Search

Abstract Complete area-coverage path planners are essential for robots performing tasks like cleaning, inspection, and surveying. However, they often involve complex calculations, mapping, and determining movement directions, leading to high computational or processing overheads and the risk of deadlocks. This paper proposes an approach for cleaning, i.e., by linear wiping of generic and discontinuous surfaces in hospital settings using inhouse assembled mobile dual-arm (MDA) robotic system. The proposed framework introduces key features: (a) a less resource-intensive approach for MDA positioning and cleaning surface mapping, (b) Modified Glasius Bioinspired Neural Network through use of heuristics (GBNN+H) to optimize surface linear wiping while obstacle avoidance, and traversal across discontinuous surfaces. The advantages of the proposed algorithm are highlighted in simulation with GBNN+H significantly reduces the number of steps and flight time required for complete coverage compared to existing algorithms. The proposed framework is also experimentally demonstrated in a hospital setting, paving the way for improved automation in cleaning and disinfection tasks. Overall, this work presents a generic and versatile, applicable to various surface orientations and complexities.

Doors are part of the building infrastructure that mobile robots have to pass through to reach zones on the other side. If robots were to clear these obstacles, they would require human assistance, advanced end-effectors, and complex control systems, making it challenging for robots. Therefore, a robot deployed in an environment should be capable of minimizing the passing through doors as well as path distance to improve overall efficiency. This paper proposes a novel Door-Density-Aware (DDA) path planning method. A vision-based door-detecting framework based on YOLOv8 has been developed to tag the door locations in a robot’s navigation map. The proposed DDA path planner uses a door-tagged map to plan an efficient path considering the cost of moving through doors and the path distance. Genetic Algorithm (GA) and Gray Wolf Optimization (GWO) have been considered for solving this optimization problem. According to the experimental results, the proposed method can effectively detect and tag doors in the navigation map and plan efficient paths. In summary, the proposed DDA path planner with GA outperformed other approaches, achieving cost reductions of 66%, 34%, 49%, and 60% compared to random selection, DDA with GWO, GA minimizing only distance, and GWO minimizing only distance, respectively.

Mobile service robots experience excessive vibrations when travelling over uneven surfaces in their workspace, increasing the degradation rate of the mechanical components or disrupting the robot’s sensing abilities for proper localization and navigational capabilities. Robot inclusivity principles can determine the suitability of a site for robot performance by considering the ground’s unevenness. This paper proposes a novel framework to autonomously evaluate the Robot Inclusivity Level of buildings based on surface unevenness (RIL-SU) by quantifying the surface unevenness of floor surfaces. The surface unevenness values are converted to RIL-SU using a rule-based approach, and the corresponding RIL-SU is tagged to the map location. A coloured heatmap based on the RIL-SU values is created as a visual representation of the RIL-SU of a given space. This heatmap would be useful for modifying the environment to make it more robot-friendly or restrict the robot’s operation in certain areas to avoid possible robot failures. The experimental results show that the proposed framework can successfully generate a valid RIL-SU heatmap for building environments.

Regular exercise plays a crucial role in promoting overall well-being in today’s lifestyle. However, individuals often find it challenging to properly execute exercises, including maintaining correct postures and appropriate movement speeds. Robotic companions have emerged as potential solutions to assist and motivate users during exercise sessions. This research paper proposes a novel robot companion designed for exercise scenarios using a reconfigurable robot. In contrast to existing non-reconfigurable robotic companions, the use of a reconfigurable robot provides added flexibility in generating emotions. The system incorporates a module that utilizes fuzzy logic to evaluate the correctness of exercise performance based on posture variations and movement speeds. The robot generates emotions and provides feedback to users based on the exercise correctness score. The robot expresses emotions through reconfigurations, motion patterns, and variations in robot speed. This emotion-based feedback could be helpful for creating engaging and interactive exercise experiences. Apart from emotion generation, the robot utilizes vocal cues as feedback. Experimental results validate the effectiveness of the proposed system in evaluating exercise correctness and demonstrating meaningful emotion transitions. The findings of this work contribute to the development of innovative robotic companions for improving exercise adherence and overall well-being.

Effective pest management in urban areas is critically challenged by the rapid proliferation of mosquito breeding sites. Traditional fumigation methods expose human operators to harmful chemicals, posing significant health risks ranging from respiratory problems to long-term chronic conditions. To address these issues, a novel fumigation robot equipped with sensor fusion technology for optimal pest control in urban landscapes is proposed. The proposed robot utilizes light detection and ranging data, depth camera inputs processed through the You Only Look Once version 8 (YOLOv8) algorithm for precise object recognition, and inertial measurement unit data. These technologies allow the robot to accurately identify and localize mosquito breeding hotspots using YOLOv8, achieving a precision of 0.81 and a mean average precision of 0.74. The integration of these advanced sensor technologies allows for detailed and reliable mapping, enhancing the robot’s navigation through complex urban terrains and ensuring precise targeting of fumigation efforts. In a test case, the robot demonstrated a 62.5% increase in efficiency by significantly reducing chemical usage through targeted hotspot fumigation. By automating the detection and treatment of breeding sites, the proposed method boosts the efficiency and effectiveness of pest management operations and significantly diminishes the health risks associated with chemical exposure for human workers. This approach, featuring real-time object recognition and dynamic adaptation to environmental changes, represents a substantial advancement in urban pest management, offering a safer and more effective solution to a persistent public health issue.

Abstract Cleaning is a fundamental routine task in human life that is now handed over to leading-edge technologies such as robotics and artificial intelligence. Various floor-cleaning robots have been developed with different cleaning functionalities, such as vacuuming and scrubbing. However, failures can occur when a robot tries to clean an incompatible dirt type. These situations will not only reduce the efficiency of the robot but also impose severe damage to the robots. Therefore, developing effective methods to classify the cleaning tasks performed in different regions and assign them to the respective cleaning agent has become a trending research domain. This article proposes a vision-based system that employs YOLOv5 and DeepSORT algorithms to detect and classify dirt to create a dirt distribution map that indicates the regions to be assigned for different cleaning requirements. This map would be useful for a collaborative cleaning framework for deploying each cleaning robot to its respective region to achieve an uninterrupted and energy-efficient operation. The proposed method can be executed with any mobile robot and on any surface and dirt, achieving high accuracy of 81.0%, for dirt indication in the dirt distribution map.

Floor‐cleaning robots play a crucial role in both industrial and domestic spaces. However, these robots often face challenges due to hazardous components in their environment, which can cause them to fail and prevent them from performing at their best. This situation necessitates continuous research in the field of floor‐cleaning robots. However, most of these efforts focus on improving the robots’ perception capabilities by incorporating additional sensors. Nevertheless, incorporating more sensors is an expensive solution for most cleaning robots. Alternatively, in this research, the feasibility of introducing a safe path on a predefined hazard map is explored. The proposed method aims to trade‐off between area coverage and the safety of the cleaning robot. Herein, the failure mode and effect analysis (FMEA) method is introduced as a tool to classify the hazards and implement a safety‐ensured coverage path‐planning process. In this approach, the risk factor defined for a point in the environment serves as the key parameter to assess the safety of the algorithm's suggested path. To validate and evaluate the proposed method, this article utilizes the hTetro mobile robot. In the experimental results, it is demonstrated that the proposed method can reduce high‐risk movements of the robot compared to existing methods.

Abstract Reconfigurable robots are suitable for cleaning applications due to their high flexibility and ability to change shape according to environmental needs. However, continuous change in morphology is not an energy-efficient approach, with the limited battery capacity. This paper presents a metaheuristic-based framework to identify the optimal morphology of a reconfigurable robot, aiming to maximize the area coverage and minimize the energy consumption in the given map. The proposed approach exploits three different metaheuristic algorithms, namely, SMPSO, NSGA-II, and MACO, to generate the optimal morphology for every unique layout of a two-dimensional grid map by considering the path-length as the energy consumption. The novel feature of our approach is the implementation of the footprint-based Complete Coverage Path Planning (CCPP) adaptable for all possible configurations of reconfigurable robots. We demonstrate the proposed method in simulations and experiments using a Tetris-inspired robot with four blocks named Smorphi, which can reconfigure into an infinite number of configurations by varying its hinge angle. The optimum morphologies were identified for three settings, i.e., 2D indoor map with obstacles and free spaces. The optimum morphology is compared with the standard Tetris shapes in the simulation and the real-world experiment. The results show that the proposed framework efficiently produces non-dominated solutions for choosing the optimal energy-efficient morphologies.

Developing guidelines for designing robot-inclusive spaces has been challenging and resource-intensive, primarily relying on physical experiments and observations of robot interactions within the built environment. These conventional methods are often costly, time-consuming, and labour-intensive, demanding manual intervention. To address these limitations, this study explores the potential of using digital twins as a promising solution to offer detailed insights, reducing the dependence on physical experiments for studying robot-built environment interactions.Although the concept of digital twins is popular in many domains, the use of digital twins for this specific problem has not been explored yet. A novel methodology for assessing existing built environment guidelines by incorporating them as an architectural digital twin asset within robot simulation software is proposed in this regard. By analysing the digital interactions between robots and the architectural digital twin assets in simulations, the compatibility of the environment with robots is evaluated, ultimately contributing to enhancing these guidelines to be robot-inclusive. The ultimate goal is to create environments that are not only inclusive but also readily accessible to Autonomous Mobile Robots (AMRs). With this objective, the proposed methodology is tested on robots of different specifications to understand the robots’ interactions with different architectural digital twin environments and obstacles. The digital twin effectively demonstrates the capability of the proposed approach in assessing the robots’ suitability for deployment in the simulated environments. The gained insights contribute to improved comprehension and strengthen the existing design guidelines.

This paper presents a comprehensive study on fall recognition and forecasting for reconfigurable stair-accessing robots by leveraging deep learning techniques. The proposed framework integrates machine learning algorithms and recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM), for fall detection of service robots on staircases. The reconfigurable stair-accessing robot sTetro serves as the platform, and the fall data required for training models are generated in a simulation environment. The two machine learning algorithms are compared and their effectiveness on the fall recognition task is reported. The results indicate that the BiLSTM model effectively classifies falls with a median categorical accuracy of 94.10% in simulation and 90.02% with limited experiments. Additionally, the BiLSTM model can be used for forecasting, which is practically valuable for making decisions well before the onset of a free fall. This study contributes insights into the design and implementation of fall detection systems for service robots used to navigate staircases through deep learning approaches. Our experimental and simulation data, along with the simulation steps, are available for reference and analysis via the shared link.

Mobile service robots employ vision systems to discern objects in their workspaces for navigation or object detection. The lighting conditions of the surroundings affect a robot’s ability to discern and navigate in its work environment. Robot inclusivity principles can be used to determine the suitability of a site’s lighting condition for robot performance. This paper proposes a novel framework for autonomously auditing the Robot Inclusivity Index of indoor environments based on the lighting condition (RII-lux). The framework considers the factors of light intensity and the presence of glare to define the RII-Lux of a particular location in an environment. The auditing framework is implemented on a robot to autonomously generate a heatmap visually representing the variation in RII-Lux of an environment. The applicability of the proposed framework for generating true-to-life RII-Lux heatmaps has been validated through experimental results.

Navigation is a primary requirement for mobile robots as they encounter hazards during operation. To improve safety for robotic deployments, hazard alert systems could be integrated into their spatial environments to reduce robotic computational load and hardware requirements, while providing benefits to visually-impaired human users in the area. Modified tactile paving markers known as the Passive Auto-Tactile Heuristic (PATH) tiles are configured for robotic usage to aid in their navigation and hazard avoidance along with a specialized Tactile Sensor Module, while providing tactile cues to visually-impaired human users. This paper proposes a novel algorithm for generating standardized layouts of ‘safe routes’ from site plans of the robot’s workspace as input. The creation of ‘safe routes’ is implemented by extracting site boundaries and outlines of interior obstacles of an input site plan, generating of the site’s Medial Axis (MA) through its connectivity graph and overlaying the corresponding PATH tile types. This implementation is explored using the Rhinoceros3D CAD program, Grasshopper development platform and associated plugins for processing the site plan and visualization of the eventual routes. The algorithm is tested on 5 sites of varying spatial functions for validation.

Abstract This work presents the vision pipeline for our in-house developed autonomous reconfigurable pavement sweeping robot named Panthera. As the goal of Panthera is to be an autonomous self-reconfigurable robot, it has to understand the type of pavement it is moving in so that it can adapt smoothly to changing pavement width and perform cleaning operations more efficiently and safely. deep learning (DL) based vision pipeline is proposed for the Panthera robot to recognize pavement features, including pavement type identification, pavement surface condition prediction, and pavement width estimation. The DeepLabv3+ semantic segmentation algorithm was customized to identify the pavement type classification, an eight-layer CNN was proposed for pavement surface condition prediction. Furthermore, pavement width estimation was computed by fusing the segmented pavement region on the depth map. In the end, the fuzzy inference system was implemented by taking input as the pavement width and its conditions detected and output as the safe operational speed. The vision pipeline was trained using the DL provided with the custom pavement images dataset. The performance was evaluated using offline test and real-time field trial images captured through the reconfigurable robot Panthera stereo vision sensor. In the experimental analysis, the DL-based vision pipeline components scored 88.02% and 93.22% accuracy for pavement segmentation and pavement surface condition assessment, respectively, and took approximately 10 ms computation time to process the single image frame from the vision sensor using the onboard computer.

Legged locomotion is essential for navigating challenging terrains where conventional robotic systems encounter difficulties. This study investigates the sensitivity of the reconfigurable Klann legged mechanism (KLM) to variations in the input geometric parameters, such as joint position location, link lengths, and angles between linkages, on the continuous coupler curve, which represents the output trace of the leg movement.The continuous coupler curve’s sensitivity is explored using global sensitivity analysis based on Sobol’s sensitivity method. Furthermore, a novel reconfigurability strategy is presented for the Klann mechanism, aiming to reduce the number of required actuators and the complexity in control. In simulation, the coupler curves obtained from the reconfigurable KLM are classified as hammering, digging, jam avoidance, and step climbing using machine learning approaches. Experimental validation is presented, discussing an approach to identifying geometric parameters and the resultant coupler curve. Illustrations of the the complete assembly of the reconfigured KLM with the obtained gaits using limited experiments are also highlighted.

Mobile service robots often have to work in dynamic and cluttered environments. Multiple safety hazards exist for robots in such work environments, which visual sensors may not detect in time before collisions or robotic damage. An alternative hazard alert system using tactile methods is explored to pre-emptively convey surrounding spatial information to robots working in complex environments or under poor lighting conditions. The proposed method for robot-inclusive tactile paving is known as Passive Auto-Tactile Heuristic (PATH) tiles. These robot-inclusive tactile paving tiles are implemented in spatial infrastructure and are aimed to allow robots to pre-emptively recognize surrounding hazards even under poor lighting conditions and potentially provide improved hazard cues to visually impaired people. A corresponding Tactile Sensing Module (TSM) was used for the digital interpretation of the PATH tiles and was mounted onboard a mobile audit robot known as Meerkat. The experiment yielded a 71.6% improvement in pre-emptive hazard detection capabilities with the TSM using a customized Graph Neural Network (GNN) model.

An automated Condition Monitoring (CM) and real-time controlling framework is essential for outdoor mobile robots to ensure the robot’s health and operational safety. This work presents a novel Artificial Intelligence (AI)-enabled CM and vibrotactile haptic-feedback-based real-time control framework suitable for deploying mobile robots in dynamic outdoor environments. It encompasses two sections: developing a 1D Convolutional Neural Network (1D CNN) model for predicting system degradation and terrain flaws threshold classes and a vibrotactile haptic feedback system design enabling a remote operator to control the robot as per predicted class feedback in real-time. As vibration is an indicator of failure, we identified and separated system- and terrain-induced vibration threshold levels suitable for CM of outdoor robots into nine classes, namely Safe, moderately safe system-generated, and moderately safe terrain-induced affected by left, right, and both wheels, as well as severe classes such as unsafe system-generated and unsafe terrain-induced affected by left, right, and both wheels. The vibration-indicated data for each class are modelled based on two sensor data: an Inertial Measurement Unit (IMU) sensor for the change in linear and angular motion and a current sensor for the change in current consumption at each wheel motor. A wearable novel vibrotactile haptic feedback device architecture is presented with left and right vibration modules configured with unique haptic feedback patterns corresponding to each abnormal vibration threshold class. The proposed haptic-feedback-based CM framework and real-time remote controlling are validated with three field case studies using an in-house-developed outdoor robot, resulting in a threshold class prediction accuracy of 91.1% and an effectiveness that, by minimising the traversal through undesired terrain features, is four times better than the usual practice.

Reconfigurable robots have the potential to perform complex tasks by adapting their morphology to different environments. However, designing optimal morphologies for these robots is challenging due to the large design space and the complex interactions between the robot and the environment. An in-house robot named Smorphi, having four holonomic mobile units connected with three hinge joints, is designed to maximize area coverage with its shape-changing features using transformation design principles (TDP). The reinforcement learning (RL) approach is used to identify the optimal morphologies out of a vast combination of hinge angles for a given task by maximizing a reward signal that reflects the robot’s performance. The proposed approach involves three steps: (i) Modeling the Smorphi design space with a Markov decision process (MDP) for sequential decision-making; (ii) a footprint-based complete coverage path planner to compute coverage and path length metrics for various Smorphi morphologies; and (iii) pptimizing policies through proximal policy optimization (PPO) and asynchronous advantage actor–critic (A3C) reinforcement learning techniques, resulting in the generation of energy-efficient, optimal Smorphi robot configurations by maximizing rewards. The proposed approach is applied and validated using two different environment maps, and the results are also compared with the suboptimal random shapes along with the Pareto front solutions using NSGA-II. The study contributes to the field of reconfigurable robots by providing a systematic approach for generating optimal morphologies that can improve the performance of reconfigurable robots in a variety of tasks.

Floor-cleaning robots are primarily designed to clean on a single floor, while multi-floor environments are usually not considered target applications. However, it is more efficient to have an autonomous floor-cleaning robot that can climb stairs and reach the next floors in a multi-floor building. To operate in such environments, the ability of a mobile robot to autonomously traverse staircases is very important. For this operation, staircase detection and localization are essential components for planning the traversal route on staircases. This article describes a deep learning approach using a convolutional neural network (CNN)-based robot operation system (ROS) framework for staircase detection, localization, and maneuvering of the robot to the detected stair. We present a real-time object detection framework to detect staircases in incoming images. We also localize these staircases using a contour detection algorithm to detect the target point: a point close to the center of the first step, and an angle of approach to the target point with respect to the current location of the robot. Experiments are performed with data from images captured on different types of staircases at different viewpoints/angles. The experimental results show that the presented approach can achieve an accuracy of 95% and a recall of 86.81%. A total runtime of 155 ms is taken to identify the presence of a staircase and the detection of the first step in the working environment, as well as being able to locate the target point with an accuracy of ±2 cm, ±1 degree.

Autonomous mobile cleaning robots are ubiquitous today and have a vast market need. Current studies are mainly focused on autonomous cleaning performances, and there exists a research gap on monitoring the robot’s health and safety. Vibration is a key indicator of system deterioration or external factors causing accelerated degradation or threats. Hence, this work proposes an artificial intelligence (AI)-enabled automated condition monitoring (CM) framework using two heterogeneous sensor datasets to predict the sources of anomalous vibration in mobile robots with high accuracy. This allows triggering proper maintenance or corrective actions based on the condition of the robot’s health or workspace, easing condition-based maintenance (CbM). Anomalous vibration sources are classified as induced by uneven Terrain, Collision with obstacles, loose Assembly, and unbalanced Structure, which causes accelerated system deterioration or potential hazards. Here, an unexplored heterogeneous sensor dataset using inertial measurement unit (IMU) and current sensors is proposed for effective recognition across different vibration classes, resulting in higher-accuracy prediction. A simple-structured 1D convolutional neural network (1D CNN) is developed for training and real-time prediction. A 2D CbM map is generated by fusing the predicted classes in real time on an occupancy grid map of the workspace to monitor the conditions of the robot and workspace remotely. The evaluation test results of the proposed method show that the usage of heterogeneous sensors performs significantly more accurately (98.4%) than previous studies, which used IMU (92.2%) and camera (93.8%) sensors individually. Also, this model is comparatively fast, fit for the environment, and ideal for real-time applications in mobile robots based on field trial validations, enhancing mobile robots’ productivity and operational safety.

An automated condition monitoring (CM) framework is essential for outdoor mobile robots to trigger prompt maintenance and corrective actions based on the level of system deterioration and outdoor uneven terrain feature states. Vibration indicates system failures and terrain abnormalities in mobile robots; hence, five vibration threshold classes for CM in outdoor mobile robots were identified, considering both vibration source system deterioration and uneven terrain. This study proposes a novel CM approach for outdoor mobile robots using a 3D LiDAR, employed here instead of its usual use as a navigation sensor, by developing an algorithm to extract the vibration-indicated data based on the point cloud, assuring low computational costs without losing vibration characteristics. The algorithm computes cuboids for two prominent clusters in every point cloud frame and sets motion points at the corners and centroid of the cuboid. The three-dimensional vector displacement of these points over consecutive point cloud frames, which corresponds to the vibration-affected clusters, are compiled as vibration indication data for each threshold class. A simply structured 1D Convolutional Neural Network (1D CNN)-based vibration threshold prediction model is proposed for fast, accurate, and real-time application. Finally, a threshold class mapping framework is developed which fuses the predicted threshold classes on the 3D occupancy map of the workspace, generating a 3D CbM map in real time, fostering a Condition-based Maintenance (CbM) strategy. The offline evaluation test results show an average accuracy of vibration threshold classes of 89.6% and consistent accuracy during real-time field case studies of 89%. The test outcomes validate that the proposed 3D-LiDAR-based CM framework is suitable for outdoor mobile robots, assuring the robot’s health and operational safety.

Steam mopping is an eco-friendly solution for cleaning and disinfecting floors, avoiding harsh chemicals or harmful UV lights. Currently, commercial steam mopping systems are manually operated, which is laborious and unsafe. This work presents the design methodology of a novel, eco-friendly autonomous steam mopping robot named ‘Snail’ for cleaning and disinfecting typical indoor floors, namely tile, carpet, concrete, vinyl, sealed wood, and rugs. Our work is mainly focused on (1) the mechanical system design, including the locomotion, steam dispenser, and mopping unit, (2) the hardware and software architecture, and (3) the motion control system’s design for smooth maneuverability and mitigating friction forces. A high-gain friction estimator is proposed, whose output is exploited by the motion controller to follow the desired path and compensate for the traction-based locomotion disturbances due to extensive mop contact with a rough-textured floor, such as carpet. The proposed autonomous steam mopping robot’s efficiency is validated with different floors with zig-zagging cleaning patterns, setting a minimum of 100 °C as the disinfecting steam criterion. Using the proposed motion control algorithm, we validate the smooth maneuverability, assuring the robot traverses with a given speed and follows the trajectory even on a rough carpeted floor. The cleaning efficiency is tested by removing hard stains, consuming less water, and faster wet floor drying. The robot used only one liter of water to clean a 5 m2 floor area, and the drying time was found to be four times faster than normal water mopping. The proposed steam mopping robot design is ideal for cleaning and disinfecting hospitals and eldercare centers, where hygiene is crucial, and promoting going green, avoiding harsh chemicals, and mitigating current cleaner labor concerns, including the risk of infection.

Reconfigurable robots design based on polyominos or n-Omino is increasingly being explored in cleaning and maintenance (CnM) tasks due to their ability to change shape using intra- and inter-reconfiguration, resulting in various footprints of the robot. On one hand, reconfiguration during a CnM task in a given environment or map results in enhanced area coverage over fixed-form robots. However, it also consumes more energy due to the additional effort required to continuously change shape while covering a given map, leading to a deterioration in overall performance. This paper proposes a new strategy for n-Omino-based robots to select a range of optimal morphologies that maximizes area coverage and minimizes energy consumption. The optimal “morphology” is based on two factors: the shape or footprint obtained by varying the angles between the n-Omino blocks and the number of n-Omino blocks, i.e., “n”. The proposed approach combines a Footprint-Based Complete coverage Path planner (FBCP) with a metaheuristic optimization algorithm to identify an n-Omino-based reconfigurable robot’s optimal configuration, assuming energy consumption is proportional to the path length taken by the robot. The proposed approach is demonstrated using an n-Omino-based robot named Smorphi, which has square-shaped omino blocks with holonomic locomotion and the ability to change from monomino to tetromino. Three different simulated environments are used to find the optimal morphologies of Smorphi using three metaheuristic optimization techniques, namely, MOEA/D, OMOPSO, and HypE. The results of the study show that the morphology produced by this approach is energy efficient, minimizing energy consumption and maximizing area coverage. Furthermore, the HypE algorithm is identified as more efficient for generating optimal morphology as it took less time to converge than the other two algorithms.

This paper introduces a wheel-based vertical propagation robot, $Ibex$ , for visual inspection of both flat and curved surfaces. The platform is modular, consisting of vacuum-based adhesion modules and steerable-wheel locomotion modules. The adhesion module consists of a unconventional design of suction cup with sufficient structural flexibility and single-axis translation freedom that helps with adhesion to flat and curved vertical surfaces. Through experiments, we validate the locomotion, adhesion, and conformation mechanisms of the robot, and collated data of the adhesion module such as differential pressure as well as normal force measurements using force sensitive resistors and a thrust force meter. We show that the platform has ability to generate a considerable amount of adhesion force while demonstrating the ability to adhere to and move on variables types of surfaces as well as various surface curvatures including plastered cement pillars and aircraft skins.

Multistorey buildings are rapidly constructing with the current world development. Cleaning of these buildings is a major concern due to several issues such as scarcity of labor. Cleaning robots are developed to cope with these issues. Among these cleaning robots, staircase cleaning robots play a vital role in a multistorey building. Therefore, this paper proposes a control strategy for a reconfigurable staircase cleaning robot to ensure area coverage and safety. A staircase cleaning robot should particularly maintain its orientation parallel to the riser while maintaining a proper clearance with the riser when performing the sideways movement on a thread to ensure safety and coverage. Two Fuzzy Logic Systems (FLSs) have been developed to correct the heading and the clearance distance during sideways movements. Precise measurements on heading error and clearance distance are essential for the decision-making process of the proposed control strategy. Thus, a vision-based perception mechanism has been developed to accurately perceives these essential measurements. The proposal of the control strategy to correct the heading and the clearance distance, and the development of the vision-based perception mechanism are the major contributions of the work. Experiments were conducted by deploying the developed robot in a typical staircase considering different conditions. The experiment results validate that the proposed method can ensure coverage and safety by properly maintaining the heading and clearance of the robot with respect to a riser.

Cleaning robots have been gaining traction amongst various industries globally, with domestic floor cleaning robots being the most widespread. These robots usually employ brushes with a vacuum unit for dry cleaning purposes. For a self-reconfigurable tiling-based floor cleaning robot, hTetro, a modular sweeping module was developed. This robot consists of four rectangular blocks connected via passive hinges and each block consists of a steering unit with two conventional wheels following the differential drive principle. This paper presents the navigation and operational principle of the hTetro robotic platform and the detailed mechanical and hardware design of two different iterations of its cleaning module. The efficiency of these modules was numerically validated via Computational Fluid Dynamics analysis. Using the loading and boundary conditions derived from analytically formulated force analysis, Finite Element based static analysis and modal analysis of the cleaning module were carried out to determine the possible structural deformations, natural frequencies and stress patterns on each bristle and rolling brush. Through these simulations, we demonstrate the safe design of the roller brushes layout and bristle pattern and efficiency of the cleaning module. By deploying the platform in the lab setting and real food court environment, we verify the cleaning performance of the platform.

The development of floor cleaning robots is an emerging area in robotics. Maximizing the area coverage is a foremost mission for a floor cleaning robot. Reconfigurable floor cleaning robots outperform floor cleaning robots with fixed morphology in the aspect of area coverage. A reconfigurable robot should be more flexible in changing its morphologies by considering the shapes of objects occupied in an environment to gain more coverage. Nevertheless, the state of the art methods of tiling robots considers only a limited number of morphologies for the reconfiguration, which is not sufficient to match the shape of an object. Therefore, this paper proposes a novel method to synthesize an appropriate morphology for a reconfigurable robot in accordance with the shape of an object. The proposed concept is named hTetro-Infi since it is not limited to a finite number of morphologies. The major novelty of the proposed concept overt the state of the art is the consideration of an infinite number of morphologies for the reconfiguration without sticking into a limited number of morphologies. Feedforward Neural Network (FNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used for determining the hinge angle required for synthesizing a given morphology. Different configurations of FNNs and ANFISs were trained and evaluated to find the most suitable configurations. The area coverage performance of the proposed hTetro-Infi was compared against that of the state of the art methods of an existing class of tiling robots, which considers only a limited number of morphologies, through simulations. According to the statistical conclusions, the proposed hTetro-Infi is capable of significantly improving area coverage compared to an existing tiling-theory based floor cleaning robot. Furthermore, the area coverage improvement of hTetro-Infi is noteworthy. Therefore, the proposed concept is beneficial in improving the abilities of a reconfigurable cleaning robot. Real-world experiments with the hardware platform of the robot for evaluating the performance is expected to be conducted in the next phase of the work. Furthermore, consideration of hTetro-Infi for navigation through confined areas is proposed for future work.

The cleaning robots are going through some significant development in recent years, driven by the greater market penetration and the demand for better cleaning performance. However, most of the robots have problems covering the total cleaning area given geometric limitations of the platforms in relation to the cleaning stage, given furniture and architecture. In this paper, we describe the hTrihex platform, which is a self-reconfigurable adaptive robot that attains three different configurations. The change in configuration gives rise to variation in the kinematic model, which in turn changes the behavior of robot locomotion. The change in configuration and the robot position is evaluated using the measurements from the on-board sensors like the encoders, LIDAR, and inertial measuring unit (IMU). These data are then utilized to develop a new cascade control strategy for motion control of h-Trihex. The control cascade comprises of a robust backstepping controller in the outer loop for tackling the varying kinematic and a conventional PID in the inner loop for speed control. This cascade is appended by a PID controller, for correcting the wheel orientation after each configuration change. The proposed design assures asymptotic path tracking and satisfactory closed-loop performance, which are validated through numerical simulations and experiments.

Floor cleaning robots have been developed to cope with the issues arisen with conventional cleaning methods that involve extensive human labor. hTetro is a self-reconfigurable floor cleaning robot that has been introduced to improve area coverage. Polyomino tiling theory is utilized by hTetro to plan area coverage. Energy usage and area coverage are distinct for different tiling arrangements, and they are often conflicting entities. Therefore, hTetro needs to maintain the tradeoff between area coverage and energy usage to improve its performance. This paper proposes a novel method to determine the tradeoff between area coverage and energy usage of a tiling theory-based self-reconfigurable floor cleaning robot per user preference. A linguistic option such as “High coverage” that represents user preference has uncertainty since fuzzy linguistic terms do not possess definitive meaning. Moreover, the meaning of such user preference depends on the present status of the robot. Thereby, a novel fuzzy inference system is proposed to determine the tradeoff between area coverage and energy usage by interpreting the meaning of user preference while accounting for the present status of the robot. A Weighted Sum Model (WSM) based Multiple-criteria decision-making (MCDM) method is adapted per user preference interpreted by the fuzzy inference system. The behavior of the proposed system has been evaluated considering heterogeneous test cases. The behavior of the test cases confirms the applicability of the proposed concept for adapting the tradeoff between area coverage and energy usage of a self-reconfigurable floor cleaning robot based on user preference.

Nature has always been an inspiration for engineers and designers to have technological inventions. The postures, locomotion and gait cycles of animals are usually smooth, dynamically stable, and highly adaptable in an unknown terrain. Based on a giraffe's leg folding pattern, we develop a quadruped hybrid drain mapping robot with modular sub-systems, four bar inversion mechanism based legs and bi-directional rolling wheel, named, Tarantula-II. The platform undergoes self-reconfiguration and achieves variable height and width, which in turn helps in navigation in a drain with multiple level-shifts. This paper describes the key features of giraffe's limb, how the folding pattern of the limbs are implemented in the robot design. In addition, we discuss the detailed mechanical design of platform, the kinematics analysis of each leg, kinematics of the platform with respect to wheels, and structural analysis of the platform under different gait condition. Applying the kinematics formulation and posture correction algorithm, we verify the mobility and level shifting capability of the platform both in lab setting and drain environment.

Self-reconfigurable robots have been proposed for a quite long period and in large numbers. However, there are very few systematic methodologies proposed to categorize and evaluate such kinds of robots. In this paper, we put forward a framework for taxonomy and evaluation (TAEV) of self-reconfigurable robots, based on the mechanism reconfigurability and the level of autonomy for reconfiguration. The mechanism reconfigurability of the robots is divided into two types: inter-reconfigurability and intra-reconfigurability which are quantified by the number of configurations and scale respectively. A combination of both the intra- and inter-reconfigurability feature is named as nested-reconfiguration. The levels of autonomy reconfigurability are ranging through different levels from manual teleoperation to fully autonomous systems. The evaluation metrics are introduced to quantify the level of autonomy and the sufficiency of self-reconfigurable robots. Detailed discussions on applications of the proposed framework are presented with real-robot examples.

Fixed morphology cleaning robots mostly face significant challenges in cleaning the narrow spaces in the environment. To overcome this, a new class of self-reconfigurable floor cleaning robot, hTetrakis, made of four triangular blocks, is built. This paper focuses on the modeling and controller design of the locomotion and reconfiguration mechanism of the platform. The locomotion of the platform with respect to the velocity of the wheels is formulated based on the kinematic analysis. By modeling the system in Simscape multibody toolbox of Matlab, the maneuverability of the platform is studied. The reconfiguration of the platform is studied based on the dynamics of individual block about the hinged joint. The control architecture of this platform has two distinct Proportional-Integral-Derivative (PID) controllers to eliminate the position error during reconfiguration, and to maintain the platform's velocity. The coverage path planning of the platform is based on the classical Travelling Salesman Problem which finds the optimal sequence of waypoints as per the minimal energy consumption. Real time experiments are carried out to validate the effectiveness of the proposed control schemes in terms of eliminating the velocity errors, and maintaining the desired trajectory.

This paper is inspired by the automation of cleaning tasks required inside the endogenous environment. This work intends to develop a robust adaptive strategy for force-position control, using robotic manipulators. With this objective, the operational/task space is decoupled into two sub-spaces, and the impedance model for the manipulator is designed using the standard second-order filters. The impedance filter generates the reference commands for the inner loop, which assures bounded position and force tracking. A delay estimation based adaptive sliding mode strategy is proposed for carrying out the tracking objective, and its convergence is proved using the Lyapunov-Razumikhin theorem. The controller uses past data to estimate the uncertainties in the error dynamics and exploits the sliding mode strategy to provide robustness in the closed-loop. This technique circumvents the under/overestimation issues, and linear/nonlinear parametrization requirements in conventional adaptive schemes. Multiple numerical simulations and experiments are performed, and the results point to the validity of the proposed control law in real-world settings.

Commercial floor cleaning robots face significant challenges in accessing convex and narrow corners due to their fixed and regular morphologies. To overcome this, we develop a new class of self-reconfigurable floor cleaning robot, hTetrakis, which is composed of tetriamonds (four equilateral triangles aligned along the edges) that adopt three distinct forms (“I”, “A”, and “U” shapes). When on a flat and rigid platform, these forms have convex corners that help to cover narrow regions. This paper addresses the mechanical structural design, reconfiguration of the robot platform through the hinge mechanism, and the electronics and navigation module of hTetrakis. Based on finite element studies, we estimate the system’s natural frequency, stress, and deformation patterns developed in the structural components of the robot, and validate the proposed design to overcome structural failure and system resonance. In order to achieve maximum area coverage using the tetriamond forms, we formulate tiling theorems and apply them for path-planning techniques during the floor cleaning process. By using a robot prototype, we conduct experiments to validate the proposed tiling theorem based on the percentage of area coverage, and demonstrate that this platform is able to cover the floor area efficiently.

The periodic cleaning pavement is a requirement for a clean environment in urban areas. The robot named Panthera introduced in this paper has the ability to change the width of the frame to help the cleaning tasks become suitable for different pavement types and friendly with the activities of the pedestrian. The Panthera cleaning operations which allow pedestrian walks freely on the pavement is modeled as a pedestrian robot cohabitant framework. To this end, the mask based deep convolutional neural network (DCNN) is used to archive segmented label maps of pedestrians in color image, then the distance from detected objects to the robot is estimated and tracked by averaging filtered depth values in the corresponding region in the refined depth image. The width and the distance from the robot to the approaching pedestrians are used to adjust the robot width. The enlarging and squeezing operations of the Panthera width are conducted by rotating one motor to change the length of the lead screw rod and the angle linkage hinges from the information of encoders sensors. The experiments carried out on real environments demonstrated the autonomous avoiding pedestrians ability by the kinematic model of Panthera.

Cleaning is an important task that is practiced in every domain and has prime importance. The significance of cleaning has led to several newfangled technologies in the domestic and professional cleaning domain. However, strategies for auditing the cleanliness delivered by the various cleaning methods remain manual and often ignored. This work presents a novel domestic dirt image dataset for cleaning auditing application including AI-based dirt analysis and robot-assisted cleaning inspection. One of the significant challenges in an AI-based robot-aided cleaning auditing is the absence of a comprehensive dataset for dirt analysis. We bridge this gap by identifying nine classes of commonly occurring domestic dirt and a labeled dataset consisting of 3000 microscope dirt images curated from a semi-indoor environment. The dirt dataset gathered using the adhesive dirt lifting method can enhance the current dirt sensing and dirt composition estimation for cleaning auditing. The dataset’s quality is analyzed by AI-based dirt analysis and a robot-aided cleaning auditing task using six standard classification models. The models trained with the dirt dataset were capable of yielding a classification accuracy above 90% in the offline dirt analysis experiment and 82% in real-time test results.

Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.

Coverage path planning technique is an essential ingredient in every floor cleaning robotic systems. Even though numerous approaches demonstrate the benefits of conventional coverage motion planning techniques, they are mostly limited to fixed morphological platforms. In this article, we put forward a novel motion planning technique for a Tetris-inspired reconfigurable floor cleaning robot named “hTetro” that can reconfigure its morphology to any of the seven one-sided Tetris pieces. The proposed motion planning technique adapts polyomino tiling theory to tile a defined space, generates reference coordinates, and produces a navigation path to traverse on the generated tile-set with an objective of maximizing the area coverage. We have summarized all these aspects and concluded with experiments in a simulated environment that benchmarks the proposed technique with conventional approaches. The results show that the proposed motion planning technique achieves significantly higher performance in terms of area recovered than the traditional methods.

Although numerous studies have focused on the development and application of polyomino tiling theories, research of this nature is typically limited to the graphics and gaming fields. In this paper, we are presenting an innovative application of the polyomino tiling theory which is applied to Tetris-inspired reconfigurable robotic cleaning device as a means of solving the area coverage problem. The robotic floor cleaner (hTetro) that was developed as part of this research leverages the polyomino tiling theory to automatically generate the global tiling set required to ensure the full area of a given space is covered. In this paper, three Tetris tiling theorems were validated using our developed hTetro robot. The results of the research clearly indicated that the proposed approach offers a strong area coverage performance across all experimental cases. This paper includes an outline of the system architecture that underpins the hTetro robot and a comprehensive overview of the three tiling theorems that were applied in this research.

False ceilings are often utilised in residential and commercial spaces as a way to contain and conceal necessary but unattractive building infrastructure, including mechanical, electrical, and plumbing services. Concealing such elements has made it difficult to perform periodic inspection safely for maintenance. To complement this, there have been increasing research interests in mobile robots in recent years that are capable of accessing hard-to-reach locations, thus allowing workers to perform inspections remotely. However, current initiatives are met with challenges arising from unstructured site conditions that hamper the robot’s productivity for false ceiling inspection. The paper adopts a top-down approach known as “Design for Robots”, taking into account four robot-inclusive design principles: activity, accessibility, safety, observability. Falcon, a class of inspection robots, was used as a benchmark to identify spatial constraints according to the four principles. Following this, a list of false ceiling design guidelines for each category are proposed.

Cleaning is one of the fundamental tasks with prime importance given in our day-to-day life. Moreover, the importance of cleaning drives the research efforts towards bringing leading edge technologies, including robotics, into the cleaning domain. However, an effective method to assess the quality of cleaning is an equally important research problem to be addressed. The primary footstep towards addressing the fundamental question of “How clean is clean” is addressed using an autonomous cleaning-auditing robot that audits the cleanliness of a given area. This research work focuses on a novel reinforcement learning-based experience-driven dirt exploration strategy for a cleaning-auditing robot. The proposed approach uses proximal policy approximation (PPO) based on-policy learning method to generate waypoints and sampling decisions to explore the probable dirt accumulation regions in a given area. The policy network is trained in multiple environments with simulated dirt patterns. Experiment trials have been conducted to validate the trained policy in both simulated and real-world environments using an in-house developed cleaning audit robot called BELUGA.

Vibration is an indicator of performance degradation or operational safety issues of mobile cleaning robots. Therefore, predicting the source of vibration at an early stage will help to avoid functional losses and hazardous operational environments. This work presents an artificial intelligence (AI)-enabled predictive maintenance framework for mobile cleaning robots to identify performance degradation and operational safety issues through vibration signals. A four-layer 1D CNN framework was developed and trained with a vibration signals dataset generated from the in-house developed autonomous steam mopping robot ‘Snail’ with different health conditions and hazardous operational environments. The vibration signals were collected using an IMU sensor and categorized into five classes: normal operational vibration, hazardous terrain induced vibration, collision-induced vibration, loose assembly induced vibration, and structure imbalanced vibration signals. The performance of the trained predictive maintenance framework was evaluated with various real-time field trials with statistical measurement metrics. The experiment results indicate that our proposed predictive maintenance framework has accurately predicted the performance degradation and operational safety issues by analyzing the vibration signal patterns raised from the cleaning robot on different test scenarios. Finally, a predictive maintenance map was generated by fusing the vibration signal class on the cartographer SLAM algorithm-generated 2D environment map.

Multirobot cooperation enhancing the efficiency of numerous applications such as maintenance, rescue, inspection in cluttered unknown environments is the interesting topic recently. However, designing a formation strategy for multiple robots which enables the agents to follow the predefined master robot during navigation actions without a prebuilt map is challenging due to the uncertainties of self-localization and motion control. In this paper, we present a multirobot system to form the symmetrical patterns effectively within the unknown environment deployed randomly. To enable self-localization during group formatting, we propose the sensor fusion system leveraging sensor fusion from the ultrawideband-based positioning system, Inertial Measurement Unit orientation system, and wheel encoder to estimate robot locations precisely. Moreover, we propose a global path planning algorithm considering the kinematic of the robot’s action inside the workspace as a metric space. Experiments are conducted on a set of robots called Falcon with a conventional four-wheel skid steering schematic as a case study to validate our proposed path planning technique. The outcome of our trials shows that the proposed approach produces exact robot locations after sensor fusion with the feasible formation tracking of multiple robots system on the simulated and real-world experiments.

During a viral outbreak, such as COVID-19, autonomously operated robots are in high demand. Robots effectively improve the environmental concerns of contaminated surfaces in public spaces, such as airports, public transport areas and hospitals, that are considered high-risk areas. Indoor spaces walls made up most of the indoor areas in these public spaces and can be easily contaminated. Wall cleaning and disinfection processes are therefore critical for managing and mitigating the spread of viruses. Consequently, wall cleaning robots are preferred to address the demands. A wall cleaning robot needs to maintain a close and consistent distance away from a given wall during cleaning and disinfection processes. In this paper, a reconfigurable wall cleaning robot with autonomous wall following ability is proposed. The robot platform, Wasp, possess inter-reconfigurability, which enables it to be physically reconfigured into a wall-cleaning robot. The wall following ability has been implemented using a Fuzzy Logic System (FLS). The design of the robot and the FLS are presented in the paper. The platform and the FLS are tested and validated in several test cases. The experimental outcomes validate the real-world applicability of the proposed wall following method for a wall cleaning robot.

Frequent inspections are essential for false ceilings to maintain the service infrastructures, such as mechanical, electrical, and plumbing, and the structure of false ceilings. Human-labor-based conventional inspection procedures for false ceilings suffer many shortcomings, including safety concerns. Thus, robot-aided solutions are demanded for false ceiling inspections similar to other building maintenance services. However, less work has been conducted on developing robot-aided solutions for false ceiling inspections. This paper proposes a novel design for a robot intended for false ceiling inspections named Falcon. The compact size and the tracked wheel design of the robot allow it to traverse obstacles such as runners and lighting fixtures. The robot’s ability to autonomously follow the perimeter of a false ceiling can improve the productivity of the inspection process since the heading of the robot often changes due to the nature of the terrain, and continuous heading correction is an overhead for a teleoperator. Therefore, a Perimeter-Following Controller (PFC) based on fuzzy logic was integrated into the robot. Experimental results obtained by deploying a prototype of the robot design to a false ceiling testbed confirmed the effectiveness of the proposed PFC in perimeter following and the robot’s features, such as the ability to traverse on runners and fixtures in a false ceiling.

Frequent inspections are essential for drains to maintain proper function to ensure public health and safety. Robots have been developed to aid the drain inspection process. However, existing robots designed for drain inspection require improvements in their design and autonomy. This paper proposes a novel design of a drain inspection robot named Raptor. The robot has been designed with a manually reconfigurable wheel axle mechanism, which allows the change of ground clearance height. Design aspects of the robot, such as mechanical design, control architecture and autonomy functions, are comprehensively described in the paper, and insights are included. Maintaining the robot’s position in the middle of a drain when moving along the drain is essential for the inspection process. Thus, a fuzzy logic controller has been introduced to the robot to cater to this demand. Experiments have been conducted by deploying a prototype of the design to drain environments considering a set of diverse test scenarios. Experiment results show that the proposed controller effectively maintains the robot in the middle of a drain while moving along the drain. Therefore, the proposed robot design and the controller would be helpful in improving the productivity of robot-aided inspection of drains.

Area coverage is a crucial factor for a robot intended for applications such as floor cleaning, disinfection, and inspection. Robots with fixed shapes could not realize an adequate level of area coverage performance. Reconfigurable robots have been introduced to overcome the limitations of fixed-shape robots, such as accessing narrow spaces and cover obstacles. Although state-of-the-art reconfigurable robots used for coverage applications are capable of shape-changing for improving the area coverage, the reconfiguration is limited to a few predefined shapes. It has been proven that the ability of reconfiguration beyond a few shapes can significantly improve the area coverage performance of a reconfigurable robot. In this regard, this paper proposes a novel robot model and a low-level controller that can facilitate the reconfiguration beyond a small set of predefined shapes and locomotion per instructions while firmly maintaining the shape. A prototype of a robot that facilitates the aim mentioned above has been designed and developed. The proposed robot model and controller have been integrated into the prototype, and experiments have been conducted considering various reconfiguration and locomotion scenarios. Experimental results confirm the validity of the proposed model and controller during reconfiguration and locomotion of the robot. Moreover, the applicability of the proposed model and controller for achieving high-level autonomous capabilities has been proven.

False-ceiling inspection is a critical factor in pest-control management within a built infrastructure. Conventionally, the false-ceiling inspection is done manually, which is time-consuming and unsafe. A lightweight robot is considered a good solution for automated false-ceiling inspection. However, due to the constraints imposed by less load carrying capacity and brittleness of false ceilings, the inspection robots cannot rely upon heavy batteries, sensors, and computation payloads for enhancing task performance. Hence, the strategy for inspection has to ensure efficiency and best performance. This work presents an optimal functional footprint approach for the robot to maximize the efficiency of an inspection task. With a conventional footprint approach in path planning, complete coverage inspection may become inefficient. In this work, the camera installation parameters are considered as the footprint defining parameters for the false ceiling inspection. An evolutionary algorithm-based multi-objective optimization framework is utilized to derive the optimal robot footprint by minimizing the area missed and path-length taken for the inspection task. The effectiveness of the proposed approach is analyzed using numerical simulations. The results are validated on an in-house developed false-ceiling inspection robot—Raptor—by experiment trials on a false-ceiling test-bed.

Cleaning is an important factor in most aspects of our day-to-day life. This research work brings a solution to the fundamental question of “How clean is clean” by introducing a novel framework for auditing the cleanliness of built infrastructure using mobile robots. The proposed system presents a strategy for assessing the quality of cleaning in a given area and a novel exploration strategy that facilitates the auditing in a given location by a mobile robot. An audit sensor that works by the “touch and inspect” analogy that assigns an audit score corresponds to its area of inspection has been developed. A vision-based dirt-probability-driven exploration is proposed to empower a mobile robot with an audit sensor on-board to perform auditing tasks effectively. The quality of cleaning is quantified using a dirt density map representing location-wise audit scores, dirt distribution pattern obtained by kernel density estimation, and cleaning benchmark score representing the extent of cleanliness. The framework is realized in an in-house developed audit robot to perform the cleaning audit in indoor and semi-outdoor environments. The proposed method is validated by experiment trials to estimate the cleanliness in five different locations using the developed audit sensor and dirt-probability-driven exploration.

Vertical gardens have emerged alongside the increase in urban density and land scarcity to reintegrate greenery in the built environment. Existing maintenance for vertical gardens is labour-intensive, time-consuming and is being increasingly complemented by robotic applications. While research has been focused on enhancing robot design to improve productivity, minimal effort has been done on ‘design for robots’ in creating suitable environments for optimal robot deployments. This paper proposed a multi-disciplinary approach that brings together architects, designers, and roboticians to adapt the design of the vertical garden infrastructure to counteract the limitations of the maintenance robot. A case study on an existing plant maintenance robot ‘Urodela’ was conducted to determine the limitations encountered by robotic aid during operation. A robot-inclusive modular design for vertical gardens is proposed based on robot-inclusive principles, namely manipulability and safety, along with architectural design considerations. Design explorations for different configurations of track layouts of the proposed robot-inclusive modular design for vertical gardens is further analysed to validate its applicability and scalability.

For glass façade cleaning, we developed a reconfigurable robot, Mantis-mini, with a dry cleaning mechanism and linear actuator based transitioning mechanism. It consists of three suction modules, connected by a support structure and each suction module has individual differential drive wheels with independent steering capability. This paper focuses on the detailed design of the platform, static structural analysis for and kinematic formulation. The adhesion performance of the suction module is evaluated using experiments, Computational Fluid Dynamics (CFD) simulations, and analytically formulated estimation. The suction pressure magnitude predicted from the simulations increases with suction impeller speed and agrees well with measurements and analytical calculation in terms of order of magnitude and qualitative trend. An adaptive proportional-integral (PI) controller is designed and implemented for regulating suction pressure and motion controller of the platform. The suction performance of the platform is validated through real-time experiments of the platform on the glass surface.