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Monitoring the safe social distancing then conducting efficient sterilization in potentially crowded public places are necessary but challenging especially during the COVID-19 pandemic. This work presents the 3D human space-based surveillance system enabling selective cleaning framework. To this end, the proposed AI-assisted perception techniques is deployed on Toyota Human Support Robot (HSR) equipped with autonomous navigation, Lidar, and RGBD vision sensor. The human density mapping represented as heatmap was constructed to identify areas with the level being likely the risks for interactions. The surveillance framework adopts the 3D human joints tracking technique and the accumulated asymmetrical Gaussian distribution scheme modeling the human location, size, and direction to quantify human density. The HSR generates the human density map as a grid-based heatmap to perform the safe human distance monitoring task while navigating autonomously inside the pre-built map. Then, the cleaning robot uses the levels of the generated heatmap to sterilize by the selective scheme. The experiment was tested in public places, including food court and wet market. The proposed framework performance analyzed with standard performance metrics in various map sizes spares about 19 % of the disinfection time and 15 % of the disinfection liquid usage, respectively.

Applying the automation in covering the areas entirely eases manual jobs in various domestic fields such as site investigation, search, rescue, security, cleaning, and maintenance. A self-reconfigurable robot with adjustable dimensions is a viable answer to improve the coverage percentage for predefined map areas. However, the shape-shifting of this robot class also adds to the complexity of locomotion components and the need for an optimal complete coverage strategy for this new type of robot. The typical complete coverage route, including the least times of shape-shifting, the shortest navigation route, and the minimum travel time, is presented in the article. By splitting the map into the sub-areas similar to the self-reconfigurable robot’s available shapes, the robot can design the ideal tileset and optimal navigation strategies to cover the workspace. To this end, we propose a Complete Tileset Energy-Aware Coverage Path Planning (CTPP) framework for a tiling self-reconfigurable robot named hRombo with four rhombus-shaped modules. The robot can reconfigure its base structure into seven distinct forms by activating the servo motors to drive the three robot hinges connecting robot modules. The problem of optimal path planning assisting the proposed hRombo robot to clear optimally all predefined tiles within the arbitrary workspace is considered a classic Travel Salesman Problem (TSP), and this TSP is solved by the reinforcement learning (RL) approach. The RL’s reward function and action space are based on robot kinematic and the required energies, including transformation, translation, and orientation actions, to move the robot inside the workspace. The CTPP for the hRombo robot is validated with conventional complete coverage methods in simulation and real workspace conditions. The results showed that the CTPP is suitable for producing Pareto plans that enable robots to navigate from source to target in different workspaces with the least consumed energy and time among considered methods.

Maintenance of ship hull involves routine tasks during dry-docking that includes inspection, paint stripping, and re-painting. Among those, paint stripping is always seen as harmful for human operators and a time-consuming task. To reduce human risk, cost, and environmental cleanliness, the shipping maintenance industries started using robotic solutions. However, most of such robotic systems cannot operate fully autonomous since it requires human in the loop to monitor the cleaning efficiency. To this end, a novel autonomous self-evaluating hull cleaning robot called Hornbill is presented in this paper. The proposed robot is capable of navigating autonomously on the hull surface and perform water jet blasting to strip off the paint coating. The robot is also enabled with a Deep Convolutional Neural Network (DCNN) based self-evaluating scheme that benchmarks cleaning efficiency. We evaluated the proposed robot’s performance by conducting experimental trials on a metal plate under three different paint coatings. While performing the paint stripping task in every experimental trial, the self-evaluating scheme would generate the heat map that depicts the plate’s cleanliness. The results indicate that the proposed self-evaluating system could successfully generate high accurate cleanliness heat maps in all considered scenarios, which simplifies the checkup process for paint inspectors.

Reconfigurable robots have received broad research interest due to the high dexterity they provide and the complex actions they could perform. Robots with reconfigurability are perfect candidates in tasks like exploration or rescue missions in environments with complicated obstacle layout or with dynamic obstacles. However, the automation of reconfigurable robots is more challenging than fix-shaped robots due to the increased possible combinations of robot actions and the navigation difficulty in obstacle-rich environments. This paper develops a systematic strategy to construct a model of hinged-Tetromino (hTetro) reconfigurable robot in the workspace and proposes a genetic algorithm-based method (hTetro-GA) to achieve path planning for hTetro robots. The proposed algorithm considers hTetro path planning as a multi-objective optimization problem and evaluates the performance of the outcome based on four customized fitness objective functions. In this work, the proposed hTetro-GA is tested in six virtual environments with various obstacle layouts and characteristics and with different population sizes. The algorithm generates Pareto-optimal solutions that achieve desire robot configurations in these settings, with O-shaped and I-shaped morphologies being the more efficient configurations selected from the genetic algorithm. The proposed algorithm is implemented and tested on real hTetro platform, and the framework of this work could be adopted to other robot platforms with multiple configurations to perform multi-objective based path planning.

This paper proposes an improved controller with a friction estimator for the path tracking of a cleaning robot having a scrubbing brush, motivated by the presence of the positive influence of brush friction on robot propulsion. For the controller and the estimator, the dynamics of a cleaning robot with a scrubbing brush is represented by a model incorporating the LuGre dynamic friction model. This dynamic robot model is transformed into appropriate forms in controller and estimator design. The controller employs a sliding mode control (SMC) law improved to exploit the friction for achieving a control target. The estimator provides estimates of the state and parameters of the dynamic model in an unscented Kalman filter (UKF) framework to calculate the friction force. The performance of the proposed controller with the estimator is tested through numerical simulations. The simulation results illustrate that the proposed approach is effective for the path tracking of cleaning robots with less input torque.

In order to automate the traditional manual process, in this paper, we demonstrate a visual-based method for sampling floor dust using a mobile microscopic platform. A floor cleaning robot with an onboard microscope is developed and deployed for the task of dust sampling. An extensive experimental study is conducted involving two sets of experiments where the first set involved three different types of sites and the second set involved three different dust types sprinkled on the same type of floor. The acquired 64 sample images from each experiment are stitched together for data logging and review purposes. The results substantiate our proposition of using a robotic platform with an onboard microscope for the automated visual sampling of floor dust. Results of the current study suggest that the proposed robotic platform could provide a viable method for the dust-sampling task in indoor settings.

Motivated by availability of autonomous reconfigurable robots, we have developed a reconfigurable quadruped robot, Scorpio, which can perform crawling and rolling locomotion. In this paper, we present an approach to the simultaneous localization and mapping (SLAM) for the quadruped robot in crawling locomotion to achieve its autonomous operation in an unknown environment. For the solution of the SLAM problem, we developed a dynamic model of the crawling quadruped robot, which considers friction offered to supporting legs and the switching of supporting legs, and applied the SLAM algorithm based on the unscented Kalman filter (UKF). The feasibility of the approach is verified through numerical simulations whose results show that the algorithm provides accurate estimates of the robot trajectory and the locations of surrounding landmarks simultaneously. This work demonstrates an effective approach for the application of the SLAM algorithm to a reconfigurable robot.

Robotics always get inspirations from the nature. The vision systems of the animals enlighten the research of the robotic area. The relative positions of two eyes of predator and prey are different which are monocular and binocular visions, respectively. A biomimetic predator-prey vision system is proposed based on a self-reconfigurable robot called Hinged-Tetro. Hinged-Tetro inspired by Tetris is composed of four hinged cubes, which is able to reconfigure into all one-sided tetrominoes (seven shapes). The robot equipped with two cameras can mimic and apply the vision mechanisms of predator and prey by changing its configurations. Finally, the experiment test is performed to illustrate the concept of the proposed robotic system.

In particular, service robotics is strongly tided to highly accurate navigational tasks where pathway tracking is a practice commonly carried out through control algorithms. This study proposes theoretical non-linear pathways presented as kth-degree polynomials as model references. Our proposal establishes a proportional control with a variable model reference at the level of second order derivatives to which the robot's motion is adapted on-line. The robot's fixed frame Cartesian trajectory model is inversely transformed to wheels' angular acceleration components, which are the reference models. Although, our proposal may be applied to any type of wheeled robot's kinematic structure, we are presenting an example of two active wheels with differential velocity modality. Obtained results raised from successful experimental practice and numerical simulations as well, which depict the capability of the proposed model control.

It is essential for robots to be highly usable to achieve increased acceptance among target population. In this paper, we put forward a metric that measures easiness of acceptance using the length of time to initiate interaction, total interaction time, time needed to identify perception-reaction phenomenon, its occurrence, number of violations and affective interaction factor. This measure would be useful as a parameter to compare different target groups, robots, and different nature of interaction. Experiments were conducted to validate the proposed metric in an eldercare nursing home using a therapeutic pet robot ERIC (Elderly Rehabilitation Interactive Companion), a dog-like robot. Results of the statistical analysis showed no significant difference in Easiness of Acceptance (EOA) estimation for between subject experiments in groups with similar characteristics for pre and post training trials. But, significant differences in EOA estimation was witnessed for within subject experiments for pre and post training trials.

Robust unconstrained real-time face recognition still remains a challenge today. The recent addition to the market of lightweight commodity depth sensors brings new possibilities for human-machine interaction and therefore face recognition.

This paper presents the development of a hand held Electromyograph (EMG) device for assisting the subject during therapy. Surface Elctromyogram (sEMG) is acquired, processed and utilized for assessment and assisting therapy. Developed device includes integrated wireless module to transmit raw EMG through wireless link to a PC for processing information required for assessment of therapy. For observatory assessment, LabVIEW based interface was developed. The interface will be used to develop interactive computer games for motivating the subjects during therapy sessions.

We present a novel robotised search procedure to find flaws in buried pipelines. The motion planning system is based on an electrochemical ground measuring technique called direct-current voltage gradient (DCVG), and we combine it with the use of a tele-robotics assistance system. Remote human-robot interaction (HRI) deploys a trade on/off of information to accomplish defects search (in semi-autonomous mode). Furthermore, in the autonomous, we formulated a novel non-linear potential field approach to give the robot capabilities to automatically perform the search. The whole system consists of a tele-system; a four-wheeled driven (4WD) mobile robot with instruments on-board; a DCVG-based motion planning model; and the human-robot interaction protocol. In this approach we provide a general mathematical solution, numerical simulations, and experimental teleoperation trades.

Increasing research and development during these years has explored Human-Robot interactions especially for the challenge of the service robot. While service robots are becoming popular, these robots are just focusing into a specific tasks, and most of them are not ready prepare for the unpredictability of human environments. The efforts to develop more reliable robots that could interact with the human, are promoting the next generation of service robot. This type of robot would be equipped with perception, motion, and manipulation among other skills, to be help humans in daily tasks in different environments. This paper presents Snowie, a service robot used as Receptionist. The Snowie combines visual clues, voice, distance and PC input to detect humans and features in the environment. Guidance is one of the tasks that the receptionist robot must perform. To complete its task Snowie could receive extra information from smart beacons located in the building. The paper also explores two modalities to the guiding missions. Finally, experiments are presented in order to highlight the relevance of the approach.

Learning by imitation represent a useful and promising alternative to programming robots. This paper presents an approach for learning by imitation based on two functional elements used by humans to understand and perform actions. One is the representation of the body that contains information of the body's capabilities. The other one is a snapshot of the body and its relation with the environment at a given instant. These elements are believed to interact to generate among other abilities, the ability to imitate. Learning new tasks is then achieved by imitating the actions' goal as demonstrated. The experimental results with a humanoid robot are presented to validate the approach.

Recently, interest in analysis and generation of human and human-like motion has increased in various areas. In robotics, in order to operate a humanoid robot, it is necessary to generate motions that have strictly dynamic consistency. Furthermore, human-like motion for robots will bring advantages such as energy optimization.

During last years, robotic research has explored Human-Robot interactions especially for the challenge of the robot companion. This type of robot would be equipped with perception, motion, and manipulation among other skills, to be help humans in daily tasks. Guidance is one of the tasks that the robot companion must perform. This paper describes Robbie, a wheeled robot companion uses for guiding missions. The Robbie combines visual clues, voice, distance and PC input to detect humans and features in the environment. To complete its task Robbie could receive extra information from smart beacons located in the building. The paper also explores two modalities to the guiding missions. Finally, experiments are presented in order to highlight the relevance of the approach.

This paper focuses on the creation of a realistic simulation of a humanoid robot in a virtual environment. This model will help researchers to implement and study different behaviours of the humanoid robot Robo-Erectus without its physical presence. The dynamics and the appearance of the robot and the other objects within the simulator are faithfully reproduced in the virtual environment. Moreover, the virtual robot can be controlled with the same program controlling the real robot. The simulator has been used successfully to generate and test behaviours for the robot to prepare for the RoboCup 2008.

As rehabilitation robots are increasingly serving to improve the quality of life for physically disabled people in clinical environments, the concept of emotional expressiveness in robots becomes increasingly important. The human perception of robot's emotional expressions plays a crucial role in human robot interaction. Virtual expression systems outperform hardware systems in realizing human like expressions due the limitations in hardware actuators and advances in animation tools. This paper evaluates the human perception of robot's emotional expressions with two different virtual emotional expression systems: one where the robot exhibits emotions through icon faced expressions and in other the robot exhibits emotions through human faced expressions in clinical environment. A rehabilitation HRI research robot Robbie is introduced and the results of the comparative study on human perception of robotpsilas emotional expressions with two different systems are discussed.

In the field of human-computer interaction, natural goals, operators, methods, and selection rules language (NGOMSL) model is one of the most popular method for modelling knowledge and cognitive processes for rapid usability evaluation. NGOMSL model is a description of the knowledge that a user must possess to operate the system represented as elementary actions for effective usability evaluations. In the last few years, mobile robots have been exhibiting a stronger presence in the commercial markets and very little work has been done with NGOMSL modelling for usability evaluations in human-robot interaction discipline. This paper focuses on applying NGOMSL modelling for usability evaluation of human-humanoid robot interaction in soccer domain. Usability evaluations were performed and adequate results were obtained. Evaluated interaction design was adopted by our Robo-Erectus Junior version of humanoid robots in the RoboCup 2007 humanoid soccer league.