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Summary: Obstacle avoidance planning is the primary element in ensuring safe robot applications such as welding, assembly, and drilling. The states in the configuration space (C‐space) provide the pose information of any part of the manipulator and are preferentially considered in motion planning. However, it is difficult to express the environmental information directly in the high dimensional C‐space, limiting the application of C‐space obstacle avoidance planning. This paper proposes a singular manifold splitting C‐space method and designs a compatible obstacle avoidance strategy. The specific method is as follows: first, according to the specific structure of industrial robots, arm‐wrist separation obstacle avoidance planning is proposed to fix the robot as a 3R manipulator to reduce the dimension of C‐space. Next, the C‐space is segmented according to the singular manifolds, and the unique domain is delineated to complete the streamlining of the volume of the C‐space. Then, with the help of the point cloud, the obstacles are enveloped and mapped to the unique domain to construct the pseudo‐obstacle map. Industrial robots' obstacle avoidance planning is completed based on the pseudo‐obstacle map combined with an improved Rapidly‐Exploring Random Trees (RRT) algorithm. This method dramatically improves the efficiency of obstacle avoidance planning in the C‐space and avoids the effect of singularities on industrial robots. Finally, the effectiveness of the method is verified by physical experiments. [ABSTRACT FROM AUTHOR]

Due to the complex maritime navigation environment, Unmanned Surface Vessels (USVs) are influenced by unknown nonlinear dynamics arising from external disturbances and internal uncertainties. Achieving effective formation control while maintaining obstacle avoidance performance presents significant challenges. This article proposes a Neural Networks (NNs) adaptive formation Artificial Potential Field (APF) obstacle avoidance control method for multiple USVs. By employing online updates of Radial Basis Function (RBF) NNs technology, the unknown nonlinear dynamics are approximated, thus addressing complex nonlinear dynamics problems. In scenarios involving multiple USVs navigating under high wind and wave conditions, collisions with obstacles frequently occur. To tackle this issue, a leader-follower control strategy is designed that effectively addresses risk assessment and obstacle avoidance under such challenging conditions. Additionally, to account for saturation constraints or potential faults in the controller inputs commonly encountered in engineering applications, it implements an asymmetric auxiliary control system. Furthermore, the Lyapunov stability theorem is utilized to ensure the stability of both the formation control and obstacle avoidance algorithms for multiple USVs. Finally, the effectiveness of the proposed algorithm is validated through simulations. [ABSTRACT FROM AUTHOR]

The use of autonomous Unmanned Aerial Vehicles (UAVs) has been increasing, and the autonomy of these systems and their capabilities in dealing with uncertainties is crucial. Autonomous landing is pivotal for the success of an autonomous mission of UAVs. This paper presents an autonomous landing system for quadrotor UAVs with the ability to perform smooth landing even in undesirable conditions like obstruction by obstacles in and around the designated landing area and inability to identify or the absence of a visual marker establishing the designated landing area. We have integrated algorithms like version 5 of You Only Look Once (YOLOv5), DeepSORT, Euclidean distance transform, and Proportional-Integral-Derivative (PID) controller to strengthen the robustness of the overall system. While the YOLOv5 model is trained to identify the visual marker of the landing area and some common obstacles like people, cars, and trees, the DeepSORT algorithm keeps track of the identified objects. Similarly, using the detection of the identified objects and Euclidean distance transform, an open space without any obstacles to land could be identified if necessary. Finally, the PID controller generates appropriate movement values for the UAV using the visual cues of the target landing area and the obstacles. To warrant the validity of the overall system without risking the safety of the involved people, initial tests are performed, and a software-based simulation is performed before executing the tests in real life. A full-blown hardware system with an autonomous landing system is then built and tested in real life. The designed system is tested in various scenarios to verify the effectiveness of the system. The code is available at this repository: https://github.com/rnjbdya/Vision-based-UAV-autonomous-landing. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimal motion planning framework that enables navigating optimally from any initial, to any final position within confined workspaces with convex, moving obstacles is presented. Our method outputs a smooth velocity vector field, which is then employed as a reference controller in order to sub-optimally avoid moving obstacles. The proposed approach leverages and extends desirable properties of reactive methods in order to provide a provably convergent and safe solution. Our algorithm is evaluated with both static and moving obstacles in synthetic environments and is compared against a variety of existing methods. The efficacy and applicability of the proposed scheme is finally validated in a high-fidelity simulation environment. [ABSTRACT FROM AUTHOR]

Abstract\nIMPLICATIONS FOR REHABILITATIONPeople with visual impairments commonly rely on the use of a white cane to navigate and avoid obstacles. Although this analog tool is highly reliable and easy to use, its drawback is the impossibility to anticipate obstacles beyond reach and routes, as well as obstacles above waist level. Electronic travel aids (ETAs) and sensory substitution devices (SSDs) are new technological solutions designed to enhance the tactile and/or auditory capabilities to access the information needed to overcome those drawbacks. In the present study, 25 individuals with visual impairments used the T-Sight, a vibrotactile SSD, and/or the white cane in a navigation task involving obstacle avoidance. While the performance achieved with the device, measured by the number of collisions and walking speed, did not surpass the white cane, the SSD did have a positive impact on ambulation. Participants reduced the number of white cane touches towards environmental obstacles and performed obstacle avoidance maneuvers earlier. These results demonstrate the potential of vibrotactile devices to address the limitations of the white cane. Assessments of secondary travel aids for people with visual impairments should include performance measures beyond speed and number of collisions.These include the number of alerts, white cane touches, and trajectory analysis.Our vibrotactile secondary ETA allows improvements in navigation and obstacle avoidance shown as a reduction in the use of the white cane and an anticipation of the avoidance maneuvers.Assessments of secondary travel aids for people with visual impairments should include performance measures beyond speed and number of collisions.These include the number of alerts, white cane touches, and trajectory analysis.Our vibrotactile secondary ETA allows improvements in navigation and obstacle avoidance shown as a reduction in the use of the white cane and an anticipation of the avoidance maneuvers. [ABSTRACT FROM AUTHOR]

This study developed a prototype for an electronic travel aids (ETAs) system named Navigation Assisting Vest (NaVest) using Bluetooth Low Energy (BLE) beacons and a smartphone application to address the challenges faced by blind or visually impaired individuals (BVIP) whilst navigating unfamiliar indoor environments. The NaVest employs ultrasonic sensors for obstacle avoidance and indoor navigation functions. Testing and evaluation of the prototype were conducted with 12 BVIP and blindfolded participants in a local university building in the United Arab Emirates. The developed prototype improved the efficiency and safety of navigation tasks, and participants were overall satisfied with the system. Future research should focus on developing ETA systems that combine several functions to help BVIP travel more independently in indoor or outdoor environments. Additionally, ETA systems with multi-language support should be considered to improve accessibility for BVIP. [ABSTRACT FROM AUTHOR]

ABSTRACT This article pays attention to the obstacle avoidance trajectory tracking control problem for uncertain Euler–Lagrange (EL) systems subject to velocity constraints and input saturation. The existing obstacle avoidance results do not consider velocity constraints under input saturation, which means that an EL system may not be able to obtain sufficient control inputs to avoid a collision with an obstacle if it has a high speed when approaching the obstacle. Therefore, the velocity constraints in the obstacle avoidance tracking control are considered in this paper. A novel velocity constraint function that depends on the distance between the system and the obstacle is proposed. Integral‐multiplicative Lyapunov‐barrier functions (LBFs) are constructed and incorporated into the backstepping procedure to design an adaptive fuzzy obstacle avoidance tracking control scheme. Moreover, an auxiliary dynamic system is designed by constructing a bounded nonlinear vector related to an auxiliary variable to compensate for the effects of saturation. Through the Lyapunov method and boundedness analysis for the barrier function, it is shown that the protocol achieves obstacle avoidance for the EL system without violating the velocity constraints inside the obstacle detection region, while also guaranteeing the ultimate uniform boundedness of all the closed‐loop signals. Numerical simulations are presented to demonstrate the efficacy of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In view of the local minimum value, unreachable target and path oscillation in the practical application of traditional artificial potential field method, this study proposes an improved artificial potential field method based on deflection angle suppression. Based on the traditional artificial potential field method, this method adopts the improved repulsive potential field function to ensure that the target point is the lowest point of the whole situation. The deflection angle inhibitor is introduced in the path solution to suppress the excessive deflection angle during the driving process of the unmanned vehicle. After the unmanned vehicle falls into the local minimum point, a new virtual target point is added to the path solution at each step until the unmanned vehicle accumulates a certain deflection angle to get rid of the obstacle group. The simulation results show that the algorithm can reduce the volatility of the planning path without affecting the obstacle avoidance of the unmanned vehicle, and enable the unmanned vehicle to smoothly escape the complex obstacle group, smoothly reach the target point, and plan an effective, short and less oscillating path. [ABSTRACT FROM AUTHOR]

This study applies a successful collision-avoidance method using buffered Voronoi cells (BVC) to control quadrotors. In particular, we consider the case of dealing with stationary obstacles using only the local information obtained from sensors, which has not been discussed in previous studies. In this study, we assume an unknown environment with grid-shaped obstacles. We demonstrate that four quadrotors can compute the information required for the BVC-based collision avoidance algorithm and move in the same environment without communicating with each other or receiving information from a central controller, using the local information obtained from the mounted 3D LiDAR. Simulations indicate that the system can handle static obstacles using point-cloud data obtained using 3D LiDAR as Voronoi seeds. We also demonstrate that the BVC-based collision avoidance algorithm can be applied to quadrotors controlled by a simple PD position controller without any modification to the controller. These findings show that the BVC-based collision avoidance can be easily implemented using existing commercial drones. [ABSTRACT FROM AUTHOR]

This study investigates a method for generating obstacle avoidance trajectories for arbitrary convex polyhedrons. We propose a formulation that converts discrete conditions into continuous equation forms to avoid convex polyhedron obstacles. The condition that the evaluation point be located outside of the convex polyhedron can be transformed into a constraint in the continuous equation form and incorporated into the optimization calculation to generate avoidance trajectories. Avoidance trajectory generation using the Legendre pseudospectral method is performed for convex polyhedral obstacles of various shapes. The results show that the proposed method successfully generates avoidance trajectories for arbitrary convex polyhedral obstacles. [ABSTRACT FROM AUTHOR]

To enhance the navigation and control efficiency of mobile robots in the field of health monitoring, a novel path planning and control strategy for mobile robots with uncertain dynamics based on improved artificial potential fields is proposed in this paper. Specifically, we propose an attractive potential field rotation method to overcome the limitation that traditional artificial potential fields tend to fall into local minima. Then, we define a new class of attractive potential fields to address the goals non-reachable with obstacles nearby (GNRON) and collisions caused by excessive attractive force at long distances from the target point. Furthermore, a control law is proposed for the mobile robot with uncertain dynamics, and the stability of the closed-loop system is rigorously proven using the Lyapunov method. Finally, the feasibility and effectiveness of the proposed method are verified by simulations and experiments. [ABSTRACT FROM AUTHOR]

The paper presents a vision-based obstacle avoidance strategy for lightweight self-driving cars that can be run on a CPU-only device using a single RGB-D camera. The method consists of two steps: visual perception and path planning. The visual perception part uses ORBSLAM3 enhanced with optical flow to estimate the car's poses and extract rich texture information from the scene. In the path planning phase, the proposed method employs a method combining a control Lyapunov function and control barrier function in the form of a quadratic program (CLF-CBF-QP) together with an obstacle shape reconstruction process (SRP) to plan safe and stable trajectories. To validate the performance and robustness of the proposed method, simulation experiments were conducted with a car in various complex indoor environments using the Gazebo simulation environment. The proposed method can effectively avoid obstacles in the scenes. The proposed algorithm outperforms benchmark algorithms in achieving more stable and shorter trajectories across multiple simulated scenes. [ABSTRACT FROM AUTHOR]

Helical growth of the root tip (circumnutation) that permits surface exploration facilitates root penetration into soil. Here, we reveal that rice actin-binding protein RMD aids in root circumnutation, manifested by wavy roots as well as compromised ability to efficiently explore and avoid obstacles in rmd mutants. We demonstrate that root circumnutation defects in rmd depend on brassinosteroid (BR) signaling, which is elevated in mutant roots. Suppressing BR signaling via pharmacological (BR inhibitor) or genetic (knockout of BR biosynthetic or signaling components) manipulation rescues root defects in rmd. We further reveal that mutations in MAPK6 suppress BR signaling and restore normal root circumnutation in rmd, which may be mediated by the interaction between MAPK6, MAPKK4 and BR signaling factor BIM2. Our study thus demonstrates that RMD and MAPK6 control root circumnutation by modulating BR signaling to facilitate early root growth. [ABSTRACT FROM AUTHOR]

Summary: This article addresses the safe tracking control problem of quadrotor unmanned aerial vehicles (QUAV) considering uncertain aerodynamics and unknown external disturbances. First, a robust adaptive backstepping controller is designed, where the bias of the control variable, that is, the tracking error of the attitude subsystem, is considered in the design process of the position subsystem. Meanwhile, the safe obstacle avoidance objective is realized by the quadratic programming (QP) method that combines the tracking controller with the exponential control barrier function (ECBF). Furthermore, an event‐triggered mechanism is used for the QP process to conserve computing resources. Simulation validates the feasibility and superiority of the proposed control scheme in presence of multi‐obstacle. [ABSTRACT FROM AUTHOR]

To address the problem of underactuated surface vessel (USV) formation control in static obstacle environments with model uncertainties and time-varying external disturbances, a model-free formation control strategy is proposed in this paper. First, based on the guiding vector field (GVF), a composite GVF is developed to guide USV formation to the desired position and to avoid multiple static obstacles. Second, a flexible constraint strategy is introduced, and the constraint boundary conditions are appropriately relaxed to avoid singularities in the obstacle environment. Then, based on the Mexican hat wavelet function, the self-structuring fuzzy Mexican hat wavelet cerebellar model articulation controller (SCMAC), and a self-structuring fuzzy Mexican hat wavelet brain emotional learning controller (SBELC), are proposed to achieve model-free control. In addition, the self-structuring algorithm is embedded into SCMAC and SBELC to achieve autonomous optimization of the controller structure and to reduce the computational effort of the control system. The salient features in the proposed control strategy are as follows. First, the proposed model-free formation control strategy does not have to rely on accurate model information. Second, collisions are effectively avoided, and good control performance is guaranteed even under the influence of disturbances and static obstacles. Third, the proposed self-structuring algorithm achieves automatic construction of the controller structure. Finally, the signals in the control system are proven to be bounded, and the simulation results verify the feasibility and superiority of the proposed model-free control strategy. • An adaptive intelligent formation control method is proposed based on SCMAC and SBELC. • A Mexican hat wavelet function is developed to enhance the ability of the formation control system to process signals. • The GVF and flexible constraints strategy are introduced to avoid static obstacles and ensure control performance. • A self-structuring algorithm is embedded to optimize the structure of SCMAC and SBELC. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The technological revolution has transformed the area of labor with reference to automation and robotization in various domains. The employment of robots automates these disciplines, rendering beneficial impacts as robots are cost-effective, reliable, accurate, productive, flexible, and safe. Usually, single robots are deployed to accomplish specific tasks. The purpose of this study is to focus on the next step in robot research, collaborative multi-robot systems, through flocking control in particular, improving their self-adaptive and self-learning abilities. This review is conducted to gain extensive knowledge related to swarming, or cluster flocking. The evolution of flocking laws from inception is delineated, swarming/cluster flocking is conceptualized, and the flocking phenomenon in multi-robots is evaluated. The taxonomy of flocking control based on different schemes, structures, and strategies is presented. Flocking control based on traditional and trending approaches, as well as hybrid control paradigms, is observed to elevate the robustness and performance of multi-robot systems for collective motion. Opportunities for deploying robots with flocking control in various domains are also discussed. Some challenges are also explored, requiring future considerations. Finally, the flocking problem is defined and an abstraction of flocking control-based multiple UAVs is presented by leveraging the potentials of various methods. The significance of this review is to inspire academics and practitioners to adopt multi-robot systems with flocking control for swiftly performing tasks and saving energy. [ABSTRACT FROM AUTHOR]

Mobile robots (MRs) are exerting a significant influence in industrial as well as residential settings. Concurrently with the escalating advancement of technology, the incorporation of artificial intelligence algorithms into the obstacle evasion issue of MRs is gaining increasing attention. The paper utilizes Deep Reinforcement Learning (DRL) to a MR that is furnished with a camera. Images captured by a stereo camera will be inputted into the YOLO-v8 model to identify obstacles situated in the path of the MR. Subsequently, the distances to these obstacles will be regarded as the state of the MR. The information was utilized to train a Deep Q-Network. Throughout this training process, the system acquires the capability to determine suitable actions for the MR to advance towards the destination while circumventing obstacles. Each action executed by the MR is accompanied by a reward, with the path yielding the most desirable outcome receiving the highest reward. The outcomes of the simulations conducted on the Robot Operating System 2 (ROS2) corroborate the effectiveness of this Deep Reinforcement Learning technique for the task of obstacle avoidance. [ABSTRACT FROM AUTHOR]

While dealing with dynamic scenarios, mobile robot (MR) path planning (PP) has proven to be extremely challenging. To overcome difficulties with grid-map representation, the paper proposes a hybrid path planning strategy. In a typical setting, an A-star algorithm first generates a global path planning (GPP). To ensure safe obstructive avoidance, the modified heuristic function is enhanced with a risk cost coefficient adjusted according to the distance to the nearest obstacle. Then, GPP is improved by eliminating redundant nodes, and smoothing algorithms. Furthermore, local path planning (LPP) has enhanced the ability to avoid obstacles in local areas by using adaptive dynamic window approach (DWA). The obtained path is hardly continuous after eliminating unnecessary path nodes and the required recalculation LPP based on DWA. Consequently, straight, and curved segments are joined into the continuous polyline smoothed by the Bezier curves. The proposed hybrid PP has been shown to be feasible and to produce optimal outcomes in both simulations and experiments. [ABSTRACT FROM AUTHOR]

Hybrid neural networks (HNNs), integrating the strengths of artificial neural networks (ANNs) and spiking neural networks (SNNs), provide a promising solution towards generic artificial intelligence. There is a prevailing trend towards designing unified SNN-ANN paradigm neuromorphic computing chips to support HNNs, but developing platforms to advance neuromorphic computing systems is equally essential. This paper presents the PAIBoard platform, which is designed to facilitate the implementation of HNNs. The platform comprises three main components: the upper computer, the communication module, and the neuromorphic computing chip. Both hardware and software performance measurements indicate that our platform achieves low power consumption, high energy efficiency and comparable task accuracy. Furthermore, PAIBoard is applied in a robot dog for tracking and obstacle avoidance system. The tracking module combines data from ultra-wide band (UWB) transceivers and vision, while the obstacle avoidance module utilizes depth information from an RGB-D camera, which further underscores the potential of our platform to tackle challenging tasks in real-world applications. [ABSTRACT FROM AUTHOR]

Summary: This work proposes a smooth time‐varying controller for trajectory tracking and posture regulation problems of car‐like mobile robots (CLMR). Currently, few studies focus on the control problems of CLMR's kinematic model. Global trajectory tracking or global posture stabilization controller for CLMR has not yet been proposed. In this study, we propose a global trajectory tracking controller for CLMR for the first time. By setting a specific reference trajectory, the controller is generalized to the posture stabilization task. Obstacle avoidance is also taken into consideration in posture stabilization task. Unlike the prototypical method of transforming the model into a nonholonomic chained‐form system, the proposed method is designed based on the original tracking error equation. Therefore our approach does not have singularities and has a global attraction region. Furthermore, the global convergence of the proposed controllers is strictly proved using the proof by contradiction and Barbalat's lemma. Finally, simulated and actual experiments on a car‐like robot demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The article addresses the event‐triggered obstacle avoidance control problem for autonomous surface vehicles subject to actuator faults. In order to tackle the challenges presented by unknown actuator faults, internal model uncertainties, and external disturbances, we propose a fault‐tolerant control scheme that relies on an extended state observer to estimate the unknown parameters. In addition, considering the occurrence of actuator faults, a novel event‐triggered mechanism is developed to reduce wear of actuator and ensures the performance of system. On this basis, we employ backstepping technique and an improved artificial potential function methods to develop an event‐triggered obstacle avoidance control scheme with the capability of fault tolerance. This proposed control strategy ensures the uniform ultimate boundedness of tracking errors. In contrast to existing results, the presented control strategy simultaneously holds the performance of obstacle avoidance and fault tolerance and reduce the update frequency of actuators. The effectiveness of the provided control scheme has been confirmed through simulation. [ABSTRACT FROM AUTHOR]

Indonesia has 127 active volcanoes, which have to be monitored continuously in normal, eruption, or after-eruption conditions, to minimize the effects of disaster. Therefore, we have developed a four-wheeled mobile robot for both exploration and monitoring of volcanoes. To finish its mission on uneven terrain full of obstacles, the robot should be able to detect and avoid these obstacles. Therefore, real-time object detection was designed using the YOLOv5s deep learning algorithm, which was implemented on a Raspberry Pi3 for the front camera of the robot. Before it was tested on a real volcano, the model of the algorithm was trained to be able to detect obstacles. The dataset was trained with three variations of epochs (100, 300, and 500) in sixteen batches of YOLOv5s. The last variant yielded the best results, at 63.4% mAP_0.5 and 40.4% mAP_0.5:0.95, with almost zero loss. This model was then implemented on a Raspberry Pi3 to detect trees and rocks captured by camera on Tangkuban Parahu Volcano. Most of the trees and rocks were successfully detected, with 90.9% recall, 79.9% precision, and 91.5% accuracy. Furthermore, the detection error was low, as indicated by low FP and FN numbers. [ABSTRACT FROM AUTHOR]

Collaborative robotic manipulators can stop in case of a collision, according to the ISO/TS 15066 and ISO 10218-1 standards. However, in a human-robot collaboration scenario where the robot and the human share the workspace, a better solution with concerns to production and operator safety would be to perceive in advance the presence of an obstacle and be able to avoid it, thereby completing the task without halting the robot. In this paper, an obstacle avoidance algorithm is tested using a sensor system for real-time human detection; the operator represents a potential dynamic obstacle that can interfere with the robot motion. The sensor system consists of three RGB-D cameras. A custom software framework has been developed in Python exploiting machine learning tools for human skeleton detection. The coordinates of the human body joints relative to the manipulator base are used as input to the obstacle avoidance algorithm. The use of multiple sensors makes it possible to limit the occlusion problem; in addition, the choice of non-wearable sensors goes in the direction of better operator comfort. A series of experimental tests were performed to verify the accuracy of skeleton detection and the ability of the system to avoid obstacles in real time. The human motion caption system, in particular, was validated through a comparison with a commercial system based on wearable IMU sensors widely used and validated in motion capture. There is an NRMSE of 3.23 % for the RGB-D camera-based skeleton detection system, against an NRMSE of 12.23 % for the IMU wearable sensor system. Test results confirm that the system is able to avoid collisions with the human body under various conditions, static or dynamic, ensuring a minimum safety distance to any part of the manipulator. In 44 tests in which the operator moves around the robot with possible collisions (at a speed typical of manufacturing operations), the minimum operator-robot distance averaged 208 mm , being 200 mm the limit safety distance set by algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human-Robot Interaction is an increasingly important topic in both research and industry fields. Since human safety must be always guaranteed and accidental contact with the operator avoided, it is necessary to investigate real-time obstacle avoidance strategies. The transfer from simulation environments, where algorithms are tested, to the real world is challenging from different points of view, e.g., the continuous tracking of the obstacle and the configuration of different manipulators. In this paper, the authors describe the implementation of a collision avoidance strategy based on the potential field method for off-line trajectory planning and on-line motion control, paired with the Motion Capture system Optitrack PrimeX 22 for obstacle tracking. Several experiments show the performance of the proposed strategy in the case of a fixed and dynamic obstacle, disturbing the robot’s trajectory from multiple directions. Two different avoidance modalities are adapted and tested for both standard and redundant robot manipulators. The results show the possibility of safely implementing the proposed avoidance strategy on real systems. [ABSTRACT FROM AUTHOR]

On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying "assembler" robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a "construction site" scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Due to the complex maritime navigation environment, Unmanned Surface Vessels (USVs) are influenced by unknown nonlinear dynamics arising from external disturbances and internal uncertainties. Achieving effective formation control while maintaining obstacle avoidance performance presents significant challenges. This article proposes a Neural Networks (NNs) adaptive formation Artificial Potential Field (APF) obstacle avoidance control method for multiple USVs. By employing online updates of Radial Basis Function (RBF) NNs technology, the unknown nonlinear dynamics are approximated, thus addressing complex nonlinear dynamics problems. In scenarios involving multiple USVs navigating under high wind and wave conditions, collisions with obstacles frequently occur. To tackle this issue, a leader-follower control strategy is designed that effectively addresses risk assessment and obstacle avoidance under such challenging conditions. Additionally, to account for saturation constraints or potential faults in the controller inputs commonly encountered in engineering applications, it implements an asymmetric auxiliary control system. Furthermore, the Lyapunov stability theorem is utilized to ensure the stability of both the formation control and obstacle avoidance algorithms for multiple USVs. Finally, the effectiveness of the proposed algorithm is validated through simulations.

For autonomous navigation, a mobile robot is required to move toward a destination while avoiding obstacles. In this paper, we present a motion planner based on CNN. In terms of obstacle avoidance, since a position of a dynamic obstacle changes with time, it is important for the robot to plan avoidance motions in consideration of the time series variation in the images. For this purpose, an LSTM block is applied to the CNN. The policy of the motion planner represented by CNN with LSTM is trained through imitation learning. In this regard, however, it is difficult for the robot to recognize unknown objects as obstacles. For obstacle recognition, a perception process is further provided between the image inputs and CNN with LSTM in the motion planner. Moreover, the robot plans different avoidance motions depending on the velocity of the dynamic obstacle. For this purpose, an obstacle state classifier based on CNN is used ahead of the motion planner. A depth-difference image generated from two depth images is fed as the input to the classifier. A classified state that indicates the velocity of the obstacle is fed as the input to the following motion planner. In the navigation experiments, we show that the robot based on the proposed motion planner is able to move toward a destination autonomously while avoiding standing and walking persons, respectively. Furthermore, we show that the robot based on the motion planner with the obstacle state input is able to plan different avoidance motions for a person walking slowly or fast using the obstacle state classifier.

People across the globe witnessed the meteoric rise of the recently discovered coronavirus, which caught governments, healthcare systems, and civilisations off guard. Robotics, self-governing systems, and intelligent devices can contribute positively to a rapid epidemic. Smart assistive robots can help patients and healthcare staff in general and make the anticipation, containment, and exacerbation of coronavirus easier by highlighting the coronavirus burden on healthcare structures during a crisis. In this type of situation, a smart-assistive robot with aid capabilities is promising, especially for those putting their lives in jeopardy by interacting with ill patients in hygienic environments. By providing regular monitoring and patient assistance, the intelligent helper robot could assist in lowering the danger of illness spreading to others. Concerning the application this paper discusses, a robot with a wheeled base and a humanoid structure can aid isolated patients and connect wirelessly anywhere in the world (using the Internet of Things) with patients far away more effectively without any physical touch. The designed robot is incredibly light (approximately 7 kg) and easily transported to the appropriate location (any hospital or medical camp, for example). Its modular design allows quick assembly and disassembly. The robot can comfortably handle 10–15 kg of load on its holding plate. This study also explains the structural capability and method of directing the robot in an unfamiliar area using ultrasonic sensors for obstacle avoidance, a camera module for real-time vision, and a Skype call feature that may enable doctors to keep tabs on a patient's health. [ABSTRACT FROM AUTHOR]

The operation of multi‐agent systems (MAS) in space necessitates considerations for obstacle avoidance, collision prevention, and connectivity among agents. This coupling conflict, stemming from spatial resource utilization, poses a significant and non‐negligible threat to the safe operation of MAS. This article proposes a consensus control scheme for the time‐varying formation of MAS, incorporating functions for maintaining connectivity, collision avoidance, and obstacle dodging. This scheme effectively constrains the time‐varying formation tracking error within an arbitrarily small range. The considered system model takes a high‐order nonlinear form, with uncertainties and disturbances present in each order. This grants the control scheme with high generality and practicability. By employing barrier Lyapunov function to delineate the safe operational space of MAS, conflicts in spatial resource utilization are avoided. This approach simultaneously fulfills the requirements for connectivity maintenance, collision avoidance, and obstacle dodging in the safe operation of MAS. An additional rotation operator is integrated into the controller to smoothly address the "minima" problem, eliminating the need for external intervention. Gaussian radial basis function is used to estimate the nonlinear terms, uncertainties, and unknown perturbation online in the system. The stability of the MAS under the proposed control scheme is analyzed through Lyapunov function. Finally, numerical simulation results are demonstrated to explain the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

Given a set of pairwise disjoint polygonal obstacles in the plane, finding an obstacle-avoiding Euclidean shortest path between two points is a classical problem in computational geometry and has been studied extensively. Previously, Hershberger and Suri (in SIAM Journal on Computing, 1999) gave an algorithm of O(n logn) time and O(n logn) space, where n is the total number of vertices of all obstacles. Recently, by modifying Hershberger and Suri’s algorithm,Wang (in SODA’21) reduced the space toO(n) while the runtime of the algorithm is still O(n logn). In this article, we present a new algorithm of O(n + h logh) time and O(n) space, provided that a triangulation of the free space is given, where h is the number of obstacles. The algorithm is better than the previous work when h is relatively small. Our algorithm builds a shortest path map for a source point s so that given any query point t, the shortest path length from s to t can be computed in O(logn) time and a shortest s-t path can be produced in additional time linear in the number of edges of the path. [ABSTRACT FROM AUTHOR]

With the publication of this article, the authors continue the research on the development and testing of a methodology for in-flight aircraft rerouting which have begun in previously published articles in the Civil Aviation High Technologies of the Moscow State Technical University of Civil Aviation. This article presents the results of the study in terms of developing the potentials of the methodology from reconfiguring a route of a hypothetical aircraft and obstacles in the horizontal plane, which were previously demonstrated, prior to rerouting in both the horizontal and vertical planes for two different types of obstacles: 1) ground natural or artificial (mountain, power line support, etc.); 2) air (squall line, prohibited flight area, etc.) and their combinations using an example of a Mi-8 helicopter flight on route using a real digital map of the terrain. As mentioned above, a large amount of aviation accidents is associated with the loss of control in-flight, as well as the collision with terrain in a controlled flight (categories LOC-I, CFIT, LALT). As a result of the investigation of the aviation accidents, it was found that these accidents are often caused by the requirement to reroute quickly due to obstacles, for example, a squall line. When determining alternatives to avoid an obstacle, as well as while implementing the selected route for avoiding action, the crew makes errors due to the increased psychophysiological load and lack of time. The methodology and the algorithms, proposed by the authors, make it possible to assess the safety of an original route, estimate options for alternatives to avoid around obstacles detected in-flight, check them for feasibility, taking into account aircraft performance, flight envelope, and also select the optimal route from the view of some criterion, for example, based on minimizing the route length increase, reducing additional fuel consumption, the time required to implement a new route of flight, etc.

In a complex and unknown battlefield environment, unmanned vehicle clusters can perform more complex tasks than a single unmanned ground vehicle (UGV). The formation and obstacle avoidance of unmanned vehicle clusters is one of the research hotspots in the field of swarm intelligence. To reduce the deviation between the actual formation position and expected formation when unmanned vehicle clusters avoid obstacles in an unknown environment, this paper proposes a cooperative formation and obstacle avoidance control method for unmanned vehicle clusters based on an improved dynamic window approach (DWA). In addition to the azimuth evaluation, obstacle evaluation, and speed evaluation factors of the path evaluation function in DWA, the direction coordination and formation maintenance factors are added. We established a mathematical model of unmanned vehicle cooperative formation and calculated in real time the expected position, direction, and speed of each following vehicle according to the expected formation. The sum of the deviation between the real driving and expected directions of each following vehicle was taken as the direction cooperation factor, and the sum of the absolute deviation between the real and expected positions of each following vehicle was taken as the formation-keeping factor. When unmanned vehicles approach unknown obstacles, the adaptive collaborative adjustment of the relative position and speed is performed based on the improved DWA, which can improve the precision of the formation positions during the obstacle avoidance process. In this paper, a simulation is conducted using a scenario of three unmanned vehicles forming a triangular formation to avoid obstacles and three obstacles. The simulation results show that the control adjustment based on the improved dynamic window and variable weight optimization algorithm is more timely than the traditional formation control based on the artificial potential field. Additionally, the formation position deviation is relatively small when avoiding obstacles. The average deviation between the actual position of the vehicle formation and the desired position of the formation near the obstacle is used as the evaluation function. The coefficients of the path evaluation function of the improved DWA are optimized based on the variable weight pigeon-inspired optimization (PIO). It can improve the formation, maintaining accuracy in obstacle avoidance. In summary, the proposed improved algorithm can make the unmanned vehicle cluster adjust the driving speed and direction cooperatively in obstacle avoidance. Moreover, it can improve the stability and accuracy of the dynamic formation.

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.

This research addresses the challenges of using the Rapidly Exploring Random Tree (RRT) algorithm as a mobile robot path planner. While RRT is known for its flexibility and wide applicability, it has limitations, including careful tuning, susceptibility to local minima, and generating jagged paths. The main objective is to improve the smoothness of RRT-generated trajectories and reduce significant path curvature. A novel approach is proposed to achieve these, integrating the RRT path planner with a modified version of the Whale Optimization Algorithm (RRT-WOA). The modified WOA algorithm incorporates parameter variation (C) specifically designed to optimize trajectory smoothness. Additionally, Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) instead of conventional splines for point interpolation further smoothes the generated paths. The modified WOA algorithm is thoroughly evaluated through a comprehensive comparative analysis, outperforming other popular population-based optimization algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Firefly Algorithm (FA) in terms of optimization time, trajectory smoothness, and improvement from the initial guess. This research contributes a refined trajectory planning approach and highlights the competitive advantage of the modified WOA algorithm in achieving smoother and more efficient trajectories compared to existing methods. [ABSTRACT FROM AUTHOR]

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Recently, the topics associated with robotics are becoming an interesting research area. Meanwhile, intelligent mobile robots or unmanned aerial vehicle (UAV) has great acceptance; however, the navigation and control of the devices are more complex. The shortage of dealing with static obstacles and evading them due to secure and safe routing is a fundamental need of the system. Therefore, an effectual robotic obstacle avoidance mechanism using deep learning (DL) model optimized with a swarm intelligence procedure is proposed. The main aim of this method is to avoid collision with static obstacles and attain the target in an optimal path. At first, the proposed method initializes the obstacles and target places for mobile robot navigation, and their positions are collected. An innovative path planning approach is presented using adaptive beetle swarm levy flight optimization (BSLFO) for optimal path calculation. Here, distance and path smoothness parameters are considered for optimal planning by the proposed BSLFO approach. The mobile robots are moved to the best positions obtained by the BSLFO approach. Additionally, optimal robots' movement with collision avoidance has been performed by employing a fuzzy rule based optimal deep neural network (FRDNN). The proposed method is implemented in the MATLAB platform and evaluates the performance in terms of performance metrics, including accuracy, recall, precision, and planning time. The maximum accuracy reached by the proposed method for static and dynamic obstacles is 98.62, which offers better obstacle avoidance and effective path planning. Consequently, the overall experimental outcomes proved that the proposed model outperforms the existing models. [ABSTRACT FROM AUTHOR]

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In order to solve the problem whereby the original DWA algorithm cannot balance safety and velocity due to fixed parameters in complex environments with many obstacles, an improved dynamic window approach (DWA) of local obstacle avoidance for robots is proposed. Firstly, to assure the path selection stationarity and enhance the navigation ability of inspection robot, the velocity cost function of the original DWA was improved and the distance cost function of the target point was added. Then, the distances among the inspection robot, observed obstacles, and target points were input into a fuzzy control module, and the fuzzy weights of the velocity and distance cost functions were obtained, by which the motion of the inspection robot can continuously self-adjust and adapt to the unknown environment. Finally, several simulations and experiments were conducted. The results show that the improved DWA algorithm can effectively improve the obstacle avoidance ability of inspection robots in complex environments. The path can be more reasonably selected and the safety of inspection robots can be enhanced, while the safe distance, path length, and the number of samples can also be optimized by the improved DWA compared to the original DWA. [ABSTRACT FROM AUTHOR]

In this study, under the limited volume of 18 cm × 18 cm × 21 cm, a small-sized mobile robot is designed and implemented. It consists of a CPU, a GPU, a 2D LiDAR (Light Detection And Ranging), and two fisheye cameras to let the robot have good computing processing and graphics processing capabilities. In addition, three functions of road detection, sign recognition, and obstacle avoidance are implemented on this small-sized robot. For road detection, we divide the captured image into four areas and use Intel NUC to perform road detection calculations. The proposed method can significantly reduce the system load and also has a high processing speed of 25 frames per second (fps). For sign recognition, we use the YOLOv4-tiny model and a data augmentation strategy to significantly improve the computing performance of this model. From the experimental results, it can be seen that the mean Average Precision (mAP) of the used model has increased by 52.14%. For obstacle avoidance, a 2D LiDAR-based method with a distance-based filtering mechanism is proposed. The distance-based filtering mechanism is proposed to filter important data points and assign appropriate weights, which can effectively reduce the computational complexity and improve the robot's response speed to avoid obstacles. Some results and actual experiments illustrate that the proposed methods for these three functions can be effectively completed in the implemented small-sized robot. [ABSTRACT FROM AUTHOR]

In this study, a cooperative robotic system comprising two Selective Compliance Assembly Robot Arm (SCARA) robots was developed and simulated. An algorithm was proposed for the coordination of robots in cooperative tasks, along with a Rapidly exploring Random Tree (RRT) path planner for obstacle avoidance. The proposed system proved effective in transferring objects between robots and in handling various scenarios of variable complexity without collisions. The implementation of advanced trajectory planning and coordination algorithms significantly improves the adaptability and autonomy of robotic systems, allowing robots to predict and react to the movements of their counterparts and changes in the environment in real time. This capability is crucial for maintaining a safe and efficient work environment. The importance of synchronization and effective communication between robots is highlighted to avoid collisions and optimize trajectories and cycle times. All tests were conducted in virtual environments, allowing for the evaluation and refinement of the performance of the robots' performance under controlled conditions. The positive results obtained in the simulations suggest that the system is well suited for future practical implementation in industrial and manufacturing applications, such as chemical handling, collaborative welding, quality inspection, among others. These findings underscore the potential of the cooperative SCARA system to improve the efficiency and safety in industrial applications using advanced algorithms and control techniques, establishing a solid foundation for future research and development in the field of cooperative robotics. [ABSTRACT FROM AUTHOR]

The purpose of this article is to control the formation of heterogeneous flying robots and to cross obstacles. The robots in question are a quadrotor and two unmanned helicopters. Independent attitude and position of robots and formation flight are controlled by the sliding mode method. Improved Artificial Potential Fields has been used to avoid collision with dynamic and static obstacles. The results of the design of the attitude and position controller showed that the attitude and position of the robots were well stabilized and converged in less than 3 s, which is a favorable time. The results of the formation flight simulation were presented in the form of 4 missions. In the first mission, the quadrotor is considered the leader, and the helicopters are the followers. The flight formation is triangular and the flight path is spiral. The results showed that the followers follow the leader. In the second mission, the robot crosses dynamic and static obstacles and the leader tracks the fixed target. In the third mission, the number of followers increases to 5 and the flight formation is hexagonal. The obstacles in this mission are dynamic and the target is moving. The results showed that in these two missions, the leader tracks the target well and the robots maintain triangular and hexagonal flight formations after crossing the obstacles. The results of simulations of group crossing of obstacles showed that the simulation error is less than 4% according to the expected position of the robots, and show the efficiency of the applied methods. [ABSTRACT FROM AUTHOR]

The existing trajectory planning research mainly considers the safety of the obstacle avoidance process rather than the anti-rollover requirements of heavy vehicles. When there are driving risks such as rollover and collision, how to coordinate the game relationship between the two is the key technical problem to realizing the anti-rollover trajectory planning under the condition of driving risk triggering. Given the above problems, this paper studies the non-cooperative game model construction method of the obstacle avoidance process that integrates the vehicle driving risk in a complex traffic environment. Then it obtains the obstacle avoidance area that satisfies both the collision and rollover profit requirements based on the Nash equilibrium. A Kmeans-SMOTE risk clustering fusion is proposed in this paper, in which more sampling points are supplemented by the SMOTE oversampling method, and then the ideal obstacle avoidance area is obtained through clustering algorithm fusion to determine the optimal feasible area for obstacle avoidance trajectory planning. On this basis, to solve the convergence problems of the existing multi-objective particle swarm optimization algorithm and analyze the influence of weight parameters and the diversity of the optimization process, this paper proposes an anti-rollover trajectory planning method based on the improved cosine variable weight factor MOPSO algorithm. The simulation results show that the trajectory obtained based on the method proposed in this paper can effectively improve the anti-rollover performance of the controlled vehicle while avoiding obstacles. [ABSTRACT FROM AUTHOR]

A system has been developed to convert manual wheelchairs into electric wheelchairs, providing assistance to users through the implemented algorithm, which ensures safe driving and obstacle avoidance. While manual wheelchairs are typically controlled indoors based on user preferences, they do not guarantee safe driving in areas outside the user's field of vision. The proposed model utilizes the dynamic window approach specifically designed for wheelchair use, allowing for obstacle avoidance. This method evaluates potential movements within a defined velocity space to calculate the optimal path, providing seamless and safe driving assistance in real time. This innovative approach enhances user assistance and safety by integrating state-of-the-art algorithms developed using the dynamic window approach alongside advanced sensor technology. With the assistance of LiDAR sensors, the system perceives the wheelchair's surroundings, generating real-time speed values within the algorithm framework to ensure secure driving. The model's ability to adapt to indoor environments and its robust performance in real-world scenarios underscore its potential for widespread application. This study has undergone various tests, conclusively proving that the system aids users in avoidance obstacles and ensures safe driving. These tests demonstrate significant improvements in maneuverability and user safety, highlighting a noteworthy advancement in assistive technology for individuals with limited mobility. [ABSTRACT FROM AUTHOR]