This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential f...Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.展开更多
Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary...Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency.展开更多
Monocular vision-based navigation is a considerable ability for a home mobile robot. However, due to diverse disturbances, helping robots avoid obstacles, especially nonManhattan obstacles, remains a big challenge. In...Monocular vision-based navigation is a considerable ability for a home mobile robot. However, due to diverse disturbances, helping robots avoid obstacles, especially nonManhattan obstacles, remains a big challenge. In indoor environments, there are many spatial right-corners that are projected into two dimensional projections with special geometric configurations. These projections, which consist of three lines,might enable us to estimate their position and orientation in 3 D scenes. In this paper, we present a method for home robots to avoid non-Manhattan obstacles in indoor environments from a monocular camera. The approach first detects non-Manhattan obstacles. Through analyzing geometric features and constraints,it is possible to estimate posture differences between orientation of the robot and non-Manhattan obstacles. Finally according to the convergence of posture differences, the robot can adjust its orientation to keep pace with the pose of detected non-Manhattan obstacles, making it possible avoid these obstacles by itself. Based on geometric inferences, the proposed approach requires no prior training or any knowledge of the camera’s internal parameters,making it practical for robots navigation. Furthermore, the method is robust to errors in calibration and image noise. We compared the errors from corners of estimated non-Manhattan obstacles against the ground truth. Furthermore, we evaluate the validity of convergence of differences between the robot orientation and the posture of non-Manhattan obstacles. The experimental results showed that our method is capable of avoiding non-Manhattan obstacles, meeting the requirements for indoor robot navigation.展开更多
This work presents a Fuzzy Logic Controller （FLC） assigned to control a robotic arm motion while avoiding the obstacles that may face the robotic arm in its movement from the initial point to the final point in an o...This work presents a Fuzzy Logic Controller （FLC） assigned to control a robotic arm motion while avoiding the obstacles that may face the robotic arm in its movement from the initial point to the final point in an optimized manner, in addition to avoid the singularity phenomenon, and without any exceeding of the physical constraints of the robot arm. A real platform （5 DOF ＂Degree Of Freedom＂ Lab Volt 5150 Robotic Arm） is used to carry this work practically, in addition to providing it by a vision sensor, where a new approach is proposed to inspect the robot work environment using a designed integrated MATLAB program having the ability to recognize the changeable locations of each of the robotic arm＇s end-effector, the goal, and the multi existed obstacles through a recorded film taken by a webcam, then these information will be treated using the FLC where its outputs represent the values that must be delivered to the robot to adopt them in its next steps till reaching to the goal in collision-free movements. The experimental results showed that the developed robotic ann travels successfully from Start to Goal where a high percentage of accuracy in arriving to Goal was achieved, and without colliding with any obstacle ensuring the harmonization between the theoretical part and the experimental part in achieving the best results of controlling the robotic arm＇s motion.展开更多
In this research work,a hierarchical controller has been designed for an autonomous navigation robot to avoid unexpected moving obstacles where the state and action spaces are continuous.The proposed scheme consists o...In this research work,a hierarchical controller has been designed for an autonomous navigation robot to avoid unexpected moving obstacles where the state and action spaces are continuous.The proposed scheme consists of two parts:1)a controller with a high-level approximate reinforcement learning(ARL)technique for choosing an optimal trajectory in autonomous navigation;and 2)a low-level,appearance-based visual servoing(ABVS)controller which controls and execute the motion of the robot.A novel approach for path planning and visual servoing has been proposed by the combined system framework.The characteristics of the on-board camera which is equipped on the robot is naturally suitable for conducting the reinforcement learning algorithm.Regarding the ARL controller,the computational overhead is quite low thanks to the fact that a knowledge of obstacle motion is not necessary.The developed scheme has been implemented and validated in a simulation system of obstacle avoidance.It is noted that findings of the proposed method are successfully verified by obtaining an optimal robotic plan motion strategy.展开更多
Placement and wiring of vast amount of sensor elements on the 3-dimensionally configured robot sur-face to form soft sensor skin is very difficult with the traditional technology. In this paper we propose a new method...Placement and wiring of vast amount of sensor elements on the 3-dimensionally configured robot sur-face to form soft sensor skin is very difficult with the traditional technology. In this paper we propose a new method to realize such a skin.By implanting infrared sensors array in an elastic body, we obtain an elastic and tough sensor skin that can be shaped freely.The developed sensor skin is a large-area, flexi-ble array of infrared sensors with data processing capabilities.Depending on the skin electronics, it en-dows its carrier with an ability to sense its surroundings.The structure, the method of infrared sensor sig-nal processing, and basic experiments of sensor skin are presented. The validity of the infrared sensor skin is investigated by preliminary obstacle avoidance trial.展开更多
Road obstacles that unexpectedly appear due to vehicle breakdowns and accidents are major causes of fatal road accidents.Connected Autonomous Vehicles(CAVs)can be used to avoid collisions to ensure road safety through...Road obstacles that unexpectedly appear due to vehicle breakdowns and accidents are major causes of fatal road accidents.Connected Autonomous Vehicles(CAVs)can be used to avoid collisions to ensure road safety through cooperative sensing and driving.However,the collision avoidance performance of CAVs with unexpected obstacles has not been studied in the existing works.In this paper,we first design a platoon-based collision avoidance framework for CAVs.In this framework,we deploy a Digital Twin(DT)system at the head vehicle in a platoon to reduce communication overhead and decision-making delay based on a proposed trajectory planning scheme.In addition,a DT-assistant system is deployed on the assistant vehicle to monitor vehicles out of the sensing range of the head vehicle for the maintenance of the DT system.In this case,the transmission frequency of kinetic states of platoon members can be reduced to ensure low-overhead communication.Moreover,we design a variable resource reservation interval that can ensure DT synchronization between DT and the assistant system with high reliability.To further improve road safety,an urgency level-based trajectory planning algorithm is proposed to avoid unexpected obstacles considering different levels of emergency risks.Simulation results show that our DT system-based scheme can achieve significant performance gains in unexpected obstacle avoidance.Compared to the existing schemes,it can reduce collisions by 95%and is faster by about 10%passing by the unexpected obstacle.展开更多
To avoid collisions between a suspended object,cables,towing robots,and obstacles in the environment in a multi-robot suspension system,obstacle avoidance planning was studied based on a collaborative optimization met...To avoid collisions between a suspended object,cables,towing robots,and obstacles in the environment in a multi-robot suspension system,obstacle avoidance planning was studied based on a collaborative optimization method for force and position.Based on the analysis of the kinematics and dynamics of the system,the inverse kinematics and inverse dynamics of the system are solved using the least variance method.The obstacle avoidance planning is performed in the solved collisionfree feasible space using the stable dung beetle optimization(SDBO)algorithm,which ensures that the suspended object can move stably to the target point in the workspace.The optimal obstacle avoidance trajectory of the multi-robot suspension system can be accurately determined by using the collaborative optimization method for force and position to plan the towing robot and the cable.Finally,the correctness of the obstacle avoidance planning method is verified by simulations.By taking a special scenario,the remarkable findings reveal that the SDBO algorithm outperforms the dung beetle optimization algorithm by reducing the length of the planned trajectory of the suspended object by 14.51%and the height by 79.88%,and reducing the minimum fitness by 95.84%and the average fitness by 94.77%.The results can help the multi-robot suspension system to perform various towing tasks safely and stably,and extend the related planning and control theory.展开更多
In recent years,the rapid evolution of unmanned aerial vehicles(UAVs)has brought about transformative changes across various industries.However,addressing fundamental challenges in UAV technology,particularly target t...In recent years,the rapid evolution of unmanned aerial vehicles(UAVs)has brought about transformative changes across various industries.However,addressing fundamental challenges in UAV technology,particularly target tracking and obstacle avoidance,remains crucial for wildlife protection,military industry security,etc.Many existing methods based on reinforcement learning to solve UAV multi-tasks need to be redesigned and retrained,and cannot be quickly and effectively extended to other scenarios.To this end,we propose a novel solution based on a hazard-aware weighted advantage combination for UAV target tracking and obstacle avoidance.First,we independently trained the UAV target tracking and obstacle avoidance using the dueling double deep Q-network reinforcement learning algorithm.Subsequently,in a multitasking scenario,we introduce the two pre-trained networks.Meanwhile,we design a weight determined by the present risk level encountered by the UAV.This weight is utilized to perform a weighted summation of the advantage values from both networks,eliminating the need for retraining to obtain the final action.We validate our approach through extensive simulation experiments in the robotics simulator known as CoppeliaSim.The results demonstrate that our method outper-forms current state-of-the-art techniques,achieving superior performance in both tracking accuracy and avoidance of collisions.展开更多
Intelligent robots are increasingly being deployed across industries ranging from manufacturing to household applications and outdoor exploration.Their autonomous obstacle avoidance capabilities in complex environment...Intelligent robots are increasingly being deployed across industries ranging from manufacturing to household applications and outdoor exploration.Their autonomous obstacle avoidance capabilities in complex environments have become a critical factor determining operational stability.Multimodal perception technology,which integrates visual,auditory,tactile,and LiDAR data,provides robots with comprehensive environmental awareness.By establishing efficient autonomous obstacle avoidance decision-making mechanisms based on this information,the system’s adaptability to challenging scenarios can be significantly enhanced.This study investigates the integration of multimodal perception with autonomous obstacle avoidance decision-making,analyzing the acquisition and processing of perceptual information,core modules and logic of decision-making mechanisms,and proposing optimization strategies for specific scenarios.The research aims to provide theoretical references for advancing autonomous obstacle avoidance technology in intelligent robots,enabling safer and more flexible movement in diverse environments.展开更多
A robot intelligent path planning system RIPPS is developed, which can be utilized for a robot off line programming tool. The system consists of three parts: geometric modeler, kinematic modeler and path planer. The...A robot intelligent path planning system RIPPS is developed, which can be utilized for a robot off line programming tool. The system consists of three parts: geometric modeler, kinematic modeler and path planer. The geometric modeler is used to construct the robot working environment cluttered with obstacles and the robot kinematic modeler to define robot manipulators by the input parameters. Giving robot start and the goal configurations, the path planer can produce a quasi optimal path. By transforming obstacles into the C space to form C obstacles, the path searching is performed in C space. The planning simulations are performed on a SGI workstation, the future research is to implement the planer on a commercial robot manipulators.展开更多
Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning,inefficient detours,and limited adaptability to complex obstacle distributions.These iss...Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning,inefficient detours,and limited adaptability to complex obstacle distributions.These issues are particularly pronounced when navigating cluttered or large-scale environments that demand both global coverage and smooth trajectory generation.To address these challenges,this paper proposes a Wave Water Simulator(WWS)algorithm,leveraging a physically motivated wave equation to achieve inherently smooth,globally consistent path planning.In WWS,wavefront expansions naturally identify safe corridors while seamlessly avoiding local minima,and selective corridor focusing reduces computational overhead in large or dense maps.Comprehensive simulations and real-world validations-encompassing both indoor and outdoor scenarios-demonstrate that WWS reduces path length by 2%-13%compared to conventional methods,while preserving gentle curvature and robust obstacle clearance.Furthermore,WWS requires minimal parameter tuning across diverse domains,underscoring its broad applicability to warehouse robotics,field operations,and autonomous service vehicles.These findings confirm that the proposed wave-based framework not only bridges the gap between local heuristics and global coverage but also sets a promising direction for future extensions toward dynamic obstacle scenarios and multi-agent coordination.展开更多
Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and education.In this paper,an optimal collision-free algorithm is designed and implemented prac...Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and education.In this paper,an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra algorithm.To achieve this research objectives,first,the MR obstacle-free environment is modeled as a diagraph including nodes,edges and weights.Second,Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target point.During its movement,the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following node.For this aim,the MR was equipped with an ultrasonic sensor used as obstacle detector.If an obstacle is found,the MR updates its diagraph by excluding the corresponding node.Then,Dijkstra algorithm runs on the modified diagraph.This procedure is repeated until reaching the target point.To verify the efficiency of the proposed approach,a simulation was carried out on a hand-made MR and an environment including 9 nodes,19 edges and 2 obstacles.The obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking sensors.This study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.展开更多
In this paper,a bio-inspired path planning algorithm in 3 D space is proposed.The algorithm imitates the basic mechanisms of plant growth,including phototropism,negative geotropism and branching.The algorithm proposed...In this paper,a bio-inspired path planning algorithm in 3 D space is proposed.The algorithm imitates the basic mechanisms of plant growth,including phototropism,negative geotropism and branching.The algorithm proposed in this paper solves the dynamic obstacle avoidance path planning problem of Unmanned Aerial Vehicle(UAV)in the case of unknown environment maps.Compared with other path planning algorithms,the algorithm has the advantages of fast path planning speed and fewer route points,and can achieve the effect of low delay real-time path planning.The feasibility of the algorithm is verified in the Gazebo simulator based on the Robot Operating System(ROS)platform.Finally,an actual UAV autonomous obstacle avoidance path planning experimental platform is built,and a UAV obstacle avoidance path planning flight test is carried out based on this actual environment.展开更多
A distributed coordination algorithm is proposed to enhance the engagement of the multi-missile network in consideration of obstacle avoidance. To achieve a cooperative interception, the guidance law is developed in a...A distributed coordination algorithm is proposed to enhance the engagement of the multi-missile network in consideration of obstacle avoidance. To achieve a cooperative interception, the guidance law is developed in a simple form that consists of three individual components for tar- get capture, time coordination and obstacle avoidance. The distributed coordination algorithm enables a group of interceptor missiles to reach the target simultaneously, even if some member in the multi-missile network can only collect the information from nearest neighbors. The simula- tion results show that the guidance strategy provides a feasible tool to implement obstacle avoid- ance for the multi-missile network with satisfactory accuracy of target capture. The effects of the gain parameters are also discussed to evaluate the proposed approach.展开更多
The flapping-wing air vehicle(FWAV)is a kind of bio-inspired robot whose wings can flap up and down like bird and insect wings.A vision-based obstacle avoidance method for FWAVs is proposed in this paper.First,the Far...The flapping-wing air vehicle(FWAV)is a kind of bio-inspired robot whose wings can flap up and down like bird and insect wings.A vision-based obstacle avoidance method for FWAVs is proposed in this paper.First,the Farneback algorithm is used to calculate the optical flow field of the first-view video frames taken by the on-board image transmission camera.Based on the optical flow information,a fuzzy obstacle avoidance controller is then designed to generate the FWAV steering commands.Experimental results show that the proposed obstacle avoidance method can accurately identify obstacles and achieve obstacle avoidance for FWAVs.展开更多
Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus...Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus algorithm. A novel distributed control model is proposed for the multi-agent system to form the anticipated formation as well as achieve obstacle avoidance. Based on the consensus algorithm, a distributed control function consisting of three terms （formation control term, velocity matching term, and obstacle avoidance term） is presented. By establishing a novel formation control matrix, a formation control term is constructed such that the agents can converge to consensus and reach the anticipated formation. A new obstacle avoidance function is developed by using the modified potential field approach to make sure that obstacle avoidance can be achieved whether the obstacle is in a dynamic state or a stationary state. A velocity matching term is also put forward to guarantee that the velocities of all agents converge to the same value. Furthermore, stability of the control model is proven. Simulation results are provided to demonstrate the effectiveness of the proposed control.展开更多
This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden environments.The MVS under consideration consists of a leader v...This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden environments.The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external disturbances.The central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction topology.Toward this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance.Furthermore,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed.It is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed.Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.展开更多
Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Fir...Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.展开更多
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
文摘Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.
基金the Natural Science Foundation of China(Project for Young Scientists:Grant No.52105010,Regular Project:Grant No.62173096)Natural Science Foundationof Guangdong Province(Regular Project:Grant No.2025A1515012124,Grant No.2022A1515010327)Guangdong-Hong Kong-Macao Key Laboratory of Multi-scaleInformation Fusion and Collaborative Optimization Control Manufacturing Process.
文摘Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency.
基金supported by the National Natural Science Foundation of China(61771146,61375122)the National Thirteen 5-Year Plan for Science and Technology(2017YFC1703303)in part by Shanghai Science and Technology Development Funds(13dz2260200,13511504300)。
文摘Monocular vision-based navigation is a considerable ability for a home mobile robot. However, due to diverse disturbances, helping robots avoid obstacles, especially nonManhattan obstacles, remains a big challenge. In indoor environments, there are many spatial right-corners that are projected into two dimensional projections with special geometric configurations. These projections, which consist of three lines,might enable us to estimate their position and orientation in 3 D scenes. In this paper, we present a method for home robots to avoid non-Manhattan obstacles in indoor environments from a monocular camera. The approach first detects non-Manhattan obstacles. Through analyzing geometric features and constraints,it is possible to estimate posture differences between orientation of the robot and non-Manhattan obstacles. Finally according to the convergence of posture differences, the robot can adjust its orientation to keep pace with the pose of detected non-Manhattan obstacles, making it possible avoid these obstacles by itself. Based on geometric inferences, the proposed approach requires no prior training or any knowledge of the camera’s internal parameters,making it practical for robots navigation. Furthermore, the method is robust to errors in calibration and image noise. We compared the errors from corners of estimated non-Manhattan obstacles against the ground truth. Furthermore, we evaluate the validity of convergence of differences between the robot orientation and the posture of non-Manhattan obstacles. The experimental results showed that our method is capable of avoiding non-Manhattan obstacles, meeting the requirements for indoor robot navigation.
文摘This work presents a Fuzzy Logic Controller （FLC） assigned to control a robotic arm motion while avoiding the obstacles that may face the robotic arm in its movement from the initial point to the final point in an optimized manner, in addition to avoid the singularity phenomenon, and without any exceeding of the physical constraints of the robot arm. A real platform （5 DOF ＂Degree Of Freedom＂ Lab Volt 5150 Robotic Arm） is used to carry this work practically, in addition to providing it by a vision sensor, where a new approach is proposed to inspect the robot work environment using a designed integrated MATLAB program having the ability to recognize the changeable locations of each of the robotic arm＇s end-effector, the goal, and the multi existed obstacles through a recorded film taken by a webcam, then these information will be treated using the FLC where its outputs represent the values that must be delivered to the robot to adopt them in its next steps till reaching to the goal in collision-free movements. The experimental results showed that the developed robotic ann travels successfully from Start to Goal where a high percentage of accuracy in arriving to Goal was achieved, and without colliding with any obstacle ensuring the harmonization between the theoretical part and the experimental part in achieving the best results of controlling the robotic arm＇s motion.
基金supported by research grants from the Natural Sciences and Engineering Research Council(NSERC)of Canadathe British Columbia Knowledge Development Fund(BCKDF)+1 种基金the Canada Foundation for Innovation(CFI)the Canada Research Chair in Mechatronics and Industrial Automation held by C.W.de Silva
文摘In this research work,a hierarchical controller has been designed for an autonomous navigation robot to avoid unexpected moving obstacles where the state and action spaces are continuous.The proposed scheme consists of two parts:1)a controller with a high-level approximate reinforcement learning(ARL)technique for choosing an optimal trajectory in autonomous navigation;and 2)a low-level,appearance-based visual servoing(ABVS)controller which controls and execute the motion of the robot.A novel approach for path planning and visual servoing has been proposed by the combined system framework.The characteristics of the on-board camera which is equipped on the robot is naturally suitable for conducting the reinforcement learning algorithm.Regarding the ARL controller,the computational overhead is quite low thanks to the fact that a knowledge of obstacle motion is not necessary.The developed scheme has been implemented and validated in a simulation system of obstacle avoidance.It is noted that findings of the proposed method are successfully verified by obtaining an optimal robotic plan motion strategy.
基金Supported by the National Natural Science Foundation of China （No.50105002）.
文摘Placement and wiring of vast amount of sensor elements on the 3-dimensionally configured robot sur-face to form soft sensor skin is very difficult with the traditional technology. In this paper we propose a new method to realize such a skin.By implanting infrared sensors array in an elastic body, we obtain an elastic and tough sensor skin that can be shaped freely.The developed sensor skin is a large-area, flexi-ble array of infrared sensors with data processing capabilities.Depending on the skin electronics, it en-dows its carrier with an ability to sense its surroundings.The structure, the method of infrared sensor sig-nal processing, and basic experiments of sensor skin are presented. The validity of the infrared sensor skin is investigated by preliminary obstacle avoidance trial.
基金partly supported by National Key R&D Program of China(No.2018YFE0117500)National Natural Science Foundation of China(No.62171104)+2 种基金EU Horizon2020(824019),EU Horizon2020(101022280)Horizon Europe(101086228)the UK EPSRC(EP/Y027787/1)。
文摘Road obstacles that unexpectedly appear due to vehicle breakdowns and accidents are major causes of fatal road accidents.Connected Autonomous Vehicles(CAVs)can be used to avoid collisions to ensure road safety through cooperative sensing and driving.However,the collision avoidance performance of CAVs with unexpected obstacles has not been studied in the existing works.In this paper,we first design a platoon-based collision avoidance framework for CAVs.In this framework,we deploy a Digital Twin(DT)system at the head vehicle in a platoon to reduce communication overhead and decision-making delay based on a proposed trajectory planning scheme.In addition,a DT-assistant system is deployed on the assistant vehicle to monitor vehicles out of the sensing range of the head vehicle for the maintenance of the DT system.In this case,the transmission frequency of kinetic states of platoon members can be reduced to ensure low-overhead communication.Moreover,we design a variable resource reservation interval that can ensure DT synchronization between DT and the assistant system with high reliability.To further improve road safety,an urgency level-based trajectory planning algorithm is proposed to avoid unexpected obstacles considering different levels of emergency risks.Simulation results show that our DT system-based scheme can achieve significant performance gains in unexpected obstacle avoidance.Compared to the existing schemes,it can reduce collisions by 95%and is faster by about 10%passing by the unexpected obstacle.
基金supported by the Excellent Graduate Student“Innovation Star”project of Education Department of Gansu Province(Grant No.2025CXZX-675)the National Natural Science Foundation of China(Grant No.51965032)+3 种基金the National Natural Science Foundation of Gansu Province of China(Grant No.22JR5RA319)the Excellent Doctoral Student Foundation of Gansu Province of China(Grant No.23JRRA842)the Open Project of State Key Laboratory of Rail Transit Vehicle System,Southwest Jiaotong University(Grant No.RVL2411)the Key Research and Development Project of Lanzhou Jiaotong University(Grant No.LZJTU-ZDYF2302).
文摘To avoid collisions between a suspended object,cables,towing robots,and obstacles in the environment in a multi-robot suspension system,obstacle avoidance planning was studied based on a collaborative optimization method for force and position.Based on the analysis of the kinematics and dynamics of the system,the inverse kinematics and inverse dynamics of the system are solved using the least variance method.The obstacle avoidance planning is performed in the solved collisionfree feasible space using the stable dung beetle optimization(SDBO)algorithm,which ensures that the suspended object can move stably to the target point in the workspace.The optimal obstacle avoidance trajectory of the multi-robot suspension system can be accurately determined by using the collaborative optimization method for force and position to plan the towing robot and the cable.Finally,the correctness of the obstacle avoidance planning method is verified by simulations.By taking a special scenario,the remarkable findings reveal that the SDBO algorithm outperforms the dung beetle optimization algorithm by reducing the length of the planned trajectory of the suspended object by 14.51%and the height by 79.88%,and reducing the minimum fitness by 95.84%and the average fitness by 94.77%.The results can help the multi-robot suspension system to perform various towing tasks safely and stably,and extend the related planning and control theory.
基金supported by the National Natural Science Foundation of China(62236002,61921004).
文摘In recent years,the rapid evolution of unmanned aerial vehicles(UAVs)has brought about transformative changes across various industries.However,addressing fundamental challenges in UAV technology,particularly target tracking and obstacle avoidance,remains crucial for wildlife protection,military industry security,etc.Many existing methods based on reinforcement learning to solve UAV multi-tasks need to be redesigned and retrained,and cannot be quickly and effectively extended to other scenarios.To this end,we propose a novel solution based on a hazard-aware weighted advantage combination for UAV target tracking and obstacle avoidance.First,we independently trained the UAV target tracking and obstacle avoidance using the dueling double deep Q-network reinforcement learning algorithm.Subsequently,in a multitasking scenario,we introduce the two pre-trained networks.Meanwhile,we design a weight determined by the present risk level encountered by the UAV.This weight is utilized to perform a weighted summation of the advantage values from both networks,eliminating the need for retraining to obtain the final action.We validate our approach through extensive simulation experiments in the robotics simulator known as CoppeliaSim.The results demonstrate that our method outper-forms current state-of-the-art techniques,achieving superior performance in both tracking accuracy and avoidance of collisions.
文摘Intelligent robots are increasingly being deployed across industries ranging from manufacturing to household applications and outdoor exploration.Their autonomous obstacle avoidance capabilities in complex environments have become a critical factor determining operational stability.Multimodal perception technology,which integrates visual,auditory,tactile,and LiDAR data,provides robots with comprehensive environmental awareness.By establishing efficient autonomous obstacle avoidance decision-making mechanisms based on this information,the system’s adaptability to challenging scenarios can be significantly enhanced.This study investigates the integration of multimodal perception with autonomous obstacle avoidance decision-making,analyzing the acquisition and processing of perceptual information,core modules and logic of decision-making mechanisms,and proposing optimization strategies for specific scenarios.The research aims to provide theoretical references for advancing autonomous obstacle avoidance technology in intelligent robots,enabling safer and more flexible movement in diverse environments.
文摘A robot intelligent path planning system RIPPS is developed, which can be utilized for a robot off line programming tool. The system consists of three parts: geometric modeler, kinematic modeler and path planer. The geometric modeler is used to construct the robot working environment cluttered with obstacles and the robot kinematic modeler to define robot manipulators by the input parameters. Giving robot start and the goal configurations, the path planer can produce a quasi optimal path. By transforming obstacles into the C space to form C obstacles, the path searching is performed in C space. The planning simulations are performed on a SGI workstation, the future research is to implement the planer on a commercial robot manipulators.
文摘Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning,inefficient detours,and limited adaptability to complex obstacle distributions.These issues are particularly pronounced when navigating cluttered or large-scale environments that demand both global coverage and smooth trajectory generation.To address these challenges,this paper proposes a Wave Water Simulator(WWS)algorithm,leveraging a physically motivated wave equation to achieve inherently smooth,globally consistent path planning.In WWS,wavefront expansions naturally identify safe corridors while seamlessly avoiding local minima,and selective corridor focusing reduces computational overhead in large or dense maps.Comprehensive simulations and real-world validations-encompassing both indoor and outdoor scenarios-demonstrate that WWS reduces path length by 2%-13%compared to conventional methods,while preserving gentle curvature and robust obstacle clearance.Furthermore,WWS requires minimal parameter tuning across diverse domains,underscoring its broad applicability to warehouse robotics,field operations,and autonomous service vehicles.These findings confirm that the proposed wave-based framework not only bridges the gap between local heuristics and global coverage but also sets a promising direction for future extensions toward dynamic obstacle scenarios and multi-agent coordination.
基金This research has been funded by Scientific Research Deanship at University of Ha’il–Saudi Arabia through Project Number BA-2107.
文摘Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and education.In this paper,an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra algorithm.To achieve this research objectives,first,the MR obstacle-free environment is modeled as a diagraph including nodes,edges and weights.Second,Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target point.During its movement,the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following node.For this aim,the MR was equipped with an ultrasonic sensor used as obstacle detector.If an obstacle is found,the MR updates its diagraph by excluding the corresponding node.Then,Dijkstra algorithm runs on the modified diagraph.This procedure is repeated until reaching the target point.To verify the efficiency of the proposed approach,a simulation was carried out on a hand-made MR and an environment including 9 nodes,19 edges and 2 obstacles.The obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking sensors.This study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.
基金the support of the Zhejiang Lab(No.2019NB0AB04)National Natural Science Foundation of China(No.61903014)+1 种基金Aeronautical Science Foundation of China(No.20181751010)Fundamental Research Funds for the Central Universities,China。
文摘In this paper,a bio-inspired path planning algorithm in 3 D space is proposed.The algorithm imitates the basic mechanisms of plant growth,including phototropism,negative geotropism and branching.The algorithm proposed in this paper solves the dynamic obstacle avoidance path planning problem of Unmanned Aerial Vehicle(UAV)in the case of unknown environment maps.Compared with other path planning algorithms,the algorithm has the advantages of fast path planning speed and fewer route points,and can achieve the effect of low delay real-time path planning.The feasibility of the algorithm is verified in the Gazebo simulator based on the Robot Operating System(ROS)platform.Finally,an actual UAV autonomous obstacle avoidance path planning experimental platform is built,and a UAV obstacle avoidance path planning flight test is carried out based on this actual environment.
基金co-supported by the National Natural Science Foundation of China(Nos.61273349 and 61175109)the Aeronautical Science Foundation of China(Nos.2014ZA18004 and 2013ZA18001)
文摘A distributed coordination algorithm is proposed to enhance the engagement of the multi-missile network in consideration of obstacle avoidance. To achieve a cooperative interception, the guidance law is developed in a simple form that consists of three individual components for tar- get capture, time coordination and obstacle avoidance. The distributed coordination algorithm enables a group of interceptor missiles to reach the target simultaneously, even if some member in the multi-missile network can only collect the information from nearest neighbors. The simula- tion results show that the guidance strategy provides a feasible tool to implement obstacle avoid- ance for the multi-missile network with satisfactory accuracy of target capture. The effects of the gain parameters are also discussed to evaluate the proposed approach.
基金This work was supported in part by the National Natural Science Foundation of China(Nos.61803025,62073031)the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)(No.FRF-IDRY-19010)the Beijing Top Discipline for Artificial Intelligent Science and Engineering,University of Science and Technology Beijing.
文摘The flapping-wing air vehicle(FWAV)is a kind of bio-inspired robot whose wings can flap up and down like bird and insect wings.A vision-based obstacle avoidance method for FWAVs is proposed in this paper.First,the Farneback algorithm is used to calculate the optical flow field of the first-view video frames taken by the on-board image transmission camera.Based on the optical flow information,a fuzzy obstacle avoidance controller is then designed to generate the FWAV steering commands.Experimental results show that the proposed obstacle avoidance method can accurately identify obstacles and achieve obstacle avoidance for FWAVs.
基金supported by the National High Technology Research and Development Program of China(Grant No.2011AA040103)the Research Foundationof Shanghai Institute of Technology,China(Grant No.B504)
文摘Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus algorithm. A novel distributed control model is proposed for the multi-agent system to form the anticipated formation as well as achieve obstacle avoidance. Based on the consensus algorithm, a distributed control function consisting of three terms （formation control term, velocity matching term, and obstacle avoidance term） is presented. By establishing a novel formation control matrix, a formation control term is constructed such that the agents can converge to consensus and reach the anticipated formation. A new obstacle avoidance function is developed by using the modified potential field approach to make sure that obstacle avoidance can be achieved whether the obstacle is in a dynamic state or a stationary state. A velocity matching term is also put forward to guarantee that the velocities of all agents converge to the same value. Furthermore, stability of the control model is proven. Simulation results are provided to demonstrate the effectiveness of the proposed control.
文摘This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden environments.The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external disturbances.The central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction topology.Toward this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance.Furthermore,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed.It is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed.Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.
文摘Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.
