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
As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehi...As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehicles(UAVs)can traverse the entire water surface in a short time through their flight field of view.In addition,Unmanned Surface Vessels(USVs)can provide battery replacement and pick up garbage.In this paper,we innovatively establish a system framework for the collaboration between UAV and USVs,and develop an automatic water cleaning strategy.First,on the basis of the partition principle,we propose a collaborative coverage path algorithm based on UAV off-site takeoff and landing to achieve global inspection.Second,we design a task scheduling and assignment algorithm for USVs to balance the garbage loads based on the particle swarm optimization algorithm.Finally,based on the swarm intelligence algorithm,we also design an autonomous obstacle avoidance path planning algorithm for USVs to realize autonomous navigation and collaborative cleaning.The system can simultaneously perform inspection and clearance tasks under certain constraints.The simulation results show that the proposed algorithms have higher generality and flexibility while effectively improving computational efficiency and reducing actual cleaning costs compared with other schemes.展开更多
As autonomous mobile robots are increasingly deployed in complex environments,traditional vision sensors and LiDAR encounter considerable limitations,particularly in detecting obstacles in blind spots or transparent o...As autonomous mobile robots are increasingly deployed in complex environments,traditional vision sensors and LiDAR encounter considerable limitations,particularly in detecting obstacles in blind spots or transparent objects.To address the issue of blind spots,we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle(HANV).This system integrates haptic sensors and LiDAR to deliver comprehensive perception,significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors.To tackle the challenge of transparent obstacles,which are often undetected by both vision and LiDAR sensors,we employ a fusion of haptic sensors and LiDAR.The haptic system provides physical contact feedback,ensuring reliable detection of transparent obstacles such as glass,while LiDAR offers long-range sensing capabilities.This combination enables HANV to navigate effectively in environments with transparent obstacles,overcoming the limitations of traditional sensing systems.Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles.HANV achieves high success rates,minimal collisions,and efficient obstacle avoidance,particularly excelling in complex,confined spaces where conventional systems prove inadequate.These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments.展开更多
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.
基金supported in part by the National Natural Science Foundation of China under Grants 62071189,62201220 and 62171189by the Key Research and Development Program of Hubei Province under Grant 2021BAA026 and 2020BAB120。
文摘As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehicles(UAVs)can traverse the entire water surface in a short time through their flight field of view.In addition,Unmanned Surface Vessels(USVs)can provide battery replacement and pick up garbage.In this paper,we innovatively establish a system framework for the collaboration between UAV and USVs,and develop an automatic water cleaning strategy.First,on the basis of the partition principle,we propose a collaborative coverage path algorithm based on UAV off-site takeoff and landing to achieve global inspection.Second,we design a task scheduling and assignment algorithm for USVs to balance the garbage loads based on the particle swarm optimization algorithm.Finally,based on the swarm intelligence algorithm,we also design an autonomous obstacle avoidance path planning algorithm for USVs to realize autonomous navigation and collaborative cleaning.The system can simultaneously perform inspection and clearance tasks under certain constraints.The simulation results show that the proposed algorithms have higher generality and flexibility while effectively improving computational efficiency and reducing actual cleaning costs compared with other schemes.
基金the National Natural Science Foundation of China(Nos.62233003 and 62073072)the Key R&D Program of Jiangsu Province(Nos.BE2020006 and BE2020006-1)the Shenzhen Science and Technology Program(Nos.JCYJ20210324132202005 and JCYJ20220818101206014)。
文摘As autonomous mobile robots are increasingly deployed in complex environments,traditional vision sensors and LiDAR encounter considerable limitations,particularly in detecting obstacles in blind spots or transparent objects.To address the issue of blind spots,we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle(HANV).This system integrates haptic sensors and LiDAR to deliver comprehensive perception,significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors.To tackle the challenge of transparent obstacles,which are often undetected by both vision and LiDAR sensors,we employ a fusion of haptic sensors and LiDAR.The haptic system provides physical contact feedback,ensuring reliable detection of transparent obstacles such as glass,while LiDAR offers long-range sensing capabilities.This combination enables HANV to navigate effectively in environments with transparent obstacles,overcoming the limitations of traditional sensing systems.Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles.HANV achieves high success rates,minimal collisions,and efficient obstacle avoidance,particularly excelling in complex,confined spaces where conventional systems prove inadequate.These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments.
