Supernumerary robotic arms(SuperLimb)are a new type of wearable robot that works closely with humans as a third hand to augment human operation capability.Accurate conveyance of wearers'intentions,allocation of ro...Supernumerary robotic arms(SuperLimb)are a new type of wearable robot that works closely with humans as a third hand to augment human operation capability.Accurate conveyance of wearers'intentions,allocation of roles,and humancentered interaction considerations are the key points in the process of human-SuperLimb collaboration.This paper proposes a human-centered intention-guided leader-follower controller that relies on the dynamic modeling of SuperLimb with application to load-carrying scenarios.The proposed leader-follower controller takes the human as the leader and the SuperLimb as the follower,achieving effective information communication,autonomous coordination,and good force compliance between SuperLimb,humans,and the environment under human safety assurance.First,the human-SuperLimb dynamic system is modeled to achieve force interaction with the environment and wearer.Second,to achieve the precise intention extraction of humans,pose data from five visual odometry sensors are fused to capture the human state,the generalized position,the velocity of hands,and the surface electromyography signals from two myoelectric bracelets sensors are processed to recognize the natural hand gestures during load-carrying scenarios by a designed Swin transformer network.Then,based on the real-time distance detection between human and mechanical limbs,the security assurance and force-compliant interaction of the human-SuperLimb system are realized.Finally,the human hand muscle intention recognition,human-robot safety strategy verification,and comparative load-carrying experiments with and without the proposed method are conducted on the SuperLimb prototype.Results showed that the task parameters are well estimated to produce more reasonable planning trajectories,and SuperLimb could well understand the wearer's intentions to switch different SuperLimb actions.The proposed sensor-based human-robot communication framework motivates future studies of other collaboration scenes for SuperLimb applications.展开更多
Target tracking is one of the hottest topics in the field of drone research.In this paper,we study the multiple unmanned aerial vehicles(multi-UAV)collaborative target tracking problem.We propose a novel tracking meth...Target tracking is one of the hottest topics in the field of drone research.In this paper,we study the multiple unmanned aerial vehicles(multi-UAV)collaborative target tracking problem.We propose a novel tracking method based on intention estimation and effective cooperation for UAVs with inferior tracking capabilities to track the targets that may have agile,uncertain,and intelligent motion.For three classic target motion modes,we first design a novel trajectory feature extraction method with the least dimension and maximum coverage constraints,and propose an intention estimation mechanism based on the environment and target trajectory features.We propose a novel Voronoi diagram,called MDA-Voronoi,which divides the area with obstacles according to the minimum reachable distance and the minimum steering angle of each UAV.In each MDA-Voronoi region,the maximum reachable region of each UAV is defined,the upper and lower bounds of the trajectory coverage probability are analyzed,and the tracking strategies of the UAVs are designed to effectively reduce the tracking gaps to improve the target sensing time.Then,we use the Nash Q-learning method to design the UAVs’collaborative tracking strategy,considering factors such as collision avoidance,maneuvering constraints,tracking cost,sensing performance,and path overlap.By designing the reward mechanism,the optimal action strategies are obtained as the control input of the UAVs.Finally,simulation analyses are provided to validate our method,and the results demonstrate that the algorithm can improve the collaborative target tracking performance for multiple UAVs with inferior tracking capabilities.展开更多
基金supported by the National Outstanding Youth Science Fund Project of the National Natural Science Foundation of China(Grant No.52025054)the Youth Science Fund Project of the National Natural Science Foundation of China(Grant No.52105016)。
文摘Supernumerary robotic arms(SuperLimb)are a new type of wearable robot that works closely with humans as a third hand to augment human operation capability.Accurate conveyance of wearers'intentions,allocation of roles,and humancentered interaction considerations are the key points in the process of human-SuperLimb collaboration.This paper proposes a human-centered intention-guided leader-follower controller that relies on the dynamic modeling of SuperLimb with application to load-carrying scenarios.The proposed leader-follower controller takes the human as the leader and the SuperLimb as the follower,achieving effective information communication,autonomous coordination,and good force compliance between SuperLimb,humans,and the environment under human safety assurance.First,the human-SuperLimb dynamic system is modeled to achieve force interaction with the environment and wearer.Second,to achieve the precise intention extraction of humans,pose data from five visual odometry sensors are fused to capture the human state,the generalized position,the velocity of hands,and the surface electromyography signals from two myoelectric bracelets sensors are processed to recognize the natural hand gestures during load-carrying scenarios by a designed Swin transformer network.Then,based on the real-time distance detection between human and mechanical limbs,the security assurance and force-compliant interaction of the human-SuperLimb system are realized.Finally,the human hand muscle intention recognition,human-robot safety strategy verification,and comparative load-carrying experiments with and without the proposed method are conducted on the SuperLimb prototype.Results showed that the task parameters are well estimated to produce more reasonable planning trajectories,and SuperLimb could well understand the wearer's intentions to switch different SuperLimb actions.The proposed sensor-based human-robot communication framework motivates future studies of other collaboration scenes for SuperLimb applications.
基金Project supported by the National Natural Science Foundation of China(No.61873033)the Science Foundation of Fujian Normal University(No.Z0210553)the Natural Science Foundation of Fujian Province,China(No.2020H0012)。
文摘Target tracking is one of the hottest topics in the field of drone research.In this paper,we study the multiple unmanned aerial vehicles(multi-UAV)collaborative target tracking problem.We propose a novel tracking method based on intention estimation and effective cooperation for UAVs with inferior tracking capabilities to track the targets that may have agile,uncertain,and intelligent motion.For three classic target motion modes,we first design a novel trajectory feature extraction method with the least dimension and maximum coverage constraints,and propose an intention estimation mechanism based on the environment and target trajectory features.We propose a novel Voronoi diagram,called MDA-Voronoi,which divides the area with obstacles according to the minimum reachable distance and the minimum steering angle of each UAV.In each MDA-Voronoi region,the maximum reachable region of each UAV is defined,the upper and lower bounds of the trajectory coverage probability are analyzed,and the tracking strategies of the UAVs are designed to effectively reduce the tracking gaps to improve the target sensing time.Then,we use the Nash Q-learning method to design the UAVs’collaborative tracking strategy,considering factors such as collision avoidance,maneuvering constraints,tracking cost,sensing performance,and path overlap.By designing the reward mechanism,the optimal action strategies are obtained as the control input of the UAVs.Finally,simulation analyses are provided to validate our method,and the results demonstrate that the algorithm can improve the collaborative target tracking performance for multiple UAVs with inferior tracking capabilities.
