为了提高围捕系统的围捕效率,提出一种基于融合蛇优化算法的多AUV协同围捕算法(Multi-AUV Cooperative Hunting Algorithm based on Fusion Snake Optimization algorithm,MACHA_FSO)。MACHA_FSO改进随机目标搜索策略,采用莱维飞行策略...为了提高围捕系统的围捕效率,提出一种基于融合蛇优化算法的多AUV协同围捕算法(Multi-AUV Cooperative Hunting Algorithm based on Fusion Snake Optimization algorithm,MACHA_FSO)。MACHA_FSO改进随机目标搜索策略,采用莱维飞行策略设置搜索目标,就近原则变更围捕AUV工作区域,保证围捕AUV的搜索效率。MACHA_FSO构建围捕系统的整体能耗模型,采用最小化围捕距离策略建立围捕联盟,提出融合蛇优化算法合理规划围捕AUV的围捕路径,有效降低围捕AUV能耗。仿真结果表明:相较于CPGBNN,RIGBNN和PRACO围捕算法,MACHA_FSO能够合理设置围捕AUV的搜索目标与围捕路径,且围捕系统平均能量消耗降低41%,围捕逃逸目标平均用时降低32%,围捕逃逸目标平均数量提高1倍,围捕系统平均生存时间提高15%。展开更多
Robotic fingers, which are the key parts of robot hand, are divided into two main kinds： dexterous fingers and under-actuated fingers. Although dexterous fingers are agile, they are too expensive. Under-actuated fing...Robotic fingers, which are the key parts of robot hand, are divided into two main kinds： dexterous fingers and under-actuated fingers. Although dexterous fingers are agile, they are too expensive. Under-actuated fingers can grasp objects self-adaptively, which makes them easy to control and low cost, on the contrary, under-actuated function makes fingers feel hard to grasp things agilely enough and make many gestures. For the purpose of designing a new finger which can grasp things dexterously, perform many gestures and feel easy to control and maintain, a concept called ＂gesture-changeable under-actuated＂ （GCUA） function is put forward. The GCUA function combines the advantages of dexterous fingers and under-actuated fingers： a pre-bending function is embedded into the under-actuated finger. The GCUA finger can not only perform self-adaptive grasping function, but also actively bend the middle joint of the finger. On the basis of the concept, a GCUA finger with 2 joints is designed, which is realized by the coordination of screw-nut transmission mechanism, flexible drawstring constraint and pulley-belt under-actuated mechanism. Principle analyses of its grasping and the design optimization of the GCUA finger are given. An important problem of how to stably grasp an object which is easy to glide is discussed. The force analysis on gliding object in grasping process is introduced in detail. A GCUA finger with 3 joints is developed. Many experiments of grasping different objects by of the finger were carried out. The experimental results show that the GCUA finger can effectively realize functions of pre-bending and self-adaptive grasping, the grasping processes are stable. The GCUA finger excels under-actuated fingers in dexterity and gesture actions and it is easier to control and cheaper than dexterous hands, becomes the third kinds of finger.展开更多
This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequentia...This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations.The feasibility of the formulated optimization problem is guaranteed under derived conditions.The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure.Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.展开更多
文摘为了提高围捕系统的围捕效率,提出一种基于融合蛇优化算法的多AUV协同围捕算法(Multi-AUV Cooperative Hunting Algorithm based on Fusion Snake Optimization algorithm,MACHA_FSO)。MACHA_FSO改进随机目标搜索策略,采用莱维飞行策略设置搜索目标,就近原则变更围捕AUV工作区域,保证围捕AUV的搜索效率。MACHA_FSO构建围捕系统的整体能耗模型,采用最小化围捕距离策略建立围捕联盟,提出融合蛇优化算法合理规划围捕AUV的围捕路径,有效降低围捕AUV能耗。仿真结果表明:相较于CPGBNN,RIGBNN和PRACO围捕算法,MACHA_FSO能够合理设置围捕AUV的搜索目标与围捕路径,且围捕系统平均能量消耗降低41%,围捕逃逸目标平均用时降低32%,围捕逃逸目标平均数量提高1倍,围捕系统平均生存时间提高15%。
基金supported by National Natural Science Foundation of China (No. 50905093)National Hi-tech Research and Development Program of China(863 Program,Grant No.2007AA04Z258)
文摘Robotic fingers, which are the key parts of robot hand, are divided into two main kinds： dexterous fingers and under-actuated fingers. Although dexterous fingers are agile, they are too expensive. Under-actuated fingers can grasp objects self-adaptively, which makes them easy to control and low cost, on the contrary, under-actuated function makes fingers feel hard to grasp things agilely enough and make many gestures. For the purpose of designing a new finger which can grasp things dexterously, perform many gestures and feel easy to control and maintain, a concept called ＂gesture-changeable under-actuated＂ （GCUA） function is put forward. The GCUA function combines the advantages of dexterous fingers and under-actuated fingers： a pre-bending function is embedded into the under-actuated finger. The GCUA finger can not only perform self-adaptive grasping function, but also actively bend the middle joint of the finger. On the basis of the concept, a GCUA finger with 2 joints is designed, which is realized by the coordination of screw-nut transmission mechanism, flexible drawstring constraint and pulley-belt under-actuated mechanism. Principle analyses of its grasping and the design optimization of the GCUA finger are given. An important problem of how to stably grasp an object which is easy to glide is discussed. The force analysis on gliding object in grasping process is introduced in detail. A GCUA finger with 3 joints is developed. Many experiments of grasping different objects by of the finger were carried out. The experimental results show that the GCUA finger can effectively realize functions of pre-bending and self-adaptive grasping, the grasping processes are stable. The GCUA finger excels under-actuated fingers in dexterity and gesture actions and it is easier to control and cheaper than dexterous hands, becomes the third kinds of finger.
基金supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China(11202318,11203721)the Australian Research Council(DP200100700)。
文摘This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations.The feasibility of the formulated optimization problem is guaranteed under derived conditions.The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure.Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.
