A new guidance law, called biased retro proportional navigation(BRPN), is proposed. The guidance law is designed to intercept high-speed targets with angular constraint, which can be used for ballistic target intercep...A new guidance law, called biased retro proportional navigation(BRPN), is proposed. The guidance law is designed to intercept high-speed targets with angular constraint, which can be used for ballistic target interception. BRPN guidance law is defined, and the exact time-varying bias for a required impact angle is derived. Furthermore, the simulation results(trajectory, variation of navigation ratio, capture region, etc) are compared with those of biased proportional navigation(BPN), proportional navigation and retro-proportional navigation. The results show that,at the cost of a higher intercept time, BRPN demands lower terminal lateral acceleration and has larger capture region compared to BPN.展开更多
RRT(Rapidly exploring Random Tree)是一种基于采样的路径规划算法,非常适用于机器人的路径规划中,但是传统RRT^(*)算法存在耗时长、占用内存较大等缺点。所以针对这些问题提出一种改进RRT^(*)算法,该算法优化了父节点选取范围,在传统...RRT(Rapidly exploring Random Tree)是一种基于采样的路径规划算法,非常适用于机器人的路径规划中,但是传统RRT^(*)算法存在耗时长、占用内存较大等缺点。所以针对这些问题提出一种改进RRT^(*)算法,该算法优化了父节点选取范围,在传统随机采样机制的基础上引入了目标偏置采样和启发式策略,减少了算法耗时且缩短了路径长度;引入了节点拒绝策略,消除转弯角太大的冗余路径的同时也进一步提升了算法效率。利用MATLAB进行了仿真实验验证,结果表明改进RRT^(*)算法能在更短的时间内搜索到一条从起点到终点的最短无碰路径,并且可以很好地应用于机械臂的路径规划中。展开更多
In order to solve the problem of path planning of mobile robots in a dynamic environment,an improved rapidly-exploring random tree^(*)(RRT^(*))algorithm is proposed in this paper.First,the target bias sampling is intr...In order to solve the problem of path planning of mobile robots in a dynamic environment,an improved rapidly-exploring random tree^(*)(RRT^(*))algorithm is proposed in this paper.First,the target bias sampling is introduced to reduce the randomness of the RRT^(*)algorithm,and then the initial path planning is carried out in a static environment.Secondly,apply the path in a dynamic environment,and use the initially planned path as the path cache.When a new obstacle appears in the path,the invalid path is clipped and the path is replanned.At this time,there is a certain probability to select the point in the path cache as the new node,so that the new path maintains the trend of the original path to a greater extent.Finally,MATLAB is used to carry out simulation experiments for the initial planning and replanning algorithms,respectively.More specifically,compared with the original RRT^(*)algorithm,the simulation results show that the number of nodes used by the new improved algorithm is reduced by 43.19%on average.展开更多
文摘A new guidance law, called biased retro proportional navigation(BRPN), is proposed. The guidance law is designed to intercept high-speed targets with angular constraint, which can be used for ballistic target interception. BRPN guidance law is defined, and the exact time-varying bias for a required impact angle is derived. Furthermore, the simulation results(trajectory, variation of navigation ratio, capture region, etc) are compared with those of biased proportional navigation(BPN), proportional navigation and retro-proportional navigation. The results show that,at the cost of a higher intercept time, BRPN demands lower terminal lateral acceleration and has larger capture region compared to BPN.
文摘RRT(Rapidly exploring Random Tree)是一种基于采样的路径规划算法,非常适用于机器人的路径规划中,但是传统RRT^(*)算法存在耗时长、占用内存较大等缺点。所以针对这些问题提出一种改进RRT^(*)算法,该算法优化了父节点选取范围,在传统随机采样机制的基础上引入了目标偏置采样和启发式策略,减少了算法耗时且缩短了路径长度;引入了节点拒绝策略,消除转弯角太大的冗余路径的同时也进一步提升了算法效率。利用MATLAB进行了仿真实验验证,结果表明改进RRT^(*)算法能在更短的时间内搜索到一条从起点到终点的最短无碰路径,并且可以很好地应用于机械臂的路径规划中。
基金National Natural Science Foundation of China(No.61903291)。
文摘In order to solve the problem of path planning of mobile robots in a dynamic environment,an improved rapidly-exploring random tree^(*)(RRT^(*))algorithm is proposed in this paper.First,the target bias sampling is introduced to reduce the randomness of the RRT^(*)algorithm,and then the initial path planning is carried out in a static environment.Secondly,apply the path in a dynamic environment,and use the initially planned path as the path cache.When a new obstacle appears in the path,the invalid path is clipped and the path is replanned.At this time,there is a certain probability to select the point in the path cache as the new node,so that the new path maintains the trend of the original path to a greater extent.Finally,MATLAB is used to carry out simulation experiments for the initial planning and replanning algorithms,respectively.More specifically,compared with the original RRT^(*)algorithm,the simulation results show that the number of nodes used by the new improved algorithm is reduced by 43.19%on average.
