基于SBL-PRM算法的柑橘采摘机器人实时路径规划

Real-time path planning for citrus picking robot based on SBL-PRM

针对动态非结构化环境下的柑橘采摘机器人实时路径规划问题,采用单次查询、双向采样与延迟碰撞检测相结合的SBL-PRM(Single-query,Bi-directional,Lazy collision checking,Probabilistic Roadmap Method)算法,对无遮挡和遮挡两种场景下的柑橘采摘情况进行仿真试验,分析最大采样点数S、邻域阈值ρ、局部路径检测阈值ε、路径平滑次数N等参数对规划时间和成功率的影响。结果表明,在S=3000,ρ=0.6,ε=0.03,N=10时,无遮挡和遮挡两种场景下路径规划的平均时间分别为1ms、60ms左右,规划成功率均为100%。仿真试验证明了SBL-PRM算法在柑橘采摘机器人实时路径规划中的有效性。

A single-query bi-directional probabilistic roadmap planner with lazy collision checking was adopted for citrus picking robot real-time path planning in dynamic and unstructured environments. Simulation of the planner was carried out in two cases of picking exposed and overlapped fruits. The effects of maximum number of milestones S, neighboring threshold p, local path checking threshold e and path smoother steps N on average time and success rate of planning were analyzed. When S, p, e, and N were 3000, 0.6, 0.03, 10 respectively, simulation results indicated that the average planning time was about lms for picking exposed fruits and 60 ms for picking overlapped ones, and the success rates of planning were both 100%. The simulation experiment shows that SBL-PRM is effective in the citrus picking robot real-time path planning.