摘要
路径规划是自动导引小车(AGV)控制中的核心问题之一。针对经典RRT算法在静态全局状态空间中随机采样搜索节点时随机性大与效率低的问题,提出了一种改进的RRT(快速搜索随机树)路径规划算法。该算法结合双向搜索功能与自适应目标引力思想,利用双向搜索速度快与自适应目标引力朝目标点方向生长的特性,使AGV在规划路径时路径搜索效率更高,路径更平滑。实验仿真结果证明,改进的RRT算法可以在有效提高路径搜索效率的同时生成最优路径。
. The path planning of automatic guided vehicle is one of the core issues in the control. For the classical RRT algorithm in global static random sampling in the state space search node is random and low efficiency problem, we propose an improved RRT （rapidly random-exploring trees） path planning algorithm. This algorithm combines the two the search function and adap- tive target gravity theory using the two-way search speed and characteristics of adaptive target gravity toward the target growth path, AGV path planning in higher searching efficiency and smoother paths. The experimental simulation results. The results show that the improved RRT algorithm can effectively improve the path search efficiency and generate the optimal path at the same time.
出处
《软件导刊》
2018年第3期28-31,共4页
Software Guide
基金
国家自然科学基金项目(61163051)
云南省教育厅科学研究基金项目(2015Y071)
