摘要
针对室内服务机器人在未知动态环境中工作时的功能需求,提出了一种局部环境增量采样的路径规划算法。该方法首先依据当前环境构建基于障碍物碰撞风险的评估概率;然后在搜索树扩展的过程中,设计了结合碰撞风险评估概率和欧氏距离的代价函数,避免了每次扩展时新节点和潜在扩展边的碰撞检测,提高了算法效率;同时,搜索树扩展借鉴了快速随机扩展图算法的扩展方式,实现在当前搜索树结构下的最优扩展;另外,提供了算法的性能分析。最后,仿真及实验结果表明该方法具有良好的规划性能,需要较少的计算时间和平均迭代次数,能够满足室内服务机器人实时路径规划的工作需求。
To meet the requirements of indoor service robot working under unknown dynamic environments, an incremental sampling path planning based on local environments is proposed in this paper. At first, the estimation of collision risk in current environment is built by a probabilistic model. Then, during the searching tree expansion process, a novel cost function using the Euclidean distance and estimation of collision risk is constructed. Thus, the collision checking for new vertex and potential extensible edges in each iteration can be reduced, and then the algorithm efficiency can be increased. Meanwhile, the best extension in current structure of searching tree can be obtained by referred the rapidly-exploring random graph algorithm. In addition, the performance analysis is provided. Finally, the simulations and experimental results show that the proposed algorithm owns good planning performances and efficiency （less calculating time and iteration times） respectively, which satisfies the needs of real time path planning for indoor service robot.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2017年第5期1093-1100,共8页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(61433016
61573134)
湖南省研究生创新项目(CX2016B124)资助
关键词
移动机器人
复杂动态环境
路径规划
避碰
mobile robots
complex dynamic environment
path planning
collision avoidance
