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考虑加速度的分层增量路径规划和依赖于情境的优化动态运动规划。

Hierarchical incremental path planning and situation-dependent optimized dynamic motion planning considering accelerations.

作者信息

Lai Xue-Cheng, Ge Shuzhi Sam, Al Mamun Abdullah

机构信息

Electrical and Computer Engineering Department, National University of Singapore, Singapore 117576.

出版信息

IEEE Trans Syst Man Cybern B Cybern. 2007 Dec;37(6):1541-54. doi: 10.1109/tsmcb.2007.906577.

Abstract

This paper studies a hierarchical approach for incrementally driving a nonholonomic mobile robot to its destination in unknown environments. The A* algorithm is modified to handle a map containing unknown information. Based on it, optimal (discrete) paths are incrementally generated with a periodically updated map. Next, accelerations in varying velocities are taken into account in predicting the robot pose and the robot trajectory resulting from a motion command. Obstacle constraints are transformed to suitable velocity limits so that the robot can move as fast as possible while avoiding collisions when needed. Then, to trace the discrete path, the system searches for a waypoint-directed optimized motion in a reduced 1-D translation or rotation velocity space. Various situations of navigation are dealt with by using different strategies rather than a single objective function. Extensive simulations and experiments verified the efficacy of the proposed approach.

摘要

本文研究了一种分层方法,用于在未知环境中逐步引导非完整移动机器人到达其目的地。对A*算法进行了修改,以处理包含未知信息的地图。在此基础上,利用周期性更新的地图逐步生成最优(离散)路径。接下来,在预测机器人姿态和运动指令产生的机器人轨迹时,考虑了不同速度下的加速度。将障碍物约束转化为合适的速度限制,以便机器人在需要时能够尽可能快地移动同时避免碰撞。然后,为了跟踪离散路径,系统在简化的一维平移或旋转速度空间中搜索指向路点的优化运动。通过使用不同的策略而非单一目标函数来处理各种导航情况。大量的仿真和实验验证了所提方法的有效性。

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