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引用次数: 0
摘要
运动和传感器不确定性下的多目标运动规划问题是寻找一组目标点的可靠策略。本文将该问题形式化为信念空间中的一个强大的旅行商问题。为了解决这一棘手的问题,我们提出了一种基于反馈信息路线图(feedback-based information roadmap, FIRM)算法的TSP-FIRM图构造算法。同时提出了离线模式下策略的在线规划和克服环境地图变化的两种算法。最后,我们将该算法应用于物理非完整移动机器人,以解决实际模型与计算模型不一致、地图更新和绑架等具有挑战性的情况。
Implementation of Multi -Goal Motion Planning Under Uncertainty on a Mobile Robot
Multi-goal motion planning under motion and sensor uncertainty is the problem of finding a reliable policy for visiting a set of goal points. In this paper, the problem is formulated as a formidable traveling salesman problem in the belief space. To solve this intractable problem, we propose an algorithm to construct a TSP-FIRM graph which is based on the feedback-based information roadmap (FIRM) algorithm. Also, two algorithms are proposed for the online planning of the obtained policy in the offline mode and overcoming changes in the map of the environment. Finally, we apply the algorithms on a physical nonholonomic mobile robot in the presence of challenging situations like the discrepancy between the real and computation model, map updating and kidnapping.