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Dynamic path planning of a mobile robot with improved Q-learning algorithm

机译:改进的Q学习算法的移动机器人动态路径规划

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Path planning of a mobile robot under dynamic environment is a difficult part of robot navigation. In this paper, a new path planning method based on improved Q-learning (IQL) algorithm and some heuristic searching strategies is proposed for mobile robot in dynamic environment. A new exploration strategy which combines ε-greedy exploration with Boltzmann exploration is used in IQL. In addition, the heuristic searching strategies are provided to reduce the search space and limit the variation range of orientation angle. From simulations, the better performance of the proposed method was certified in terms of time taken and optimal path comparison with classical Q-learning (CQL) and other planning methods. Meanwhile, the reduction in orientation angle and path length has significance in the robotics literature of the energy consumption.
机译:动态环境下移动机器人的路径规划是机器人导航的难点。提出了一种基于改进Q学习(IQL)算法和启发式搜索策略的移动机器人在动态环境下的路径规划新方法。 IQL中使用了一种将ε贪婪探索与Boltzmann探索相结合的新探索策略。另外,提供了启发式搜索策略以减小搜索空间并限制取向角的变化范围。通过仿真,该方法在传统的Q学习(CQL)和其他规划方法所花费的时间和最佳路径比较方面得到了较好的验证。同时,减少方向角和路径长度在能源消耗的机器人学文献中具有重要意义。

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