多功能移动机器人分层路径规划研究

现有移动机器人规划算法生成轨迹目标单一，无法满足多样化的任务需求，为此，提出一种多指标维度反馈的路径规划策略。基于强化学习在线决策机制，寻求多条抵达目标的无碰撞路径；进而根据路径性能特征建立价值评估体系，动态更新机器人运动节点间的动作代价值，并且在不同权重配比下进行了仿真测试。结果表明：该算法能根据需求针对性提高全局路径相关性能，并结合全局路径信息，反馈控制局部运动决策，从而使得移动机器人能够在相同环境中解算出不同满足期望的运动决策。

Layered Motion Planning of Multifunctional Mobile Robot

Aiming at the single trajectory target generated by the existing planning algorithm, which cannot meet the diversified task requirements of mobile robots, a motion planning strategy for multi-dimensional information feedback is proposed. Based on the reinforcement learning online decision-making mechanism, multiple collision-free paths to reach the goal are sought. According to the path performance characteristics, a value evaluation system is established to dynamically update the generation value between the nodes of the robot motion state. Simulation tests are carried out under different weight ratios, and the results verify that the algorithm can control local path decisions in combination with global path information feedback, and achieve the goal of multi-dimensional control of generating paths, so that mobile robots can solve different motion decisions that meet expectations in the same environment.