基于移动机器人目标动态避障跟随的 路径规划方法研究

近些年来移动机器人市场发展迅速，在许多涉及人机交互领域的应用中，具 备自主跟随目标移动能力的机器人已成为必需。然而，现实世界的环境异常复杂， 充斥着大量的动态障碍物，给机器人的移动带来了巨大挑战。路径规划方法可以 根据环境的变化实时调整机器人的运动轨迹，使其能够快速、安全地避开动态障 碍物。因此，路径规划方法对于解决机器人在复杂动态环境中的局限性至关重要。 本文旨在研究路径规划方法，以克服这些挑战，实现机器人的自主、高效移动。主 要研究内容如下： （1）针对传统路径规划方法在处理动态避障时成功率较低的问题，提出了一 种基于改进 A* 和 TEB 算法的混合动态避障方法。该方法结合了全局规划与局部 规划的优势，通过改进 A* 算法实现高效的全局路径搜索，通过改进 TEB 算实现 动态障碍物的实时避让，具有较高的搜索效率和良好的动态避障能力。 （2）将提出的混合动态避障方法与现有的目标检测和目标跟踪算法相结合， 构建了一套完整的机器人行人跟随系统，并在真实场景中（包括静态环境和动态 环境）对该方法的可行性进行了验证。 （3）针对传统 DQN 算法在探索性方面的不足，提出了一种基于融合噪声网 络的改进 DQN 方法。通过引入噪声网络增加了评价函数估计的随机性，从而增强 了智能体探索未知环境的能力。通过与传统 DQN 的对比实验，证明了加入噪声网 络的改进 DQN 方法在提高探索效率等方面的显著优势。

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