提出实时动态路径规划方法，让机器人更好应对移动障碍物。
On-the-Go Path Planning and Repair in Static and Dynamic Scenarios
- 结合传统规划器与实时响应机制，动态调整路径。
- 仿真验证可在已知和未知环境中有效避障。
- 适合城市环境中的无人机、机器人等自主系统使用。
自主系统（如机器人和无人机）在城市等动态环境中导航面临巨大挑战，障碍物、交通流和行人活动持续变化。虽然传统的波前规划和梯度下降规划等基于势函数的方法在静态环境中表现良好，但在环境持续变化时效果不佳。本文提出一种动态、实时的路径规划方法，专为自主系统设计，可有效避开静态与动态障碍物，提升整体适应性。该方法融合传统规划器的效率与对移动障碍物及环境变化的快速响应能力。文章通过仿真结果证明了该方法的有效性，表明其适用于包含移动物体、代理或潜在威胁的已知与未知环境中的机器人路径规划。
原文摘要 · Abstract (English)
Autonomous systems, including robots and drones, face significant challenges when navigating through dynamic environments, particularly within urban settings where obstacles, fluctuating traffic, and pedestrian activity are constantly shifting. Although, traditional motion planning algorithms like the wavefront planner and gradient descent planner, which use potential functions, work well in static environments, they fall short in situations where the environment is continuously changing. This work proposes a dynamic, real-time path planning approach specifically designed for autonomous systems, allowing them to effectively avoid static and dynamic obstacles, thereby enhancing their overall adaptability. The approach integrates the efficiency of conventional planners with the ability to make rapid adjustments in response to moving obstacles and environmental changes. The simulation results discussed in this article demonstrate the effectiveness of the proposed method, demonstrating its suitability for robotic path planning in both known and unknown environments, including those involving mobile objects, agents, or potential threats.
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