摘要：

随着应用场景的日益复杂，机器人对旨在生成无碰撞路径（轨迹）的自主运动规划技术的需求也变得更加迫切。虽然目前已产生了大量适应于不同场景的规划算法，但如何妥善地对现有成果进行归类，并分析不同方法间的优劣异同仍是需要深入思考的问题。以此为切入点，首先，阐释运动规划的基本内涵及经典算法的关键步骤；其次，针对实时性与解路径（轨迹）品质间的矛盾，以是否考虑微分约束为标准，有层次地总结了现有的算法加速策略；最后，面向不确定性（即传感器不确定性、未来状态不确定性和环境不确定性）下的规划和智能规划提出的新需求，对运动规划领域的最新成果和发展方向进行了评述，以期为后续研究提供有益的参考。

关键词: 机器人, 组合运动规划, 基于采样的运动规划, 基于优化的运动规划, 反馈运动规划

Abstract:

As application scenarios become more complex， the need for autonomous motion planning techniques which aims at generating collision-free path （trajectory） becomes more urgent. Although a large number of planning algorithms adapted to different scenarios have been proposed already， how to properly classify the existing results and analyze the advantages and disadvantages of different methods is still a problem that needs in-depth consideration. In this paper， the basic connotation of motion planning and the key steps of classical algorithms are explained. Secondly， aiming at the contradiction between real-time performance and the quality of solution path （trajectory）， the existing algorithm acceleration strategies are analyzed and summarized hierarchically based on whether differential constraint is considered. Finally， facing the new requirements of planning under uncertainty （i.e.， sensor uncertainty， future state uncertainty and environmental uncertainty） and intelligent planning， the latest achievements and development direction in the field of motion planning are reviewed. It is expected that the review can provide ideas for future research.

Key words: robot, combined motion planning, sample-based motion planning, optimized-based motion planning, feedback motion planning

中图分类号: 

• V249.121