发表机构
College of Information Science and Technology, Jinan University; University of Connecticut; Guangzhou Maritime University; University of Chinese Academy of Sciences; Global College, Shanghai Jiao Tong University; Wuyi University; South China University of Technology（暨南大学信息科学技术学院; 康涅狄格大学; 广州海事大学; 中国科学院大学; 上海交通大学全球学院; 五邑大学; 华南理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述回顾2015至2025年动态环境下基于学习的运动规划代表性研究，提出学习作用分类法，分析相关影响因素，探讨安全可验证规划等开放挑战与未来方向。
AI 中文摘要
动态环境中的运动规划是机器人学的基础问题，旨在生成安全、高效的路径、轨迹或控制动作，需应对移动障碍物、不确定预测及多智能体交互等情况，广泛应用于自动驾驶、服务机器人、仓储物流、人机协作、人群导航和多机器人系统等领域。本综述主要回顾2015至2025年间发表的代表性研究，重点关注近期基于学习的进展如何扩展、补充或与经典规划基础相互作用。首先，回顾经典规划方法，作为基于学习的扩展的算法基础和参考框架；接着提出学习作用分类法，根据学习在规划流程中的参与方式对现有方法分类，包括直接策略学习、学习增强型经典规划、混合规划及训练增强方法，对每个类别总结主要问题设定、代表性算法、核心思想、集成机制、优势与局限性；进一步分析观测表示、预测不确定性、交互建模、规划器集成、安全约束及训练策略如何影响动态环境下基于学习的运动规划；最后探讨开放挑战与未来方向，包括现实差距、安全可验证规划、密集人群导航、感知-规划耦合及具身AI。
英文摘要
Motion planning in dynamic environments is a fundamental problem in robotics, aiming to generate safe and efficient paths, trajectories, or control actions in the presence of moving obstacles, uncertain predictions, and multi-agent interactions. It has broad applications in autonomous driving, service robotics, warehouse logistics, human-robot collaboration, crowd navigation, and multi-robot systems. This survey reviews representative works published primarily between 2015 and 2025, with a particular focus on how recent learning-based advances extend, complement, or interact with classical planning foundations. We first revisit classical planning methods as algorithmic foundations and reference frameworks for learning-based extensions. We then propose a role-of-learning taxonomy that categorizes existing methods according to how learning participates in the planning pipeline, including direct policy learning, learning-augmented classical planning, hybrid planning, and training enhancement methods. For each category, we summarize the main problem settings, representative algorithms, key ideas, integration mechanisms, strengths, and limitations. We further analyze how observation representations, prediction uncertainty, interaction modeling, planner integration, safety constraints, and training strategies shape learning-based motion planning in dynamic environments. Finally, we discuss open challenges and future directions, including sim-to-real gap, safe and certifiable planning, dense crowd navigation, perception-planning coupling, and embodied AI.
