A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures

Abstract

Motion planning is critical to realize the autonomous operation of mobile robots. As the complexity and randomness of robot application scenarios increase, the planning capability of the classical hierarchical motion planners is challenged. With the development of machine learning, deep reinforcement learning (DRL)-based motion planner has gradually become a research hotspot due to its several advantageous features. DRL-based motion planner is model-free and does not rely on the prior structured map. Most importantly, DRL-based motion planner achieves the unification of the global planner and the local planner. In this paper, we provide a systematic review of various motion planning methods. First, we summarize the representative and state-of-the-art works for each submodule of the classical motion planning architecture and analyze their performance features. Subsequently, we concentrate on summarizing RL-based motion planning approaches, including motion planners combined with RL improvements, map-free RL-based motion planners, and multi-robot cooperative planning methods. Last but not least, we analyze the urgent challenges faced by these mainstream RL-based motion planners in detail, review some state-of-the-art works for these issues, and propose suggestions for future research.

References in corpus (10)

• A Brief Survey of Deep Reinforcement Learning
• Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic
• Learning to Navigate in Complex Environments
• R-MADDPG for Partially Observable Environments and Limited Communication
• IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning
• Realtime Collision Avoidance for Mobile Robots in Dense Crowds using Implicit Multi-sensor Fusion and Deep Reinforcement Learning
• Improving Target-driven Visual Navigation with Attention on 3D Spatial Relationships
• Learning World Transition Model for Socially Aware Robot Navigation
• Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation in Dense Mobile Crowds
• Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning