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

2021/08/31 by Lu Dong, Zichen He, Dong, Lu +5 · 9 citations
Computer Science · Engineering · #Artificial intelligence #Computer science #FOS: Computer and information sciences #Mobile robot #Motion (physics) #Motion planning #Planner #Reinforcement Learning in Robotics #Reinforcement learning #Robot #Robotic Locomotion and Control #Robotic Path Planning Algorithms #Robotics (cs.RO) #cs.RO

paper · pdf · doi:10.48550/arxiv.2108.13619

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2021/08/31 · openalex created_date 2021/09/13 · arxiv created 2022/02/23 · arxiv updated 2022/02/24 · openalex updated_date 2026/08/06

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.

Citations

• Prioritized Experience Replay
• Learning to Navigate in Complex Environments
• Continual Learning with Deep Generative Replay
• Realtime Collision Avoidance for Mobile Robots in Dense Crowds using Implicit Multi-sensor Fusion and Deep Reinforcement Learning
• A Brief Survey of Deep Reinforcement Learning
• Learning World Transition Model for Socially Aware Robot Navigation
• CURL: Contrastive Unsupervised Representations for Reinforcement Learning

Cited by

• Infrastructure-based Autonomous Mobile Robots for Internal Logistics -- Challenges and Future Perspectives
• DDBot: Differentiable Physics-based Digging Robot for Unknown Granular Materials
• Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
• Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning

Related

• Harnessing Reinforcement Learning for Neural Motion Planning
• Deep Reinforcement Learning for Mobile Robot Path Planning
• A review of motion planning algorithms for intelligent robotics
• Deep Reinforcement Learning for Motion Planning of Mobile Robots
• URPlanner: A Universal Paradigm For Collision-Free Robotic Motion Planning Based on Deep Reinforcement Learning