Motion Planning in Dynamic Environments: A Survey from Classical to Modern Methods

2026-06-01

AI Summary

This study addresses the critical need for real-time path adaptation in robotic navigation within dynamic environments, a challenge inadequately covered by existing surveys. Systematically reviewing 138 studies from 2015 to 2025, this work presents the first unified taxonomy of motion planning approaches, categorizing them into sampling-based, graph-search, model predictive control, learning-based, and classical local planners, while integrating both classical and learning-driven methods. It critically examines how dynamic perception influences planning, with in-depth analysis of core challenges including prediction uncertainty, human-robot interaction, and the “freezing robot” problem. The review encompasses key techniques such as velocity obstacles, potential fields, dynamic window approaches, supervised and reinforcement learning, and perception modalities leveraging cameras, LiDAR, and event-based sensors. By establishing a structured methodological framework, this paper offers researchers a comprehensive understanding of the principles, strengths, and limitations across planning paradigms, thereby advancing the field.

Abstract

Motion planning in dynamic environments requires robots to continuously adapt their paths in response to environmental changes for safe and uninterrupted navigation. While many surveys have reviewed planning in static settings, systematic reviews focused on dynamic environments remain limited. This paper presents a comprehensive survey of 138 works, primarily published between 2015 and 2025, spanning both classical and learning-based approaches. The motion planning methods are grouped into five categories based on the concepts of sampling, graph search, model predictive control, learning, and additional classical local planning approaches, including velocity obstacles, potential fields and dynamic windows. The learning techniques include supervised learning and reinforcement learning. We also discuss the role of dynamic perception in motion planning, covering techniques for detecting and modeling moving obstacles using cameras, LiDAR, and event-based sensors. The survey analyzes the principles, strengths, and limitations of each method, with particular attention to challenges unique to dynamic environments, such as prediction uncertainty, human-robot interaction, and the freezing robot problem. The survey provides researchers with a structured understanding of motion planning methods in dynamic environments.

Problem

• motion planning
• dynamic environments
• robot navigation
• moving obstacles
• environmental changes

Innovation

• motion planning
• dynamic environments
• learning-based methods
• dynamic perception
• robot navigation