Mobile Robot Path Planning in Dynamic Environments: A Survey

TL;DR

This survey reviews recent advances in mobile robot path planning within dynamic environments, focusing on global and local strategies, including Velocity Obstacle and reinforcement learning methods, highlighting their strengths and weaknesses.

Contribution

It provides a comprehensive overview of recent path planning techniques for dynamic environments, emphasizing the analysis of Velocity Obstacle and reinforcement learning approaches.

Findings

Velocity Obstacle methods are effective for local planning in dynamic environments.

Reinforcement learning algorithms are widely adopted for model-free path planning.

The survey identifies strengths and weaknesses of current methods.

Abstract

There are many challenges for robot navigation in densely populated dynamic environments. This paper presents a survey of the path planning methods for robot navigation in dense environments. Particularly, the path planning in the navigation framework of mobile robots is composed of global path planning and local path planning, with regard to the planning scope and the executability. Within this framework, the recent progress of the path planning methods is presented in the paper, while examining their strengths and weaknesses. Notably, the recently developed Velocity Obstacle method and its variants that serve as the local planner are analyzed comprehensively. Moreover, as a model-free method that is widely used in current robot applications, the reinforcement learning-based path planning algorithms are detailed in this paper.