Mobile Robot Path Planning in Dynamic Environments: A Survey
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Jun 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1.
5 papers.
- 2021Conflict Avoidance in Social Navigation -- a Surveycited by 14
- 2022Learning Social Navigation from Demonstrations with Conditional Neural Processescited by 3
- 2024Personalized Embodied Navigation for Portable Object Findingcited by 3
- 2021OctoPath: An OcTree Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robotscited by 1
- 2023POA: Passable Obstacles Aware Path-planning Algorithm for Navigation of a Two-wheeled Robot in Highly Cluttered Environmentscited by 0
- 2024Personalized Embodied Navigation for Portable Object Finding14 Mar 2024
- 2023POA: Passable Obstacles Aware Path-planning Algorithm for Navigation of a Two-wheeled Robot in Highly Cluttered Environments16 Jul 2023
- 2022Learning Social Navigation from Demonstrations with Conditional Neural Processes7 Oct 2022
- 2021Conflict Avoidance in Social Navigation -- a Survey23 Jun 2021
- 2021OctoPath: An OcTree Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots2 Jun 2021