Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms
Motion planning in dynamic environments is a fundamental problem in robotics, aiming to generate safe and efficient paths, trajectories, or control actions in the presence of moving obstacles, uncertain predictions, and multi-agent interactions. It has broad applications in autonomous driving, service robotics, warehouse logistics, human-robot collaboration, crowd navigation, and multi-robot systems. This survey reviews representative works published primarily between 2015 and 2025, with a particular focus on how recent learning-based advances extend, complement, or interact with classical planning foundations. We first revisit classical planning methods as algorithmic foundations and ref...
| Authors | Zongyuan Shen, Shalabh Gupta, Shancheng Zhao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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