SafeMove-RL: A Certifiable Reinforcement Learning Framework for Dynamic Motion Constraints in Trajectory Planning

TL;DR

SafeMove-RL introduces a reinforcement learning framework that ensures safe, feasible, and efficient local motion planning in dynamic, uncertain environments by integrating real-time optimization, adaptive safety margins, and online learning.

Contribution

It presents a novel certifiable RL framework combining safety margins, trajectory optimization, and adaptive learning for dynamic motion constraints in trajectory planning.

Findings

Achieves higher success rates than existing methods.

Demonstrates improved computational efficiency.

Validated on both simulated and real robotic platforms.

Abstract

This study presents a dynamic safety margin-based reinforcement learning framework for local motion planning in dynamic and uncertain environments. The proposed planner integrates real-time trajectory optimization with adaptive gap analysis, enabling effective feasibility assessment under partial observability constraints. To address safety-critical computations in unknown scenarios, an enhanced online learning mechanism is introduced, which dynamically corrects spatial trajectories by forming dynamic safety margins while maintaining control invariance. Extensive evaluations, including ablation studies and comparisons with state-of-the-art algorithms, demonstrate superior success rates and computational efficiency. The framework's effectiveness is further validated on both simulated and physical robotic platforms.