SMCPP: Safety Margin Controllable Path Planning via Safety Constraint Propagation

Existing path planning methods often prioritize geometric shortest paths while lacking explicit control over safety margins, which may lead to unreliable navigation in complex environments. To address this issue, this paper proposes a Safety Margin Controllable Path Planning (SMCPP) framework. The key idea of SMCPP is to propagate safety constraints throughout the entire planning pipeline, ensuring that safety requirements are consistently maintained during search, path point filtering, and path smoothing. Unlike conventional planners that enforce safety through obstacle inflation after path generation, SMCPP integrates safety awareness directly into all planning stages and preserves it throughout the pipeline, rather than treating planning stages as independent modules. Extensive experiments demonstrate that SMCPP produces paths with larger obstacle clearance and smoother paths while achieving reliable navigation performance, particularly in narrow and cluttered environments. These results indicate that maintaining consistent safety constraints during planning significantly improves navigation robustness. Overall, SMCPP provides a practical and effective framework for safe and smooth autonomous navigation in complex environments.