A Unified Local Risk Map for Uncertainty-Aware Mobile Robot Navigation in Cluttered and Dynamic Environments.

Achieving safe and efficient navigation in cluttered and dynamic environments remains an open challenge for mobile robots, especially when perception and actuation are uncertain. Standard navigation stacks typically handle obstacle avoidance through fixed safety margins or costmap inflation layers. While effective in simple settings, these approaches are difficult to tune in practice: conservative inflation can prevent traversal through narrow passages, whereas less conservative settings may lead to unsafe behavior. Moreover, they usually encode risk only as a function of obstacle proximity. We propose a unified probability-inspired risk-cost map that integrates perception uncertainty, actuation uncertainty, dynamic obstacle prediction, and occlusion-aware memory into a single spatial representation. The resulting risk map is used by a local path-modification module that adapts a reference global path using the proposed risk map and interfaces with a standard Model Predictive Path Integral (MPPI) controller. The proposed method is compatible with standard navigation pipelines. We validate the resulting framework in Gazebo simulations under different sensing and actuation uncertainty conditions and in environments containing unknown static and dynamic obstacles. The results show that the proposed method is more robust than conventional costmap-based baselines, resulting in fewer aborted goals in cluttered environments and substantially fewer collision events when dynamic obstacles are present.