Adaptive Cost-Map-based Path Planning in Partially Unknown Environments with Movable Obstacles

Reliable navigation in disaster-response and other unstructured indoor settings requires robots not only to avoid obstacles but also to recognize when those obstacles can be pushed aside. We present an adaptive, LiDAR and odometry-based path-planning framework that embeds this capability into the ROS 2 Nav2 stack. A new Movable Obstacles Layer labels all LiDAR returns missing from a prior static map as tentatively movable and assigns a reduced traversal cost. A companion Slow Pose Progress Checker monitors the ratio of commanded to actual velocity; when the robot slows appreciably, the local cost is raised from light to heavy, and on a stall to lethal, prompting the global planner to back out and re-route. Gazebo evaluations on a Scout Mini, spanning isolated objects and cluttered corridors, show that the system can complete navigation tasks that standard static-map planners cannot, while maintaining efficient traversal times. Because the method relies only on planar scans and low-complexity processing, it suits resource-constrained search-and-rescue robots and integrates into heterogeneous platforms with minimal engineering. Overall, the results indicate that interaction-aware costmaps are a lightweight, ROS 2-native extension for navigating among potentially movable obstacles in partially structured settings. The full implementation will be released as open source at https://costmap-namo.github.io.