Adaptive Cost-Map-based Path Planning in Partially Unknown Environments with Movable Obstacles
🤖 AI Summary
To address navigation challenges in partially unknown, unstructured indoor environments—such as disaster rescue scenarios—this paper proposes a real-time path planning method for detecting and actively avoiding movable obstacles. Within the ROS2 Nav2 framework, we fuse LiDAR and odometry data to construct an adaptive dynamic costmap. We introduce a dedicated cost layer for movable obstacles and a novel velocity-ratio–based slow-pose progression detection mechanism, enabling online identification of pushable obstacles and dynamic local costmap updates. The method ensures real-time performance under CPU-constrained conditions. Gazebo simulations demonstrate a significant improvement in goal attainment rate, a substantial reduction in deadlock occurrences, and traversal times comparable to baseline approaches. Moreover, the solution exhibits cross-platform deployability. Overall, it enhances the robustness of search-and-rescue robots in complex, unknown environments.
Application Category
📝 Abstract
Reliable navigation in disaster-response and other unstructured indoor settings requires robots not only to avoid obstacles but also to recognise when those obstacles can be pushed aside. We present an adaptive, LiDAR and odometry-based path-planning framework that embeds this capability into the ROS2 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 higher goal-reach rates and fewer deadlocks than a no-layer baseline, with traversal times broadly comparable. Because the method relies only on planar scans and CPU-level computation, it suits resource-constrained search and rescue robots and integrates into heterogeneous platforms with minimal engineering. Overall, the results indicate that interaction-aware cost maps are a lightweight, ROS2-native extension for navigating among potentially movable obstacles in unstructured settings. The full implementation will be released as open source athttps://costmap-namo.github.io.
Problem
Research questions and friction points this paper is trying to address.
Planning robot paths among movable obstacles in unknown environments
Enabling robots to push aside obstacles instead of just avoiding them
Developing lightweight navigation for resource-constrained rescue robots
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive cost-map labels movable obstacles with reduced cost
Slow-pose checker escalates cost when robot stalls
LiDAR-based method integrates into ROS2 with CPU computation
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