Collision-Aware Path Planning for Articulated Arms in Narrow Irregular Tunnels Under Partial Observability

Abstract

Path planning for articulated robotic arms in confined spaces requires reasoning about collision geometry in environments where the workspace boundary is irregular, partially observed, and may change during operation (e.g., due to water flow or debris movement). Classical motion planning algorithms assume complete workspace knowledge or rely on sensor-complete reconstruction, neither of which is practical for battery-powered robots operating in Indian sewers with limited onboard compute. We present a collision-aware planning approach that maintains a probabilistic occupancy representation of the workspace, updated incrementally from wrist-mounted depth sensors, and plans arm trajectories using a learned collision cost field. The planner operates in a receding-horizon fashion, replanning every 200ms as new depth observations arrive. Evaluated on the SafAI platform performing sludge extraction in irregular tunnel sections, our approach reduces collision events by 67% compared to a geometry-unaware baseline while maintaining 91% of the baseline's task completion rate. The method requires less than 15% of available onboard compute, leaving sufficient headroom for simultaneous VLA model inference.

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