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This paper focuses on the emerging paradigm shift of collision-inclusivemotion planning and control for impact-resilient mobile robots, and develops aunified hierarchical framework for navigation in unknown andpartially-observable cluttered spaces. At the lower-level, we develop adeformation recovery control and trajectory replanning strategy that handlescollisions that may occur at run-time, locally. The low-level system activelydetects collisions (via embedded Hall effect sensors on a mobile robot builtin-house), enables the robot to recover from them, and locally adjusts thepost-impact trajectory. Then, at the higher-level, we propose a search-basedplanning algorithm to determine how to best utilize potential collisions toimprove certain metrics, such as control energy and computational time. Ourmethod builds upon A* with jump points. We generate a novel heuristic function,and a collision checking and adjustment technique, thus making the A* algorithmconverge faster to reach the goal by exploiting and utilizing possiblecollisions. The overall hierarchical framework generated by combining theglobal A* algorithm and the local deformation recovery and replanning strategy,as well as individual components of this framework, are tested extensively bothin simulation and experimentally. An ablation study draws links to relatedstate-of-the-art search-based collision-avoidance planners (for the overallframework), as well as search-based collision-avoidance and sampling-basedcollision-inclusive global planners (for the higher level). Results demonstrateour method's efficacy for collision-inclusive motion planning and control inunknown environments with isolated obstacles for a class of impact-resilientrobots operating in 2D.

Online Search-based Collision-inclusive Motion Planning and Control for Impact-resilient Mobile Robots