Hierarchical motion planning for self-reconfigurable modular robots

Motion planning for a self-reconfigurable robot involves coordinating the movement and connectivity of each of its homogeneous modules. Reconfiguration occurs when the shape of the robot changes from some initial configuration to a target configuration. Finding an optimal solution to reconfiguration problems involves searching the space of possible robot configurations. As this space grows exponentially with the number of modules, optimal planning becomes intractable. We propose a hierarchical planning approach that computes heuristic global reconfiguration strategies efficiently. Our approach consists of a base planner that computes an optimal solution for a few modules and a hierarchical planner that calls this base planner or reuses pre-computed plans at each level of the hierarchy to ultimately compute a global suboptimal solution. We present results from a prototype implementation of the method that efficiently plans for self-reconfigurable robots with several thousand modules. We also discuss tradeoffs and performance issues including scalability, heuristics and plan optimality.