Auto-Optimizing Connection Planning Method for Chain-Type Modular Self-Reconfiguration Robots

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Abstract
Chain-type modular robots are capable of self-reconfiguration (SR), where the connection relationship between modules is changed according to the environment and tasks. This article focuses on the connection planning of SR based on multiple in-degree single out-degree (MISO) modules. The goal is to calculate the optimal connection planning solution: the sequence with the fewest detachment and attachment actions. To this end, we propose an auto-optimizing connection planning method that contains a polynomial-time algorithm to calculate near-optimal solutions and an exponential-time algorithm to further optimize the solutions automatically when some CPUs are idle. The method combines rapidity and optimality in the face of an NP-complete problem by using configuration pointers, strings that uniquely specify the robot's configuration. Our polynomial-time algorithm, in-degree matching (IM) uses the interchangeability of connection points to reduce reconfiguration steps. Our exponential-time algorithm, tree-based branch and bound (TBB) further optimizes the solutions to the optimum by a new branching strategy and stage cost. In the experiments, we verify the feasibility of the auto-optimizing method combining IM and TBB, and demonstrate the superiority of IM over Greedy-CM in the SR of MISO modules and the near-optimality of IM compared to the optimal solutions of TBB.
Funding Information
  • National Natural Science Foundation of China (62073274, AC01202101103)
  • Shenzhen Institute of Artificial Intelligence and Robotics for Society

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