Fault-tolerant modular robotic swarm through cooperative reconfiguration for large-scale on-orbit assembly

Robots are playing an increasingly important role in tasks such as space assembly and manufacturing, but the extreme space environment makes them highly prone to failure. This work proposes a cooperative reconfiguration strategy for the modular robotic swarm, enabling the system to tolerate joint failures and sustain its manipulation capability autonomously. Notably, this process avoids the need for spare modules to replace faulty modules, which improves adaptability to the resource-constrained and extreme conditions of on-orbit missions. Especially under task space constraints, traditional fault-tolerant methods that rely solely on increasing redundancy remain ineffective, whereas cooperative reconfiguration can regenerate manipulation capability. A kinematic self-modeling method for modular robots is developed, leveraging topological representations and the product of exponentials (PoE) formula. After a failure occurs, the target configuration for reconfiguration is searched via particle swarm optimization (PSO), with the objective of maximizing the manipulation capability of the modular robot. A cooperative reconfiguration method considering joint constraints is developed on the basis of an improved rapidly exploring random tree (RRT) algorithm. This method enables the modular robot to transition from the initial faulty configuration to a new configuration capable of accomplishing the task. Experimental analysis of the assembly task validates that the proposed method can enhance the fault tolerance of a robot. In extreme cases involving multiple joint failures, the upper limit of fault tolerance for the number of faulty joints has been increased by 7 times. This fault-tolerant strategy holds significant potential for safeguarding the assembly and manufacturing capabilities of space robotic systems.