Parallel self-assembly is an efficient approach to accelerate the assembly process for modular robots. However, these approaches cannot accommodate complicated environments with obstacles, which restricts their applications. This paper considers the surrounding stationary obstacles and proposes a parallel self-assembly planning algorithm named SAPOA. With this algorithm, modular robots can avoid immovable obstacles when performing docking actions, which adapts the parallel self-assembly process to complex scenes. To validate the efficiency and scalability, we have designed 25 distinct grid maps with different obstacle configurations to simulate the algorithm. From the results compared to the existing parallel self-assembly algorithms, our algorithm shows a significantly higher success rate, which is more than 80%. For verification in real-world applications, a multi-agent hardware testbed system is developed. The algorithm is successfully deployed on four omnidirectional unmanned surface vehicles, CuBoats. The navigation strategy that translates the discrete planner, SAPOA, to the continuous controller on the CuBoats is presented. The algorithm's feasibility and flexibility were demonstrated through successful self-assembly experiments on 5 maps with varying obstacle configurations.

翻译：并行自组装是加速模块化机器人组装过程的有效方法。然而，现有方法无法适应包含障碍物的复杂环境，这限制了其应用。本文考虑周围存在的静态障碍物，提出了一种名为SAPOA的并行自组装规划算法。通过该算法，模块化机器人在执行对接动作时可规避不可移动障碍物，从而将并行自组装过程适配至复杂场景。为验证算法的效率与可扩展性，我们设计了25种不同障碍物配置的栅格地图进行仿真。与现有并行自组装算法相比，本算法的成功率显著提升，超过80%。为开展实际应用验证，我们开发了多智能体硬件测试平台，并成功将算法部署于四艘全向无人水面艇CuBoats上。本文提出了将离散规划器SAPOA转化为CuBoats连续控制器的导航策略。通过在5种不同障碍物配置地图上开展的自组装实验，验证了算法的可行性与灵活性。