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高效策略。然而，现有方法无法适应包含障碍物的复杂环境，这限制了其应用场景。本文考虑环境中静止障碍物，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，使并行自组装过程适配于复杂场景。为验证算法效率与可扩展性，我们设计了25种具有不同障碍物配置的网格地图进行仿真实验。与现有并行自组装算法的对比结果表明，本算法成功率显著提升（超过80%）。为验证实际应用效果，开发了多智能体硬件测试平台系统。该算法成功部署于四艘全向无人水面艇CuBoats上，并提出了将离散规划器SAPOA映射至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完成自组装实验，验证了算法的可行性与灵活性。