Parallel Self-assembly for a Multi-USV System on Water Surface with Obstacles

TL;DR

This paper introduces SAPOA, a parallel self-assembly planning algorithm for multi-USV systems that effectively navigates obstacles, demonstrating high success rates in simulations and real-world experiments with CuBoats.

Contribution

The paper presents a novel obstacle-aware self-assembly algorithm, SAPOA, enabling modular robots to assemble in complex environments, extending previous methods to obstacle-rich scenarios.

Findings

Success rate exceeds 80% in diverse obstacle configurations

Algorithm successfully deployed on four CuBoats in real-world tests

Demonstrated effective self-assembly in 5 different obstacle maps

Abstract

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.