This research paper addresses the challenges of exploration and navigation in unknown environments from an evolutionary swarm robotics perspective. Path formation plays a crucial role in enabling cooperative swarm robots to accomplish these tasks. The paper presents a method called the sub-goal-based path formation, which establishes a path between two different locations by exploiting visually connected sub-goals. Simulation experiments conducted in the Argos simulator demonstrate the successful formation of paths in the majority of trials. Furthermore, the paper tackles the problem of inter-collision (traffic) among a large number of robots engaged in path formation, which negatively impacts the performance of the sub-goal-based method. To mitigate this issue, a task allocation strategy is proposed, leveraging local communication protocols and light signal-based communication. The strategy evaluates the distance between points and determines the required number of robots for the path formation task, reducing unwanted exploration and traffic congestion. The performance of the sub-goal-based path formation and task allocation strategy is evaluated by comparing path length, time, and resource reduction against the A* algorithm. The simulation experiments demonstrate promising results, showcasing the scalability, robustness, and fault tolerance characteristics of the proposed approach.

翻译：本研究论文从进化群机器人学视角出发，探讨未知环境下的探索与导航挑战。路径形成是实现群机器人协作完成上述任务的关键环节。本文提出一种名为"基于子目标的路径形成"方法，通过利用视觉连接的子目标在两地之间建立路径。在Argos仿真器中进行的模拟实验表明，该方法在多数试验中成功实现了路径形成。此外，论文解决了大规模参与路径形成的机器人间碰撞（交通拥堵）问题——该问题会降低基于子目标方法的性能。为缓解此问题，提出一种结合局部通信协议与光信号通信的任务分配策略。该策略通过评估点间距离并确定路径形成任务所需的机器人数量，减少无效探索与交通拥堵。通过对比路径长度、时间及资源消耗，本文将该方法性能与A*算法进行了评估。仿真实验展现了该方法的扩展性、鲁棒性和容错特性，结果令人鼓舞。