进化群体机器人：未知环境中基于动态子目标路径形成与任务分配的探索与导航
Evolutionary Swarm Robotics: Dynamic Subgoal-Based Path Formation and Task Allocation for Exploration and Navigation in Unknown Environments
AI总结：
本文提出基于子目标的路径形成与任务分配策略，解决未知环境中群体机器人的探索导航及交通拥堵问题，仿真验证其优于A*算法，具有可扩展性和鲁棒性。
摘要：
本研究论文从进化群体机器人学的视角，探讨了在未知环境中进行探索与导航所面临的挑战。路径形成在使协作式群体机器人能够完成这些任务中起着至关重要的作用。论文提出了一种称为基于子目标的路径形成方法，该方法通过利用视觉上连接的子目标，在两个不同位置之间建立路径。在Argos模拟器中进行的仿真实验表明，在大多数试验中，路径形成均能成功实现。此外，论文还解决了大量参与路径形成的机器人之间的相互碰撞（交通拥堵）问题，该问题对基于子目标方法的性能产生了负面影响。为了缓解这一问题，提出了一种任务分配策略，该策略利用本地通信协议和基于光信号的通信。该策略评估点之间的距离，并确定路径形成任务所需的机器人数量，从而减少不必要的探索和交通拥堵。通过将路径长度、时间和资源减少与A*算法进行比较，评估了基于子目标的路径形成和任务分配策略的性能。仿真实验展示了令人鼓舞的结果，证明了所提出方法的可扩展性、鲁棒性和容错特性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
