基于动态子目标的路径形成与任务分配：一种面向可扩展群体机器人的 NeuroFleets 方法
Dynamic Subgoal based Path Formation and Task Allocation: A NeuroFleets Approach to Scalable Swarm Robotics
AI总结：
本文提出 NeuroFleets 方法，通过有限状态机、视觉连通子目标和基于光信号通信的任务分配策略，实现未知环境中群体机器人的去中心化路径形成与最优资源部署，并在 Argos 仿真中验证其可扩展性、鲁棒性和容错性。
摘要：
本文从进化群体机器人的视角探讨未知环境中的探索与导航挑战。一个关键关注点是路径形成，这对于使协作群体机器人能够有效导航至关重要。我们基于有限状态机设计了任务分配和路径形成过程，确保系统化决策和高效的状态转换。该方法采用去中心化方式，允许每个机器人根据局部信息独立做出决策，从而增强了可扩展性和鲁棒性。我们提出了一种新颖的基于子目标的路径形成方法，通过利用视觉连通的子目标在位置之间建立路径。在 Argos 模拟器中进行的仿真实验表明，该方法在大多数试验中成功形成了路径。然而，在路径形成过程中，大量机器人之间的相互碰撞（交通拥堵）可能对性能产生负面影响。为解决这一问题，我们提出了一种任务分配策略，该策略使用局部通信协议和基于光信号的通信来管理机器人部署。该策略评估点之间的距离，并确定路径形成任务所需的最优机器人数量，从而减少不必要的探索和交通拥堵。通过将路径长度、时间和资源使用情况与 A* 算法进行比较，评估了基于子目标的路径形成方法和任务分配策略的性能。仿真结果证明了我们方法的有效性，突显了其可扩展性、鲁棒性和容错能力。
英文摘要：
This paper addresses the challenges of exploration and navigation in unknown environments from the perspective of evolutionary swarm robotics. A key focus is on path formation, which is essential for enabling cooperative swarm robots to navigate effectively. We designed the task allocation and path formation process based on a finite state machine, ensuring systematic decision-making and efficient state transitions. The approach is decentralized, allowing each robot to make decisions independently based on local information, which enhances scalability and robustness. We present a novel subgoal-based path formation method that establishes paths between locations by leveraging visually connected subgoals. Simulation experiments conducted in the Argos simulator show that this method successfully forms paths in the majority of trials. However, inter-collision (traffic) among numerous robots during path formation can negatively impact performance. To address this issue, we propose a task allocation strategy that uses local communication protocols and light signal-based communication to manage robot deployment. This strategy assesses the distance between points and determines the optimal number of robots needed for the path formation task, thereby reducing unnecessary exploration and traffic congestion. The performance of both the subgoal-based path formation method and the task allocation strategy is evaluated by comparing the path length, time, and resource usage against the A* algorithm. Simulation results demonstrate the effectiveness of our approach, highlighting its scalability, robustness, and fault tolerance.
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。
