Kotsinis Dimitrios, Bechlioulis Charalampos P
Division of Systems and Automatic Control, Department of Electrical and Computer Engineering, University of Patras, Rio, 26504 Patras, Greece.
Athena Research Center, Robotics Institute, Artemidos 6 & Epidavrou, 15125 Maroussi, Greece.
Sensors (Basel). 2024 May 15;24(10):3134. doi: 10.3390/s24103134.
Multi-agent systems are utilized more often in the research community and industry, as they can complete tasks faster and more efficiently than single-agent systems. Therefore, in this paper, we are going to present an optimal approach to the multi-agent navigation problem in simply connected workspaces. The task involves each agent reaching its destination starting from an initial position and following an optimal collision-free trajectory. To achieve this, we design a decentralized control protocol, defined by a navigation function, where each agent is equipped with a navigation controller that resolves imminent safety conflicts with the others, as well as the workspace boundary, without requesting knowledge about the goal position of the other agents. Our approach is rendered sub-optimal, since each agent owns a predetermined optimal policy calculated by a novel off-policy iterative method. We use this method because the computational complexity of learning-based methods needed to calculate the global optimal solution becomes unrealistic as the number of agents increases. To achieve our goal, we examine how much the yielded sub-optimal trajectory deviates from the optimal one and how much time the multi-agent system needs to accomplish its task as we increase the number of agents. Finally, we compare our method results with a discrete centralized policy method, also known as a Multi-Agent Poli-RRT* algorithm, to demonstrate the validity of our method when it is attached to other research algorithms.
多智能体系统在研究领域和工业界的应用越来越频繁，因为它们比单智能体系统能更快、更高效地完成任务。因此，在本文中，我们将提出一种在简单连通工作空间中解决多智能体导航问题的最优方法。该任务要求每个智能体从初始位置出发，沿着最优的无碰撞轨迹到达其目的地。为实现这一目标，我们设计了一种由导航函数定义的分散控制协议，其中每个智能体都配备了一个导航控制器，该控制器能够在不要求了解其他智能体目标位置的情况下，解决与其他智能体以及工作空间边界的紧迫安全冲突。由于每个智能体都拥有通过一种新颖的离策略迭代方法计算出的预定最优策略，我们的方法是次优的。我们使用这种方法是因为随着智能体数量的增加，计算全局最优解所需的基于学习的方法的计算复杂度变得不切实际。为实现我们的目标，我们研究了随着智能体数量的增加，产生的次优轨迹与最优轨迹的偏差程度以及多智能体系统完成任务所需的时间。最后，我们将我们的方法结果与一种离散集中式策略方法（也称为多智能体Poli-RRT*算法）进行比较，以证明我们的方法与其他研究算法结合时的有效性。
