Liu Kexin, Zhang Yinyan
IEEE Trans Neural Netw Learn Syst. 2025 Mar;36(3):5795-5802. doi: 10.1109/TNNLS.2024.3377433. Epub 2025 Feb 28.
Tasks allocation plays a pivotal role in cooperative robotics. This study proposes a novel fully distributed task allocation method for target tracking, by which mobile robots only need to share state information with communication neighbors. The proposed method adopts a distributed k winners-take-all (k-WTA) network to select the k mobile robots closest to the moving target to perform the target tracking task. In addition, an innovative robot control law is designed, incorporating speed feedback and nonlinear activation functions to achieve finite-time error convergence. Unlike previous approaches, our distributed task allocation method yields finite-time error convergence, does not rely on consensus filters, and eliminates the need for a central computing unit to get the k-WTA result during the control process. We demonstrate the effectiveness of the proposed method through theoretical analysis and simulations. Compared to traditional methods, our method leads to smaller total moving distances and speed norms, which underscores the significance of our method in enhancing the efficiency and performance of mobile robots in dynamic task allocation.
任务分配在协作机器人技术中起着关键作用。本研究提出了一种用于目标跟踪的新型全分布式任务分配方法，通过该方法移动机器人仅需与通信邻居共享状态信息。所提出的方法采用分布式k胜者全得（k-WTA）网络来选择距离移动目标最近的k个移动机器人以执行目标跟踪任务。此外，设计了一种创新的机器人控制律，纳入速度反馈和非线性激活函数以实现有限时间误差收敛。与先前的方法不同，我们的分布式任务分配方法实现了有限时间误差收敛，不依赖于一致性滤波器，并且在控制过程中无需中央计算单元来获取k-WTA结果。我们通过理论分析和仿真证明了所提出方法的有效性。与传统方法相比，我们的方法导致总移动距离和速度范数更小，这突出了我们的方法在提高移动机器人动态任务分配效率和性能方面的重要性。
