This paper presents a cooperative multi-robot multi-target tracking framework aimed at enhancing the efficiency of the heterogeneous sensor network and, consequently, improving overall target tracking accuracy. The concept of normalized unused sensing capacity is introduced to quantify the information a sensor is currently gathering relative to its theoretical maximum. This measurement can be computed using entirely local information and is applicable to various sensor models, distinguishing it from previous literature on the subject. It is then utilized to develop a distributed coverage control strategy for a heterogeneous sensor network, adaptively balancing the workload based on each sensor's current unused capacity. The algorithm is validated through a series of ROS and MATLAB simulations, demonstrating superior results compared to standard approaches that do not account for heterogeneity or current usage rates.

翻译：本文提出了一种面向异构传感器网络的多机器人协同多目标跟踪框架，旨在提升网络效率并改善整体目标跟踪精度。引入归一化未利用感知容量概念，用以量化传感器当前采集信息与其理论最大值之间的相对关系。该指标完全基于局部信息计算，适用于多种传感器模型，与现有文献方法存在显著区别。进而基于此指标开发了异构传感器网络的分布式覆盖控制策略，能够根据各传感器当前未利用容量自适应平衡工作负载。通过一系列ROS与MATLAB仿真实验验证了算法性能，相较于未考虑异构性或当前利用率的标准方法展现出更优效果。