论文标题
通过重新配置，具有未知数的目标数量的多杆多目标跟踪中的弹性
Resilience in multi-robot multi-target tracking with unknown number of targets through reconfiguration
论文作者
论文摘要
我们解决了在感兴趣的环境中执行未知数量目标的分布式跟踪的网络多机器人团队中维持资源可用性的问题。基于我们的模型，机器人配备了传感和计算资源，使它们能够使用分布式概率假设密度（PHD）滤波器在环境中合作跟踪一组目标。我们使用机器人传感器测量噪声协方差矩阵的痕迹来量化其传感质量。在执行跟踪任务时，如果机器人会经历传感器质量降低，则重新配置了机器人团队的通信网络，以使带有错误传感器的机器人可以与其他机器人共享信息，以提高团队的目标跟踪能力，而无需实施大量主动通信链接的数量。监视团队执行所有网络重新配置计算的中央系统。我们在本文中考虑了两种不同的博士学位融合方法，并提出了四个不同的混合整数半准编程（MISDP）制定（每种博士学位融合方法的两种配方）来实现我们的目标。所有四个MISDP公式都在模拟中验证。
We address the problem of maintaining resource availability in a networked multi-robot team performing distributed tracking of unknown number of targets in an environment of interest. Based on our model, robots are equipped with sensing and computational resources enabling them to cooperatively track a set of targets in an environment using a distributed Probability Hypothesis Density (PHD) filter. We use the trace of a robot's sensor measurement noise covariance matrix to quantify its sensing quality. While executing the tracking task, if a robot experiences sensor quality degradation, then robot team's communication network is reconfigured such that the robot with the faulty sensor may share information with other robots to improve the team's target tracking ability without enforcing a large change in the number of active communication links. A central system which monitors the team executes all the network reconfiguration computations. We consider two different PHD fusion methods in this paper and propose four different Mixed Integer Semi-Definite Programming (MISDP) formulations (two formulations for each PHD fusion method) to accomplish our objective. All four MISDP formulations are validated in simulation.
