Resilient Multi-Robot Multi-Target Tracking

We address the problem of ensuring resource availability in a networked multi-robot system performing distributed target tracking. Specifically, we consider a multi-target tracking scenario where the targets are driven by exogenous inputs that are unknown to the robots performing the tracking task. Robots track the positions of targets using a form of the Distributed Kalman Filter (DKF). We use the trace of each robot’s sensor measurement noise covariance matrix as a measure of its sensing quality. When a robot’s sensing quality deteriorates, the team’s communication graph is modified by adding edges such that the robot with deteriorating sensor quality may share information with other robots to improve the team’s target tracking ability. This computation is performed centrally and is designed to work without a large change in the number of active inter-robot communication links. Our method generates coordinates for the robots such the new communication graph can be realized in 3D. To achieve this, we propose two mixed integer semi-definite programming formulations, namely an ‘agent-centric’ strategy and a ‘team-centric’ strategy. We implement both formulations and a greedy, baseline strategy in simulation. Our simulation results show that the team-centric approach outperforms both agent-centric and greedy methods. Additionally, we show the effectiveness of our method in real-world settings through a multi-robot experiment performed in real-time. Note to Practitioners—This paper is motivated by the need to track multiple targets by means of a multi-robot team. When robots in the team experience degradation in their sensing quality our method reconfigures the team’s communication graph by repositioning robots. This allows the robot with deteriorated sensing quality to benefit from sensor measurements from its neighbors. In previous work, we solved this problem under the strong assumption the targets moved under inputs that were known to the tracking team. In this paper we remove this assumption. Our experiments show comparable tracking accuracy for both settings, suggesting that this strong assumption is not always necessary for good tracking results. Our method is straightforward to implement in Python using off-the-shelf optimization solvers. We assume that the team performs target tracking using a distributed algorithm, but has access to a powerful centralized base station to solve the computationally expensive underlying optimization problems. Developing a fully decentralized method and finding better ways to solve the underlying optimization problems are potential directions for future work.