We study the problem of multiple target tracking using multiple mobile robots. Our approach is to divide the cooperative multi-target tracking problem into two sub-problems: target tracking using a single mobile robot and on-line motion strategy design for multi-robot coordination.
For single robot-based tracking, we address two key challenges: how to separate the egomotion of the robot from the motions of external objects, and how to compensate this ego-motion to detect and track moving objects robustly. An ego-motion compensation method using salient feature tracking and a probabilistic filter design to handle the noise and uncertainty of sensor inputs are presented. The proposed method has been tested in various outdoor environments using three different robot platforms, which have unique ego-motion characteristics.
For multi-robot coordination, we propose an algorithm based on treating the densities of robots and targets as properties of the environment in which they are embedded. By suitably manipulating these densities a control law for each robot is proposed; we term our approach Regionbased. We derive two specialized versions of the control law for the case when the topology of the environment is known in advance and the case when the environment is unstructured. These coordination approaches have been tested in simulation and in real robot systems. Experiments indicate that our treatment of the coordination problem based on environmental characteristics is effective and efficient.