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Tracking multiple moving objects of interest (OOI) with multiple robotsystems (MRS) has been addressed by active sensing that maintains a sharedbelief of OOIs and plans the motion of robots to maximize the informationquality. Mobility of robots enables the behavior of pursuing better visibility,which is constrained by sensor field of view (FoV) and occlusion objects. Wefirst extend prior work to detect, maintain and share occlusion informationexplicitly, allowing us to generate occlusion-aware planning even if a priorisemantic occlusion information is unavailable. The efficacy of active sensingapproaches is often evaluated according to estimation error and informationgain metrics. However, these metrics do not directly explain the level ofcooperative behavior engendered by the active sensing algorithms. Next, weextract different emergent cooperative behaviors that stem from the sameunderlying algorithms but manifest differently under differing scenarios. Inparticular, we highlight and demonstrate three emergent behavior patterns inactive sensing MRS: (i) Change of tracking responsibility between agents whentracking trajectories with divergent directions or due to a re-allocation ofthe resource among heterogeneous agents; (ii) Awareness of occlusions to atrajectory and temporal leave-and-return of the sensing agent; (iii) Sharing oflocal occlusion objects in MRS that subsequently improves the awareness ofocclusion.

Emergent Cooperative Behavior in Distributed Target Tracking with Unknown Occlusions