  Tracking multiple moving objects of interest (OOI) with multiple robot
systems (MRS) has been addressed by active sensing that maintains a shared
belief of OOIs and plans the motion of robots to maximize the information
quality. Mobility of robots enables the behavior of pursuing better visibility,
which is constrained by sensor field of view (FoV) and occlusion objects. We
first extend prior work to detect, maintain and share occlusion information
explicitly, allowing us to generate occlusion-aware planning even if a priori
semantic occlusion information is unavailable. The efficacy of active sensing
approaches is often evaluated according to estimation error and information
gain metrics. However, these metrics do not directly explain the level of
cooperative behavior engendered by the active sensing algorithms. Next, we
extract different emergent cooperative behaviors that stem from the same
underlying algorithms but manifest differently under differing scenarios. In
particular, we highlight and demonstrate three emergent behavior patterns in
active sensing MRS: (i) Change of tracking responsibility between agents when
tracking trajectories with divergent directions or due to a re-allocation of
the resource among heterogeneous agents; (ii) Awareness of occlusions to a
trajectory and temporal leave-and-return of the sensing agent; (iii) Sharing of
local occlusion objects in MRS that subsequently improves the awareness of
occlusion.
