We study the problem of sensor scheduling for multisensor multitarget tracking-to determine which sensors to activate over time to trade off tracking error with sensor usage costs. Formulating this problem as a Partially Observable Markov Decision Process (POMDP) gives rise to a non-myopic sensor-scheduling scheme. Our method combines sequential multisensor Joint Probabilistic Data Association (MS-JPDA) and particle filtering for belief-state estimation, and uses simulationbased Q-value approximation method for "lookahead." The example of focus in this paper involves the activation of multiple sensors simultaneously for tracking multiple targets, illustrating the effectiveness of our approach.
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 à 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.
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