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.

翻译：多机器人系统（MRS）对多个感兴趣移动目标（OOI）的跟踪，已通过主动感知方法得以解决，该方法维护对OOI的共享信念，并规划机器人运动以最大化信息质量。机器人的移动性使其能够追求更优可视性，但受限于传感器视场（FoV）和遮挡物体。我们首先将先前工作扩展为显式检测、维护和共享遮挡信息，从而在缺乏先验语义遮挡信息时生成遮挡感知规划。主动感知方法的有效性通常依据估计误差和信息增益指标进行评估，然而这些指标无法直接解释主动感知算法所激发的协作行为水平。随后，我们提取出源自相同底层算法但不同场景下表现各异的涌现协作行为。具体而言，我们重点阐释并演示了主动感知MRS中的三种涌现行为模式：（i）当跟踪轨迹方向发散或因异构代理间的资源重分配时，代理间跟踪责任的转移；（ii）对轨迹遮挡的感知，以及感知代理的临时离开与返回行为；（iii）MRS中局部遮挡物体的共享，进而提升整体遮挡感知能力。