Observing and filming a group of moving actors with a team of aerial robots is a challenging problem that combines elements of multi-robot coordination, coverage, and view planning. A single camera may observe multiple actors at once, and the robot team may observe individual actors from multiple views. As actors move about, groups may split, merge, and reform, and robots filming these actors should be able to adapt smoothly to such changes in actor formations. Rather than adopt an approach based on explicit formations or assignments, we propose an approach based on optimizing views directly. We model actors as moving polyhedra and compute approximate pixel densities for each face and camera view. Then, we propose an objective that exhibits diminishing returns as pixel densities increase from repeated observation. This gives rise to a multi-robot perception planning problem which we solve via a combination of value iteration and greedy submodular maximization. %using a combination of value iteration to optimize views for individual robots and sequential submodular maximization methods to coordinate the team. We evaluate our approach on challenging scenarios modeled after various kinds of social behaviors and featuring different numbers of robots and actors and observe that robot assignments and formations arise implicitly based on the movements of groups of actors. Simulation results demonstrate that our approach consistently outperforms baselines, and in addition to performing well with the planner's approximation of pixel densities our approach also performs comparably for evaluation based on rendered views. Overall, the multi-round variant of the sequential planner we propose meets (within 1%) or exceeds the formation and assignment baselines in all scenarios we consider.

翻译：使用空中机器人团队观测和拍摄一组移动演员是一个具有挑战性的问题，它融合了多机器人协调、覆盖范围以及视角规划等要素。单个摄像头可同时观测多个演员，而机器人团队也能从多个视角观测单个演员。随着演员移动，群体可能分裂、合并、重组，拍摄这些演员的机器人应能平滑适应演员编队的此类变化。我们不采用基于显式编队或分配的方法，而是提出一种直接优化视角的方案。我们将演员建模为移动多面体，并计算每个面与摄像头视角的近似像素密度。接着，我们提出一个目标函数，该函数会随重复观测导致像素密度增加而呈现收益递减特性。由此产生一个多机器人感知规划问题，我们通过结合值迭代与贪心子模最大化来求解。我们基于模拟各类社交行为、包含不同数量机器人与演员的具有挑战性的场景评估该方法，并观察到机器人分配与编队会依据演员群体的移动而隐式形成。仿真结果表明，我们的方法始终优于基线方法，并且除了在规划器近似像素密度的场景中表现良好外，在基于渲染视图的评估中也具有可比性能。总体而言，我们提出的顺序规划器的多轮变体在所有考虑场景中均达到（误差在1%以内）或超越了编队与分配基线方法。