  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.
