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Deployment of teams of aerial robots could enable large-scale filming ofdynamic groups of people (actors) in complex environments for novelapplications in areas such as team sports and cinematography. Toward this end,methods for submodular maximization via sequential greedy planning can be usedfor scalable optimization of camera views across teams of robots but facechallenges with efficient coordination in cluttered environments. Obstacles canproduce occlusions and increase chances of inter-robot collision which canviolate requirements for near-optimality guarantees. To coordinate teams ofaerial robots in filming groups of people in dense environments, a more generalview-planning approach is required. We explore how collision and occlusionimpact performance in filming applications through the development of amulti-robot multi-actor view planner with an occlusion-aware objective forfilming groups of people and compare with a greedy formation planner. Toevaluate performance, we plan in five test environments with complexmultiple-actor behaviors. Compared with a formation planner, our sequentialplanner generates 14% greater view reward over the actors for three scenariosand comparable performance to formation planning on two others. We also observenear identical performance of sequential planning both with and withoutinter-robot collision constraints. Overall, we demonstrate effectivecoordination of teams of aerial robots for filming groups that may split,merge, or spread apart and in environments cluttered with obstacles that maycause collisions or occlusions.

Greedy Perspectives: Multi-Drone View Planning for Collaborative Coverage in Cluttered Environments