Deployment of teams of aerial robots could enable large-scale filming of dynamic groups of people (actors) in complex environments for applications in areas such as team sports and cinematography. Toward this end, methods for submodular maximization via sequential greedy planning can enable scalable optimization of camera views across teams of robots but face challenges with efficient coordination in cluttered environments. Obstacles can produce occlusions and increase chances of inter-robot collision which can violate requirements for near-optimality guarantees. To coordinate teams of aerial robots in filming groups of people in dense environments, a more general view-planning approach is required. We explore how collision and occlusion impact performance in filming applications through the development of a multi-robot multi-actor view planner with an occlusion-aware objective for filming groups of people and compare with a formation planner and a greedy planner that ignores inter-robot collisions. We evaluate our approach based on five test environments and complex multi-actor behaviors. Compared with a formation planner, our sequential planner generates 14% greater view reward for filming the actors in three scenarios and comparable performance to formation planning on two others. We also observe near identical view rewards for sequential planning both with and without inter-robot collision constraints which indicates that robots are able to avoid collisions without impairing performance in the perception task. Overall, we demonstrate effective coordination of teams of aerial robots in environments cluttered with obstacles that may cause collisions or occlusions and for filming groups that may split, merge, or spread apart.

翻译：部署空中机器人团队能够在复杂环境中大规模拍摄动态人群（演员），应用于团队体育和电影摄影等领域。为此，通过序列贪婪规划进行子模最大化的方法可实现跨机器人团队的相机视点可扩展优化，但在杂乱环境中的高效协调面临挑战。障碍物可能导致遮挡并增加机器人间碰撞概率，从而违反近似最优性保证的要求。为在密集环境中协调空中机器人团队拍摄人群，需要更通用的视点规划方法。我们通过开发具有遮挡感知目标的多机器人-多演员视点规划器，研究碰撞和遮挡如何影响拍摄应用性能，并与忽略机器人间碰撞的编队规划器和贪婪规划器进行比较。我们在五个测试环境和复杂多演员行为中评估所提方法。与编队规划器相比，我们的序列规划器在三种场景中为拍摄演员生成高出14%的视点奖励，在另外两种场景中与编队规划性能相当。我们还观察到序列规划在有无机器人间碰撞约束下获得近乎相同的视点奖励，这表明机器人能够在避免碰撞的同时不影响感知任务性能。总体而言，我们证明了空中机器人团队在可能引发碰撞或遮挡的障碍物杂乱环境中，以及对可能分裂、合并或分散的人群进行拍摄时的有效协调能力。