贪婪视角：面向杂乱环境中协同感知的多无人机视角规划
Greedy Perspectives: Multi-Drone View Planning for Collaborative Perception in Cluttered Environments
AI总结：
本文提出遮挡感知的多机器人多演员顺序贪婪视角规划器，在杂乱环境中协同拍摄动态人群，实验表明其可在避免机器人间碰撞的同时提升或保持视角奖励。
摘要：
部署空中机器人团队可在复杂环境中对动态人群（演员）进行大规模拍摄，适用于团队运动和电影摄影等领域。为此，通过顺序贪婪规划实现子模最大化的方法，能够在机器人团队间对相机视角进行可扩展优化，但在杂乱环境中进行高效协调时面临挑战。障碍物会造成遮挡，并增加机器人间碰撞的可能性，这可能违反近似最优性保证的要求。为了在密集环境中协调空中机器人团队拍摄人群，需要一种更通用的视角规划方法。我们通过开发一个多机器人、多演员视角规划器来探究碰撞和遮挡如何影响拍摄应用中的性能，该规划器采用面向人群拍摄的遮挡感知目标，并与编队规划器以及忽略机器人间碰撞的贪婪规划器进行比较。我们基于五个测试环境和复杂的多演员行为评估了所提方法。与编队规划器相比，我们的顺序规划器在三个场景中拍摄演员时产生的视角奖励高14%，并在另外两个场景中取得与编队规划相当的性能。我们还观察到，在有和没有机器人间碰撞约束的情况下，顺序规划得到的视角奖励几乎相同，这表明机器人能够在不损害感知任务性能的情况下避免碰撞。总体而言，我们展示了在布满障碍物、可能导致碰撞或遮挡的环境中，以及面对可能分散、合并或拉开的拍摄群体时，对空中机器人团队的有效协调。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
