Multi-Robot Planning for Filming Groups of Moving Actors Leveraging Submodularity and Pixel Density
<|reference_start|>Multi-Robot Planning for Filming Groups of Moving Actors Leveraging Submodularity and Pixel Density: 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 a 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 that we solve via a combination of value iteration and greedy submodular maximization. We evaluate our approach on challenging scenarios modeled after various social behaviors and featuring different numbers of robots and actors and observe that robot assignments and formations arise implicitly given 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 formation and assignment baselines in all scenarios.<|reference_end|>
@article{hughes2024multi-robot,
    title={Multi-Robot Planning for Filming Groups of Moving Actors Leveraging
  Submodularity and Pixel Density},
    author={Skyler Hughes, Rebecca Martin, Micah Corah, Sebastian Scherer},
    journal={arXiv preprint arXiv:2404.03103},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03103},
    primaryClass={cs.RO cs.SY eess.SY}
}