In existing solutions for outdoor MoCap systems, including ours [1] [2] [3], the front and back end are developed independently. The formation control algorithms of the existing aerial MoCap front ends consider that their objective should be to center the person in every UAV's camera image and she/he should be within a threshold distance to each MAV. These assumptions are intuitive and important. Also, experimentally it has been shown that it leads to a good MoCap estimate. However, it remains sub-optimal without any feedback from the human pose estimation back-end of the MoCap system. This is because the estimated human pose is strongly dependent on the viewpoints of the UAVs.

Methodology:  In our work [4], we introduce a deep reinforcement learning (RL) based multi-robot formation controller for the task of autonomous aerial human motion capture (MoCap). We focus on vision-based MoCap, where the objective is to estimate the trajectory of body pose and shape of a single moving person using multiple micro aerial vehicles. State-of-the-art solutions to this problem are based on classical control methods, which depend on hand-crafted system and observation models. Such models are difficult to derive and generalize across different systems. Moreover, the non-linearity and non-convexities of these models lead to sub-optimal controls. We formulate this problem as a sequential decision making task to achieve the vision-based motion capture objectives, and solve it using a deep neural network-based RL method. We leverage proximal policy optimization (PPO) to train a stochastic decentralized control policy for formation control. The neural network is trained in a parallelized setup in synthetic environments. We performed extensive simulation experiments to validate our approach. Finally, real-robot experiments demonstrate that our policies generalize to real world conditions.

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