Reinforcement Learning Driven Cooperative Ball Balance in Rigidly Coupled Drones
<|reference_start|>Reinforcement Learning Driven Cooperative Ball Balance in Rigidly Coupled Drones: Multi-drone cooperative transport (CT) problem has been widely studied in the literature. However, limited work exists on control of such systems in the presence of time-varying uncertainties, such as the time-varying center of gravity (CG). This paper presents a leader-follower approach for the control of a multi-drone CT system with time-varying CG. The leader uses a traditional Proportional-Integral-Derivative (PID) controller, and in contrast, the follower uses a deep reinforcement learning (RL) controller using only local information and minimal leader information. Extensive simulation results are presented, showing the effectiveness of the proposed method over a previously developed adaptive controller and for variations in the mass of the objects being transported and CG speeds. Preliminary experimental work also demonstrates ball balance (depicting moving CG) on a stick/rod lifted by two Crazyflie drones cooperatively.<|reference_end|>
@article{barawkar2024reinforcement,
    title={Reinforcement Learning Driven Cooperative Ball Balance in Rigidly
  Coupled Drones},
    author={Shraddha Barawkar and Nikhil Chopra},
    journal={arXiv preprint arXiv:2404.19070},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.19070},
    primaryClass={cs.RO}
}