Multiple-Pilot Collaboration for Advanced Remote Intervention using Reinforcement Learning
This addresses workload and error issues in remote intervention systems, but appears incremental as it builds on existing methods like DDPG and fuzzy logic.
The paper tackles the high workload and lack of correction in traditional teleoperation by proposing a co-pilot-in-the-loop framework using DDPG-based arbitration and IT2 T-S fuzzy identification, which enhances command robustness and reconstructs force feedback without delay, validated in two experimental applications.
The traditional master-slave teleoperation relies on human expertise without correction mechanisms, resulting in excessive physical and mental workloads. To address these issues, a co-pilot-in-the-loop control framework is investigated for cooperative teleoperation. A deep deterministic policy gradient(DDPG) based agent is realised to effectively restore the master operators' intents without prior knowledge on time delay. The proposed framework allows for introducing an operator (i.e., co-pilot) to generate commands at the slave side, whose weights are optimally assigned online through DDPG-based arbitration, thereby enhancing the command robustness in the case of possible human operational errors. With the help of interval type-2(IT2) Takagi-Sugeno (T-S) fuzzy identification, force feedback can be reconstructed at the master side without a sense of delay, thus ensuring the telepresence performance in the force-sensor-free scenarios. Two experimental applications validate the effectiveness of the proposed framework.