Multiple-Pilot Collaboration for Advanced Remote Intervention using Reinforcement Learning

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.

Kinematic Parameter Optimization of a Miniaturized Surgical Instrument Based on Dexterous Workspace Determination

Miniaturized instruments are highly needed for robot assisted medical healthcare and treatment, especially for less invasive surgery as it empowers more flexible access to restricted anatomic intervention. But the robotic design is more challenging due to the contradictory needs of miniaturization and the capability of manipulating with a large dexterous workspace. Thus, kinematic parameter optimization is of great significance in this case. To this end, this paper proposes an approach based on dexterous workspace determination for designing a miniaturized tendon-driven surgical instrument under necessary restraints. The workspace determination is achieved by boundary determination and volume estimation with partition and least-squares polynomial fitting methods. The final robotic configuration with optimized kinematic parameters is proved to be eligible with a large enough dexterous workspace and targeted miniature size.