Costi Leone, Scimeca Luca, Maiolino Perla, Lalitharatne Thilina Dulantha, Nanayakkara Thrishantha, Hashem Ryman, Iida Fumiya
Bio Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
NAVER AI Lab, NAVER Corp, Seongnam-si, South Korea.
Front Robot AI. 2021 Sep 27;8:730946. doi: 10.3389/frobt.2021.730946. eCollection 2021.
Communication delay represents a fundamental challenge in telerobotics: on one hand, it compromises the stability of teleoperated robots, on the other hand, it decreases the user's awareness of the designated task. In scientific literature, such a problem has been addressed both with statistical models and neural networks (NN) to perform sensor prediction, while keeping the user in full control of the robot's motion. We propose shared control as a tool to compensate and mitigate the effects of communication delay. Shared control has been proven to enhance precision and speed in reaching and manipulation tasks, especially in the medical and surgical fields. We analyse the effects of added delay and propose a unilateral teleoperated leader-follower architecture that both implements a predictive system and shared control, in a 1-dimensional reaching and recognition task with haptic sensing. We propose four different control modalities of increasing autonomy: , , , and . When analyzing how the added delay affects the subjects' performance, the results show that the is very sensitive to the delay: users are not able to stop at the desired position and trajectories exhibit wide oscillations. The degree of autonomy introduced is shown to be effective in decreasing the total time requested to accomplish the task. Furthermore, we provide a deep analysis of environmental interaction forces and performed trajectories. Overall, the shared control modality, , represents a good trade-off, having peak performance in accuracy and task time, a good reaching speed, and a moderate contact with the object of interest.
一方面，它会损害遥控机器人的稳定性，另一方面，它会降低用户对指定任务的感知。在科学文献中，已经通过统计模型和神经网络（NN）来解决这个问题，以进行传感器预测，同时让用户完全控制机器人的运动。我们提出共享控制作为一种补偿和减轻通信延迟影响的工具。共享控制已被证明可以提高在到达和操作任务中的精度和速度，特别是在医疗和外科领域。我们分析了增加延迟的影响，并提出了一种单边遥控主从架构，该架构在具有触觉传感的一维到达和识别任务中既实现了预测系统又实现了共享控制。我们提出了四种不同的增加自主性的控制模式： ， ， ，和 。在分析增加的延迟如何影响受试者的表现时，结果表明 对延迟非常敏感：用户无法在期望位置停止，轨迹呈现出较大的振荡。所引入的自主程度被证明在减少完成任务所需的总时间方面是有效的。此外，我们对环境相互作用力和执行的轨迹进行了深入分析。总体而言，共享控制模式 代表了一种很好的权衡，在准确性和任务时间方面具有最佳性能，具有良好的到达速度，并且与感兴趣的对象有适度的接触。
