A Haptic Shared Control Architecture for Tracking of a Moving Object

In this work, we propose a haptic shared control framework that integrates human and robotic control to track moving objects. Uncertainty in object detection is estimated with a Kalman filter. An obstacle avoidance algorithm is designed in conjunction with the artificial potential field method. The operator input and robot’s historical trajectory are taken into consideration and used to infer the human and robot’s control willingness according to the principle of maximum entropy. The fuzzy inference method is used to simulate human decision-making behavior and blend agents’ control. A pouring task and a docking task were designed to compare the proposed method with existing control methods. Based on the objective evaluation results, the proposed method was superior to manual control and shared control based on piecewise functions. Compared to manual control, the proposed method reduced completion time, operator input, and cognitive load by 17%, 25%, and 20% on Task1, respectively, and by 23%, 17%, and 28% on Task2, respectively. Compared to automatic control, the proposed method was more practicable although some performance indicators were less inferior. The subjective evaluation results indicated that the proposed method made it easier for agents to perceive each other’s intentions and promoted mutual trust.