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.

中文翻译：

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用于跟踪移动物体的触觉共享控制架构


在这项工作中，我们提出了一种触觉共享控制框架，该框架集成了人类和机器人控制来跟踪移动物体。使用卡尔曼滤波器估计对象检测的不确定性。结合人工势场法设计了避障算法。考虑操作员输入和机器人的历史轨迹，并根据最大熵原理推断人和机器人的控制意愿。采用模糊推理方法来模拟人类的决策行为和混合智能体的控制。设计了浇注任务和对接任务，以将所提出的方法与现有的控制方法进行比较。基于客观评价结果，该方法优于手动控制和基于分段函数的共享控制。与手动控制相比，所提出的方法在任务 1 上分别减少了 17%、25% 和 20%，在任务 2 上分别减少了 23%、17% 和 28% 的完成时间、操作员输入和认知负荷。与自动控制相比，虽然某些性能指标稍逊一筹，但该方法更具有实用性。主观评价结果表明，该方法使代理人更容易感知彼此的意图，促进相互信任。