Unified Human Intention Recognition and Heuristic-Based Trajectory Generation for Haptic Teleoperation of Non-Holonomic Vehicles

In this paper, a novel bilateral shared control approach is proposed to address the issue of strong dependence on the human, and the resulting burden of manipulation, in classical haptic teleoperation systems for vehicle navigation. A Hidden Markov Model (HMM) is utilized to handle the Human Intention Recognition (HIR), according to the force input by the human-including the HMM solution, i.e., Baum-Welch algorithm, and HMM decoding, i.e., Viterbi algorithm-and the communication delay in teleoperation systems is added to generate a temporary goal. Afterwards, a heuristic and sampling method for online generation of splicing trajectory based on the goal is proposed innovatively, ensuring the vehicle can move feasibly after the change in human intention is detected. Once the trajectory is available, the vehicle velocity is then converted to joystick position information as the haptic cue of the human, which enhances the telepresence. The shared teleoperation control framework is verified in the simulation environment, where its excellent performance in the complex environment is evaluated, and its feasibility is confirmed. The experimental results show that the proposed method can achieve simple and efficient navigation in a complex environment, and can also give a certain situational awareness to the human.