Abstract: The operational load on operators, imposed by the complex structural characteristics of hexapod robots, leads to muscle fatigue and operational errors during long-duration teleoperation across irregular terrains. Existing autonomous decisionmaking methods for robots are insufficient to meet the demands for high precision and real-time performance. To address these challenges, this paper proposes a remote human–robot collaborative control method for hexapod robots. First, the relationship between terrain features and control commands is analyzed, and a deep learning-based model is constructed to generate autonomous walking commands for the robot in various terrains. Next, a human–robot command allocation method based on state machine switching is designed to optimize command authority allocation and dynamically adjust command combinations, thereby reducing errors. Furthermore, a command fusion method based on human–robot decision reliability is proposed. By evaluating the reliability of decisions made by the operator and the robot system in real time, this method dynamically adjusts the level of human–robot collaboration, thereby improving control accuracy and stability. Finally, human-in-the-loop driving experiments are conducted to validate the effectiveness of the proposed method in complex environments. The experimental results demonstrate that the proposed method significantly enhances the robot’s control precision and motion stability.

Key words: Hexapod robot')">Hexapod robot, Autonomous decision-making, Human–robot collaboration, Authority allocation, Command fusion