Human–robot collaborative control method based on command-weighted fusion strategy for manned legged robot

This paper proposes a human–robot collaborative control method based on command-weighted fusion strategy for manned legged robot, addressing challenges posed by the complex structure of manned legged robots. These challenges affect both the safety of autonomous decision-making algorithms and the complexity of manual control. First, we design an autonomous command optimization method integrating terrain information and cost functions to enhance decision-making in complex terrains. Subsequently, a method for optimizing driving weighting factors is designed, utilizing a prior mechanism and rule knowledge base, while considering the influence of driver reliability and terrain complexity on driving safety and stability. Through analysis of human-machine driving intentions and the autonomous driving weighting factor, a commands weighted fusion strategy for human-machine commands is devised to achieve rational dynamic allocation of driving weighting and command fusion. Finally, validation through a human–robot collaborative control experiment demonstrates that the proposed control strategy effectively leverages the strengths of both human drivers and intelligent systems, yielding satisfactory control performance.