A heuristic gait template planning and dynamic motion control for biped robots

Abstract

Biped robots with dynamic motion control have shown strong robustness in complex environments. However, many motion planning methods rely on models, which have difficulty dynamically modifying the walking cycle, height, and other gait parameters to cope with environmental changes. In this study, a heuristic model-free gait template planning method with dynamic motion control is proposed. The gait trajectory can be generated by inputting the desired speed, walking cycle, and support height without a model. Then, the stable walking of the biped robot can be realized by foothold adjustment and whole-body dynamics model control. The gait template can be changed in real time to achieve gait flexibility of the biped robot. Finally, the effectiveness of the method is verified by simulations and experiments of the biped robot BHR-B2. The research presented here helps improve the gait transition ability of biped robots in dynamic locomotion.

Hybrid Momentum Compensation Control by Using Arms for Bipedal Dynamic Walking

“…We use the heuristic gait template proposed in previous work to plan the bipedal movements [ 25 ]. The heuristic gait template is a model-free method.…”

“…In our previous work [ 25 ], the gait template generated the trajectory online and realized motion tracking control through a whole-body dynamic control (WBC) approach. For dynamic motion control of biped robots, please refer to our previous work.…”

“… Framework for momentum compensation control of swinging arm. The gray area marked by * is the walking control without arm swing in our previous work [ 25 ]. …”

Stability Control of Quadruped Robot Based on Active State Adjustment

“…The model design of this method is complicated, and has not been adjusted for different environments. Some robots use a gait planning method to move in different types of complex environments [ 24 , 25 , 26 ], which requires visual assistance and real-time calculation and greatly reduces the efficiency of motion [ 27 ]. Inspired by this, on the premise of ensuring the motion effect and efficiency, properly simplifying the model and making active adjustments for different complex environments will have a better applicability.…”