Biped robots are expected to be able to work in complex environments. However, these robots will inevitably fall at times and such falls may cause injury to the robot itself or to people nearby. Therefore, it is necessary to detect that the robot is falling to be able to warn the robot in sufficient time when it is about to fall and to switch its controller to protect the vulnerable parts of the robot. Modeling and analysis of the biped robot falling problem cannot be fully accurate and many current learning-based methods rely on large quantities of fall data that are difficult to use to train fragile robots. A machine learning-based fall detection method is therefore proposed in this paper. This method requires only a small amount of training data to obtain good fall detection, making the training process on the robot platform much safer. A support vector machine is used to determine the state of the robot and the decision boundary of the stable state is updated during motion to enable the classifier to match the motion capability of the robot.
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.
Biped robots swing their legs alternately to achieve highly dynamic walking, which is the basic ability required for them to perform tasks. However, swinging of the swinging leg in the air will disturb the interaction between the supporting leg and the ground and affect the upper body’s balance during dynamic walking. To allow the robot to use its own intrinsic motion characteristics to maintain stable movement like a human when its lower limbs are affected by unknown disturbances during dynamic walking, the ability to use its arms to resist disturbances is essential. This article presents a hybrid momentum compensation control method for torque-controlled biped robots to adapt to unknown disturbances during dynamic walking. First, a hybrid angular momentum and linear momentum regulator is designed to compensate for the disturbance caused by the swinging leg. Second, based on real-time dynamic state changes of the legs, a mixed-momentum quadratic programming controller is designed to realize stable dynamic walking. The proposed method allows the force-controlled robot to maintain its balance while walking down an unknown platform, and it maintains good straightness in the forward direction of dynamic motion. The proposed method’s effectiveness is verified experimentally on the BHR-B2 force-controlled biped robot platform.
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