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In this paper, dynamic stair climbing and descending are experimentally realized for a biped humanoid robot, HUBO. Currently, in addition to biped walking on the ground, other types of biped walking such as running, jogging, and stair walking (climbing and descending) have been also studied since the end of 1990. In spite of many years of research works on stair walking, it is still a challengeable topic that requires high performance of control technique. For dynamic stair walking, we designed stair climbing and descending patterns according to a known stair configuration. Next, we defined stair climbing and descending stages for a switching control strategy. In each stage, we designed and adopted several online controllers to maintain the balance. For the simplicity and easy application, the online controllers only use the force and torque signals of the force/torque sensors of the feet. Finally, the effectiveness and performance of the proposed strategy are verified through stair climbing and descending experiments of HUBO.

This paper presents a novel online walking control that replans the gait pattern based on our proposed foot placement control using the actual center of mass (COM) state feedback. The analytic solution of foot placement is formulated based on the linear inverted pendulum model (LIPM) to recover the walking velocity and to reject external disturbances. The foot placement control predicts where and when to place the foothold in order to modulate the gait given the desired gait parameters. The zero moment point (ZMP) references and foot trajectories are replanned online according to the updated foothold prediction. Hence, only desired gait parameters are required instead of predefined or fixed gait patterns. Given the new ZMP references, the extended prediction self-adaptive control (EPSAC) approach to model predictive control (MPC) is used to minimize the ZMP response errors considering the acceleration constraints. Furthermore, to ensure smooth gait transitions, the conditions for the gait initiation and termination are also presented. The effectiveness of the presented gait control is validated by extensive disturbance rejection studies ranging from single mass simulation to a full body humanoid robot COMAN in a physics based simulator. The versatility is demonstrated by the control of reactive gaits as well as reactive stepping from standing posture. We present the data of the applied disturbances, the prediction of sagittal/lateral foot placements, the replanning of the foot/ZMP trajectories, and the COM responses.

This paper presents a control method of the torso for dynamic walking of biped robots. Specifically, we want to save the energy and improve the walking stability by the planning and control of torso orientation at landing. First, the impact process of leg exchange is formulated using a simplified model. Based on this, the influence of the torso orientation at landing on walking performance is investigated. Second, a control method of torso orientation, which is an under actuated control, is proposed to regulate the torso orientation in the single support phase (SSP). Third, the control module of the torso is integrated into the previously established control frame of 3D biped walking to implement the control objective of a 3D humanoid robot. The results of a number of simulations show the feasibility of the proposed method, and also explore the relations between the speed, stability and energy consumption with the landing orientation of the torso.

The authors have proposed a novel method for high-speed gait generation of limit-cycle walkers based on the forward-tilting impact posture. Based on this approach, the robot can overcome the potential barrier at mid-stance easily and can generate a high-speed level gait only by extending the stance leg during stance phases. The problem was that there is not enough time-margin for stance-leg actuation due to the excessive high-speed motion. In this paper, we then attach forefeet to the legs of a telescopic-legged rimless wheel for the purposes of braking and tilting the impact posture more. The length of forefeet is finite and the stance leg rotates around the tiptoe just prior to heel strike. The simulation results show that the geometric effect of forefeet on the impact posture strongly improves the gait efficiency in terms of walking speed and specific resistance.

In this paper we show that exchanging curved feet and rigid ankles by flat feet and compliant ankles improves the range of gait parameters for a bipedal dynamic walker. The new lower legs were designed such that they fit to the old set-up, allowing for a direct and quantitative comparison. The dynamic walking robot RunBot, controlled by an reflexive neural network, uses only few sensors for generating its stable gait. The results show that flat feet and compliant ankles extend RunBot's parameter range especially to more leaning back postures. They also allow the robot to stably walk over obstacles with low height.

This paper presents a novel foot placement control algorithm for adaptive bipedal walking. In this method, the torso attitude and height are stabilized by synergic patterns so that the forward velocity and its change have a stable and nearly linear relation with the foot placement. Hence, our proposed online linear regression analysis well represents the local linear models by estimating continuously from measured data. Based on this estimation, an appropriate foot placement can be determined to control the forward velocity. Our simulation study successfully demonstrates the natural gait with accurate tracking of walking velocity, and the robustness of walking over uneven terrain.