This paper introduces a design method for an efficient and agile quadruped robot. A mixed-integer optimization formulation including the number of gear teeth is derived to obtain the optimal gear ratio that minimizes cost for a running-trot with the target speed of 3 m/s. With the inclusion of integer constraints related to the number of gear teeth, detailed design considerations of gear trains can be included in the optimization process. Thermal dissipation of the motor controller is also taken into account in the optimization to consider heat generation during high-speed running. KAIST Hound, a 45 kg robot, designed with the obtained design parameters has successfully demonstrated a 3 m/s running-trot using a nonlinear model predictive controller (NMPC). Furthermore, the robot has proved its robustness by the demonstration of additional experiments such as 22° slope climbing, 3.2 km walking, and traversing a 35 cm obstacle.

This paper proposes a novel algorithm for joint space position/torque hybrid control of a mammal-type quadruped robot. With this control algorithm, the robot demonstrated both dynamic locomotion and push reaction abilities without the need for torque control in the ab/ad joints. Based on the tipping and slipping condition of the legged robot, we showed that reaction to a typical push in the horizontal direction does not require full contact-force-control in the frontal plane. Furthermore, we showed that position/torque hybrid control in Cartesian space is directly applicable to joint space hybrid control due to the joint configuration of the quadruped robot. We conducted experiments on our legged robot platform to verify the performance of our hybrid control algorithm. With this approach, the robot displayed stability while walking and reacting to external push disturbances.

In this paper, we propose a biped walking controller that optimized three push recovery strategies: the ankle, hip, and stepping strategies. We suggested formulations that related the effects of each strategy to the stability of walking based on the linear inverted pendulum with flywheel model. With these relations, we could set up an optimization problem that integrates all the strategies, including step time change. These strategies are not applied hierarchically, but applied according to each weighting factor. Various combinations of weighting factors can be used to determine how the robot should respond to an external push. The optimization problem derived here includes many nonlinear components, but it has been linearized though some assumptions and it can be applied to a robot in real time. Our method is designed to be robust to modeling errors or weak perturbations, by exploiting the advantages of the foot. Hence, it is very practical to apply this algorithm to a real robot. The effectiveness of the walking controller has been verified through abstracted model simulation, full dynamics simulation, and a practical robot experiments.

This work proposes a humanoid robot center of mass (COM) estimation framework based on the compliant inverted pendulum model and the robust estimator. Humanoids' limited structural stiffness and relatively long legs result in undesired flexibility, and this undesired motion hinders the state estimation. The models used in previous studies were either not suitable for estimation or too simple to express these key characteristics of humanoid robots. Here, to enhance the estimation performance, the compliant inverted pendulum model, which is developed by attaching a spring and damper to the original pendulum, is adopted. The additional elements can represent the mechanical deformation and undesired flexibility. This model can reflect the important characteristics of the humanoid robot while taking advantage of the merits of the sing-mass model. In addition, a robust state estimator that was proposed in our previous work is adopted to compensate for an estimation error caused by a modeling error. Using these two factors, an improved COM kinematics estimates could be obtained.

Most state-of-the-art torque control-based legged robots show excellent performance, exceeding that of conventional position control-based robots. Many conventional position control-based legged robots have high gear ratios, but do not have joint torque sensors. In addition, some robots cannot generate current for controlling the motor torque. To apply torque control-based walking algorithms to a position control-based humanoid robot, we proposed current control using a motor thermal model and realized joint torque control by compensating for the joint dynamics and robot dynamics. We conducted experiments to verify the performance of the Hubo2 platform developed in 2008 by applying a full-body dynamics control framework. The results confirmed the possibility of using torque control algorithms with existing position-based robots.

This paper describes a humanoid robotics platform (DRC-HUBO+) developed for the Defense Advanced Research Projects Agency Robotics Challenge (DRC) Finals. This paper also describes the design criteria, hardware, software framework, and experimental testing of the DRC-HUBO+ platform. The purpose of DRC-HUBO+ is to perform tasks by teleoperation in hazardous environments that are unsafe for humans, such as disaster zones. We identified specific design concepts for DRC-HUBO+ to achieve this goal. For a robot to be capable of performing human tasks, a human-like shape and size, autonomy, mobility, manipulability, and power are required, among other features. Furthermore, modularized joints and a user-friendly software framework were emphasized as design concepts to facilitate research on the robot tasks. The DRC-HUBO+ platform is based on DRC-HUBO-1 and HUBO-2. The torque of each joint is increased compared to that in DRC-HUBO-1 owing to its high reduction ratio and air-cooling system. DRC-HUBO+ is designed with an exoskeletal structure to provide it with sufficient stiffness relative to its mass. All wires are enclosed within the robot body using a hollow shaft and covers to protect the wires from external shock. Regarding the vision system, active cognition of the environment can be realized using a light-detection and ranging sensor and vision cameras on the head. To achieve stable mobility, the robot can transition from the bipedal walking mode to the wheel mode using wheels located on both knees. DRC-HUBO+ has 32 degrees of freedom (DOFs), including seven DOFs for each arm and six DOFs for each leg, and a solid and light body with a height of 170 cm and a mass of 80 kg. A software framework referred to as PODO, with a Linux kernel and the Xenomai patch, is used in DRC-HUBO+.

The unified approach algorithm to control a mobile robot is useful in simplifying path planning and inverse kinematics by considering a mobile platform as hypothetical joints. Applying the damped least square method by using the Jacobian prevents the divergence of the joint angle. However, it is possible to violate the nonholonomic constraint on mobility by distorting the Jacobian. In robot manipulation, the end-effector position error due to a violation of the constraint can be fatal, because such an error can damage a nearby object or the robot itself. In this paper, we propose a method that strictly guarantees nonholonomic constraints on a mobile manipulator. We also propose a compensation technique for faster convergence of the position and orientation of the end effector. The effectiveness of the proposed methods was confirmed through simulations.

In this paper we introduce a biped foot placement controller based on capture point (CP) feedback control. This capture point feedback controller generates desired Zero Moment Point (ZMP) according to current capture point error. We call this desired ZMP as Control-ZMP (cZMP). Using this cZMP, we constructed (i) a disturbance adapting walking pattern generator, (ii) an ankle torque reference generator, (iii) a landing foot position adjustor and, (iv) a step time adjustor. By applying these controllers, the biped system becomes robust over uneven terrain and external pushing disturbances. We considered cZMP as a key indicator of bipedal walking stability. If the cZMP is within the support polygon, the robot will track the cZMP by using (i) and (ii). If the cZMP is outside of the support polygon, the robot will change footstep position and time by using (iii) and (iv) (COM pattern will be changed by (i) too). Capture point dynamics is described based on a linear inverted pendulum (LIP) model. The performance of the suggested control algorithm is validated in the Choreonoid simulator with a model of DRC-HUBO+ [11], [12].

In this paper, a current control scheme for the brushless DC (BLDC) motor is proposed. In order to estimate the phase current, it employs the adaptive extended Kalman filter (EKF) and measurement of a single DC link current. When estimating the motor phase current, measurement of a single DC link current is scaled by PWM (Pulse Width Modulation) duty. If the PWM duty is very small, the sensitivity of the measurement noise is dramatically increased. In order to solve this problem adaptive EKF is applied. Therefore, estimated phase current using adaptive EKF prevents the possible occurrence of an unstable situation due to the sensitivity. The accuracy of the proposed current control scheme was confirmed by comparing the estimated current by adaptive EKF and the measured actual phase current. In additions, the effectiveness was verified through the current-control time response of the when the PWM duty for the control was close to zero. By using the proposed current control scheme, robust current control of the BLDC motor was possible with a single DC link current.