Hyoin Bae

Robot System of DRC‐HUBO+ and Control Strategy of Team KAIST in DARPA Robotics Challenge Finals

This paper summarizes how Team KAIST prepared for the DARPA Robotics Challenge (DRC) Finals, especially in terms of the robot system and control strategy. To imitate the Fukushima nuclear disaster situation, the DRC performed a total of eight tasks and degraded communication conditions. This competition demanded various robotic technologies such as manipulation, mobility, telemetry, autonomy, localization, etc. Their systematic integration and the overall system robustness were also important issues in completing the challenge. In this sense, this paper presents a hardware and software system for the DRC-HUBO+, a humanoid robot that was used for the DRC; it also presents control methods such as inverse kinematics, compliance control, a walking algorithm, and a vision algorithm, all of which were implemented to accomplish the tasks. The strategies and operations for each task are briefly explained with vision algorithms. This paper summarizes what we learned from the DRC before the conclusion. In the competition, 25 international teams participated with their various robot platforms. We competed in this challenge using the DRC-HUBO+ and won first place in the competition.

Improvement Trend of a Humanoid Robot Platform HUBO2

This paper covers improvement of the humanoid robot platform HUBO2, known as the HUBO2+. As a necessity of the growth of the humanoid platform, a robust, reliable and user friendly platform is needed. From this standpoint, HUBO2+ is the most improved humanoid robot platform in the HUBO series. The mechanical design has been changed to increase the movable range and to stop joint compulsion. Additionally, all of the electrical parts are re-designed to be un-breakable in an unexpected situation. A smart power controller with robot status check panel is attached on the back. Additionally, a diagnosis tool, the HUBO-i, has been developed. Moreover, each joint motor controller of HUBO2+ has a Protection Function and a PODO system is provided for handling the robot easily.

Walking-wheeling dual mode strategy for humanoid robot, DRC-HUBO+

For robots, a flexible approach is very important. Even when the robot is a humanoid, it does not have to perfectly mimic human motion. If necessary, the humanoid robot can adapt to other technologies even when these technologies make it different from humans. In a real environment or a disaster environment, humanoid robots are a good solution to assist humans. However, the stability problem of biped locomotion remains. In this paper, we introduce a new strategy of attaching a wheel to our humanoid robot in order to achieve mobility efficiency with a bipedal walking and wheel driven mode. Additionally, this strategy can enhance the stability. Through the proposed method, the humanoid robot can traverse uneven terrain and climb stairs with a walking motion, and can move safely with wheels on the ground. In addition, a method will be presented that can enhance the performance of wheel movement by using redundant joints and sensors on the humanoid robot. Various experiments and the DARPA Robotics Challenge results validate the efficiency and the feasibility of the proposed strategy.

Novel state estimation framework for humanoid robot

Abstract This study proposes a new Kalman filter-based framework for humanoid robot state estimation. The conventional Kalman filter generates optimal estimation solutions only when the nominal equations of the model and measurement include zero-mean, uncorrelated, white Gaussian noise. Because a humanoid robot is a complex system with multiple degrees of freedom, its mathematical model is limited in terms of expressing the system accurately, resulting in the generation of non-zero-mean, non-Gaussian, correlated modeling errors. Therefore, it is difficult to obtain accurate state estimates if the conventional Kalman filter-based approaches are used with such inexact humanoid models. The proposed modified Kalman filter framework consists of two loops: a loop to estimate the state, and a loop to estimate the disturbance generated by the modeling errors (a dual-loop Kalman filter). The disturbance values estimated by the disturbance estimation loop are provided as feedback to the state estimation loop, thereby improving the accuracy of the model-based prediction process. By considering the correlation between the state and disturbance in the estimation process, the disturbance can be accurately estimated. Therefore, the proposed estimator allows the use of a simple model, even if it implies the presence of a large modeling error. In addition, it can estimate the humanoid state more accurately than the conventional Kalman filter. Furthermore, the proposed filter has a simpler structure than the existing robust Kalman filters, which require the solution of complex Riccati equations; hence, it can facilitate recursive online implementation. The performance and characteristics of the proposed filter are verified by comparison with other existing linear/nonlinear estimators using simple examples and simulations. Furthermore, the feasibility of the proposed filter is verified by implementing it on a real humanoid robot platform.

Humanoid state estimation using a moving horizon estimator

Abstract In this research, a new state estimator based on moving horizon estimation theory is suggested for the humanoid robot state estimation. So far, there are almost no studies on the moving horizon estimator (MHE)-based humanoid state estimator. Instead, a large number of humanoid state estimators based on the Kalman filter (KF) have been proposed. However, such estimators cannot guarantee optimality when the system model is nonlinear or when there is a non-Gaussian modeling error. In addition, with KF, it is difficult to incorporate inequality constraints. Since a humanoid is a complex system, its mathematical model is normally nonlinear, and is limited in its ability to characterize the system accurately. Therefore, KF-based humanoid state estimation has unavoidable limitations. To overcome these limitations, we propose a new approach to humanoid state estimation by using a MHE. It can accommodate not only nonlinear systems and constraints, but also it can partially cope with non-Gaussian modeling error. The proposed estimator framework facilitates the use of a simple model, even in the presence of a large modeling error. In addition, it can estimate the humanoid state more accurately than a KF-based estimator. The performance of the proposed approach was verified experimentally.

Biped robot state estimation using compliant inverted pendulum model

Abstract This study proposes a biped robot state estimation framework based on a compliant inverted pendulum model and a robust state estimator. A proper model that can express the key physical characteristics while considering limited computing power should be defined for the biped robot state estimation. A biped robot’s limited structural stiffness and relatively long legs compared with the cross section of the body lead to undesired flexibility. However, the models used in previous research are either not suitable for state estimation or too simple to express the essential characteristics of the biped robot. A compliant inverted pendulum model is adopted herein to enhance the estimation accuracy. This model is made by adding a virtual spring and a damper to the conventional inverted pendulum. The additional elements represent the mechanical deformation and the undesired flexible movement. Adopting this model makes it possible to reflect the important characteristics of the biped robot while taking advantage of the merits of the single-mass model. In addition, a robust state estimator that we previously proposed is adopted to compensate for the estimation error caused by the modeling error. Using these two factors, the improved COM-kinematics estimate is obtained with respect to the existing simple-model-based biped state estimators.

Inverse kinematics with strict nonholonomic constraints on mobile manipulator

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.

BLDC motor current control using filtered single DC link current based on adaptive extended Kalman filter

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.

Analytic Inverse Kinematics Considering the Joint Constraints and Self-Collision for Redundant 7DOF Manipulator

This study proposes an analytic inverse kinematic solution considering joint limit and self-collision avoidance for a redundant 7DOF manipulator with spherical shoulder and wrist joints. An analytic approach is used to satisfy the sub-task. The arm angle is used to restrain the manipulator redundancy. The analytic inverse kinematic solution set satisfying the sub-task is proposed by determining the range of the feasible arm angle. The effectiveness of the proposed method was verified through a simulation. We confirmed that the solution of the proposed method always satisfies the joint limit constraints and self-collision avoidance.

Low-cost indoor positioning system using BLE (bluetooth low energy) based sensor fusion with constrained extended Kalman Filter

In this paper, a BLE (Bluetooth Low Energy) based new sensor fusion algorithm is proposed. We can estimate distance by using the signal strength from the BLE beacons. Other sensors support the estimation process which uses the proposed “constrained extended Kalman Filter”. BLE is more efficient than the existing wireless communication, and is easy to install. The proposed algorithm effectively reduces offline tasks and accurately estimates indoor position. It is verified with practical experiments.