This study introduces a two-wheeled self-balancing mobile robot based on a control moment gyroscope module. Two-wheeled mobile robots are able to achieve better mobility and rotation in small spaces and to move faster than legged robots such as humanoid type robots. For this reason, the two-wheeled mobile robot is generally used as a mobile robot platform. However, to maintain its balance, the two-wheeled robot needs to use movements of its two wheels. When an unexpected disturbance affects the robot, the robot maintains its balance with movements of the wheels and tilting of the body. If the disturbance exceeds the response capability of the robot, the robot will lose its stability. At the same time, the safety of the robot may be put at risk by movements to maintain balance. To address these issues, a robot was designed with a control moment gyroscope module to improve balance while minimizing movement. When a disturbance is applied to the robot, the disturbance is estimated by a disturbance observer and the control moment gyroscope controller compensates the disturbance. Using the control moment gyroscope module, the robot can maintain balance with just small movements of its wheels. Improved performance and stability were verified with experiments and simulations.

This paper describes a model-based dynamic posture stabilization of a biped robot on slope changing grounds. The ground slope generally is changing when a biped robot is walking outdoors on uneven grounds. Hence, if biped robots are used outside, their robustness against slope-changing grounds is necessary for stable walking. In this paper, a Zero Moment Point (ZMP) controller on level ground was designed as a basic posture stabilizer, and a disturbance observer (DOB) was additionally designed to reject a disturbance due to the ground slope. A simple single inverted pendulum model with a flexible joint is used for the ZMP controller and DOB designs. Finally, the performance of the proposed ZMP-DOB controller was verified through MATLAB simulations and walking in place experiments using a biped robot, SUBO-1 on a slope-changing treadmill.

We describe the stabilization of a hopping humanoid robot against a disturbance. In the proposed scheme, the method of control is selected according to the size of the disturbance. A posture balance controller is used when the disturbance is small, and the posture balance controller and a foot placement method are activated together when the disturbance is large. A simplified model is used to develop the novel controller for the foot placement method, and a linearized Poincare map for single hopping is made. The control law is designed using the pole placement method. The proposed method is verified through simulation and experiment. In the experiment, HUBO2 hops well against various disturbance.

This paper describes online balance controllers for running in a humanoid robot and verifies the validity of the proposed controllers via experiments. To realize running in the humanoid robot, the overall control structure is composed of an offline controller and an online controller. The main purpose of the online controller is to maintain dynamic stability while the humanoid robot hops or runs. The online controller is composed of the posture balance control in the sagittal plane, the transient balance control in the frontal plane and the swing ankle pitch compensator in the sagittal plane. The posture balance controller makes the robot maintain balance using an inertial measurement unit sensor in the sagittal plane. The transient balance controller makes the robot keep its balance in the frontal plane using gyros attached to each upper leg. The swing ankle pitch compensator prevents the swing foot from hitting the ground at unexpected times while the robot runs forward. HUBO2 was used for the running experiment. It was designed for the running experiment, and is lighter and more powerful than the previous walking robot platform, HUBO. With the proposed controllers, HUBO2 ran forward stably at a maximum speed of 3.24 km/h and this result verified the effectiveness of the proposed algorithm. In addition, in order to show the contribution of the stability, the running performance according to the existence of each controller was described by experiment. (C) Koninklijke Brill NV, Leiden, 2011

This paper describes a novel walking pattern generation method for a biped humanoid robot using a convolution sum. For a biped walking model, a single mass inverted pendulum model is generally used and the zero moment point (ZMP) equation is described by a decoupled linear differential equation. As a walking pattern generation method for the robot model, a novel method using a convolution sum is proposed in this paper. From the viewpoint of the linear system response, walking pattern generation can be regarded as a convolution of an arbitrary reference ZMP and the walking pattern for an impulse reference ZMP. For the calculation of convolution, the walking pattern for an impulse reference ZMP is first derived from the analytic walking pattern for a step reference ZMP. The convolution sum is then derived in two recursive forms, which can be applied online and offline, respectively. The proposed algorithm requires low computation power, since the walking pattern equation is composed of a recursive form. As the algorithm is expressed in analytic form, it is not necessary to solve optimization problems or calculate the fast Fourier transform, contrary to previous approaches. A computer simulation of walking demonstrates that the proposed algorithm yields excellent accuracy compared to the preview control method-one of the most highly regarded walking pattern generation methods. In addition, the application on the multi-point mass model is shown with the computer simulation. (C) Koninklijke Brill NV, Leiden, 2011

This paper discusses the generation of a running pattern for a humanoid biped and verifies the validity of the proposed method of running pattern generation via experiments. Two running patterns are generated independently in the sagittal plane and in the frontal plane and the two patterns are then combined. When a running pattern is created with resolved momentum control in the sagittal plane, the angular momentum of the robot about the Center of Mass (COM) is set to zero, as the angular momentum causes the robot to rotate. However, this also induces unnatural motion of the upper body of the robot. To solve this problem, the biped was set as a virtual under-actuated robot with a free joint at its support ankle, and a fixed point for a virtual under-actuated system was determined. Following this, a periodic running pattern in the sagittal plane was formulated using the fixed point. The fixed point is easily determined in a numerical approach. In this way, a running pattern in the frontal plane was also generated. In an experiment, a humanoid biped known as KHR-2 ran forward using the proposed running pattern generation method. Its maximum velocity was 2.88 km/h.

Even though many humanoid robots have been developed and they have locomotion ability, their balancing ability is not sufficient. In the future, humanoid robots will work and act within the human environment. At that time, the humanoid robot will be exposed to various disturbances. This paper proposes a balancing strategy for hopping humanoid robots against various magnitude of disturbance. The proposed balancing strategy for a hopping humanoid robot consists of two controllers, the posture balance controller and the landing position controller. The posture balance controller is used for small disturbances, and its role is to maintain stability by controlling the ankle torque of the robot. On the other hand, if disturbance is large, the landing position controller, which changes the landing position of the swing foot, works with the posture balance controller simultaneously. In this way, the landing position controller reduces large disturbances, and the posture balance controller controls the remaining disturbances. The landing position controller is derived by the principle of energy conservation. An experiment conducted with a real humanoid robot, HUBO2, verifies the proposed method. HUBO2 made a stable and continuous hopping action with the proposed balancing strategy overcoming various disturbances placed in the way of the robot.

Even though many humanoid robots have been developed and they have locomotion ability, their balancing ability is not sufficient. In the future, humanoid robots will work and act within the human environment. At that time, the humanoid robot will be exposed to various disturbances. This paper proposes a balancing strategy for hopping humanoid robots against various magnitude of disturbance. The proposed balancing strategy for a hopping humanoid robot consists of two controllers, the posture balance controller and the landing position controller. The posture balance controller is used for small disturbances, and its role is to maintain stability by controlling the ankle torque of the robot. On the other hand, if disturbance is large, the landing position controller, which changes the landing position of the swing foot, works with the posture balance controller simultaneously. In this way, the landing position controller reduces large disturbances, and the posture balance controller controls the remaining disturbances. The landing position controller is derived by the principle of energy conservation. An experiment conducted with a real humanoid robot, HUBO2, verifies the proposed method. HUBO2 made a stable and continuous hopping action with the proposed balancing strategy overcoming various disturbances placed in the way of the robot.

In Korea, the underwater robot Crabster200 was developed to perform missions such as accident correspondence, artifact exploration, and resource extraction. To explore the shallow sea successfully, it was designed to walk and swim with six legs. Besides, to develop the walking algorithm efficiently, a small model of Crabster200, Little Crabster200, was developed as well. This article describes the development of a walking algorithm on the uneven terrain for the hexapod robot Little Crabster200. The main purpose of the algorithm is for the robot to maintain a horizontal posture while it walks on uneven terrain. The pattern generator makes a trajectory of normal walking on the flat ground, and the online controller helps Little Crabster200 maintain stable walking on uneven terrain. The proposed algorithm was verified by experiments using the Little Crabster200 on a treadmill equipped with obstacles such as wood boards and bricks. The Little Crabster200 walked stably on the uneven terrain while maintaining a horizontal posture in the experiments.

This paper presents a technical overview of Team DRC-Hubo@UNLV's approach to the 2015 DARPA Robotics Challenge Finals (DRC-Finals). The Finals required a robotic platform that was robust and reliable in both hardware and software to complete tasks in 60 min under degraded communication. With this point of view, Team DRC-Hubo@UNLV integrated methods and algorithms previously verified, validated, and widely used in the robotics community. For the communication aspect, a common shared memory approach that the team adopted to enable efficient data communication under the DARPA controlled network is described. A new perception head design (optimized for the tasks of the Finals) and its data processing are then presented. In the motion planning and control aspect, various techniques, such as wheel-driven navigation, zero-moment-point (ZMP) -based locomotion, and position-based manipulation and controls, are described in this paper. By introducing strategically critical elements and key lessons learned from DRC-Trials 2013 and the testbed of Charleston, we also illustrate how DRC-Hubo has evolved successfully toward the DRC-Finals. (C) 2017 Wiley Periodicals, Inc.

The KAIST HUBO team and the Hanson Robotics team jointly developed an android-type humanoid robot termed Albert HUBO, which may be the world's first robot that incorporates an expressive human face on a walking biped robot. Albert HUBO adopts the techniques of the HUBO design for the body of Albert HUBO and uses technology from Hanson Robotics for the head. The head and the body are two independent systems that use different computers to control each system. The head uses RC servo motors for facial expressions. It is controlled using a PC that utilizes RS232 communication. The head PC also processes vocal and visual information. The body (including the arms, fingers, legs, and torso) PC controls walking and body movements. It is connected via CAN communication to body servo controllers. The PCs are connected by RS232, which is able to transfer command data to each system. The height and weight of Albert HUBO are 137 cm and 57 kg, respectively. The robot has 66 DOFs (31 for the head motions and 35 for the body motions). The head part uses a material known as 'Frubber', which serves as a smooth human-like skin. In the head, 28 servo motors for facial movements and three servo motors for neck movements are used to generate a full range of facial expressions; thus, the robot is able to laugh, act surprised, and express sadness or anger. The body is modified from HUBO (KHR-3), which was introduced in 2004, and is joined with the head of Albert HUBO with 35 DC motors that are embedded for the purpose of imitating various human-like body motions. The biped humanoid robot with a human-like android head was realized by integrating two independent systems as a self-contained robotic system. The integration of the two systems is in the topic of this paper. (c) 2007 Elsevier B.V. All rights reserved.

This paper discusses the generation of a running pattern for a humanoid biped and verifies the validity of the proposed method of running pattern generation via experiments. Two running patterns are generated independently in the sagittal plane and in the frontal plane and the two patterns are then combined. When a running pattern is created with resolved momentum control in the sagittal plane, the angular momentum of the robot about the Center of Mass (COM) is set to zero, as the angular momentum causes the robot to rotate. However, this also induces unnatural motion of the upper body of the robot. To solve this problem, the biped was set as a virtual under-actuated robot with a free joint at its support ankle, and a fixed point for a virtual under-actuated system was determined. Following this, a periodic running pattern in the sagittal plane was formulated using the fixed point. The fixed point is easily determined in a numerical approach. In this way, a running pattern in the frontal plane was also generated. In an experiment, a humanoid biped known as KHR-2 ran forward using the proposed running pattern generation method. Its maximum velocity was 2.88 km/h.