Jeong-Ki Yoo

Modifiable Walking Pattern of a Humanoid Robot by Using Allowable ZMP Variation

In order to handle complex navigational commands, this paper proposes a novel algorithm that can modify a walking period and a step length in both sagittal and lateral planes. By allowing a variation of zero moment point (ZMP) over the convex hull of foot polygon, it is possible to change the center of mass (CM) position and velocity independently throughout the single support phase. This permits a range of dynamic walking motion, which is not achievable using the 3-D linear inverted pendulum mode (3D-LIPM). In addition, the proposed algorithm enables to determine the dynamic feasibility of desired motion via the construction of feasible region, which is explicitly computed from the current CM state with simple ZMP functions. Moreover, adopting the closed-form functions makes it possible to calculate the algorithm in real time. The effectiveness of the proposed algorithm is demonstrated through both computer simulation and experiment on the humanoid robot, HanSaRam-VII, developed at the Robot Intelligence Technology (RIT) laboratory, Korea Advanced Institute of Science and Technology (KAIST).

Recent progress and development of the humanoid robot HanSaRam

This paper presents an overview of the recent progress and development of the humanoid robot, HanSaRam series, which have been developed in the Robot Intelligence Technology (RIT) Laboratory, KAIST since 2000. The HanSaRam series have been designed and developed as a small-sized robot for researching walking gate generation, navigation, task planning and HuroCup of FIRA. In particular, the performance of the 7th and 8th versions have been remarkably improved in the aspect of walking pattern generation and task planning. This paper describes the overall design and architecture of recently developed two versions of HanSaRam along with a developed vision simulator tool and the real-time walking gate generation scheme, modifiable walking pattern generator.

Fuzzy Integral-Based Gaze Control Architecture Incorporated With Modified-Univector Field-Based Navigation for Humanoid Robots

When a humanoid robot moves in a dynamic environment, a simple process of planning and following a path may not guarantee competent performance for dynamic obstacle avoidance because the robot acquires limited information from the environment using a local vision sensor. Thus, it is essential to update its local map as frequently as possible to obtain more information through gaze control while walking. This paper proposes a fuzzy integral-based gaze control architecture incorporated with the modified-univector field-based navigation for humanoid robots. To determine the gaze direction, four criteria based on local map confidence, waypoint, self-localization, and obstacles, are defined along with their corresponding partial evaluation functions. Using the partial evaluation values and the degree of consideration for criteria, fuzzy integral is applied to each candidate gaze direction for global evaluation. For the effective dynamic obstacle avoidance, partial evaluation functions about self-localization error and surrounding obstacles are also used for generating virtual dynamic obstacle for the modified-univector field method which generates the path and velocity of robot toward the next waypoint. The proposed architecture is verified through the comparison with the conventional weighted sum-based approach with the simulations using a developed simulator for HanSaRam-IX (HSR-IX).

Evolutionary Multiobjective Footstep Planning for Humanoid Robots

This paper proposes a novel evolutionary multiobjective footstep planner for humanoid robots. First, a footstep planner using a univector field navigation method is proposed to provide a command state (CS), which is to be an input of a modifiable walking pattern generator (MWPG) at each footstep. Then, the MWPG generates corresponding trajectories for every leg joint of the humanoid robot at each footstep to follow the CS. Second, a multiobjective evolutionary algorithm (MOEA) is employed to optimize the univector fields satisfying multiple objectives in navigation. Finally, a preference-based selection algorithm based on a fuzzy measure and fuzzy integral is proposed to select the preferred one out of various nondominated solutions obtained by the MOEA. The effectiveness of the proposed evolutionary multiobjective footstep planner is demonstrated through computer simulations for a simulation model of a small-sized humanoid robot, HanSaRam-VIII.

Compensation for the landing impact force of a humanoid robot by time domain passivity approach

In this paper, a method to reduce the landing impact force is proposed for a stable dynamic walking of a humanoid robot. To measure the meaningful landing impact force, a novel foot mechanism, which uses FSRs (force sensing resistors), is introduced as well. Humanoid robot might become unstable during the walking due to the impulsive contact force from the sudden landing of its foot. Therefore a new control method to decrease the landing impact force has been required. In this paper, time domain passivity control approach is applied for this purpose. Ground and the foot of the robot are modeled as two one-port network systems which are connected and exchanging energy each other. And, the time domain passivity controller which has the landing impact force as input and foots position to trim off the force as output, is implemented. Unlike previous works, the proposed controller can guarantee the stability of the robot system without any dynamic model information at all. The small sized humanoid robot, HanSaRam-VI which has 25 DOFs, with the proposed foot mechanism is developed to verify the proposed approach through dynamic walking experiments

Landing Force Controller for a Humanoid Robot: Time-Domain Passivity Approach

For the purpose of a humanoid robots stable walking or running, it is important to absorb landing force or ground reaction force which is generated when the robots foot lands on the ground surface. The force can make the robot unstable, and the problem becomes serious if the robot runs. This paper proposes a control system, which can absorb the landing force of a humanoid robot. Time-domain passivity control approach is applied for this purpose. Ground and the robots foot are modeled as two one-port network systems, which are connected and exchange energy with each other. The time-domain passivity controller has the landing force as input and controls the foots position to reduce the force. The proposed controller can guarantee the stability of the robot system without need of any dynamic model information or control parameters. Using small sized humanoid robot, dynamic walking experiments are performed to verify the proposed scheme, and its efficiency is shown from the comparison with the other scheme.

Stabilization control for humanoid robot to walk on inclined plane

In this paper, a novel stabilization control is proposed for humanoid robot to walk dynamically on an inclined plane. Online walking control is indispensable to obtain a stable dynamic walking, even if the walking pattern is provided based on a zero moment point and an angular momentum because modeling errors and external disturbances, which are not expected in the modeling stage, may exist in the actual condition. Considering these issues, this paper proposes a stabilization control, which consists of landing force controller, posture controller and walking pattern generator to walk on the inclined plane. The proposed control does not require a complex dynamic equation of robot and the adjustment of control parameters because it is based on time-domain passivity approach. Moreover, it can guarantee the stability of the controller without requiring any dynamic model information. The proposed control is verified through dynamic walking simulations using Webots simulator.

Modifiable walking pattern generation using real-time ZMP manipulation for humanoid robots

Complex navigational commands require a walking pattern generator that is able to modify the pattern at any point in the walking gait. This paper utilizes the 3D-LIPM (linear inverted pendulum model) for generating a walking pattern, but introduces a method that allows for manipulation of the ZMP over the convex hull of the foot polygon whilst in single support phase. This permits a range of dynamic walking states that are not achievable using the conventional 3D-LIPM. These walking states are defined as a feasible region. A real-time algorithm is then developed, which follows the cue from a complex navigational command exactly when the desired walking state is in the feasible region and chooses the nearest feasible motion when it lies outside the feasible region. The proposed scheme is both simulated and implemented on the humanoid robot HanSaRam-VII developed at RIT laboratory, KAIST.

Hybrid Architecture for Kick Motion of Small-sized Humanoid Robot, HanSaRam-VI

This paper presents a hybrid architecture designed for the kick motion of a humanoid robot. The principal components of this architecture are vision system, path planner and motion generator. The vision system utilizes captured images for calculating ball and goal positions and simple self-localization using a proposed image preprocessing technique. Based on this information, appropriate path and motion procedures are calculated in the path planner and motion generator embedded within the PDA. As all deliberative decision is made in above three modules, there is temporal independence between deliberative layer and reactive layer of robot control. By virtue of this, all procedures of behavior selection and execution could be done efficiently according to the situation. The performance of the proposed scheme is demonstrated through the kick behavior using small-sized humanoid robot, HSR-VI, developed by RIT lab in KAIST

Navigation framework for humanoid robots integrating gaze control and modified-univector field method to avoid dynamic obstacles

This paper proposes a navigation framework for humanoid robots, which integrates gaze control and modified univector field-based path planning to cope with moving obstacles. To make navigation robust, obstacles are modeled according to their relative velocities and positions. Moreover, partial evaluation values for gaze control architecture are also considered for modifying their virtual size and moving trajectory. In addition, gaze control architecture is proposed, which estimates the size of local map confidence area, self-localization error, surrounding obstacles and obstacle-free distance against those obstacles in the local map. The proposed framework is verified through computer simulations by using a developed simulator for HanSaRam-VIII.