Young-Dae Hong

Laser-Based Kinematic Calibration of Robot Manipulator Using Differential Kinematics

This paper proposes a novel systematic technique to estimate entire kinematic parameter errors of robot manipulator. Small errors always exist in link length and link twist for physical manipulators, which affect the precision in kinematic equations leading to calculate wrong joint angle values in inverse kinematic equations. In order to solve these problems, the proposed technique employs a structured laser module (SLM), a stationary camera, the Jacobian matrices, and an extended Kalman filter (EKF). The SLM is attached to the end-effector of the manipulator arm and the stationary camera is used to determine an accurate position where the laser comes out. Variances between actual and measured positions of laser beams are represented by the Jacobian matrices formulated from differential kinematics. Then, the EKF is used to estimate kinematic parameters. Effectiveness of the proposed technique is verified with 7 DOF humanoid manipulator arm by computer simulation and 4 DOF manipulator by actual experiment.

Command State-Based Modifiable Walking Pattern Generation on an Inclined Plane in Pitch and Roll Directions for Humanoid Robots

Previous research related to walking on an inclined plane for humanoid robots, including the 3-D linear inverted pendulum model (3D-LIPM) approach, were unable to modify walking period, step length, and walking direction independently without any additional step for adjusting the center of mass (CoM) motion. Moreover, the inclination along the pitch direction was only considered for walking. To solve these problems, a novel command state (CS)-based modifiable walking pattern generator for humanoid robots is proposed for modifiable walking on an inclined plane in both pitch and roll directions. The dynamic equation of the 3D-LIPM on the inclined plane in both pitch and roll directions is derived to obtain the CoM motion. Using the CoM motion, a method for modifiable walking pattern generation on the inclined plane is developed to follow a given CS composed of walking periods, step lengths, and walking directions for both legs. The effectiveness of the proposed walking pattern generator is demonstrated through both simulation and experiment for the small-sized humanoid robot, HanSaRam-IX (HSR-IX).

Stable Bipedal Walking With a Vertical Center-of-Mass Motion by an Evolutionary Optimized Central Pattern Generator

This paper proposes a method for stable bipedal walking with a vertical center-of-mass (COM) motion by an evolutionary optimized central pattern generator (CPG). To generate a walking pattern for a bipedal robot, a modifiable walking pattern generator (MWPG) is employed, which extends a conventional 3-D linear inverted pendulum model (3-D LIPM) to allow a zero-moment-point variation by the closed-form functions. By using the MWPG, the robot is able to modify the walking pattern in real time while walking. For the vertical COM motion of the 3-D LIPM, the vertical COM trajectory is generated by the CPG. The disturbance caused by the vertical COM motion is compensated by utilizing the sensory feedback in the CPG. To obtain the desired output signals from the CPG, the CPG is optimized by the two-phase evolutionary programming (TPEP), which is suitable to solve the constrained optimization problems. By combining the MWPG with the CPG, stable bipedal walking with a larger stride is obtained. The validity of the proposed method is verified through real experiments for the small-sized bipedal robot, HanSaRam-IX.

3-D Command State-Based Modifiable Bipedal Walking on Uneven Terrain

Bipedal robots in previous research work were unable to independently modify the elements of a walking pattern on uneven terrain without any extra footstep for adjusting the center of mass (COM) motion. To solve this problem, this paper extends the modifiable walking pattern generator (MWPG). The MWPG can independently modify the elements of the walking pattern without any extra footstep on flat terrain only. The extended MWPG can be applied on uneven terrain. In the extended MWPG, a 3-D command state is defined to generate the walking pattern on uneven terrain. Instead of using the constant COM height, the vertical COM trajectory is generated to satisfy the foot height of the swing leg. Also, an additional trajectory, generated by the cubic spline interpolation, is supplied to the vertical foot trajectory of the MWPG. The proposed MWPG is implemented on the small-sized bipedal robot, HanSaRam-IX (HSR-IX) and the effectiveness of the proposed MWPG is demonstrated through the experiment.

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.

Full-body joint trajectory generation using an evolutionary central pattern generator for stable bipedal walking

Central pattern generator (CPG) is used to control the locomotion of vertebrate and invertebrate animals, such as walking, running or swimming. It consists of biological neural networks that can produce coordinated rhythmic signals by using simple input signals. In this paper, a full-body joint trajectory generator is proposed for stable bipedal walking by using an evolutionary optimized CPG. Sensory feedback pathways are proposed in the CPG structure, which uses force sensing resistor (FSR) signals. In order to optimize the parameters of CPG, quantum-inspired evolutionary algorithm is employed. Then, controller is developed to control the position of both ankles and pelvis and the pitching angles of shoulders. The proposed trajectory generator controls the position of the center of pelvis along lateral direction, and the pitching angle of both shoulders in addition to the position of both ankles for stable biped locomotion. The stability of biped locomotion along lateral direction is improved by controlling the position of the center of pelvis along lateral direction. To reduce yawing momentum, the pitching angle of both shoulders are controlled. The effectiveness is demonstrated by simulations with the Webot model of a small-sized humanoid robot, HSR-IX and real experiments with HSR-IX.

Evolutionary-Optimized Central Pattern Generator for Stable Modifiable Bipedal Walking

In this paper, an evolutionary-optimized central pattern generator (CPG) considering equality constraints is proposed for stable modifiable bipedal walking. The proposed CPG generates the position trajectories of the swing foot and the center of pelvis in the Cartesian coordinate system at single and double support phases. The significance of the proposed CPG is that it can change the sagittal and lateral step lengths just before the beginning of each single support phase while maintaining the desired values of single and double support times, which are set in the beginning of bipedal walking. To deal with environmental perturbations, the sensory feedbacks in the CPG are designed using the force sensing resistors such that the bipedal robot can maintain its balance. For the optimized parameters of the CPG, a two-phase evolutionary programming is employed. The effectiveness of the method is demonstrated by computer simulation with the Webots model of a small-sized humanoid robot, HSR-IX, and the experiment with HSR-IX developed in the RIT Laboratory, KAIST, Daejeon, Korea.

AN EVOLUTIONARY OPTIMIZED FOOTSTEP PLANNER FOR THE NAVIGATION OF HUMANOID ROBOTS

In this paper, an evolutionary optimized footstep planner for the navigation of humanoid robots is proposed. A footstep planner based on a univector field navigation method is proposed to generate a command state (CS) as an input to a modifiable walking pattern generator (MWPG) at each footstep. The MWPG generates associated trajectories of every leg joint to follow the given CS. In order to satisfy various objectives in the navigation, the univector fields are optimized by evolutionary programming. The three objectives, shortest elapsed time to get to a destination, safety without obstacle collision, and less energy consumption, are considered with mechanical constraints of a real humanoid robot, that is, the maximum step length and allowable yawing range of the feet. The effectiveness of the proposed algorithm is demonstrated through both computer simulation and experiment for a small-sized humanoid robot, HanSaRam-IX.

Experimental Study on Modifiable Walking Pattern Generation for Handling Infeasible Navigational Commands

To accommodate various navigational commands, a humanoid should be able to change its walking motion in real time. Using the modifiable walking pattern generation (MWPG) algorithm, a humanoid can handle dynamic walking commands by changing its walking period, step length, and direction independently. If the humanoid is given a command to perform an infeasible movement, the algorithm substitutes the infeasible command with a feasible one using binary search. The feasible navigational command is subsequently translated into the desired center-of-mass (CM) state. Every sample time CM reference is generated using a zero-moment-point (ZMP) variation scheme. Based on this algorithm, various complex walking patterns can be generated, including backward and sideways walking, without detailed consideration of the feasibility of the navigational commands. In a previous study, the effectiveness of the MWPG algorithm was verified by dynamic simulation. This paper presents experimental results obtained using the small-sized humanoid robot platform DARwIn-OP.

An evolutionary central pattern generator for stable bipedal walking by the increased double support time

Central pattern generator (CPG) consisting of neural oscillators, generates rhythmic signals using simple input signal. It can modify motor patterns to handle environmental perturbations by sensory feedback. In this paper, an evolutionary CPG for stable bipedal walking by the increased double support time is proposed. The proposed CPG generates swing motion of arms as well as ankle and the center of pelvis (COP) motions in Cartesian coordinate system. Sensory feedback pathways in the proposed CPG use force sensing resistor (FSR) signals. The sensory feedback maintains humanoid robots balance and prevents it from falling down to the ground. To optimize the parameters of the proposed CPG, evolutionary algorithm is employed. The effectiveness of the scheme is demonstrated by simulations with the Webot model of a small-sized humanoid robot, HSR-IX, developed in the RIT Lab., KAIST.