• Journal of the Korean Society for Precision Engineering
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• v.23 no.7 s.184
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• pp.84-92
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• 2006

This paper deals with determination of motions of a humanoid robot using genetic algorithm. A humanoid robot has some problems of the structural instability basically. So, we have to consider the stable walking gait in gait planning. Besides, it is important to make the smoothly optimal gait for saving the electric power. A mobile robot has battery to move autonomously. But a humanoid robot needs more electric power in order to drive many joints. So, if movements of walking joint don't maintain optimally, it is hard to sustain the battery power during the working period. Also, if a gait trajectory doesn't have optimal state, the expected lift span of joints tends to be decreased. Also, if a gait trajectory doesn't have optimal state, the expected lift span of joints tends to be decreased. To solve these problems, the genetic algorithm is employed to guarantee the optimal gait trajectory. The fitness functions in a genetic algorithm are introduced to find out optimal trajectory, which enables the robot to have the less reduced jerk of joints and get smooth movement. With these all process accomplished by PC-based program, the optimal solution could be obtained from the simulation. In addition, we discuss the design consideration fur the joint motion and distributed computation of tile humanoid, ISHURO, and suggest its result such as structure of the network and a disturbance observer.

• Korean Journal of Applied Biomechanics
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• v.16 no.1
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• pp.11-17
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• 2006

The purpose of this study was to evaluate the smoothness of movement during various walking speeds. Based on the maximum smoothness theory (or the minimum jerk theory), we hypothesized that the walking speed at the maximum smoothness (or minimum normalized jerk) is the same as that at the minimum energy consumption. Eleven university students participated in treadmill walking experiment with 11 different walking speeds (1.11, 1.19, 1.25, 1.33, 1.56, 1.78, 1.9, 2, 211, 233, and 2.47m/sec). Normalized jerk at 15 markers and the center of mass was calculated. Results showed that there existed a quadratic relationship between the normalized jerk of the vertical direction at the center of mass and the walking speed As the walking speed increased, the normalized jerk of all directions at the heel decreased Our hypothesis that the previously published energetically optimal walking speed ($1.25\;{\sim}\;1.4m/s$) is the same as the minimum jerk speed (1.78m/s) did not agree with this result. The minimum normalized jerk at the center of mass occurred at the walking speed of 1.78m/s which was the preferred walking speed by subjects' questionaries. Further studies concerning the energetically optimal walking speed, preferred walking speed, and walk-run transition speed or run-walk transition speed are necessary based on actual energy consumption experiment and various multi-dimensional analysis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.450-453
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• 2002

This paper is concerned with smooth walking robot using genetic algorithm. The new walking algorithm is proposed and we simulated and experimented the algorithm. We suggested the leg trajectory algorithm and balancing trajectory algorithm by applying genetic algorithm. First the leg trajectory algorithm generated the smooth trajectory. Also the balancing trajectory generated the optimal trajectory. We compared results with the previous walking algorithm. It showed that the new proposed algorithm generated the better walking trajectory.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.77-83
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• 2005

This paper deals with determination of motions of a humanoid robot using genetic algorithm. A humanoid robot has some problems of the structural instability basically. So, we have to consider the stable walking gait in gait planning. Besides, it is important to make the smoothly optimal gait for saving the electric power. A mobile robot has a battery to move autonomously. But a humanoid robot needs more electric power in order to drive many joints. So, if movements of walking joints don't maintain optimally, it is difficult for a robot to have working time for a long time. Also, if a gait trajectory doesn't have optimal state, the expected life span of joints tends to be decreased. To solve these problems, the genetic algorithm is employed to guarantee the optimal gait trajectory. The fitness functions in a genetic algorithm are introduced to find out optimal trajectory, which enables the robot to have the less reduced jerk of joints and get smooth movement. With these all process accomplished by a PC-based program, the optimal solution could be obtained from the simulation. In addition, we discuss the design consideration for the joint motion and distributed computation of the humanoid, ISHURO, and suggest its result such as the structure of the network and a disturbance observer.

• The Journal of Korea Robotics Society
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• v.8 no.2
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• pp.92-103
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• 2013

Humanoid robot is the most intimate robot platform suitable for human interaction and services. Biped walking is its basic locomotion method, which is performed with combination of joint actuator's rotations in the lower extremity. The present work employs humanoid robot simulator and numerical optimization method to generate optimal joint trajectories for biped walking. The simulator is developed with Matlab based on the robot structure constructed with the Denavit-Hartenberg (DH) convention. Particle swarm optimization method minimizes the cost function for biped walking associated with performance index such as altitude trajectory of clearance foot and stability index concerning zero moment point (ZMP) trajectory. In this paper, instead of checking whether ZMP's position is inside the stable region or not, reference ZMP trajectory is approximately configured with feature points by which piece-wise linear trajectory can be drawn, and difference of reference ZMP and actual one at each sampling time is added to the cost function. The optimized joint trajectories realize three phases of stable gait including initial, periodic, and final steps. For validation of the proposed approach, a small-sized humanoid robot named DARwIn-OP is commanded to walk with the optimized joint trajectories, and the walking result is successful.

• Journal of Drive and Control
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• v.15 no.3
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• pp.1-7
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• 2018

Humanoid robots have fascinated many researchers since they appeared decades ago. For the requirement of both accurate tracking control and the safety of physical human-robot interaction, torque control is basically desirable for humanoid robots. Humanoid robots are highly nonlinear, coupled, complex systems, accordingly the calculation of robot model is difficult and even impossible if precise model of the humanoid robots are unknown. Therefore, it is difficult to control using traditional model-based techniques. To realize model-free torque control, time-delay control (TDC) for humanoid robot was proposed with time-delay estimation technique. Using optimal walking trajectory obtained by particle swarm optimization, TDC with proposed scheme is implemented on whole body of a humanoid, not on biped legs even though it is performed by a virtual humanoid robot. The simulation results show the validity of the proposed TDC for humanoid robots.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2493-2503
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• 2018

Passive dynamic walking exhibits humanoid and energy efficient gaits. However, optimal design of passive walker at multi-variable level is not well studied yet. This paper presents a Chaotic Particle Swarm Optimization (CPSO) algorithm and applies it to the optimal design of flexible passive walker. Hip torsional stiffness and damping were incorporated into flexible biped walker, to imitate passive elastic mechanisms utilized in human locomotion. Hybrid dynamics were developed to model passive walking, and period-one gait was gained. The parameters global searching scopes were gained after investigating the influences of structural parameters on passive gait. CPSO were utilized to optimize the flexible passive walker. To improve the performance of PSO, multi-scroll Jerk chaotic system was used to generate pseudorandom sequences, and chaotic disturbance would be triggered if the swarm is trapped into local optimum. The effectiveness of CPSO is verified by comparisons with standard PSO and two typical chaotic PSO methods. Numerical simulations show that better fitness value of optimal design could be gained by CPSO presented. The proposed CPSO would be useful to design biped robot prototype.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.833-839
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• 2004

This paper proposes a method that minimizes the consumed energy by searching the optimal locations of the mass centers of links composing of a biped robot using Real-Coded Genetic Algorithm. Generally, in order to utilize optimization algorithms, the system model and design variables must be defined. Firstly, the proposed model is a 6-DOF biped robot composed of seven links, since many of the essential characteristics of the human walking motion can be captured with a seven-link planar biped walking in the saggital plane. Next, Fourth order polynomials are used for basis functions to approximate the walking gait. The coefficients of the fourth order polynomials are defined as design variables. In order to use the method generating the optimal gait trajectory by searching the locations of mass centers of links, three variables are added to the total number of design variables. Real-Coded GA is used for optimization algorithm by reason of many advantages. Simulations and the comparison of three methods to generate gait trajectories including the GCIPM were performed. They show that the proposed method can decrease the consumed energy remarkably and be applied during the design phase of a robot actually.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.499-500
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• 2008

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.169-172
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• 2002

This paper is concerned with walking trajectory generation by applying the genetic algorithm. The walking trajectory is generated though three via-points and genetic algorithm is employed to find velocity and acceleration at each via-point. Also genetic algorithm is applied for balancing joint trajectory. Fitness function is used for minimizing the trajectory. As a result, new algorithm generated the smooth trajectory. The proposed algorithm is verified by the experiment of biped walking robot developed in our Control laboratory, and we compared the result with the previous walking algorithm. It showed that the new proposed algorithm generated the better walking trajectory.