• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2492-2497
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• 2003

This paper presents optimal walking trajectory generation for a quadruped robot with genetic-fuzzy algorithm. In order to move a quadruped robot smoothly, both generations of optimal leg trajectory and free walking are required. Generally, making free walking is difficult to realize for a quadruped robot, because the patterned trajectory may interfere in the free walking. In this paper, we suggest the generation method for the leg trajectory satisfied with free walking pattern so as to avoid obstacle and walk smoothly. We generate via points of leg with respect to body motion, and then we use the genetic-fuzzy algorithm to search for the optimal via velocity and acceleration information of legs. All these methods are verified with PC simulation program, and implemented to SERO-V robot.

• 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 the Korean Society for Precision Engineering
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• v.15 no.3
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• pp.157-167
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• 1998

In this paper it is purpose that reduces joint torques and their rate of change through optimizing trajectory planning of biped walking machine. The motion of biped walking machine is divided into leg motion for walking and body motion for keeping balance. The leg motion is planned by three phases, that are deploy, swing, and place phases, in terms of the state of foot against floor. The distribution of time assigned to each phase is optimized and that causes leg joint torques and their rate of change to minimize. The body notion is produced by using optimal control theory which minimizes body joint torques and satisfies Z.M.P. constraints defined as region of each phase.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1193-1200
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• 2015

An effective swing trajectory of legged robots is different from the swing trajectories of humans or animals because of different dynamic characteristics. Therefore, it is important to find optimal parameters through experiments. This paper proposes a real-time nonlinear programming (RTNLP) method for optimization of the swing trajectory of the legged robot. For parameterization of the trajectory, the swing trajectory is approximated to parabolic and cubic spline curves. The robotic leg is position-controlled by a high-gain controller, and a cost function is selected such that the sum of the motor inputs and tracking errors at each joint is minimized. A simplified dynamic model is used to simulate the dynamics of a robotic leg. The purpose of the simulation is to find the feasibility of the optimization problem before an actual experiment occurs. Finally, an experiment is carried out on a real robotic leg with two degrees of freedom. For both the simulation and the experiment, the design variables converge to a feasible point, reducing the cost value.

• Journal of Mechanical Science and Technology
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• v.20 no.5
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• pp.612-620
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• 2006

This paper proposes an optimal trajectory generation method for biped robots for walking up-and-down stairs using a Real-Coded Genetic Algorithm (RCGA). The RCGA is most effective in minimizing the total consumption energy of a multi-dof biped robot. Each joint angle trajectory is defined as a 4-th order polynomial of which the coefficients are chromosomes or design variables to approximate the walking gait. Constraints are divided into equalities and inequalities. First, equality constraints consist of initial conditions and repeatability conditions with respect to each joint angle and angular velocity at the start and end of a stride period. Next, inequality constraints include collision prevention conditions of a swing leg, singular prevention conditions, and stability conditions. The effectiveness of the proposed optimal trajectory is shown in computer simulations with a 6-dof biped robot model that consists of seven links in the sagittal plane. The optimal trajectory is more efficient than that generated by the Modified Gravity-Compensated Inverted Pendulum Mode (MGCIPM). And various trajectories generated by the proposed GA method are analyzed from the viewpoint of the consumption energy: walking on even ground, ascending stairs, and descending stairs.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.304-306
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• 2001

This paper presents the obstacle avoidance of a biped walking robot using GA-Fuzzy algorithm. In the case of our previous studies the surface has been assumed to be flat. For the case of the environment with obstacles, however, the walking robot might be unnatural. Thus, we considered the surface contained obstacles that the robot can pass through. We propose the optimal leg trajectory data-base by using genetic algorithm and optimal leg trajectory movement about obstacles that exist in front of the robot using fuzzy approach. It is shown that the robot can move more naturally on the surface that contains obstacles.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.831-836
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• 2007

This paper proposes an optimal galloping trajectory which costs low energy and guarantees the stability of the quadruped robot. In the realization of the fast galloping, the trajectory design is important. As a galloping trajectory, we propose an elliptic leg trajectory, which provides simplified locomotion to complex galloping motions of animals. However, the elliptic trajectory, as an imitation of animal galloping motion, does not guarantee stability and minimal energy consumption. We propose optimization based on the energy and stability using a genetic algorithm, which provides the robust and global solution to a multi-body, highly nonlinear dynamic system. To evaluate and verify the effectiveness of the proposed trajectory, computer simulations were carried out.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.4 s.181
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• pp.75-82
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• 2006

In this paper, we propose an optimal trajectory for biped robots to move up-and-down stairs using a genetic algorithm and a computed-torque control for biped robots to be dynamically stable. First, a Real-Coded Genetic Algorithm (RCGA) which of operators are composed of reproduction, crossover and mutation is used to minimize the total energy. Constraints are divided into equalities and inequalities: Equality constraints consist of a position condition at the start and end of a step period and repeatability conditions related to each joint angle and angular velocity. Inequality constraints include collision avoidance conditions of a swing leg at the face and edge of a stair, knee joint conditions with respect to the avoidance of the kinematic singularity, and the zero moment point condition with respect to the stability into the going direction. In order to approximate a gait, each joint angle trajectory is defined as a 4-th order polynomial of which coefficients are chromosomes. The effectiveness of the proposed optimal trajectory is shown in computer simulations with a 6-dof biped robot that consists of seven links in the sagittal plane. The trajectory is more efficient than that generated by the modified GCIPM. And various trajectories generated by the proposed GA method are analyzed in a viewpoint of the consumption energy: walking on even ground, ascending stairs, and descending stairs.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.7 s.172
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• pp.112-121
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• 2005

This paper is concerned with smooth trajectory generation of biped robot which has inverted pendulum type balancing weight. Genetic algorithm is used to generate the trajectory of the leg and balancing weight. Balancing trajectory can be determined by solving the second order differential equation under the condition that the reference ZMP (Zero moment point) is settled. Reference ZMP effect on gait pattern absolutely but the problem is how to determine the reference ZMP. Genetic algorithm can find optimal solution under the high order nonlinear situation. Optimal trajectory is generated when use genetic algorithm which has some genes and a fitness function. In this paper, minimization of balancing joints motion is used for the fitness function and set the weight factor of the two balancing joints at the fitness function. Inverted pendulum type balancing weight is very similar with human and this model can be used fur humanoid robot. Simulation results show ZMP trajectory and the walking experiment made on the real biped robot IWR-IV.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2345-2347
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• 1998

This paper deals with the trajectory planning of IWR biped robot using genetic algorithm. The trajectory of a swing leg is generated by 5th order polynomial equation. Velocities and Acceleration properties on a viapoints are needed. These constants are given by heuristic method. The optimal values are determined by G.A to minimize the jerk of a trajectory. As a result, trajectory planning is implemented not on between two viapoints but on a whole interval. Efficient numerical calculation routines and walking algorithms for simulation are accomplished by MATLAB package.