This paper deals with the locomotion control of quadruped robots inspired by the biological concept of central pattern generator (CPG). A control architecture is proposed with a 3-D workspace trajectory generator and a motion engine. The workspace trajectory generator generates adaptive workspace trajectories based on CPGs, and the motion engine realizes joint motion imputes. The proposed architecture is able to generate adaptive workspace trajectories online by tuning the parameters of the CPG network to adapt to various terrains. With feedback information, a quadruped robot can walk through various terrains with adaptive joint control signals. A quadruped platform AIBO is used to validate the proposed locomotion control system. The experimental results confirm the effectiveness of the proposed control architecture. A comparison by experiments shows the superiority of the proposed method against the traditional CPG-joint-space control method.

CPG-Inspired Workspace Trajectory Generation and Adaptive Locomotion Control for Quadruped Robots

Gait generation is very important as it directly affects the quality of locomotion of legged robots. As this is an optimization problem with constraints, it readily lends itself to Evolutionary Computation methods and solutions. This paper reviews the techniques used in evolution-based gait optimization, including why Evolutionary Computation techniques should be used, how fitness functions should be composed, and the selection of genetic operators and control parameters. This paper also addresses further possible improvements in the efficiency and quality of evolutionary gait optimization, some problems that have not yet been resolved and the perspectives for related future research.

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A review of gait optimization based on evolutionary computation

This paper proposes a method of adaptively generating a gait pattern of biped robot. The gait synthesis is based on human's gait pattern analysis. The proposed method can easily be applied to generate the natural and stable gait pattern of any biped robot. To analyze the human's gait pattern, sequential images of the human's gait on the sagittal plane are acquired from which the gait control values are extracted. The gait pattern of biped robot on the sagittal plane is adaptively generated by a genetic algorithm using the human's gait control values. However, gait trajectories of the biped robot on the sagittal plane are not enough to construct the complete gait pattern because the biped robot moves on 3-dimension space. Therefore, the gait pattern on the frontal plane, generated from Zero Moment Point (ZMP), is added to the gait one acquired on the sagittal plane. Consequently, the natural and stable walking pattern for the biped robot is obtained. Keywords—Biped robot, Gait pattern, Genetic Algorithm. I. INTRODUCTION UMANOID robots are expected to assist human activities in daily life. Therefore, humanoid robots are asked to walk natural to provide intimacy to human. The human gait is a complex dynamic activity. The complicated human model that has 3D deformable frame, high DOF, and complex mechanical structure cannot be applied directly to simplified biped robots. To overcome these problems, many researches have assumed that the human gait is optimized in the view of the gait energy, and they have defined the natural gait of the biped robot as the movement that minimizes its gait energy. Therefore, many researches about the gait pattern generation of the biped robot focus on minimizing the gait energy of them(1,2,3,4). Since a lot of the parameter values cannot be obtained only from minimizing the gait energy of the biped robot, adaptive techniques such as Genetic Algorithm and Neural Network Algorithm have been studied. Capi(5) proposed a gait synthesis method of biped robots using GA. The gait synthesis during walking is analyzed from the minimum consume energy (MCE) and minimum torque change (MTC). The stability of the robot

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Adaptive Gait Pattern Generation of Biped Robot based on Human's Gait Pattern Analysis

In this paper, we propose optimal trajectories of biped robots to move a staircase up and down using genetic algorithms and computed-torque controller to be dynamically stable. Firstly, in order to minimize the total energy efficiency, the real-coded genetic algorithm (RCGA) which of operators are composed of the reproduction, crossover and mutation is used. Constraints are divided into equalities and inequalities: equality constraints consist of position conditions at the start and end of stride period and repeatability conditions related to each joint angle and angular velocity. Next, inequality constraints include the collision avoidance conditions of swing leg at the face and edge of a stair, the knee joint conditions with respect to the singular avoidance, and the zero moment point condition with respect to the stability into the going direction. Finally, in order to approximate the walking gait, each joint angle trajectory is defined as a 4th order polynomial of which coefficients are chromosomes. In the computer simulation used 6 degree of freedom biped robot model that consists of seven links, we analyze the energy efficiency of proposed optimal trajectories about the following cases: walking on ground, ascending stairs, and descending stairs.

Optimal trajectory generation for a biped robot walking a staircase based on genetic algorithms

This paper presents the design of walking motions of a biped robot by a computational intelligent method. The proposed method finds the parameters of the output functions that produce such motion in the closed-loop system which fulfill the requirements of stability and minimal energy control by formulating a constrained optimization problem. A set of parameters which produces periodic motions with low energy control magnitudes and its closed-loop system trajectories are shown through a numerical study case of a 3-DOF biped robot.

Generation of walking periodic motions for a biped robot via genetic algorithms

This paper proposes a method that minimizes the energy consumption in the locomotion of a biped robot. A real-coded genetic algorithm is employed in order to search for the optimal locomotion pattern, and at the same time the optimal locations of the mass centers of the links that compose the biped robot. 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, a 6-DOF biped robot that consists of seven links is used as the model used in the work. For trajectories of the robot in a single stride, fourth-order polynomials are used as their basis functions to approximate the locomotion gait. The coefficients of the polynomials are defined as design variables. For the optimal locations of the mass centers of the links, three variables are added to the design variables under the assumption that the left and right legs are identical. Simulations were performed to compare locomotion trajectories obtained with the genetic algorithm and the one obtained with the gravity-compensated inverted pendulum mode (GCIPM). They show that the proposed trajectory with the optimized mass centers significantly reduces the energy consumption, indicating that the proposed optimized method is a valuable tool in the design of biped robots.

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Moosung Choi

Papers

Generation of an Optimal Gait Trajectory for Biped Robots Using a Genetic Algorithm