Abstract
In this article, an energy-efficient gait planning algorithm that utilizes both 3D body motion and an allowable zero moment point region (AZR) is presented for biped robots based on a five-mass inverted pendulum model. The product of the load torque and angular velocity of all joint motors is used as an energy index function (EIF) to evaluate the energy consumption during walking. The algorithm takes the coefficients of the finite-order Fourier series to represent the motion space of the robot body centroid, and the motion space is gridded by discretizing these coefficients. Based on the geometric structure of the leg joints, an inverse kinematics method for calculating grid intersection points is designed. Of the points that satisfy the AZR constraints, the point with the lowest EIF value in each network line is selected as the seed. In the neighborhood of the seed, the point with the minimum EIF value in the motion space is successively approximated by the gradient descent method, and the corresponding joint angle sequence is stored in the database. Given a distance to be traveled, our algorithm plans a complete walking trajectory, including two starting steps, multiple cyclic steps, and two stopping steps, while minimizing the energy consumption. According to the preset AZR, the joint angle sequences of the robot are read from the database, and these sequences are adjusted for each step according to the zero-moment-point feedback during walking. To determine the effectiveness of the proposed algorithm, both dynamic simulation and walking experiment in the real environment were carried out. The experimental results show that compared with algorithms based on the fixed body height or vertical body motion, our gait algorithm has a significant energy-saving effect.
Highlights
Bipedal robots have human-like structures and appearances, which can adapt to the human environment, and are ideal robots for replacing human work
We describe the simplified biped robot model and formulate the problem of gait planning
The gait planning optimization (GPO) algorithm takes a long time and is suitable for offline operation. It completes the calculation of S and H and copies the offline database to the online database, which is called in real time in the gait synthesis (GSYN) algorithm. (b) Given the required walking distance d and the value of h for the allowable zero moment point region (AZR), the GSYN algorithm plans the step sequence S* to achieve the minimum E
Summary
Bipedal robots have human-like structures and appearances, which can adapt to the human environment, and are ideal robots for replacing human work. One is to utilize high-speed optical motion capture systems to obtain data on human motion according to external characteristics of human walking and apply these features to the generation of robot motion modes.[5,6] The other approach is to use a central pattern generator to simulate the neural network control of human walking to generate rhythmic signals, thereby solving the problem of robot gait generation.[7,8,9] The third approach is to simplify the biped robot into a linear inverted pendulum model consisting of a point mass and a massless telescopic leg[10,11,12,13] or a cart-table model consisting of a table without mass and a cart driving on the table with all the mass concentrated in it.[14]. According to the algorithm in this article and various control methods proposed in the related literature, the fourth section presents the simulation and experimental results for the performance analysis of the proposed algorithm. The algorithm is summarized, and possible follow-up work is presented
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