Adaptive gait generation for Spider Robots using NARX nodel combining evolutionary neural network model

Abstract

Legged robots' gait is a major factor in how well they walk. Because present solutions lack a precise approach to the incredibly complicated
structure and sensitive motions of legged creatures, gait creation remains a highly challenging subject. This article proposes a gait generation
model (WPG) for a spider robot to walk straight and follow the designed reference ZMP trajectory in 2 step cycles with two different speeds.
Initially, the robot spider's gait parameters are determined using a nonlinear recurrent evolutionary neural network model (NARX+EANN),
which is then used in a walking pattern generator (WPG). Next, a new gait pattern generator (WPG) that depends only on four parameters
(step length, leg lift, knee bend, stride) of the small-sized spider robot is designed, by relying on realistic gait analysis of the spider robot and
kinematic analysis. Simultaneously, by using analytical techniques to solve the inverse kinematics issue, 12 joint angle orbits at the spider
robot's four legs will be determined from the hip and foot orbits at the spider robot's four legs. Then, the optimal weights of the NARX+EANN
model are identified using the Jaya optimization algorithm for training with the objective function of minimizing the total error between the
actual ZMP coordinates and the reference ZMP in two-step cycles of different speeds. The actual ZMP point is determined based on 12 joint
angle orbits at the four legs of the spider robot by solving the forward kinematics problem using analytical methods. Finally, this proposal is
applied to the experimental model of the B3-SBOT spider robot. The obtained results demonstrate that B3-SBOT walks steadily and strongly
without tilting, closely following the designed reference ZMP trajectory in 2 step cycles with two different speeds.