Problem
Research questions and friction points this paper is trying to address.
biped robot
trajectory generation
dynamic stability
slope inclination
ZMP
Walking on the Slope: Stable Bipedal Gaits with Genetic-Algorithm-Optimized Trajectories
🤖 AI Summary
本文通过使用遗传算法优化轨迹，解决了8自由度双足机器人在平坦和倾斜地形上行走的稳定步态问题。
📝 Abstract
This paper presents the kinematic and dynamic modeling, trajectory generation, and stability analysis of an 8-degree-of-freedom (DOF) biped robot walking on flat and inclined terrain. Denavit-Hartenberg (DH) parameters and homogeneous transformations are used to derive the forward kinematics, while closed-form inverse kinematics maps the desired hip and swing-foot Cartesian trajectories, generated with cubic splines, to joint angles. Joint torques are computed using the Newton-Euler iterative algorithm, and dynamic stability is evaluated using the zero moment point (ZMP) criterion. A genetic algorithm (GA) optimizes the hip height, maximum swing-foot lift, and frontal-plane tilt angle by minimizing the work done by the joints subject to a ZMP feasibility penalty. Simulation results in MATLAB show that the nominal 8-DOF model remains ZMP-stable for step completion times down to 0.5 s and for slope inclinations up to 22.5 degrees with the given foot geometry. Beyond these limits, the ZMP leaves the support polygon, and either the foot dimensions or the trajectory parameters must be modified. The results also show that ZMP stability is governed by the mass distribution among the links rather than the total mass of the robot.
Innovation
Methods, ideas, or system contributions that make the work stand out.
genetic algorithm
bipedal walking
trajectory optimization
zero moment point (ZMP)
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