智能决策改进的四足机器人ZMP爬行步态算法

提出了一种基于反馈控制和贪婪决策的四足机器人爬行步态规划算法。该算法利用机载惯性传感器IMU（Inertial Measurement Unit）来实时计算零力矩点和姿态角，以稳态裕度为指标在支撑平面内实时规划期望零力矩点（Zero Moment Point，ZMP）轨迹，结合非线性反馈控制器实现对机体ZMP点的连续平滑调节，保证机器人在按给定速度矢量进行连续爬行的同时具有抵抗一定外力扰动的能力。步态规划采用动态步态周期，基于机器人结构约束和贪婪决策实现跨腿的自动触发，提高了步态自适应性。最终通过样机行走实验验证了所提算法应用于微型四足机器人中的可行性，机器人实现了在平坦地面上稳定地全向行走和旋转，所提算法同时兼顾了自适应性和稳定裕度。

Improved ZMP Crawling Gait Algorithm for Quadruped Robot Based on Intelligent Decision Making

A novel crawling gait planning algorithm of quadruped robot based on feedback control and greedy decision is proposed in this paper. The proposed algorithm utilizes the on-board inertial sensor IMU（Inertial Measurement Unit） to calculate the real-time Zero Moment Point（ZMP） of the robot, and plans its expected trajectory in the polygon composed of the ground foot with stability margin as the performance index. The proposed algorithm adopts the nonlinear PD controller to continuously adjust the ZMP, which can guarantee the robot has the ability to resist the external disturbance. Based on the proposed area greedy decision and robot’s physical structure constraint, the swing leg can automatically trigger without fixed gait cycle and order. The experimental results on actual quadruped robots show that the robot can realize stable crawling and rotation on the flat ground. The proposed algorithm has anti-disturbance performance, which can be easily realized on small, low-cost quadruped robots.