仿尺蠖爬壁机器人的曲面步态规划与CPG网络构建OA

To address the motion planning challenges of wall-climbing robots on complex curved surfaces,this study pro-poses a bio-inspired gait planning method based on an adaptive central pattern generator(CPG).Inspired by the locomotion of inchworms in nature,a five-degree-of-freedom bipedal wall-climbing robot is designed.First,the conditions for stable adhesion of the robot's bipedal suction cups are analyzed.A CPG network model for the ro-bot's three joints is constructed by coupling multiple tunable Hopf oscillators using a supervised learning method.The CPG network parameters for curved-surface flipping are obtained by learning the joint angles,amplitudes,and other parameters of the robot's T-joint during planar flipping gaits.Online modifications are then applied to the joint angles and amplitudes mapped by the CPG network for curved-surface flipping to achieve gait planning.Sub-sequently,a simulation model is established using Matlab/Simulink and Adams to verify the robot's ability of adap-ting to flipping gaits on surfaces with varying curvatures.Finally,flipping gait experiments are conducted on real curved surfaces,with experimental results consistent with simulations.The findings demonstrate that after learning planar gaits,the CPG network can transform them into curved-surface gaits,validating the effectiveness of the on-line adaptive gait planning method based on CPG.