迁移差分蜣螂优化算法在六足机器人能耗最优足端轨迹规划中的应用研究OACHSSCD

Aims:Hexapod robots suffer from excessive energy consumption during locomotion;and existing foot trajectory planning methods struggle to simultaneously satisfy kinematic feasibility and energy consumption constraints.To solve the above problems,this paper proposed a migration differential dung beetle optimizer(MDDBO)algorithm combined with the 5-7-5 piecewise polynomial interpolation to achieve energy optimal feet trajectory planning.Methods:Firstly,Latin hypercube sampling was introduced to initialize the population,improving the spatial uniformity and diversity of the initial population.Secondly,a path migration guidance strategy was designed to enhance the search direction guidance ability and global search efficiency during the ball-rolling phase.Finally,an elite differential mutation strategy was developed to balance global exploration and local exploitation,which effectively avoided premature convergence.Results:Comparative experiments on the CEC2017 benchmark test suite verified that the proposed MDDBO achieved significant superiority in convergence accuracy,convergence speed and algorithm stability.In the energy-optimal trajectory planning experiment for hexapod robots,the optimized foot trajectories maintained smooth and continuous profiles.Meanwhile,the fluctuations of joint angular velocity and angular acceleration were substantially suppressed.The overall kinematic performance was greatly improved;and the total locomotion energy consumption was reduced by 26.70%.Conclusions:The proposed MDDBO algorithm can effectively solve the energy optimal feet trajectory planning problem of hexapod robots,which possesses great feasibility and practical potential for engineering applications.