基于能效优化的仿人机器人跑步步态优化与控制

针对高能耗导致的仿人机器人难以大规模实用化的问题,提出了一种新的仿人机器人参数化跑步步态优化方法。分析了不同跑步步态参数对仿人机器人水平、垂直方向的稳定性及能耗的影响,将机器人步态优化问题转化为对步态参数的多目标寻优问题,根据连杆模型得到机器人跑步过程中水平、垂直方向的稳定裕度及能耗表达式,并构造目标函数,采用基于对位学习的遗传算法对机器人参数化跑步步态进行多目标寻优,在保证机器人俯仰、翻滚和偏摆各方向力矩平衡的前提下降低整体能量消耗；针对传统遗传算法早熟及收敛速度慢的问题,提出基于领域知识的精细化初始成员策略,采取生成种群成员对位点的方式更新种群,以加快收敛速度；为提高轨迹跟踪性能,设计了自适应控制器,并给出了稳定性证明。仿真实验表明:该方法能有效降低能耗并保证其稳定性。

Running Gait Planning and Control for Humanoid Robot Based on Energy Efficiency Optimization

A novel parametric running gait optimization algorithm was proposed for solving the fatal problem of high energy consumption for humanoid robot,limiting the practical application of humanoid robot.After analyzing the impact of different gait parameters on horizontal and vertical stability,the gait planning problem is transformed to the multi-objective optimization problem,and the expressions of stability and energy consumption are obtained according to connective link model.In order to achieve the ideal running gait,a method based on opposite learning generic algorithm was proposed,which helps to reduce energy consumption and obtain good stability margin in pitching,rolling and yawing axis.In view of the problems of early maturing and slow convergence of traditional GA algorithm,an effective policy of initiating population based on domain knowledge was developed and the population was updated by generating opposite entity.To improve the trajectory tracking performance,an adaptive controller was designed and the stability proof was provided.The simulation results prove the validity of the method.