摘要
针对当前仿人机器人运动优化算法多采用对能耗、稳定性及速度等单目标优化而存在一定的局限性的问题,提出了一种基于多目标优化的仿人机器人上楼梯运动优化方法。针对NSGA-Ⅱ——经典的带精英策略的非支配排序遗传算法(NSGA)的快速非支配排序效率较低的问题,提出了一种基于自调整二叉搜索树的改进NSGA-Ⅱ方法,并采用改进的NSGA-Ⅱ算法实现了仿人机器人上楼梯运动参数优化。通过仿真和实际实验对比了优化前后仿人机器人的能耗和稳定性。实验表明,采用这种方法能克服单目标优化的缺点,在同时满足多个目标需求的同时有效地实现仿人机器人上楼梯。
Based on the analysis of the certain limitations of current methods for optimization of humanoid robots＇ motion caused by their optimization of single objectives such as energy, stability and speed, a optimization method based on multi-objective optimization to optimize the motion parameters of a humanoid robot in stepping upstairs was presented. In consideration of the low efficiency of fast nondominated sorting of the NSGA-Ⅱ , a typical nondominated sorting genetic algorithm （NSGA） with the elitist tactics, an improved NSGA-Ⅱ method based on self-adjusting binary search trees was proposed, and by using it, the motion parameter optimization for a humanoid robot in stepping upstairs was achieved. The humanoid robot＇s energy consumption and stability before and after the optimization were measured and compared by computer simulations and experiment. The experimental results show that the use of this method can overcome the disadvantages of the single objective optimization, and effectively realize the humanoid robot＇ s motion planning when it stepping upstairs in the circumstances of meeting multiple objectives requirements.
出处
《高技术通讯》
CAS
CSCD
北大核心
2014年第9期982-990,共9页
Chinese High Technology Letters
基金
863计划(2007AA041603)
国家自然科学基金(61075077)
黑龙江省自然科学基金(F201323)资助项目
关键词
仿人机器人
多目标优化
带精英策略的非支配排序遗传算法(NSGA—Ⅱ)
自调
整二叉搜索树
humanoid robot, multi-objective optimization, nondominated sorting genetic algorithm （NSGA） with the elitist tactics （NSGA）-Ⅱ, self-adjusting binary search trees
