为了提高四足机器人对角小跑步态的平稳性,提出一种基于贝塞尔曲线的足端轨迹规划方法。针对四足机器人的对角小跑步态,建立了四足机器人并联腿运动学模型,运用几何法求解了正逆运动学,并基于零冲击原则和比例微分(Proportion and Diffe...为了提高四足机器人对角小跑步态的平稳性,提出一种基于贝塞尔曲线的足端轨迹规划方法。针对四足机器人的对角小跑步态,建立了四足机器人并联腿运动学模型,运用几何法求解了正逆运动学,并基于零冲击原则和比例微分(Proportion and Differentiation,PD)控制器对摆动相和支撑相的足端轨迹进行优化;利用Webots仿真平台和实验样机进行对比试验。结果表明:当X方向上比例系数取20,微分系数取0.75 s,Y方向上比例系数取20,微分系数取0.5 s时,四足机器人的足端轨迹平滑且无速度、加速度突变,对角小跑步态更为稳定。展开更多
More and more biological evidences have been found that neural networks in the spinal cord, referred to as ＂central pattern generators＂ （CPGs）, govern locomotion. CPGs are capable of producing rhythmic movements, ...More and more biological evidences have been found that neural networks in the spinal cord, referred to as ＂central pattern generators＂ （CPGs）, govern locomotion. CPGs are capable of producing rhythmic movements, such as swimming, flying, and walking, even when isolated from the brain and sensory inputs. If we could build up any models that have similar functions as CPGs, it will be much easier to design better locomotion for robots. In this paper, a self-training environment is designed and through genetic algorithm （GA）, walking trajectories for every foot of AIBO are generated at first. With this acquired walking pattern, AIBO gets its fastest locomotion speed. Then, this walking pattern is taken as a reference to build CPGs with Hopf oscillators. By changing corresponding parameters, the frequencies and the amplitudes of CPGs＇ outputs can be adjusted online. The limit cycle behavior of Hopf oscillators ensures the online adjustment and the walking stability against perturbation as well. This property suggests a strong adaptive capacity to real environments for robots. At last, simulations are carried on in Webots and verify the proposed method.展开更多
文摘为了提高四足机器人对角小跑步态的平稳性,提出一种基于贝塞尔曲线的足端轨迹规划方法。针对四足机器人的对角小跑步态,建立了四足机器人并联腿运动学模型,运用几何法求解了正逆运动学,并基于零冲击原则和比例微分(Proportion and Differentiation,PD)控制器对摆动相和支撑相的足端轨迹进行优化;利用Webots仿真平台和实验样机进行对比试验。结果表明:当X方向上比例系数取20,微分系数取0.75 s,Y方向上比例系数取20,微分系数取0.5 s时,四足机器人的足端轨迹平滑且无速度、加速度突变,对角小跑步态更为稳定。
基金supported by National Natural Science Foundation of China (Grant No. 60875057)National Hi-tech Research and Development Program of China(863 Program, Grant No. 2009AA04Z213)
文摘More and more biological evidences have been found that neural networks in the spinal cord, referred to as ＂central pattern generators＂ （CPGs）, govern locomotion. CPGs are capable of producing rhythmic movements, such as swimming, flying, and walking, even when isolated from the brain and sensory inputs. If we could build up any models that have similar functions as CPGs, it will be much easier to design better locomotion for robots. In this paper, a self-training environment is designed and through genetic algorithm （GA）, walking trajectories for every foot of AIBO are generated at first. With this acquired walking pattern, AIBO gets its fastest locomotion speed. Then, this walking pattern is taken as a reference to build CPGs with Hopf oscillators. By changing corresponding parameters, the frequencies and the amplitudes of CPGs＇ outputs can be adjusted online. The limit cycle behavior of Hopf oscillators ensures the online adjustment and the walking stability against perturbation as well. This property suggests a strong adaptive capacity to real environments for robots. At last, simulations are carried on in Webots and verify the proposed method.
