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Optimal trajectory generation for biped robots walking up-and-down stairs

机译:两足机器人上下楼梯的最佳轨迹生成

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This paper proposes an optimal trajectory generation method for biped robots for walking up-and-down stairs using a Real-Coded Genetic Algorithm (RCGA). The RCGA is most effective in minimizing the total consumption energy of a multi-dof biped robot. Each joint angle trajectory is defined as a 4-th order polynomial of which the coefficients are chromosomes or design variables to approximate the walking gait. Constraints are divided into equalities and inequalities. First, equality constraints consist of initial conditions and repeatability conditions with respect to each joint angle and angular velocity at the start and end of a stride period. Next, inequality constraints include collision prevention conditions of a swing leg, singular prevention conditions, and stability conditions. The effectiveness of the proposed optimal trajectory is shown in computer simulations with a 6-dof biped robot model that consists of seven links in the sagittal plane. The optimal trajectory is more efficient than that generated by the Modified Gravity-Compensated Inverted Pendulum Mode (MGCIPM). And various trajectories generated by the proposed GA method are analyzed from the viewpoint of the consumption energy: walking on even ground, ascending stairs, and descending stairs.
机译:本文提出了一种使用实码遗传算法(RCGA)的两足机器人上下楼梯行走的最佳轨迹生成方法。 RCGA在最小化多足足两足机器人的总能耗方面最有效。每个关节角度轨迹都定义为4阶多项式,其系数为染色体或设计变量,以近似步态。约束分为平等和不平等。首先,等式约束包括关于步幅周期开始和结束时每个关节角度和角速度的初始条件和可重复性条件。接下来,不等式约束包括秋千腿的碰撞防止条件,奇异防止条件和稳定性条件。所建议的最佳轨迹的有效性在计算机仿真中得到了证明,该计算机仿真采用6轴Biped机器人模型,该模型由矢状面中的七个链接组成。最佳轨迹比修正重力补偿倒立摆模式(MGCIPM)生成的轨迹更有效。并从能量消耗的角度分析了所提出的遗传算法产生的各种轨迹:在平坦的地面上行走,上楼梯和下楼梯。

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