一种双足机器人实时步态规划方法及相关数值算法

A Realtime Gait Programming Method for Biped Robot and Its Related Numerical Algorithm

针对步行双足机器人实时步态规划问题,提出了一种改进的非线性模型预测控制(NMPC)方法.采用扩展的关节坐标,将单腿支撑相(SSP)和双腿支撑相(DSP)统一表示为一个非线性动力学模型.通过对SSP和DSP的3个阶段设定运动学和动力学虚拟约束,将复杂实时步态规划问题转化为4个以预测时域内控制量二次型为代价函数的NMPC问题.采用直接法将连续优化问题参数化为有限维优化问题,并采用惩罚函数法将状态变量约束转化为代价函数中的惩罚项,从而得到能够用渐进二次规划(SQP)求解的有限维静态优化问题.仿真结果表明,应用该方法对BIP机器人模型进行实时步态规划,实现了包含足部转动的动态步行,且机器人满足稳定性条件,不发生侧滑,从而证明了该方法的有效性和可实现性.

A modified nonlinear model predictive control （NMPC） strategy is proposed for the realtime gait programming problem of biped robot. Extended joint coordinates are used to represent both the single support phase （SSP） and the double support phase （DSP） as an identical nonlinear dynamical model. Through setting kinetic and kinematic virtual constraints to SSP and the three sub-phases of DSP, the complex realtime gait programming problem is transformed into four NMPC problems which use the quadratics of inputs in the predictive horizon as the cost functions. Direct method is used to parameterize the continuous optimization problem as a finite dimensional optimization problem, penalty function method is used to transform the state constraints into the penalty items in cost function, and the finite dimensional static optimization problem which can be solved with sequential quadratic programming （SQP） is acquired. Simulation has been made to use the presented method to implement realtime gait programming of BIP robot, and the results show that dynamic walking （including foot rotation） is realized, and biped stability is satisfied without any side slipping. So effectiveness and applicability of the presented approach are verified.