Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada.
Department of Cell Biology and Anatomy, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
PLoS Comput Biol. 2019 Nov 21;15(11):e1007444. doi: 10.1371/journal.pcbi.1007444. eCollection 2019 Nov.
It is widely held that quadrupeds choose steady gaits that minimize their energetic cost of transport, but it is difficult to explore the entire range of possible footfall sequences empirically. We present a simple model of a quadruped that can spontaneously produce any of the thousands of planar footfall sequences available to quadrupeds. The inelastic, planar model consists of two point masses connected with a rigid trunk on massless legs. It requires only center of mass position, hind and forelimb proportions and a stride-length to speed relationship as input. Through trajectory optimization of a work and force-rate cost, and a large sample of random initial guesses, we provide evidence for the global optimality of symmetrical four-beat walking at low speeds and two beat running (trotting) at intermediate speeds. Using input parameters based on measurements in dogs (Canis lupus familiaris), the model predicts the correct phase offset in walking and a realistic walk-trot transition speed. It also spontaneously reproduces the double-hump ground reaction force profile observed in walking, and the smooth single-hump profile observed in trotting. Actuation appears elastic, despite the model's lack of springs, suggesting that spring-like locomotory behaviour emerges as an optimal tradeoff between work minimization and force-rate penalties.
人们普遍认为,四足动物会选择稳定的步态,以最小化其能量传输成本,但很难从经验上探索所有可能的足步序列。我们提出了一个简单的四足动物模型,它可以自发地产生四足动物可用的数千种平面足步序列中的任何一种。这个无弹性的平面模型由两个质点通过无质量的腿部与刚性躯干连接而成。它只需要质心位置、后肢和前肢的比例以及步长与速度的关系作为输入。通过对工作和力率成本的轨迹优化,以及大量随机初始猜测,我们为低速时对称的四拍步行和中速时两拍跑步(小跑)的全局最优性提供了证据。使用基于犬(Canis lupus familiaris)测量的输入参数,该模型预测了步行中的正确相位偏移和现实的走跑过渡速度。它还自发地再现了在步行中观察到的双驼峰地面反作用力曲线,以及在小跑中观察到的平滑单驼峰曲线。尽管该模型缺乏弹簧,但运动似乎是弹性的,这表明类似弹簧的运动行为是在最小化功和力率惩罚之间的最优权衡。