论文标题
单杆立足点选择和四足球的约束评估
Single-shot Foothold Selection and Constraint Evaluation for Quadruped Locomotion
论文作者
论文摘要
在本文中，我们提出了一种选择用于腿部系统的最佳立足点的方法。提出的方法的目的是在当地高程图上找到最佳的摇摆腿立足点。我们应用卷积神经网络来了解局部高程图与潜在立足的质量之间的关系。提出的网络评估了高程图上每个单元的几何特性，检查运动学约束和碰撞。在执行时间内，控制器从神经模型中获得了每个潜在立足点的定性测量。此方法允许评估数百个潜在的立足点，并在单个步骤中检查多个约束，该步骤在没有GPGPU的标准计算机上需要10〜MS。这些实验是在模拟和真正的机器人平台上的崎terrain地形上行走的四倍的机器人上进行的。
In this paper, we propose a method for selecting the optimal footholds for legged systems. The goal of the proposed method is to find the best foothold for the swing leg on a local elevation map. We apply the Convolutional Neural Network to learn the relationship between the local elevation map and the quality of potential footholds. The proposed network evaluates the geometrical characteristics of each cell on the elevation map, checks kinematic constraints and collisions. During execution time, the controller obtains the qualitative measurement of each potential foothold from the neural model. This method allows to evaluate hundreds of potential footholds and check multiple constraints in a single step which takes 10~ms on a standard computer without GPGPU. The experiments were carried out on a quadruped robot walking over rough terrain in both simulation and real robotic platforms.
