论文标题
在非结构化动态环境中避免碰撞的模型预测性轮廓控制
Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments
论文作者
论文摘要
本文介绍了一种在具有静态和移动障碍（例如人类）的非结构化环境中进行本地运动计划的方法。在给定参考路径和速度的情况下，我们基于优化的后水解方法计算了一种局部轨迹，该轨迹可以最大程度地减少跟踪误差，同时避免障碍物。我们以非线性模型预测性轮廓控制（MPCC）为基础，并将其扩展到通过计算，在线计算，在自由空间中的一组凸区域来合并静态图。我们将移动障碍物作为椭圆形建模，并提供正确的结合，以近似碰撞区域，由椭圆形和圆的Minkowsky总和给出。我们的框架对机器人模型不可知。我们通过在室内环境中导航的移动机器人介绍了实验结果。我们的方法无需外部支持就可以在船上完全执行，并且可以应用于其他机器人形态，例如自动驾驶汽车。
This paper presents a method for local motion planning in unstructured environments with static and moving obstacles, such as humans. Given a reference path and speed, our optimization-based receding-horizon approach computes a local trajectory that minimizes the tracking error while avoiding obstacles. We build on nonlinear model-predictive contouring control (MPCC) and extend it to incorporate a static map by computing, online, a set of convex regions in free space. We model moving obstacles as ellipsoids and provide a correct bound to approximate the collision region, given by the Minkowsky sum of an ellipse and a circle. Our framework is agnostic to the robot model. We present experimental results with a mobile robot navigating in indoor environments populated with humans. Our method is executed fully onboard without the need of external support and can be applied to other robot morphologies such as autonomous cars.
