Robust predictive control for mobile robots in uncertain environments

首先，提出了一种新的基于模型预测控制(MPC)的轨迹生成方法，以防止预测时间步长之间的时间间隔内的碰撞。将该方法推广到基于预测控制的多车编队控制中。对于基于MPC的碰撞避免问题，我们还提出了一种减少混合整数问题所产生的子问题的算法，从而减少了MPC在每个采样时刻的计算时间。此外，针对现有编队控制方法中存在的一个问题，即在车辆数量较多时难以确定每辆车的目标位置，提出了一种新的基于编队模式提取的编队方法。更准确地说，我们引入了表示队形的位置、方向和形状的抽象变量，并提出了一种自动控制队形的方法，使其在保持算子给定的队形宽度的同时沿目标路径移动。提出了蛇形机器人的路径跟踪控制方法，为所提出的队形控制方法奠定了基础。仿真和实验证明了这些方法的有效性。

First, a new trajectory generation method based on model predictive control (MPC) has been proposed to prevent the collisions in time intervals between prediction time steps. This method has been extended to the multi-vehicle formation control based on MPC. We have also proposed an algorithm to decrease sub-problems generated to solve mixed integer problems for MPC based collision avoidance, which allows us to decrease the computation time for MPC at each sampling time. Furthermore, we have focused on a problem of most existing formation control method i.e. difficulty of determining the target position of each vehicle when the number of the vehicles is large, and have proposed a new formation method based on the abstraction of formation pattern. More precisely, we have introduced the abstraction variables which represent the position, orientation and shape of the formation, and proposed a method to control the shape of the formation automatically so as to move along the target path while keeping the width of the formation given by operators. We have also proposed a path tracking control method for snake-like robot which gives the basis of the proposed formation control method. The effectiveness of these methods has been demonstrated by simulations and experiments.