Virtual-Goal-Guided RRT for Visual Servoing of Mobile Robots With FOV Constraint
提出一种虚拟目标引导的快速探索随机树算法，在未知图像深度下规划满足视场和速度约束的轨迹，并通过图像特征跟踪实现非完整移动机器人的视觉伺服控制。
In this article, a virtual-goal-guided rapidly exploring random tree (RRT)-based visual servoing approach is proposed for nonholonomic mobile robots to simultaneously satisfy the field-of-view (FOV) constraint and the velocity constraints during the motion toward the desired pose. The presented approach contains two parts: 1) trajectory planning in the scaled Euclidean space and 2) trajectory tracking control. For the trajectory planning part, a new virtual-goal-guided RRT algorithm is designed to guarantee the FOV constraint and the velocity constraints by iteratively exploring the scaled Euclidean space in the presence of unknown image depth. Specifically, a virtual goal directly behind the desired pose is set to guide the tree to extend laterally into the area wherein the robot is easier to satisfy the FOV constraint. In addition, the lateral extension of the tree also helps decrease the lateral error of the robot as much as possible. Following each successful extension toward the virtual goal node, a greedy extension from the newly explored node to the desired pose is attempted using a polar stabilization controller, so that the planned trajectory can accurately arrive at the desired pose. Each newly explored edge in the scaled space is projected into the image space to check for the FOV limit. For the visual tracking part, the final searched trajectory in the scaled space is first transformed into image feature trajectories, which are then tracked by an image-based visual tracking controller. Experiments validate the effectiveness of the proposed approach.