2D Visual Servoing meets Rapidly-exploring Random Trees for collision avoidance

Visual Servoing is a well-known subject in robotics. However, there are still some challenges on the visual control of robots for applications in human environments. In this article, we propose a method for path planning and correction of kinematic errors using visual servoing. 3D information provided by external cameras will be used for segmenting the environment and detecting the obstacles in the scene. Rapidly-exploring Random Trees are then used to calculate a path through the obstacles to a given, previously calculated, end-effector goal pose. This allows for model-free path planning for cluttered environments by using a point cloud representation of the environment. The proposed path is then followed by the robot in open-loop. Error correction is performed near the goal pose by using real-time calculated image features as control points for an Image-Based Visual Servoing controller that drives the end-effector towards the desired goal pose. With this method, we intend to achieve the navigation of a robotic arm through a cluttered environment towards a goal pose with error correction performed at the end of the trajectory to mitigate both the weaknesses of Image Based Visual Servoing and of open-loop trajectory following. We made several experiments in order to validate our approach by evaluating each individual main component (environment segmentation, trajectory calculation and error correction through visual servoing) of our solution. Furthermore, our solution was implemented in ROS using the Baxter Research Robot.