Abstract
To solve the visual servoing tasks in complex environment, a path planning method based on improved rapidly exploring random trees algorithm is proposed. First, the improved rapidly exploring random trees planning method is adopted, which keeps the observed feature points in the field of view. The start node and the desired node are initialized as roots of multi-trees which grow harmoniously to plan path of the robot. Then, the planned path is used to project the three-dimensional target feature points into the image space and obtain the feature trajectory for the image-based visual servoing controller. Finally, the feature trajectory is tracked by the image-based visual servoing controller. The proposed visual servoing design method takes field of view constraints, camera retreat problem, and obstacle avoidance into consideration, which can significantly improve the ability of the robotic manipulator, especially in the narrow space. Simulation and experiment on 6-degree-of-freedom robot are conducted. The results present the effectiveness of the proposed algorithm.
Highlights
The visual servoing control system refers to a control system using visual feedback
The spatio-temporal context (STC) learning algorithm is introduced into a visual tracking issue.[1]
According to the definition of system error, the current visual servoing can be divided into position-based visual servoing (PBVS), image-based visual servoing (IBVS), and hybrid visual servoing
Summary
The visual servoing control system refers to a control system using visual feedback. The control goal is to adjust the task function e(sà À s) to a minimum, where s and sà are the current state and expected state of the system, respectively. Unlike a conventional control system, s is based on the image information observed from the camera fixed to the robot’s work space (eye-tohand) or mounted on the robot’s end-effector (eye-inhand) in visual servoing system. This visual servoing system has higher dimension and greater information than using a traditional sensor, it can improve the flexibility of the robot system. The essential problem of the position-based visual servoing control[2] is the use of image features to estimate the current pose of the target.
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