Collision Avoidance for Redundant Robots in Position-Based Visual Servoing

To tackle the problem on trajectory planning or the design of control law, this paper introduces a visual servoing system for a manipulator with redundant joints that the trajectory of the manipulator approaching the target is determined spontaneously by the visual control law. The proposed method resolves joint solution for visual servoing and obstacle avoidance. The work comprises of two procedures, feature extraction for position-based visual servoing (PBVS) and collision avoidance within the working envelope. In the PBVS control, the target pose must be reconstructed with respect to the robot and this results in a Cartesian motion-planning problem. Once the geometric relationship between the target and the end effector is determined, a secure inverse kinematics method incorporating trajectory planning is used to solve the solution of the redundant manipulator by the virtual repulsive torque method. Therefore, the links of the manipulator can always maintain a safe distance from obstacles while approaching the target smoothly. The proposed method is verified with its applicability in experiments using an eye-in-hand manipulator with seven joints. For reusability and extensibility, the system has been coded and constructed in the framework of the Robot Operating System so as that the developed algorithms can be disseminated to different platforms.

[1]  Kao-Shing Hwang,et al.  An Adaptive Strategy Selection Method With Reinforcement Learning for Robotic Soccer Games , 2018, IEEE Access.

[2]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

[3]  H. Jin Kim,et al.  Vision-Guided Aerial Manipulation Using a Multirotor With a Robotic Arm , 2016, IEEE/ASME Transactions on Mechatronics.

[4]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[5]  Kao-Shing Hwang,et al.  Decoupled Visual Servoing With Fuzzy Q-Learning , 2018, IEEE Transactions on Industrial Informatics.

[6]  Ying Wang,et al.  A Hybrid Visual Servo Controller for Robust Grasping by Wheeled Mobile Robots , 2010, IEEE/ASME Transactions on Mechatronics.

[7]  Tao Liu,et al.  Eye-in-Hand Tracking Control of a Free-Floating Space Manipulator , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Chi-Yi Tsai,et al.  A Hybrid Switched Reactive-Based Visual Servo Control of 5-DOF Robot Manipulators for Pick-and-Place Tasks , 2015, IEEE Systems Journal.

[9]  E. Malis,et al.  Deeper understanding of the homography decomposition for vision-based control , 2007 .

[10]  Danica Kragic,et al.  Survey on Visual Servoing for Manipulation , 2002 .

[11]  François Chaumette,et al.  Potential problems of stability and convergence in image-based and position-based visual servoing , 1997 .

[12]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[13]  Kao-Shing Hwang,et al.  A Virtual Torque-Based Approach to Kinematic Control of Redundant Manipulators , 2017, IEEE Transactions on Industrial Electronics.

[14]  Hemanta Kumar Bhuyan,et al.  Privacy preserving sub-feature selection in distributed data mining , 2015, Appl. Soft Comput..

[15]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[16]  Hemanta Kumar Bhuyan,et al.  Privacy preserving sub-feature selection based on fuzzy probabilities , 2014, Cluster Computing.

[17]  Kao-Shing Hwang,et al.  An adaptive decision-making method with fuzzy Bayesian reinforcement learning for robot soccer , 2018, Inf. Sci..

[18]  Francois Chaumette,et al.  Potential problems of unstability and divergence in image-based and position-based visual servoing , 1999, 1999 European Control Conference (ECC).