A Segmented Geometry Method for Kinematics and Configuration Planning of Spatial Hyper-Redundant Manipulators

With many degrees of freedom (DOFs), a hyper-redundant manipulator has superior dexterity and flexible manipulation ability. However, its inverse kinematics and configuration planning are very challenging. With the increase in the number of DOFs, the corresponding computation load or training set will be much larger for traditional methods (such as the generalized inverse method and the artificial neural network method). In this paper, a segmented geometry method is proposed for a spatial hyper-redundant manipulator to solve the above problems. Similar to the human arm, the hyper-redundant manipulator is segmented into three sections from geometry, i.e., shoulder, elbow, and wrist. Then, its kinematics can be solved separately according to the segmentation, which reduces the complexity of the solution and simplifies the computation of the inverse kinematics. Furthermore, the configuration is parameterized by several parameters, i.e., the arm-angle, space arc parameters, and desired direction vector. The shoulder has proximal four DOFs, which is redundant for positioning the elbow and avoiding the joint limit. The arm-angle parameter is defined to solve the redundancy. The wrist consists of the distal two DOFs, and its joints are determined to match the desired direction vector of the end-effector. All the other joints (except for the joints belonging to shoulder and wrist) compose the elbow. These joint angles are solved by using space arc-based method. The configuration planning for avoiding joint limit, obstacles, and inspecting narrow pipeline are detailed for practical applications. Finally, circular trajectory tracking and pipeline inspection are, respectively, simulated and experimented on a 20-DOFs hyper-redundant manipulator. The results show that the proposed method can give solutions of the three-dimensional-pose-determining problem and the configuration-planning problem. The computation of the inverse kinematics is simplified for real-time control. It can also be applied to other spatial hyper-redundant manipulators with similar serial configurations.

[1]  Farbod Fahimi,et al.  Obstacle Avoidance for Spatial Hyper-Redundant Manipulators Using Harmonic Potential Functions and the Mode Shape Technique , 2003, J. Field Robotics.

[2]  Kazuo Tanaka,et al.  Range-Sensor-Based Semiautonomous Whole-Body Collision Avoidance of a Snake Robot , 2015, IEEE Transactions on Control Systems Technology.

[3]  Anthony A. Maciejewski,et al.  Kinematic Design of Manipulators with Seven Revolute Joints Optimized for Fault Tolerance , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[4]  C. Torras,et al.  Closed-Loop Inverse Kinematics for Redundant Robots: Comparative Assessment and Two Enhancements , 2015, IEEE/ASME Transactions on Mechatronics.

[5]  Raul Ordonez,et al.  Real-time Inverse Kinematics of (2n + 1) DOF hyper-redundant manipulator arm via a combined numerical and analytical approach , 2015 .

[6]  Mahmoud Moghavvemi,et al.  Geometrical approach of planar hyper-redundant manipulators: Inverse kinematics, path planning and workspace , 2011, Simul. Model. Pract. Theory.

[7]  Min Wang,et al.  Dynamic Learning From Adaptive Neural Control of Robot Manipulators With Prescribed Performance , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[8]  Gregory S. Chirikjian,et al.  A modal approach to hyper-redundant manipulator kinematics , 1994, IEEE Trans. Robotics Autom..

[9]  Howie Choset,et al.  Continuum Robots for Medical Applications: A Survey , 2015, IEEE Transactions on Robotics.

[10]  Wenfu Xu,et al.  Singularity Analysis and Avoidance for Robot Manipulators With Nonspherical Wrists , 2016, IEEE Transactions on Industrial Electronics.

[11]  Sunil Kumar Agrawal,et al.  Hyper-redundant planar manipulators: motion planning with discrete modal summation procedure , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[12]  Shugen Ma,et al.  Control of a multijoint manipulator "Moray arm" , 2002 .

[13]  Gregory S. Chirikjian,et al.  A Geometric Approach to Hyper-Redundant Manipulator Obstacle Avoidance , 1992 .

[14]  Gregory S. Chirikjian,et al.  A general numerical method for hyper-redundant manipulator inverse kinematics , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[15]  Renquan Lu,et al.  Trajectory-Tracking Control of Mobile Robot Systems Incorporating Neural-Dynamic Optimized Model Predictive Approach , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[16]  S. S. Yang,et al.  A new inverse kinematics method for three dimensional redundant manipulators , 2009, 2009 ICCAS-SICE.

[17]  Shuai Li,et al.  Kinematic Control of Redundant Manipulators Using Neural Networks , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Yoshiaki Ohkami,et al.  Capability evaluation of reconfigurable brachiating space robot , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[19]  Matteo Cianchetti,et al.  Soft robotics: Technologies and systems pushing the boundaries of robot abilities , 2016, Science Robotics.

[20]  Robert Bogue,et al.  Industrial Robot : An International Journal Snake robots : A review of research , products and applications , 2014 .

[21]  Changyin Sun,et al.  Adaptive Neural Network Control of Biped Robots , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[22]  S. Ma,et al.  An obstacle avoidance scheme for hyper-redundant manipulators-global motion planning in posture space , 1997, Proceedings of International Conference on Robotics and Automation.