Joint Trajectory Planning Based on Minimum Euclidean Distance of Joint Angles of a Seven-Degrees-of-Freedom Manipulator for a Sequential Reaching Task

Yoshiaki Taniai; Tomohide Naniwa

doi:10.20965/jaciii.2019.p0997

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JACIII Vol.23 No.6 pp. 997-1003

doi: 10.20965/jaciii.2019.p0997

(2019)

Paper:

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Joint Trajectory Planning Based on Minimum Euclidean Distance of Joint Angles of a Seven-Degrees-of-Freedom Manipulator for a Sequential Reaching Task

Yoshiaki Taniai and Tomohide Naniwa

Graduate School of Engineering, University of Fukui
3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan

Received:

October 30, 2017

Accepted:

June 25, 2019

Published:

November 20, 2019

Keywords:

optimal planning, sequential reaching task, 7-DOF manipulator, minimum Euclidean distance, movable ranges

Abstract

When a nuclear power disaster occurs at a nuclear power plant, it is hazardous for humans to enter the plant. If robots could remove radioactive substances adhering to a plane such as a plant wall, humans would be able to enter the plant to investigate the situation and to work. In this study, to efficiently remove radioactive substances from a wall with a manipulator, we examined joint trajectory planning based on the minimum Euclidean distance of joint angles of a seven-degrees-of-freedom (7-DOF) serial link manipulator for a sequential reaching task on a plane. We demonstrate the planning for the sequential reaching task, which is an iterative point-to-point reaching movement between positions on a plane. The joint angles for each target position were obtained based on the inverse kinematics for an arm angle, and the optimal arm angles within the constraints of the joint angles were computed by the sequential quadratic programming method. The optimal trajectories for the arm angles were compared with the trajectories of the joint angles that were the eight inverse kinematic solutions for a fixed arm angle. The result showed that through optimal planning, an efficient trajectory within the movable ranges of the joint angles could be obtained for the sequential reaching task.

Cite this article as:

Y. Taniai and T. Naniwa, “Joint Trajectory Planning Based on Minimum Euclidean Distance of Joint Angles of a Seven-Degrees-of-Freedom Manipulator for a Sequential Reaching Task,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.6, pp. 997-1003, 2019.

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References

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[6] M. Shimizu, H. Kakuya, W.-K. Yoon, K. Kitagaki, and K. Kosuge, “Analytical Inverse Kinematic Computation for 7-DOF Redundant Manipulators With Joint Limits and Its Application to Redundancy Resolution,” IEEE Trans. on Robotics, Vol.24, No.5, pp. 1131-1142, 2008.
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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] S. Kawatsuma, “Robots for Nuclear Emergency in Germany and France,” J. of the Robotics Society of Japan, Vol.32, No.1, pp. 25-31, 2014 (in Japanese).

[2] [2] L. Morikawa, T. Hashimoto, N. Shimonabe, M. Yano, H. Onitsuka, and J. Fujita, “Development of the Remote Decontamination Robot “MHI-MEISTeR II” for an Upper Floor of Reactor Building in Fukushima Daiichi NPP,” E-J. of Advanced Maintenance, Vol.9, No.2, pp. 126-131, 2017.

[3] [3] E. J. Minehara, “Laser decontamination device,” US Patent No.US9174304B2, 2015.

[4] [4] K. Kreutz-Delgado, M. Long, and H. Seraji, “Kinematic Analysis of 7-DOF Manipulators,” Int. J. of Robotics Research, Vol.11, No.5, pp. 469-481, 1992.

[5] [5] Y. Zhang, B. Cai, L. Zhang, and K. Li, “Bi-criteria Velocity Minimization of Robot Manipulators Using a Linear Variational Inequalities-Based Primal-Dual Neural Network and PUMA560 Example,” Advanced Robotics, Vol.22, No.13-14, pp. 1479-1496, 2008.

[6] [6] M. Shimizu, H. Kakuya, W.-K. Yoon, K. Kitagaki, and K. Kosuge, “Analytical Inverse Kinematic Computation for 7-DOF Redundant Manipulators With Joint Limits and Its Application to Redundancy Resolution,” IEEE Trans. on Robotics, Vol.24, No.5, pp. 1131-1142, 2008.

[7] [7] H. Asada and J.-J. E. Slotine, “Robot Analysis and Control,” John Wiley & Sons, 1986.

[8] [8] P. E. Gill, W. Murray, and M. A. Saunders, “SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization,” SIAM Review, Vol.47, No.1, pp. 99-131, 2005.