Abstract
The number of a redundant manipulator’s DOF is greater than the number of the workspace’s dimensions, creating difficulty in solving its inverse kinematics. In the workspace, each pose corresponds to infinite number of an-gles of the arm. As a result this means that the traditional inverse kinematics so-lution method is computationally large and complex. With the aim of trajectory planning, an inverse kinematics algorithm based on the genetic algorithm has been proposed in this paper to generate a smooth trajectory. The initial pose of the arm was taken as the initial condition, then the constraint function, a reduction in the search interval and a smooth reward function was used, and finally the genetic algorithm was implemented. It was found that this method greatly reduced the solution of the large angular momentum of the arm and tended to choose a smoother solution. Through the use of the constraint function and the smooth reward function as the optimization index, the inverse kinematic solution corresponding to the preset trajectory was able to be effectively solved.
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References
Menasri R, Nakib A, Daachi B, et al. A trajectory planning of redundant manipulators based on bilevel optimization [J]. Applied Mathematics & Computation, 2015, 250:934-947.
Shukla A, Singla E, Wahi P, et al. A direct variational method for planning monotonically optimal paths for redundant manipulators in constrained workspaces [J]. Robotics & Au-tonomous Systems, 2013, 61(2):209-220.
Daniel R R, J. A P G, Wenger P. Trajectory planning of kinematically redundant parallel manipulators by using multiple working modes [J]. Mechanism & Machine Theory, 2016, 98:216-230.
Gregoryabb J. Energy-optimal trajectory planning for robot manipulators with holonomic constraints [J]. Systems & Control Letters, 2012, 61(2):279-291.
Gómez-Bravo F, Carbone G, Fortes J C. Collision free trajectory planning for hybrid ma-nipulators [J]. Mechatronics, 2012, 22(6):836-851.
Zhang L, Jia Q, Chen G, et al. Pre-impact trajectory planning for minimizing base attitude disturbance in space manipulator systems for a capture task [J]. Chinese Journal of Aero-nautics, 2015, 28(4):1199-1208.
Liu H, Lai X, Wu W. Time-optimal and jerk-continuous trajectory planning for robot ma-nipulators with kinematic constraints [J]. Robotics & Computer Integrated Manufacturing, 2013, 29(2):309-317.
Huang J, Hu P, Wu K, et al. Optimal time-jerk trajectory planning for industrial robots [J]. Mechanism & Machine Theory, 2018, 121:530-544.
Boscariol P, Gasparetto A. Model-based trajectory planning for flexible-link mechanisms with bounded jerk [J]. Robotics & Computer Integrated Manufacturing, 2013, 29(4):90-99.
Wu J, Wu H, Song Y, et al. Genetic algorithm trajectory plan optimization for EAMA: EAST Articulated Maintenance Arm [J]. Fusion Engineering & Design, 2016, 109-11
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Dong, H., Wu, W., Yao, L., Sun, H. (2019). A novel algorithm combined with single mapping workspace and Genetic Algorithm for solving inverse kinematic problem of redundant manipulators. In: Uhl, T. (eds) Advances in Mechanism and Machine Science. IFToMM WC 2019. Mechanisms and Machine Science, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-030-20131-9_30
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DOI: https://doi.org/10.1007/978-3-030-20131-9_30
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Online ISBN: 978-3-030-20131-9
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