A VELOCITY SELF-LEARNING ALGORITHM FOR TIME-OPTIMAL TRAJECTORY PLANNING ALONG FULLY SPECIFIED PATH – Part II
DOI:
https://doi.org/10.59277/RRST-EE.2025.2.15Keywords:
Industrial handling robot, Velocity self-learning, Hermite interpolation, Correction trajectory, Actual joint torqueAbstract
In response to the problem of uncertainty in the system dynamics model during time-optimal trajectory planning for industrial handling robots, a novel online, self-learning, model-free time-optimal trajectory planning method is proposed. First, offline kinematic constraints and the Hermite interpolation algorithm are used to obtain the optimal spline velocity curve under kinematic constraints. Then, online trajectory data of the robot's operation is collected, and the trajectory generation method using a self-learning strategy is employed to iteratively refine the trajectory iteratively, resulting in a time-optimal trajectory under actual dynamic constraints. Finally, taking the UR5e cooperative robot and the ABB IRB9670-235 industrial robot as the experimental platform, experiments verify the effectiveness and efficiency of the proposed method.
References
(1) H. Pham, Q.C. Pham, A new approach to time–optimal path parameterization based on reachability analysis, IEEE Transactions on Robotics, 34, 3, pp. 645–659 (2018).
(2) E. Barnett, C. Gosselin, A bisection algorithm for time-optimal trajectory planning along fully specified paths, IEEE Transactions on Robotics, 37, 1, pp. 131–145 (2020).
(3) K. Shin, N. McKay, Minimum-time control of robotic manipulators with geometric path constraints, IEEE Transactions on Automatic Control, 30, 6, pp. 531–541 (1985).
(4) J.A. Rojas-Quintero, F. Dubois, H.C. Ramírez-De-Ávila, Riemannian formulation of Pontryagin’s maximum principle for the optimal control of robotic manipulators, Mathematics, 10, 7, pp. 1117–1128 (2022).
(5) K. Shin, N. McKay, A dynamic programming approach to trajectory planning of robotic manipulators, IEEE Transactions on Automatic Control, 31, 6, pp. 491–500 (1986).
(6) S. Singh, M. Leu, Optimal trajectory generation for robotic manipulators using dynamic programming, J. Dyn. Syst. Meas. Control, 109, 2, pp. 88–96 (1987).
(7) Q.C. Pham, A general, fast, and robust implementation of the time-optimal path parameterization algorithm, IEEE Transactions on Robotics, 30, 6, pp. 1533–1540 (2014).
(8) D. Verscheure, B. Demeulenaere, J. Swevers, et al, Time-optimal path tracking for robots: A convex optimization approach, IEEE Transactions on Automatic Control, 54, 10, pp. 2318–2327 (2009).
(9) Z. Kingston, M. Moll, L.E. Kavraki, Sampling-based methods for motion planning with constraints, Annual review of control, robotics, and autonomous systems, 1, 1, pp. 159–185 (2018).
(10) F. Debrouwere, W.V. Loock, G. Pipeleers, et al, Time-optimal path following for robots with convex–concave constraints using sequential convex programming, IEEE Transactions on Robotics, 29, 6, pp. 1485–1495 (2013).
(11) A. Tharwat, M. Elhoseny, A.E. Hassanien, et al, Intelligent Bézier curve-based path planning model using chaotic particle swarm optimization algorithm, Cluster Computing, 22, 4, pp. 1–22 (2019).
(12) L. Zhang, et al, Time-optimal trajectory planning of serial manipulator based on adaptive cuckoo search algorithm, Journal of Mechanical Science and Technology, 35, 7, pp. 3171–3181 (2021).
(13) A. Steinhauser, J. Swevers, An efficient iterative learning approach to time-optimal path tracking for industrial robots, IEEE Transactions on Industrial Informatics, 14, 11, pp. 5200–5207 (2018).
(14) L. Yu, X. Shao, Y. Wei, K. Zhou, Intelligent land-vehicle model transfer trajectory planning method based on deep reinforcement learning, Sensors, 18, 9, 2905 (2018).
(15) X. Lei, Z. Zhang, P. Dong, Dynamic path planning of unknown environment based on deep reinforcement learning, Journal of Robotics (2018).
(16) S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, D. Quillen, Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection, The International Journal of Robotics Research, 37, 4-5, pp. 421–436 (2018).
(17) K. Chatzilygeroudis, R. Rama, R. Kaushik, D. Goepp, V. Vassiliades, J.B. Mouret, Black-box data-efficient policy search for robotics, In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 51–58 (2017).
(18) S.A. Khader, H. Yin, P. Falco, et al, Data-efficient model learning and prediction for contact-rich manipulation tasks, IEEE Robotics and Automation Letters, 5, 3, pp. 4321–4328 (2020).
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