Research on Trajectory Planning of Upper Limb Rehabilitation Robot Based on Improved Particle Swarm Optimization Algorithm
( Pp. 102-113)
More about authors
Yu Daquan
postgraduate student, Institute of Mathematics and Computer Technologies, .
Far Eastern Federal University
Vladivostok, Russian Federation Pustovalov Evgenii V. Dr. Sci. (Phys.-Math.), Associate Professor; Professor, Institute of Mathematics and Computer Technologies, Far Eastern Federal University; Vladivostok, Russian Federation
Far Eastern Federal University
Vladivostok, Russian Federation Pustovalov Evgenii V. Dr. Sci. (Phys.-Math.), Associate Professor; Professor, Institute of Mathematics and Computer Technologies, Far Eastern Federal University; Vladivostok, Russian Federation
Abstract:
There is a significant increase in the number of patients with upper limb dysfunction caused by the social aging of the population. Using a rehabilitation robot for training is sure to become a new trend. In this paper, the object of research is the movement speed of 4-degree-of-freedom upper limb rehabilitation robot. This paper presented a segmentation trajectory planning method for upper limb rehabilitation robots based on quintic polynomials. The algorithm of particle swarm optimization, which solved the problem of how to optimize the motion trajectory of the upper limb rehabilitation robot to ensure the security of rehabilitation training, is presented and successfully verified. In order to give the best training effect to patients, doctors can adjust the working speed of the rehabilitation robot according to the rehabilitation status of the patients’ arm to meet the needs of patients for rehabilitation training in different periods of rehabilitation. The simulation results from Matlab software confirm the feasibility of this method.
How to Cite:
Daquan Yu and Pustovalov E.V. Research on trajectory planning of upper limb rehabilitation robot based on improved Particle Swarm Optimization algorithm. Computational Nanotechnology. 13, 1 (2026), 102–113. DOI: 10.33693/2313-223X-2026-13-1-102-113. EDN: MFOXKL
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Kim B., Ahn K., Nam S. et al. Upper extremity exoskeleton system to generate customized therapy motions for stroke survivors. Robotics and Autonomous Systems. 2022. Vol. 154. Art. 104128. DOI: 10.1016/j.robot.2022.104128.
Kong M., Ji C., Chen Z. et al. Smooth and near time-optimal trajectory planning of robotic manipulator with smooth constraint based on cubic B-spline. In: IEEE International Conference on Robotics and Biomimetics (ROBIO). 2013. Pp. 2334–2339. DOI: 10.1109/ROBIO.2013.6739818.
Li G., Fang Q., Xu T. et al. Inverse kinematic analysis and trajectory planning of a modular upper limb rehabilitation exoskeleton. Technology and Health Care. 2019. Vol. 27. Pp. 123–132. DOI: 10.3233/THC-199012.
Nguiadem C., Raison M., Achiche S. Motion planning of upper-limb exoskeleton robots: A review. Applied Sciences. 2020. Vol. 10. No. 21. Art. 7626. DOI: 10.3390/app10217626.
Piotrowski A.P., Napiorkowski J.J., Piotrowska A.E. Particle swarm optimization or differential evolution-a comparison. Engineering Applications of Artificial Intelligence. 2023. Vol. 121. Art. 106008. DOI: 10.1016/j.engappai.2023.106008.
Porawagama C.D., Munasinghe S.R. Reduced jerk joint space trajectory planning method using 5-3-5 spline for robot manipulators. In: International Conference on Information and Automation for Sustainability (ICIAfS). Colombo: IEEE, 2015. Pp. 1–6.
Rodgers H., Bosomworth H., Krebs H.I. et al. Robot assisted training for the upper limb after stroke (RATULS): A multicentre randomised controlled trial. The Lancet. 2019. Vol. 394. No. 10192. Pp. 51–62. DOI: 10.1016/S0140-6736(19)31055-4.
Rout A., Deepak B.B.V.L., Biswal B.B. Optimal time-jerk trajectory planning of 6 axis welding robot using TLBO method. Procedia Computer Science. 2018. Vol. 133. Pp. 537–544. DOI: 10.1016/j.procs.2018.07.067.
Stinear C.M., Lang C.E., Zeiler S. et al. Advances and challenges in stroke rehabilitation. Lancet Neurology. 2020. Vol. 19. No. 4. Pp. 348–360. DOI: 10.1016/S1474-4422(19)30415-6.
Wang F., Zhang H., Zhou A. A particle swarm optimization algorithm for mixed-variable optimization problems. Swarm and Evolutionary Computation. 2021. Vol. 60. Art. 100808. DOI: 10.1016/j.swevo.2020.100808.
Xu L., Cao M., Song B. A new approach to smooth path planning of mobile robot based on quartic bezier transition curve and improved PSO algorithm. Neurocomputing. 2022. Vol. 473. Pp. 98–106.
Yang M., Li C. Path planning and tracking for multirobot system based on improved PSO algorithm. In: IEEE International Conference on Mechatronics and Automation (ICMA). 2011. Pp. 1671–1674. DOI: 10.1109/ICMA.2011.6025799.
Keywords:
rehabilitation robot, trajectory planning, quintic polynomials, Matlab, Particle Swarm Optimization algorithm.
