Time optimal trajectory planning of five degrees of freedom manipulator based on PSO algorithm

Jiang, Zhengshuai; Zhang, Qizhi

doi:10.1109/icmsp55950.2022.9858972

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…Time-optimal trajectory planning is one of the most extensively explored applications of the PSO algorithm in the context of solving motion planning problems for robotic arms. Several research publications aimed to enhance the PSO algorithm through various modifications [59][60][61][62][63]. One of the initial approaches introduced a modification where the entire trajectory was divided into segments, and optimization was performed at connection points called knot points.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review

Juříček,

Parák,

Kůdela

2023

Computation

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The significance of robot manipulators in engineering applications and scientific research has increased substantially in recent years. The utilization of robot manipulators to save labor and increase production accuracy is becoming a common practice in industry. Evolutionary computation (EC) techniques are optimization methods that have found their use in diverse engineering fields. This state-of-the-art review focuses on recent developments and progress in their applications for industrial robotics, especially for path planning problems that need to satisfy various constraints that are implied by both the geometry of the robot and its surroundings. We discuss the most-used EC method and the modifications that suit this particular purpose, as well as the different simulation environments that are used for their development. Lastly, we outline the possible research gaps and the expected directions future research in this area will entail.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review

Juříček,

Parák,

Kůdela

2023

Computation

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“…The approach also reuses the optimized parameters, resulting in improved PSO convergence in motion planning. The polynomial interpolation algorithm and the PSO are presented to achieve a shorter running time and better stability of the industrial robot (Jiang and Zhang, 2022; Du and Chen, 2022). A similar type of work using few modifications and improvements in PSO for trajectory optimization with a minimized time of motion is presented by Li and Li (2022) and Yu et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Optimal trajectory planning of the industrial robot using hybrid S-curve-PSO approach

Patle

Chen

Singh

et al. 2023

RIA

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Purpose The paper aims to develop an efficient and compact hybrid S-curve-PSO (particle swarm optimization) controller for the optimal trajectory planning of industrial robots in the presence of obstacles, especially those used in pick-and-place operations. Design/methodology/approach The proposed methodology comprises a monotonic trajectory through bounded entropy of speed, velocity, acceleration and jerk. Thus, the robot’s trajectory planning corresponds with S-curve-PSO duality. This is achieved by dual navigation with minimal computational complexity. The matrix algebra-based computational complexity transforms the trajectory from random to compact. The linear programming problem represents the proposed robot in Euclidean space, and its optimal solution sets the corresponding optimal trajectory. Findings The proposed work ensures the efficient trajectory planning of the industrial robot in the presence of obstacles with optimized path length and time. The real-time and simulation analysis of the robot is presented for performance measurement, and their outcomes demonstrate a good correlation. Compared with the existing controller, it gives a noteworthy improvement in performance. Originality/value The novel S-curve-PSO hybrid approach is presented here, along with the LIDAR sensors, which generate the environment map and detect obstacles for autonomous trajectory planning. Based on the sensory information, the proposed approach generates the optimal trajectory by avoiding obstacles and minimizing the travel time, jerk, velocity and acceleration. The hybrid S-curve-PSO approach for optimal trajectory planning of the industrial robot in the presence of obstacles has not been presented by any researchers. This method considers the robot’s kinematics as well as its dynamics. The implementation of the PSO makes it computationally superior and faster. The selection of best-fit parameters by PSO assures the optimized trajectory in the presence of obstacles and uncertainty.

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Full-Dimensional Collision Avoidance of Autonomous Vehicles Using Sequence Convex Programming

Wang,

Sun,

Dang

et al. 2023

2023 42nd Chinese Control Conference (CCC)

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Time optimal trajectory planning of five degrees of freedom manipulator based on PSO algorithm

Cited by 3 publications

References 1 publication

Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review

Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review

Optimal trajectory planning of the industrial robot using hybrid S-curve-PSO approach

Full-Dimensional Collision Avoidance of Autonomous Vehicles Using Sequence Convex Programming

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