Trajectory planning for a 6-axis robotic arm with particle swarm optimization algorithm

Ekrem, Özge; Aksoy, Bekir

doi:10.1016/j.engappai.2023.106099

Cited by 36 publications

(14 citation statements)

References 18 publications

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“…The collection point is taken as the starting point of the arm movement path, and at the same time, it is necessary to limit The target apple position on the tree is taken as the end point of the arm motion path, and the apple-picking direction calculated is used as the speed and direction of the robot arm at the end point. To avoid the end-effector of the robotic arm colliding with other apples, a transition point needs to be set to adjust the arm movement path (Ekrem and AKsoy, 2023). Therefore, the arm movement path is determined by five conditions: the coordinate P 0 , the starting point direction V 0 , the transition point coordinate P m , the end point coordinate P 1 and the end point direction V 1 .…”

Section: Control Strategy Of a Robotic Arm For An Applepicking Robotmentioning

confidence: 99%

Research on system integration and control methods of an apple-picking robot in unstructured environment

Zhou,

Gao,

et al. 2024

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Purpose The purpose of this article is to solve the issue that apple-picking robots are easily interfered by branches or other apples near the target apple in an unstructured environment, leading to grasping failure and apple damage. Design/methodology/approach This study introduces the system units of the apple-picking robot prototype, proposes a method to determine the apple-picking direction via 3D point cloud data and optimizes the path planning method according to the calculated picking direction. Findings After the field experiments, the average deviation of the calculated picking direction from the desired angle was 11.81°, the apple picking success rate was 82% and the picking cycle was 11.1 s. Originality/value This paper describes a picking control method for an apple-picking robot that can improve the success and reliability of picking in an unstructured environment and provides a basis for automated and mechanized picking in the future.

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Section: Control Strategy Of a Robotic Arm For An Applepicking Robotmentioning

confidence: 99%

Research on system integration and control methods of an apple-picking robot in unstructured environment

Zhou,

Gao,

et al. 2024

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“…Another explored area is the optimization of the trajectory itself. Enhanced versions of the PSO algorithm have inspired numerous research proposals [66][67][68][69][70]. One of the first modifications involved the utilization of the normalized step cost (NSC) for particle initialization.…”

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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“…Temporal optimal trajectory planning aims to minimize the running time of the robot and plan the temporal optimal trajectory according to the motion rules and specified constraints [3]. Ekrem class [4] uses quintic polynomials combined with particle swarm optimization (PSO) algorithm for motion planning of manipulator, so that each joint can operate smoothly, but the convergence r ate of the traditional particle swarm algorithm is slow. Yin Fengjian [5] uses the adaptive genetic algorithm to plan the robot time, but the convergence is slow and tends to fall into local optimum.…”

Section: Introductionmentioning

confidence: 99%

Trajectory optimization of manipulator based on particle swarm optimization with mutation strategy

Yi,

Yan,

Qiang

et al. 2024

Preprint

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In order to solve the problems of slow convergence speed and low convergence accuracy of adaptive particle swarm algorithm, a particle swarm optimization algorithm based on the mutation strategy in the differential evolution algorithm was proposed.Based on the adaptive inertia weight update formula with the improved power finger function and the particle swarm optimization with dynamic learning factor, the variation link in the differential evolution algorithm is introduced to enhance the ability of particles to explore the solution space and improve the convergence speed and accuracy of the algorithm.Taking the medium-sized manipulator PUMA560 as the research object, the posture of each joint under the constraint of key points was determined by the robot inverse kinematics, and the spatial pose curve fitting of the six joints of the manipulator was carried out by using the five-order polynomial.Under the constraint of the maximum angular velocity of rad/s, the shortest motion time of the manipulator is the goal to be optimized, and the mutation strategy is introduced in the iteration, and the particle population under the current iteration is used as the parent to mutate and generate a new population, so as to improve the diversity of the population.Simulation experiments on the MATLAB platform show that the convergence speed and convergence accuracy of the particle swarm optimization algorithm with the mutation strategy for the adaptive particle swarm optimization algorithm, and the pose curves of each joint change are smooth and continuous.The final optimization time was reduced by 50.01% compared to the initial planning time.

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Trajectory planning for a 6-axis robotic arm with particle swarm optimization algorithm

Cited by 36 publications

References 18 publications

Research on system integration and control methods of an apple-picking robot in unstructured environment

Research on system integration and control methods of an apple-picking robot in unstructured environment

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

Trajectory optimization of manipulator based on particle swarm optimization with mutation strategy

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