Research on PAGV path planning based on artificial immune ant colony fusion algorithm

Liao, Jinquan

doi:10.3233/jifs-169635

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…The application of the improved GA to solving a variety of AGV dynamic path planning problems under an environment map proved the effectiveness of the improved algorithm. (24) Alajmi and Almeshal optimized the trajectory of mobile robots by using the quantum particle swarm optimization (QPSO) algorithm, which has a high performance, enhances the ergodic property of the particle space, and improves the search ability. (25) All the above algorithms demonstrated their applicability in the research field after improvement, and the performances of the algorithms themselves were also improved.…”

Section: Methodsmentioning

confidence: 99%

Automatic Guided Vehicle Global Path Planning Considering Multi-objective Optimization and Speed Control

Song¹

2021

Sensors and Materials

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Over the past few decades, as the main tool of intelligent material transportation, automatic guided vehicles (AGVs) have been widely used in modern production systems, logistics, transportation, industry, and commerce to further improve productivity, reduce labor costs, raise energy efficiency, and enhance safety. Path planning is a key issue in the field of AGVs to ensure that they do not collide with obstacles during movement and reach the destination as fast as possible to complete the assigned task. We propose two different and crucial operating environments in this paper. More specifically, in a static environment, a multi-objective mathematical model is established with the shortest path and the maximum smoothness, and the improved Levy random quantum particle swarm optimization (LRQPSO) algorithm is used to solve the proposed model and screen the AGV's driving path. In a dynamic environment, an inductive steering algorithm (ISA) that considers the movement of obstacles is proposed to make the AGV avoid obstacles rationally. By combining the steering characteristics of the two environments, AGV speed control rules are set and applied to the steering process in complex environments to ensure that the AGV can travel more smoothly and quickly. Simulation results show that the proposed method can ensure the obstacle avoidance and flexible steering of an AGV, and improve the driving speed and work efficiency in the two environments. In addition, compared with the conventional algorithm, the smoothness, operation speed, and work efficiency of the AGV are significantly increased using the improved LRQPSO algorithm and ISA.

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Section: Methodsmentioning

confidence: 99%

Automatic Guided Vehicle Global Path Planning Considering Multi-objective Optimization and Speed Control

Song¹

2021

Sensors and Materials

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“…Update the pheromone. e pheromone is updated according to equation (5), and the amount of pheromone is limited by equation (10).…”

Section: Application Of Improved Ant Colony Algorithm In Path Planningmentioning

confidence: 99%

“…In recent years, some researchers have adopted bionic intelligent optimization algorithms to solve the problem of path planning. ese bionic intelligent optimization algorithms mainly include ant colony algorithm [5], genetic algorithm [6], particle swarm optimization algorithm [7], immune algorithm [8], simulated annealing algorithm [9], and the combined optimization algorithm among the algorithms [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Wang

Zhao

et al. 2021

Journal of Robotics

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Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

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Energy-efficient path planning for a single-load automated guided vehicle in a manufacturing workshop

Zhang

et al. 2021

Computers & Industrial Engineering

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Research on PAGV path planning based on artificial immune ant colony fusion algorithm

Cited by 8 publications

References 2 publications

Automatic Guided Vehicle Global Path Planning Considering Multi-objective Optimization and Speed Control

Automatic Guided Vehicle Global Path Planning Considering Multi-objective Optimization and Speed Control

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Energy-efficient path planning for a single-load automated guided vehicle in a manufacturing workshop

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