Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators

Das, Pradipta Kumar; Jena, Prabir Kumar

doi:10.1016/j.asoc.2020.106312

Cited by 110 publications

(40 citation statements)

References 39 publications

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“…For the multi-AGVs path planning, the coupled approach regards the group of robots as a single entity, such that all paths are planned simultaneously in a joint or composite configuration space, and therefore could guarantee completeness, but these solutions do not scale well with large robot teams and they usually cannot be solved in real-time [21]. Das et al [22] employ an improved version of particle swarm optimization (IPSO) with evolutionary operators (EOPs) to calculate an optimal collision-free trajectory path for each robot in a known and complex environment. During the path generation process, each robot calculates its next local optimal coordinates in a stepwise manner to avoid path conflicts of multiple AGVs.…”

Section: Related Workmentioning

confidence: 99%

Research on Multi-AGVs Path Planning and Coordination Mechanism

et al. 2020

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Reasonable automatic guided vehicle path planning can shorten the transportation time of materials and improve the production efficiency of the intelligent assembly workshop. Ant colony algorithm is a widely used path planning method, however, it suffers from the shortcomings that being easy to fall into local optimum and low search efficiency. To overcome these shortcomings, first, this paper proposes a step optimization method to improve the search efficiency of the ant colony algorithm, and a path simplification method to avoid getting blindly tortuous paths; Second, to overcome the problem that the ant colony algorithm is easy to fall into the local optimum, this paper proposes an adaptive pheromone volatilization coefficient strategy, which uses different pheromone volatilization coefficients at different stages of the search path; third, for the path conflict problem of multiple automatic guided vehicles, this paper proposes a load balancing strategy to avoid it, which is based on the consideration that, path conflicts are caused by excessive concentration of multiple automatic guided vehicles paths. Extensive simulation results demonstrate the feasibility and efficiency of the proposed methods. INDEX TERMS ant colony algorithm, intelligent assembly, multiple automatic guided vehicles, path planning;

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Section: Related Workmentioning

confidence: 99%

Research on Multi-AGVs Path Planning and Coordination Mechanism

et al. 2020

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“…On one hand, at the initial level, researchers are taking interest in modeling and controlling a single aerial vehicle. On the other hand, more and more academics are now studying multi-UAV-based scenarios i.e., trajectory tracking, formation control, and path planning of swarms [ 1 , 2 , 3 ]. Scientists are studying the natural behavior of a flock of birds, how the birds do the tasks and successfully cooperate within a flock.…”

Section: Introductionmentioning

confidence: 99%

Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture

Ali

Han

Masood³

2021

Sensors

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This study proposes a collective motion and self-organization control of a swarm of 10 UAVs, which are divided into two clusters of five agents each. A cluster is a group of UAVs in a dedicated area and multiple clusters make a swarm. This paper designs the 3D model of the whole environment by applying graph theory. To address the aforesaid issues, this paper designs a hybrid meta-heuristic algorithm by merging the particle swarm optimization (PSO) with the multi-agent system (MAS). First, PSO only provides the best agents of a cluster. Afterward, MAS helps to assign the best agent as the leader of the nth cluster. Moreover, the leader can find the optimal path for each cluster. Initially, each cluster contains agents at random positions. Later, the clusters form a formation by implementing PSO with the MAS model. This helps in coordinating the agents inside the nth cluster. However, when two clusters combine and make a swarm in a dynamic environment, MAS alone is not able to fill the communication gap of n clusters. This study does it by applying the Vicsek-based MAS connectivity and synchronization model along with dynamic leader selection ability. Moreover, this research uses a B-spline curve based on simple waypoint defined graph theory to create the flying formations of each cluster and the swarm. Lastly, this article compares the designed algorithm with the NSGA-II model to show that the proposed model has better convergence and durability, both in the individual clusters and inside the greater swarm.

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“…In response to these problems, researchers have extensively studied PSO improvement in recent years. Das and Jena (2020) used a genetic algorithm that inherits multiple crossover operators and bee colony operators as two evolutionary operators to improve the optimization ability of the PSO. Shao et al (2020) designed the constant acceleration coefficient and the maximum speed as the adaptive linear variation to adapt to the optimization process.…”

Section: Introductionmentioning

confidence: 99%

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

Cheng

Zheng³

et al. 2021

Front. Neurorobot.

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The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local optimum, which could lead to the failure of fast optimal path obtainment. In order to solve these problems, this paper proposes an improved PSO combined gray wolf optimization (IPSO-GWO) algorithm with chaos and a new adaptive inertial weight. The gray wolf optimizer can sort the particles during evolution to find the particles with optimal fitness value, and lead other particles to search for the position of the particle with the optimal fitness value, which gives the PSO algorithm higher global search capability. The chaos can be used to initialize the speed and position of the particles, which can reduce the prematurity and increase the diversity of the particles. The new adaptive inertial weight is designed to improve the global search capability and convergence speed. In addition, when the algorithm falls into a local optimum, the position of the particle with the historical best fitness can be found through the chaotic sequence, which can randomly replace a particle to make it jump out of the local optimum. The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods.

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Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators

Cited by 110 publications

References 39 publications

Research on Multi-AGVs Path Planning and Coordination Mechanism

Research on Multi-AGVs Path Planning and Coordination Mechanism

Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

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