An Improved Method of Particle Swarm Optimization for Path Planning of Mobile Robot

Li, Xun; Wu, Dan-Dan; He, Jifeng; Bashir, Muhammad; Ma, Lei

doi:10.1155/2020/3857894

Cited by 38 publications

(24 citation statements)

References 18 publications

(27 reference statements)

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“…This path network includes also both the starting point and the ending point, then a valid and short path has to be found from the start to the end passing through some segments of the connection network, for that reason it is necessary to apply a decision or optimization algorithm [11] to choose the best path inside on that network. A lot of different optimization algorithms has been used for the path planning issue such as ant colony optimization [12], [13], particle swarm for mobile robots [14], [15], [16], [17], chaotic particle swarm [18] particle swarm for manipulators [19], brain storm optimization [20], Fuzzy-Wind Driven algorithm [21], rapidly-exploring trees [22], gray wolf algorithm [23] among others.…”

Section: Introductionmentioning

confidence: 99%

Path Optimization for Mobile Robots using Genetic Algorithms

Santa¹,

Sarmiento²,

Ariza³

2022

IJACSA

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This article proposes a path planning strategy for mobile robots based on image processing, the visibility graphs technique, and genetic algorithms as searching/optimization tool. This proposal pretends to improve the overall execution time of the path planning strategy against other ones that use visibility graphs with other searching algorithms. The global algorithm starts from a binary image of the robot environment, where the obstacles are represented in white over a black background. After that four keypoints are calculated for each obstacle by applying some image processing algorithms and geometric measurements. Based on the obtained keypoints, a visibility graph is generated, connecting all of these along with the starting point and the ending point, as well as avoiding collisions with the obstacles taking into account a safety distance calculated by means of using an image dilation operation. Finally, a genetic algorithm is used to optimize a valid path from the start to the end passing through the navigation network created by the visibility graph. This implementation was developed using Python programming language and some modules for working with image processing ang genetic algorithms. After several tests, the proposed strategy shows execution times similar to other tested algorithms, which validates its use on applications with a limited number of obstacles presented in the environment and low-medium resolution images.

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Section: Introductionmentioning

confidence: 99%

Path Optimization for Mobile Robots using Genetic Algorithms

Santa¹,

Sarmiento²,

Ariza³

2022

IJACSA

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show abstract

“…Thus, many researchers have sought to improve the original PSO. The studies (Hantash et al 2020; Li et al 2020) proposed improvements by integrating uniform distribution, exponential attenuation inertia weight, cubic spline interpolation function and learning factor of enhanced control. These studies have signi cantly contributed to a high e ciency of the improved algorithms.…”

Section: Introductionmentioning

confidence: 99%

Minimize Power Loss and Cost for Power Grids with The Placement of Distributed Generation Units by Using Improved Equilibrium Optimizer

Pham

Nguyen

2022

Preprint

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This paper proposes a novel approach for determining optimal location and effective size of distributed generation units (DGUs) in the distribution systems. The goal of this study is to minimize both the total power loss on all distribution branches and the total cost of DGUs including investment cost, operation cost and maintenance cost. The major constraints of the systems regarding harmonic distortions, branch current and voltage must be kept within allowed operating limits. The proposed improved equilibrium optimizer (IEO) is developed from the original equilibrium optimizer (EO), which was motivated by control volume mass balance models. This novel algorithm can effectively expand the search area and avoid the premature convergence to low quality solution spaces. With the determined solutions from IEO, the total power loss is significantly reduced from 0.2110 MW to 0.0815 MW, 0.2245 MW to 0.0720 MW and 0.3161 MW to 0.1510 MW for IEEE 33-bus, IEEE 69-bus and IEEE 85-bus radial distribution systems, respectively. Not only that, the total cost of DGUs is also more economical and consumes only $7.0231 million, $6.6357 million and $6.2721 million corresponding to the three systems for a 20-year planning period. The performance of the proposed algorithm is compared to three other implemented methods consisting of artificial bee colony (ABC) algorithm, Salp swarm algorithm (SSA) and EO, and eight previously published methods for the three test systems. The comparisons of results imply that IEO is better than other methods in terms of performance, stability and convergence characteristic.

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“…In References [23,24], a genetic algorithm (GA) is utilized to find the optimal knowledge domain factors for path planning of mobile robot. An improved particle swarm optimization (PSO) is applied instead of the conventional algorithms for path planning of mobile robot 25,26 . In Reference [27], the whale optimization algorithm (WOA) is utilized for the tuning of FL parameters for the energy management of AVs.…”

Section: Introductionmentioning

confidence: 99%

“…An improved particle swarm optimization (PSO) is applied instead of the conventional algorithms for path planning of mobile robot. 25,26 In Reference [27], the whale optimization algorithm (WOA) is utilized for the tuning of FL parameters for the energy management of AVs. In Reference [28], the GA, the memetics algorithms (MA), and mesh adaptive direct search (MADS) are applied to tune the gains of PID controller for AVs.…”

mentioning

confidence: 99%

Optimal design of low computational burden model predictive control based on SSDA towards autonomous vehicle under vision dynamics

Elsisi

Ebrahim

2021

Int J of Intelligent Sys

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The adjusting of autonomous vehicles (AVs) steering angle represents a challenging issue in the intelligent vehicular applications. Among different control strategies, the model predictive control (MPC) is devoted to providing efficacious steering control for the AVs. However, the formulation of MPC with a few parameters is the main challenge to overcome the complicated computation that happens due to the long prediction and control horizons. Hybridization between discrete‐time Laguerre function (DTLF) and MPC is suggested to formulate the MPC with few parameters. While the hybrid DTLF–MPC requires an adequate tuning technique for their gains to improve the system performance. A new low computational burden design is proposed in this paper to adjust the gains of the hybrid DTLF–MPC based on the social ski driver algorithm (SSDA). This algorithm is developed as a recently artificial intelligence technique. The tuned hybrid DTLF–MPC by SSDA is devoted to control AV's steering angle. Furthermore, the proposed system takes into account the vision dynamics. The system performance based on the tuned DTLF–MPC by SSDA is evaluated with the MPC based on the neural network algorithm for comparison. Different scenarios are carried out to confirm the superiority of the suggested control mechanism to handle small as well as large‐scale variations of road curvatures.

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An Improved Method of Particle Swarm Optimization for Path Planning of Mobile Robot

Cited by 38 publications

References 18 publications

Path Optimization for Mobile Robots using Genetic Algorithms

Path Optimization for Mobile Robots using Genetic Algorithms

Minimize Power Loss and Cost for Power Grids with The Placement of Distributed Generation Units by Using Improved Equilibrium Optimizer

Optimal design of low computational burden model predictive control based on SSDA towards autonomous vehicle under vision dynamics

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