Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

Kamil, Rakaa T.; Mohamed, Mohamed J.; Oleiwi, Bashra Kadhim

doi:10.30684/etj.v38i9a.1100

Cited by 22 publications

(26 citation statements)

References 14 publications

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“…[ [149][150][151] Genetic Algorithm Derived from evolutionary algorithms, they involve different operators, e.g., mutation, crossover, and selection operator (refer to Figure 7). [152][153][154][155][156][157][158][159][160] Simulated Annealing…”

Section: Seminal Work Sourcementioning

confidence: 99%

“…Genetic Algorithm (a) Faster convergence rate and optimization capability [46] (b) Combine well with other algorithms [46] (c) Because of easy implementation integrate well with other algorithms [181] (a) Get stuck in local minima problem when environment complexity increase [254] (b) Produce oscillations in system [255] Simulation T ≥ 0(n 2 ) ABC (a) Requires fewer control parameters [151] (b) Requires less computational time [181] (c) Because of easy implementation integrate well with other algorithms [181] (a) Slow convergence rate [256] Simulation T ≥ 0(n 2 )…”

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confidence: 99%

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A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

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In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented.

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Section: Seminal Work Sourcementioning

confidence: 99%

mentioning

confidence: 99%

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

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“…The second category is according to time, including online and offline planning [6]. The third category depends on the nature of obstacles inside the environment, where the obstacles might be stationary or moving [7,8]. Many researchers have used different approaches in the navigation of Differential Wheel Mobile Robot (DWMR).…”

Section: Introductionmentioning

confidence: 99%

“…From previous studies, it was seen that many optimization methods produce low-quality solutions or suffer from an early convergence in the primary stages. These issues are observed in PSO [14], Ant colony [7], GWO [15]. etc., therefore there is a need to study and improve these methods to overcome their limitations.…”

Section: Introductionmentioning

confidence: 99%

Online Path Planning of Mobile Robots Based on African Vultures Optimization Algorithm in Unknown Environments

Abed¹,

Lutfy²,

Al-Doori³

2022

JESA

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Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest root to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The main drawbacks of classic methods are the incapacity to move the robot in a dynamic and unknown environment, deadlock in a local minimum and complicated environments, and incapacity to foretell the speed vector of obstacles and non-optimality of the route. This article exhibits a recent path planning approach that utilizes the African Vultures Optimization (AVOA) for navigation of the mobile robot in static and dynamic unknown environments with a dynamic target. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, an environment with unknown dynamic obstacles, and an environment with a dynamic target. The proposed approach can solve a local minima problem in the environment with static obstacles. The online optimization method is performed using two phases which are the sensors’ reading phase and the path calculation phase and the results are given based on computer simulation in different unknown environments. A comparative study was conducted between the suggested algorithm and two other algorithms and the results showed that the AVOA algorithm was better in avoiding obstacles successfully including the local minima situation. Finally, the average enhancement rates in the path length compared with the Adaptive Particle Swarm Optimization (APSO) and the Hybrid Fuzzy-Wind Driven Optimization (WDO) are 2.21% and 1.02207%, respectively.

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“…Mobile robots have an important part in many areas of situations [1]. Mobile robots sometimes work with insufficient knowledge about the areas.…”

Section: Introductionmentioning

confidence: 99%

Outdoor Localization in Mobile Robot with 3D LiDAR Based on Principal Component Analysis and K-Nearest Neighbors Algorithm

Atiyah¹,

Mohammed²

2021

ETJ

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Localization is one of the potential challenges for a mobile robot. Due to the inaccuracy of GPS systems in determining the location of the moving robot alongside weathering effects on sensors such as RGBs (e.g. rain and light-sensitivity(. This paper aims to improve the localization of mobile robots by combining the 3D LiDAR data with RGB-D images using deep learning algorithms. The proposed approach is to design an outdoor localization system. It is divided into three stages. The first stage is the training stage where 3D LiDAR scans the city and then reduces the dimensions of 3D LiDAR data to 2.5D image. This is based on PCA method where these data are used as training data. The second stage is the testing data stage. RGB and depth image in IHS method are combined to generate 2.5D fusion image. The training and testing of these datasets are based on using Convolution Neural Network. The third stage consists of using the K-Nearest Neighbor algorithm. This is the classification stage to get high accuracy and reduces the training time. The experimental results obtained prove the superiorly of the proposed approach with accuracy up to 97.52%, Mean Square of Error of 0.057568, and Mean error in distance equals 0.804 meters.

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Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

Cited by 22 publications

References 14 publications

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

Online Path Planning of Mobile Robots Based on African Vultures Optimization Algorithm in Unknown Environments

Outdoor Localization in Mobile Robot with 3D LiDAR Based on Principal Component Analysis and K-Nearest Neighbors Algorithm

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