Using the Bees Algorithm for wheeled mobile robot path planning in an indoor dynamic environment

Darwish, Ahmed Haj; Joukhadar, Abdulkader; Kashkash, Mariam

doi:10.1080/23311916.2018.1426539

Cited by 23 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, bees algorithm was used in [213] to present a real-time path planning method in an indoor dynamic environment. The bees algorithm was used to generate the path in static environment which is later updated online using modified bees algorithm to enable preventing hitting obstacles in dynamic environment.…”

Section: Artificial Bee Colony Path Planningmentioning

confidence: 99%

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Ayawli

Chellali

Appiah

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

Safe and smooth mobile robot navigation through cluttered environment from the initial position to goal with optimal path is required to achieve intelligent autonomous ground vehicles. There are countless research contributions from researchers aiming at finding solution to autonomous mobile robot path planning problems. This paper presents an overview of nature-inspired, conventional, and hybrid path planning strategies employed by researchers over the years for mobile robot path planning problem. The main strengths and challenges of path planning methods employed by researchers were identified and discussed. Future directions for path planning research is given. The results of this paper can significantly enhance how effective path planning methods could be employed and implemented to achieve real-time intelligent autonomous ground vehicles.

show abstract

Section: Artificial Bee Colony Path Planningmentioning

confidence: 99%

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Ayawli

Chellali

Appiah

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Darwish et al [181] implemented the bees algorithm in real-time path planning. The path is generated in an offline mode, which is later updated using the bees algorithm to avoid collision with obstacles.…”

Section: Application To Ground Vehiclesmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…Different types of intelligent classification techniques are used in the field of systems engineering and biomedical (Darwish et al, 2018;Souliman et al, 2013). At the present stage, among different varieties of artificial intelligence techniques, artificial neural networks, and their architectures (Joukhadar et al, 2020;Souliman et al, 2014), the feedforward artificial neural network (ANN) with error backpropagation training algorithm was chosen for the classification process (see Figure 9) because it is considered the most widely used model for the purpose of pattern recognition in the biomedical field (Ahmad, 2011;Kumar et al, 2018;Uğuz, 2012).…”

Section: Classificationmentioning

confidence: 99%

A portable Raspberry Pi-based system for diagnosis of heart valve diseases using automatic segmentation and artificial neural networks

et al. 2020

Self Cite

View full text Add to dashboard Cite

This study proposes a Raspberry Pi-based system for the diagnosis of heart valve diseases as a primary tool to improve the diagnostic accuracy of physicians. The proposed system is able to detect and classify nine common valvular heart cases encompassing eight types of heart valve diseases as well as the normal case of valves. The design and development of the proposed system are mainly divided into two phases, namely development of a disease classification approach, and design and implementation of the diagnostic hardware system. The developed disease classification approach is comprised of five stages, namely obtaining phonocardiogram (PCG) signals, preprocessing, segmentation using a proposed automatic algorithm, feature extraction in three domains (time, frequency, and wavelet decomposition domains) and classification using a backpropagation neural network. The hardware of the diagnostic system consists of a PCG signal acquisition module connected to a processing and displaying unit, which is represented by a Raspberry Pi connected to a touch screen. Where the developed disease classification approach is implemented in

show abstract

Using the Bees Algorithm for wheeled mobile robot path planning in an indoor dynamic environment

Cited by 23 publications

References 11 publications

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

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

A portable Raspberry Pi-based system for diagnosis of heart valve diseases using automatic segmentation and artificial neural networks

Contact Info

Product

Resources

About