An Embedded Real-Time Neuro-Fuzzy Controller for Mobile Robot Navigation

Zhang, Nian; Beetner, Daryl G.; Wunsch, Donald C.; Hemmelman, Brian; Hasan, A. O.

doi:10.1109/fuzzy.2005.1452413

Cited by 15 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…21 An RAM-based neuro-fuzzy technique for mobile robots was described. 22 The authors employed the NN for managing the robot's bearing angle while moving and a fuzzy controller for analyzing the sensor data. The neuro-fuzzy detector is developed for controlling the steering of a robotic in such an unfamiliar area.…”

Section: Related Workmentioning

confidence: 99%

“…An impediment avoidance approach is suggested for wheeled robot navigation position stabilization utilizing periodic type‐2 fuzzy NN controllers 21 . An RAM‐based neuro‐fuzzy technique for mobile robots was described 22 . The authors employed the NN for managing the robot's bearing angle while moving and a fuzzy controller for analyzing the sensor data.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid approach to implement multi‐robotic navigation system using neural network, fuzzy logic, and bio‐inspired optimization methodologies

Ayub

Singh

Hussain

et al. 2022

Computational Intelligence

View full text Add to dashboard Cite

Mobile robots have been increasingly popular in a variety of industries in recent years due to their ability to move in variable situations and perform routine jobs effectively. Path planning, without a dispute, performs a crucial part in multi-robot navigation, making it one of the very foremost investigated issues in robotics. In recent times, meta-heuristic strategies have been intensively investigated to tackle path planning issues in the similar way that optimizing issues were handled, or to design the optimal path for such multi-robotics to travel from the initial point to such goal. The fundamental purpose of portable multi-robot guidance is to navigate a mobile robot across a crowded area from initial point to target position while maintaining a safe route and creating optimum length for the path. Various strategies for robot navigational path planning were investigated by scientists in this field. This work seeks to discuss bio-inspired methods that are exploited to optimize hybrid neuro-fuzzy analysis which is the combination of neural network and fuzzy logic is optimized using the Particle Swarm optimization technique (PSO) in realtime scenarios. Several optimization approaches of bio-inspired techniques are explained briefly. Its simulation findings, which are displayed for two simulated scenarios reveal that hybridization increases multi-robot navigation accuracy in terms of navigation duration and length of the path.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Hybrid approach to implement multi‐robotic navigation system using neural network, fuzzy logic, and bio‐inspired optimization methodologies

Ayub

Singh

Hussain

et al. 2022

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Several local methods based on neuro and fuzzy paradigms have been reported recently [1]- [8]. Many of them employ fuzzy rules extracted from heuristic knowledge [4]- [5]. They are usually simple but often require a tedious and unreliable trial-error adjustment which does not obtain optimal results.…”

Section: Introductionmentioning

confidence: 99%

A Simple Neuro-Fuzzy Controller for Car-Like Robot Navigation Avoiding Obstacles

Baturone

Gersnoviez

2007

2007 IEEE International Fuzzy Systems Conference

View full text Add to dashboard Cite

This paper describes how the combination of neuro-fuzzy techniques with geometric analysis offers a good trade-off between purely heuristics and purely physical approaches when solving the problem of car-like robot navigation. The controller described, which follows a reactive technique, generates trajectories of near-minimal lengths when no obstacles are detected and, in presence of obstacles, generates minimum deviations from them. All these reference paths meet the kinematic constraints of car-like robots and take into account dynamic issues. Besides its efficiency, the proposed controller is very simple and linguistically interpretable. The whole controller has been designed and verified by using the CAD tools of the Xfuzzy environment.

show abstract

Advancements and Challenges in Mobile Robot Navigation: A Comprehensive Review of Algorithms and Potential for Self-Learning Approaches

Al Mahmud,

Kamarulariffin,

Ibrahim

et al. 2024

J Intell Robot Syst

View full text Add to dashboard Cite

Mobile robot navigation has been a very popular topic of practice among researchers since a while. With the goal of enhancing the autonomy in mobile robot navigation, numerous algorithms (traditional AI-based, swarm intelligence-based, self-learning-based) have been built and implemented independently, and also in blended manners. Nevertheless, the problem of efficient autonomous robot navigation persists in multiple degrees due to the limitation of these algorithms. The lack of knowledge on the implemented techniques and their shortcomings act as a hindrance to further development on this topic. This is why an extensive study on the previously implemented algorithms, their applicability, their weaknesses as well as their potential needs to be conducted in order to assess how to improve mobile robot navigation performance. In this review paper, a comprehensive review of mobile robot navigation algorithms has been conducted. The findings suggest that, even though the self-learning algorithms require huge amounts of training data and have the possibility of learning erroneous behavior, they possess huge potential to overcome challenges rarely addressed by the other traditional algorithms. The findings also insinuate that in the domain of machine learning-based algorithms, integration of knowledge representation with a neuro-symbolic approach has the capacity to improve the accuracy and performance of self-robot navigation training by a significant margin.

show abstract

An Embedded Real-Time Neuro-Fuzzy Controller for Mobile Robot Navigation

Cited by 15 publications

References 9 publications

Hybrid approach to implement multi‐robotic navigation system using neural network, fuzzy logic, and bio‐inspired optimization methodologies

Hybrid approach to implement multi‐robotic navigation system using neural network, fuzzy logic, and bio‐inspired optimization methodologies

A Simple Neuro-Fuzzy Controller for Car-Like Robot Navigation Avoiding Obstacles

Advancements and Challenges in Mobile Robot Navigation: A Comprehensive Review of Algorithms and Potential for Self-Learning Approaches

Contact Info

Product

Resources

About