Obstacle avoidance for mobile robots: A Hybrid Intelligent System based on Fuzzy Logic and Artificial Neural Network

Alves, Raulcezar M. F.

doi:10.1109/fuzz-ieee.2016.7737802

Cited by 17 publications

(10 citation statements)

References 4 publications

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“…To deal with the effect of noise generation during information collection using sensors, a Hybrid Intelligent System (HIS) was proposed by Alves and Lopes [268]. They adopted FL and ANN to control the navigation of the robot.…”

Section: Neuro-fuzzy 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

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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.

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Section: Neuro-fuzzy 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

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“…Noisy sensory information is treated using fuzzy rules before being fed into a neural network in [33]. When compared with a purely neural-based model, the proposed method proved to be superior both in terms of obstacle avoidance success rate and the smoothness of navigation.…”

Section: B Obstacle Avoidancementioning

confidence: 99%

Adaptive and intelligent navigation of autonomous planetary rovers — A survey

Wong

Yang

Yan

et al. 2017

2017 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)

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Abstract-The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors.

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“…Previous robots required research on navigation or obstacle avoidance, which are significant issues and challenging tasks for mobile robots; they must be able to navigate safely [1] in different circumstances, mainly to avoid a collision. Mobile robots are composed of mechanical and electronic parts: actuators, sensors, computers, power units, electronics, and so on.…”

Section: Introductionmentioning

confidence: 99%

Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence

et al. 2021

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Obstacle–Avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors and generate information for the models based on artificial intelligence to obtain a reactive decision. The main contribution is focused on the discussion of aspects that allow for comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies a fuzzy inference system (FIS) to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on a multilayer perceptron (MLP) architecture, which is a class of feedforward artificial neural network (ANN), and ultrasonic sensors to decide how to move in an uncontrolled environment. The analysis of both options offers perspectives to choose between reactive Obstacle–Avoidance systems based on ultrasonic or Kinect sensors, models that infer optimal decisions applying fuzzy logic or artificial neural networks, with key elements and methods to design mobile robots with wheels. Therefore, we show how AI or Fuzzy Logic techniques allow us to design mobile robots that learn from their “experience” by making them safe and adjustable for new tasks, unlike traditional robots that use large programs to perform a specific task.

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Obstacle avoidance for mobile robots: A Hybrid Intelligent System based on Fuzzy Logic and Artificial Neural Network

Cited by 17 publications

References 4 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

Adaptive and intelligent navigation of autonomous planetary rovers — A survey

Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence

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