Neuro-fuzzy expert system E_S_CO_V for the obstacle avoidance behavior of intelligent autonomous vehicles

Chohra, Amine; Farah, Ahcene; Belloucif, M.

doi:10.1163/156855399x00045

Cited by 11 publications

(9 citation statements)

References 21 publications

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“…The learning of the two proposed networks are based on the supervised Gradient Back-Propagation algorithm as detailed in (Chohra , et al, 1999). The training is performed in a learning environment where all the situations T j and O j arc represented and only free-collision action is permitted in each situation.…”

Section: Learning Algorithm and Learning Environmentmentioning

confidence: 99%

Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

Farah

Chohra

2002

SQU Journal for Science

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This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles with more autonomy and intelligence is discussed. Second, the system for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN) obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.

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Section: Learning Algorithm and Learning Environmentmentioning

confidence: 99%

Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

Farah

Chohra

2002

SQU Journal for Science

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“…Chohra et.al. [4] use the neural network, connecting the hybrid intelligent systems (HIS),for the obstacle avoidance, and can make a cognitive decision-making of mobile robot to have the ability to avoid the barrier same as people. Yang et.al.…”

Section: Introductionmentioning

confidence: 99%

A potential function and artificial neural network for path planning in dynamic environments based on self-reconfigurable mobile robot system

Chang²,

2012

2012 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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A dynamic environment is defined that either the obstacles or the goal or both are in motion. There are many methods to deal with the problems of the path planning of the robot in dynamic environment. In the case of the dynamic environment, one method for path planning is to take the velocity of the goal and obstacles into account. In this paper, we propose a method for path planning in dynamic environments that uses a potential function which indicates the probability that a robot will collide with an obstacle. The traditional potential function method has many shortcomings that are not suitable for the robot in the dynamic environment. So a modified method of potential function is proposed, and artificial neural network (ANN) is also used in order to get the information of velocity and positions of the obstacles and goal. This paper will discuss how to define the attractive force and repulsive force, and how to predict the velocity of the obstacle and the distance between obstacle and the robot.

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“…The mathematical models refer to the precise mathematical description of a ships' dynamics and its neighbouring environment. Hybrid autonomous navigation systems (Chohra et al, 1999, Hwang et al, 2001 propose a possible optimal combination of all, or a subset of the above methods for collision free ship navigation. In other words, these mathematical algorithms are not intelligent algorithms but they can be described as measuring algorithms to solve the collision avoidance problem in autonomous ship navigation. '…”

Section: Introductionmentioning

confidence: 99%

“…These properties can aid ship CA. Hybrid autonomous navigation systems . Hybrid autonomous navigation systems (Chohra et al ., 1999, Hwang et al ., 2001) propose a possible optimal combination of all, or a subset of the above methods for collision free ship navigation.It is worth noting that COLREGs have been applied in most reviewed research work. Further, most of the papers commonly use the following terminology: Own-ship : the ship to be navigated, Target-ship or strange-ship : the ship to be avoided.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques

2007

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This study provides both a spherical understanding about autonomous ship navigation for collision avoidance (CA) and a theoretical background of the reviewed work. Additionally, the human cognitive abilities and the collision avoidance regulations (COLREGs) for ship navigation are examined together with water based collision avoidance algorithms. The requirements for autonomous ship navigation are addressed in conjunction with the factors influencing ship collision avoidance. Humans are able to appreciate these factors and also perform ship navigation at a satisfactory level, but their critical decisions are highly subjective and can lead to error and potentially, to ship collision. The research for autonomous ship navigation may be grouped into the classical and soft computing based categories. Classical techniques are based on mathematical models and algorithms while soft-computing techniques are based on Artificial Intelligence (AI). The areas of AI for autonomous ship collision avoidance are examined in this paper are evolutionary algorithms, fuzzy logic, expert systems, and neural networks (NN), as well as a combination of them (hybrid system).

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Neuro-fuzzy expert system E_S_CO_V for the obstacle avoidance behavior of intelligent autonomous vehicles

Cited by 11 publications

References 21 publications

Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

A potential function and artificial neural network for path planning in dynamic environments based on self-reconfigurable mobile robot system

Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques

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