Browsing by Author "Hachour, Ouarda"

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Cognitive tasks behavior of intelligent autonomous mobile robots
(2011) Hachour, Ouarda
In this paper we propose a neural network based navigation for intelligent autonomous mobile robots. The proposed neural networks algorithm deals with unknown static obstacles. Neural Networks deal with cognitive tasks such as learning, adaptation generalization and they are well appropriate when knowledge based systems are involved. To solve navigation problems, neural networks prove interesting to deal with the behaviour of autonomous mobile robots near the human being in reasoning. This paper deals with an algorithm for two dimensional (2D) path planning to a target for mobile robot in unknown environment. A complete path planning algorithm should guarantee that the robot can reach the target if possible, or prove that the target can not be reached. Just as human being, a neural network relies on previously solved examples to build a system of “neurons” that makes new decisions, classification and forecasts. Networks of neurons can achieve complex classification based on the elementary capability of each neuron to distinguish classes its activation function. In designing a Neural Networks navigation approach, the ability of learning must provide robots with capacities to successfully navigate in the environments like our proposed maze environment. The simulation results display the ability of the neural networks based approach providing autonomous mobile robots with capability to intelligently navigate in several environments
A genetic learning motion planning of an autonomous mobile robots
(2010) Hachour, Ouarda
This paper describes how soft computing technology as Genetic Algorithms (GAs) can be applied for path planning of an Autonomous Mobile Robot (AMR). GAs are search algorithms based on the mechanics of natural genetics. They combine survival of the littlest among string structures with a structured yet randomized information exchange to form a search algorithm with some of the innovative flair of human search. The proposed GA approach has an advantage of adaptivity such that the GA works perfectly even if an environment is unknown. . These environments were randomly generated . While randomized, GAs are no simple random walk. They efficiently exploit historical information to speculate on new search points (sub path positions) with expected improved performance. We measure the number of generations of candidates. The coding of GA is to affect label 0 for free cell and 1 for hazardous cell. This way of work is very useful later if the substring is inherited to new generations by genetic operators. The objective is to find a feasible and flexible path from initial area source to destination target area, flexible because the user can change the position of obstacles it has no effect since the environment is unknown. This robust method can deal a wide number of environments and gives to our robot the autonomous decision of how to avoid obstacles and how to attend the target. More, the path planning procedure covers the environments structure and the propagate distances through free space from the source position. For any starting point within the environment representing the initial position of the mobile robot, the shortest path to the goal is traced. The results gotten of the GA on randomly generated terrains are very satisfactory and promising
Intelligent autonomous path planning systems
(2011) Hachour, Ouarda
The theory and practice of Intelligent Autonomous Robot IAR are currently among the most intensively studied and promising areas in computer science and engineering which will certainly play a primary goal role in future. These theories and applications provide a source linking all fields in which intelligent control plays a dominant role. Cognition, perception, action, and learning are essential components of such-systems and their use is tending extensively towards challenging applications (service robots, micro-robots, bio-robots, guard robots, warehousing robots). The present paper studies the problem of motion of a mobile robot that moves inside an unknown environment with stationary unknown obstacles. This paper deals with the main principles of Intelligent Autonomous Systems IAS Path Planning and illustrates some criteria to be taken into account in any intelligent navigation control of IAS. For any starting point within the environment representing the initial position of the mobile robot
Neural path planning for mobile robots
(2011) Hachour, Ouarda
Navigation is a major challenge for autonomous, mobile robots. The problem can basically be divided into positioning and path planning. The proposed path finding strategy is designed in a known static environments. The proposed method starts from an initial point to a target point establishing a control nodes neural networks for which connections are made to determine the form of the path. This algorithm provides the robot the possibility to move from the initial position to the final position (target). The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. The proposed algorithm can deal with any shape obstacles even if it is the case of circular obstacles. This case is the hardest one in any navigation problem. The problem is solved by proposing neural networks navigation systems. Indeed, NNs are well adapted in appropriate form when knowledge based systems are involved. Since the network is able to take into account and respond to new constraints and data related to the external environments, the adaptation here is largely related to the learning capacity. Besides, Networks of neurons can achieve complex classification based on the elementary capability of each neuron to distinguish classes its activation function. Some useful solutions are proposed for each situation. For any proposed environment, the robot succeeds to reach its target without collisions. The results are satisfactory to see the great number of environments treated The simulation results display the ability of the neural networks based approach providing autonomous mobile robots with capability to intelligently navigate in several environments
Novel mobile robot path planning algorithm
(2010) Hachour, Ouarda
In this present work we propose a novel mobile robot path planning algorithm. Autonomous robots which work without human operators are required in robotic fields. In order to achieve tasks, autonomous robots have to be intelligent and should decide their own action. When the autonomous robot decides its action, it is necessary to plan optimally depending on their tasks. More, when a robot moves from a point to a target point in its given environment, it is necessary to plan an optimal or feasible path avoiding obstacles in its way and answer to some criterion of autonomy requirements such as : thermal, energy, time, and safety for example. First, we assume that the goal position is unknown. Secondly, only obstacles in the “relevant” area (according to the logical position ) are consider, i.e. the obstacles that are far, or in the direction opposite to the movement of the robot are not relevant. In this context, a full range of “main sub_position concepts” for vehicle control have been investigated by the execution of the asked mission. These feasible sub_position works demonstrate that obstacle detection and collision avoidance are improved with good results. While this model has been successful for the path planning problem, it is problematic for robots to react, act, decide, and to take a suitable action ”high level reasoning”. Much of the challenge of the mobile robots requires intelligence at subconscious level. In this context, the proposed path planning algorithm provides the robot the possibility to move from the initial position to the final position (target). The results are satisfactory to see the great number of environments treated
Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control
(Elsevier, 2017) Bakdi, Azzeddine; Hentout, Abdelfetah; Boutami, Hakim; Maoudj, Abderraouf; Hachour, Ouarda; Bouzouia, Brahim
Path planning of mobile robot in an indoor environment using dynamic replanning and RRT stra alrorithm.
(2021) Djebbar, Amira; Hachour, Ouarda
Mobile robots are currently being introduced in many fields such as hospitality, healthcare, manufacturing, transportation, delivery. And research in the development of mobile robots is heading towards increasing their level of autonomy in performing different tasks of different levels of complexity. One of these tasks is path planning which is taking a significant amount of importance during the last decade, this is because mobile robots nowadays are requiring more intelligence in order to plan feasible paths in various types of environments. The sampling-based method RRT* is one of the most efficient algorithms to perform path planning because of their many advantages such as the adaptability with complex and large environments. This project introduces a path planning method for a ground wheeled mobile robot in an indoor environment in the presence of random obstacles using RRT* and Dynamic Replanning. the robot is uninformed about the obstacle’s positions, but informed about its static environment. and it combines both global and local path planning in order to achieve the goal. The results demonstrate successful execution of the algorithm using the same number of tree nodes.
The proposed fuzzy logic navigation approach of autonomous mobile robots in unknown environments
(2009) Hachour, Ouarda
In this paper we discuss the ability to deal with a fuzzy logic navigation approach for intelligent autonomous mobile robots in unknown environments. The aim of this work is to develop hybrid intelligent system combining Fuzzy Logic (FL) and Expert System (ES). This combination provides the robot the possibility to move from the initial position to the final position (target) without collisions. This combination is necessary to bring the machine behaviour near the human one in reasoning, decision-making and action. That was the current reason to replace the classical approaches related to navigation pr oblems by the current approaches based on the fuzzy logic and expert system. The robot moves within the environment by sensing and avoiding the obstacles coming across its way towards the unknown target. The focus is on the ability to move and on being self-suffici ent to evolve in an unknown environment. The proposed hybrid na vigation strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. This approach must make the robot able to achieve these tasks: to avoid obstacles, and to make ones way toward its target by ES_FL system capturing the behavior of a human expert. The integration of ES and FL has prove n to be a way to develop useful real-world applications, and hybrid systems involving robust adaptive control. The proposed approach has the advantage of being generic and can be changed at the user demand. The results are satisfactory to see the great number of environments treated. The results are satisfactory and promising
The proposed grid _based navigation approach
(2010) Hachour, Ouarda
Navigation is a major challenge for autonomous, mobi le robots. The problem can basically be divided into positioning and path planning. In this paper we present a scheme for path finding, we focus on positioning. Starting out from an initial position in the grid, the mobile robot can autonomously head for destination cells in the grid. On its way it determines the current location in the grid using a connectivity_cell principle by picking up line Crossing cells. This principle will be clarified in detail. A key ability needed by an autonomous, mobile robot is the possibility to navigate through the space. The focus is on the ability to move and on being self sufficient. The robot navigates on a grid which regularly divides the ground into rectangular cells. To carry out tasks in various environments as in space applications, the robot succeeds to reach its target without collisions. The proposed approach can deal a wide number of environments. This navigation approach has an advantage of adaptivity such that the intelligent autonomous mobile robot approach works perfectly even if an environment is unknown. The results are promising for next future work of this domain
The proposed hybrid intelligent system for path planning of Intelligent Autonomous Systems
(2009) Hachour, Ouarda
In this paper, , we discuss the ability to deal with a Hybrid Intelligent Systems (HIS) for Intelligent Autonomous Vehicles IAV in unknown environment. The aim of this work is to develop HIS combining Genetic Algorithms (GA), Fuzzy Logic (FL), Neural Networks (NN) and Expert Systems (ES). This project deals with a simulation program that allows a robot to identify a path to reach a specified target avoiding obstacles. The combination of (ES FL, NN, GA) offers design flexibility and robust integration and has the benefits of reduced communications overhead and improved runtime performance. This integration provides the robot the possibility to move from the initial position to the final position (target) without collisions. The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. The algorithm permits the robot to move from the initial position to the desired position following an estimated trajectory. The proposed hybrid navigation strategy is designed in unknown environment with static unknown obstacles. This approach must make the robot able to achieve these tasks: to avoid obstacles, and to make ones way toward its target by ES_FL_GA_NN system capturing the behavior of a human expert. The integration of these technologies (FL, NN, ES, and GA) has proven to be a way to develop useful real-world applications, and hybrid systems involving robust adaptive control. The proposed approach has the advantage of being generic and can be changed at the user demand. The results are satisfactory to see the great number of environments treated. The results are satisfactory and promising. the proposed method is computationally efficient and is suitable for more integration of hybrid intelligent systems
The proposed neural networks navigation approach
(2010) Hachour, Ouarda
in this present work we present a neural network navigation approach. To deal with cognitive asks such as learning and generalization; the use of Neural Networks (NN) is necessary to bring the behaviour of Intelligent Autonomous Systems (IAS). Indeed, NNs are well adapted in appropriate form when knowledge based systems are involved. Since the network is able to take into account and respond to new constraints and data related to the external environments, the adaptation here is largely related to the learning capacity. To build a system of "neurons" that makes new decisions, classification and forecasts just as human being, a neural network is relied on previously solved examples. Besides, Networks of neurons can achieve complex classification based on the elementary capability of each neuron to distinguish classes its activation function. In designing a Neural Networks navigation approach, the ability of learning must provide robots with capacities to successfully navigate in the environments like our proposed maze environment. Also, robots must learn during the navigation process, build a map representing the knowledge from sensors, update this one and use it for intelligently planning and controlling the navigation. The simulation results display the ability of the neural networks based approach providing autonomous mobile robots with capability to intelligently navigate in several environments
Route map generation
(2011) Hachour, Ouarda
In this paper we present a route map generation of an autonomous mobile robot. The work in path planning has led into issues of map representation for a real world. Therefore, this problem is considered as one challenge in the field of mobile robots because of its direct effect for having a simple computationally efficient path planning strategy. For the real application in a real environment, it is necessary for the mobile robot to have a real time section while executing the planned path connected the start point and the goal point. The robot must then be able to understand the structure of the environment to find a way towards its target without collisions. To perform well this task several requirements must be satisfied and intelligent components become a necessity. More, world understanding and data interpreting is very solicited in any way of navigation When the target position is detected, the path planner will generate the proper path between the start and the goal position. This is called path planning step. The next step is to generate the geometric information of the generated path by searching the ways around the robot along the paths. This is called route map generation. When a route map generation is done, the next work is to control the robot itself to execute the route map, in order to achieve the goal planned by path planner and it is named as route runner. This is will be more clarified by the proposed work while answering to some interesting questions. The software implementation is very interesting to see the main factors are realized
Towards an approach of fuzzy control motion for mobile robots in unknown environments
(2009) Hachour, Ouarda
World understanding and planning missions
(2011) Hachour, Ouarda
In this paper, we present an intelligent control of an autonomous mobile robot in unknown environments. When an autonomous robot moves from an initial point to a target point in its given environment, it is necessary to plan an optimal or feasible path avoiding obstacles in its way and answer to some criterion of autonomy requirements such as : thermal, energy, time, and safety for example. Therefore, the major main work for path planning for autonomous mobile robot is to search a collision free path. . A key prerequisite for a truly autonomous robot is that it can navigate safely within its environment and executing the task without doubt. The problem of achieving this mobility is one of the most active areas in mobile robotics research. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In order to get an intelligent component, the proposed approach based on intelligent computing offers to the autonomous mobile system the ability to realize these factors: recognition, learning, decision-making, and action (the principle obstacle avoidance problems) which are the main factors to be considered in any design of navigation approach. The acquisition of these faculties constitutes the key of a certain kind of intelligence. Building this kind of intelligence is, up to now, a human ambition in the design and development of intelligent vehicles. However, the mobile robot is an appropriate tool for investing optional artificial intelligence problems relating to world understanding and taking a suitable action, such as, planning missions, avoiding obstacles, and fusing data from many sources. In this context we discuss this ability by proposing this approach. The results are promising for next developments