Safe navigation and target recognition for a mobile robot using neural networks

Jebur, Mustafa H.; Ali, Mohammad M.

doi:10.1109/ssd.2017.8166921

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…All of these components are separately controllable, thus enabling the Robotino ® to travel freely in all directions. Furthermore, it has an open electrical interface to easily install any additional sensors or actuators [17].…”

Section: Mobile Robot System Descriptionmentioning

confidence: 99%

Obstacles Avoidance for Mobile Robot Using Type-2 Fuzzy Logic Controller

2022

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Intelligent mobile robots need to deal with different kinds of uncertainties in order to perform their tasks, such as tracking predefined paths and avoiding static and dynamic obstacles until reaching their destination. In this research, a Robotino® from Festo Company was used to reach a predefined target in different scenarios, autonomously, in a static and dynamic environment. A Type-2 fuzzy logic controller was used to guide and help Robotino® reach its predefined destination safely. The Robotino® collects data from the environment. The rules of the Type-2 fuzzy logic controller were built from human experience. They controlled the Robotino® movement, guiding it toward its goal by controlling its linear and angular velocities, preventing it from colliding obstacles at the same time, as well. The Takagi–Sugeno–Kang (TSK) algorithm was implemented. Real-time and simulation experimental results showed the capability and effectiveness of the proposed controller, especially in dealing with uncertainty problems.

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Section: Mobile Robot System Descriptionmentioning

confidence: 99%

Obstacles Avoidance for Mobile Robot Using Type-2 Fuzzy Logic Controller

2022

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“…The authors introduced a simulation only in the presence of static and dynamic environment. Jebur et al [29] proposed MLP to recognize the target and avoid…”

Section: Neural Networkmentioning

confidence: 99%

Assessment and Review of the Reactive Mobile Robot Navigation

Yosif¹,

Mahmood²,

Al-Khayyt³

2021

(AREJ)

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Nowadays, the mobile robot can be seen in different fields of engineering and science. The mobile robot can do some tasks that are so difficult or very risky to be performed by a human. Most of the works currently focus on implementing artificially intelligent algorithms and other algorithms that depend on the behaviour of nature. These approaches have been used in mobile robot navigation along uncertain manner. Mobile robot navigation strategies can be divided into two approaches: the classical approach and reactive approach. The classical approach related to static environment, whiles the reactive navigation is based on an unstructured environment. Path planning is one of the most important parts of the navigation system. In this paper, review and assessment of path planning strategies that can concern with the reactive approach are discussed, because it deal with the problem of dynamic environment. Numerous reactive methods have been introduced. Most of these presented works were concerned with simulation and a few of them have shown experimental implementation. Many papers tried to make a combination between two algorithms or more to increase the efficiency. It is concluded that reactive algorithms need more learning phases, complex in design, and require large memory storage.

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“…The Neural Network (NN) is used in many fields and applications such as signal processing, image processing, pattern recognition, and mobile robot navigation [9]. Jebur et al [10] proposed MLP to recognize the target and avoid the obstacle. They used an IR camera to filter the red target from others.…”

Section: Introductionmentioning

confidence: 99%

A combination between Adaptive Neuro-fuzzy inference system and neural network for mobile robot dynamic obstacles avoidance

Yosif

Mahmood

Alkhayat

2023

Preprint

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When a mobile robot has the ability to avoid obstacles while traveling is called an autonomous robot. There are various methods and techniques used to get a collision-free path until gets to the target point. The dynamic obstacle problems are handled by reactive mobile robot navigation techniques. In this paper, the problem of dynamic obstacle avoidance has been addressed by proposing a combination between an Adaptive Neuro-Fuzzy inference system and a Neural network. The proposed system consists of three main parts. The first part was abstracted by using A* algorithm to get the initial path from the start to the goal point. The second part of the system classifies Obstacle(s). The classification estimate whether the obstacle is dangerous and may collide with the mobile robot or not. The relative velocity and distance between the mobile robot and obstacle (s) determine whether the obstacle(s) are dangerous or not. Bayesian regularization Back-Propagation Neural Network is used to train the data for obstacle severity classification. Where obstacle is divided into five zones where zone 1 is dangerous and zone 5 is safe. When obstacle gets into critical regions classified as dangerous. The third part of the system is related to avoiding obstacles if these obstacles are classified as a danger to the mobile robot. The Adaptive Neuro-Fuzzy Inference System has been adopted in the process of avoiding obstacles during the mobile robot motion. Obstacle avoidance is a reaction taken by the robot to avoid collision with dynamic obstacles around it, which are classified as dangerous obstacles by the neural networks. Three important criteria were used as input to the Adaptive Neuro-Fuzzy Inference System, which are the relative speed, distance, and angle between the robot and the obstacle, the output was a suggested steering angle and speed for the mobile robot. The simulation results for the tested cases show the capability of the proposed controller for avoiding static and dynamic obstacles in a fully known environment. The Adaptive Neuro-Fuzzy Inference System enhances the performance of the proposed controller resulting in the reduction of path length, processing time, and the number of iterations.

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Safe navigation and target recognition for a mobile robot using neural networks

Cited by 4 publications

References 5 publications

Obstacles Avoidance for Mobile Robot Using Type-2 Fuzzy Logic Controller

Obstacles Avoidance for Mobile Robot Using Type-2 Fuzzy Logic Controller

Assessment and Review of the Reactive Mobile Robot Navigation

A combination between Adaptive Neuro-fuzzy inference system and neural network for mobile robot dynamic obstacles avoidance

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