A Review on Path Planning Algorithms for Mobile Robots

Abed, Mustafa S.; Lutfy, Omar Farouq; Al-Doori, Qusay F.

doi:10.30684/etj.v39i5a.1941

Cited by 17 publications

(11 citation statements)

References 62 publications

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“…The basic computing unit of an ANN is a neuron, which has the ability to store and replicate experiential information in a manner comparable to the human brain. These have been widely employed in numerous search optimization, learning, and pattern recognition problems due to their ability to produce simple and optimal solutions in complex scenarios while maintaining the integrity of the specifications [22]. A feedforward net is one that was trained using the backpropagation training algorithm.…”

Section: Neural Networkmentioning

confidence: 99%

Artificial Techniques Based on Neural Network and Fuzzy Logic Combination Approach for Avoiding Dynamic Obstacles

Yosif¹,

Mahmood²,

Saeed³

2022

JESA

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The autonomous mobile robot must be capable of avoiding static and dynamic obstacles in the environment and navigating towards the target without any human effort. A valid low-cost path from start to goal is obtained by A* algorithm. Neural network used for Zone classification. The relative values between mobile robot and obstacle are used for classification which are distance, velocity, and angle. Zone1 is very dangerous while zone 5 is not dangerous. If the neural network classifies the obstacle as a dangerous obstacle and activates the controller. The fuzzy logic makes a decision as a reaction of mobile robot to prevent collision. There are three inputs to the fuzzy logic (relative velocity, relative distance, and relative angle) between mobile robot and obstacle. The outputs of fuzzy logic are velocity and steering angle of mobile robot. Static obstacles have been added to the environment in addition to dynamic obstacles to make the environment more complex. Three dangerous dynamic obstacles to the mobile robot are tested. While mobile robot is avoiding one obstacle, another obstacle enters critical zone and becomes dangerous to mobile robot. The mobile robot avoids the second obstacle while it is avoiding the first obstacle. Then the velocities of mobile robot and obstacles have been increased to prove that the proposed system can handle cases with faster velocities. The simulation results for the tested cases shows the capability of the proposed method for avoiding static and dynamic obstacles in fully known environment.

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Section: Neural Networkmentioning

confidence: 99%

Artificial Techniques Based on Neural Network and Fuzzy Logic Combination Approach for Avoiding Dynamic Obstacles

Yosif¹,

Mahmood²,

Saeed³

2022

JESA

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“…Робот може рухатися у площині або у тривимірному просторі (повітряні або підводні роботи). Крім того, оточуюче АМР середовище можна розділити на детерміноване та невизначене відповідно до кількості наявної щодо нього інформації [3]. Складність завдання побудови шляху зростає за необхідності побудови оптимального шляху.…”

Section: вступunclassified

Bio-inspired methods for planning the path of mobile robots

Gurko¹,

Hurko²

2022

Bulletin of KhNAHU

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Problem. The issue of path planning for a mobile robot is one of the most important ones of mobile robotics. Proper path planning ensures the safety of the robot and its environment, the efficiency of the tasks carried out by a robot, saves time and energy consumption for these tasks, etc. Therefore, research is constantly conducted on the implementation of new and improving existing optimization methods for the path planning for a mobile robot. The utilization of classical optimization methods is limited by their significant drawbacks, such as computational complexity and long time for searching the optimal path. To eliminate these issues, heuristic and then metaheuristic methods have been developed. Among metaheuristic methods, bio-inspired optimization methods, which are based on evolutionary processes in nature, as well as the behaviour of living organisms, are becoming increasingly popular. Goal. This paper aims to analyse the most popular bio-inspired algorithms used for mobile robot path planning. Methodology. The paper briefly reviews the bio-inspired optimization methods that are applicable to the path planning of a mobile robot. Particular emphasis is given to swarm intelligence algorithms, in which the relatively simple behaviour of individual agents interacting with each other and with the environment allows a swarm of these agents to achieve a given goal. Results. A classification of bio-inspired optimization methods used for mobile robot path planning is proposed. Pseudocodes for swarm optimization algorithms that are most frequently applied in mobile robotics are presented. Originality. This paper is one of the first in Ukraine to offer a comprehensive overview of bio-inspired methods of optimization used for mobile robot path planning. Practical value. The implementation of the considered algorithms in mobile robot control systems will improve the efficiency of robots in performing their assigned tasks. The given pseudocodes will simplify the development of software to implement the mentioned algorithms.

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“…Recently, the motion planning issue of a mobile robot is an important topic of study in the area of mobile robots in current research [1,2]. Route planning is a process to obtain a sensible and collision-free path between the beginning point and the destination, where the need to plan the route becomes important for a fully or partly automated process [3,4]. Generally, planning the movement of a mobile robot in an unfamiliar environment has been divided into three categories.…”

Section: Introductionmentioning

confidence: 99%

Online Path Planning of Mobile Robots Based on African Vultures Optimization Algorithm in Unknown Environments

Abed¹,

Lutfy²,

Al-Doori³

2022

JESA

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Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest root to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The main drawbacks of classic methods are the incapacity to move the robot in a dynamic and unknown environment, deadlock in a local minimum and complicated environments, and incapacity to foretell the speed vector of obstacles and non-optimality of the route. This article exhibits a recent path planning approach that utilizes the African Vultures Optimization (AVOA) for navigation of the mobile robot in static and dynamic unknown environments with a dynamic target. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, an environment with unknown dynamic obstacles, and an environment with a dynamic target. The proposed approach can solve a local minima problem in the environment with static obstacles. The online optimization method is performed using two phases which are the sensors’ reading phase and the path calculation phase and the results are given based on computer simulation in different unknown environments. A comparative study was conducted between the suggested algorithm and two other algorithms and the results showed that the AVOA algorithm was better in avoiding obstacles successfully including the local minima situation. Finally, the average enhancement rates in the path length compared with the Adaptive Particle Swarm Optimization (APSO) and the Hybrid Fuzzy-Wind Driven Optimization (WDO) are 2.21% and 1.02207%, respectively.

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A Review on Path Planning Algorithms for Mobile Robots

Cited by 17 publications

References 62 publications

Artificial Techniques Based on Neural Network and Fuzzy Logic Combination Approach for Avoiding Dynamic Obstacles

Artificial Techniques Based on Neural Network and Fuzzy Logic Combination Approach for Avoiding Dynamic Obstacles

Bio-inspired methods for planning the path of mobile robots

Online Path Planning of Mobile Robots Based on African Vultures Optimization Algorithm in Unknown Environments

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