Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment

Ajeil, Fatin Hassan; Ibraheem, Ibraheem Kasim; Azar, Ahmad Taher; Humaidi, Amjad J.

doi:10.1177/1729881420929498

Cited by 72 publications

(28 citation statements)

References 44 publications

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“…In formula (7) and (8), r represents the turning radius (radius), L is the turning angle determined by the robot, and the time is t . 1 q and 2 q are the angular velocities of the left and right wheels of the robot, and the constraint equations constrain the angular velocities of the two driving wheels [7] .…”

Section: Hierarchical Fuzzy Obstacle Avoidance Control For Omnidirectmentioning

confidence: 99%

Hierarchical fuzzy obstacle avoidance control of omnidirectional mobile robot for substation inspection

Ma¹

2021

E3S Web Conf.

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There are some limitations in the practical application of robot obstacle avoidance control methods. In order to realize the high-speed planning and obstacle avoidance processing of mobile robot, the path model, inspection route and path obstacles of the robot must be fully considered. Through feature extraction, the reasonable planning of substation inspection obstacle avoidance route is realized, and the control quantity set satisfying substation inspection is given. The hierarchical fuzzy obstacle avoidance control method of mobile robot is realized by using the optimal control set design. The simulation results show that the control method can solve various complex problems when the robot contour collides with obstacles, and can realize safe and fast control. The flexibility and robustness of obstacle avoidance are improved, and the motion state and speed of obstacles are also improved.

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Section: Hierarchical Fuzzy Obstacle Avoidance Control For Omnidirectmentioning

confidence: 99%

Hierarchical fuzzy obstacle avoidance control of omnidirectional mobile robot for substation inspection

Ma¹

2021

E3S Web Conf.

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“…The velocity of each particle is updated according to the following equation [ 42 , 43 ]:

where,

represents the inertia coefficient,

represents the personal acceleration coefficient and

represents the social acceleration coefficient. The position of each particle is updated by the equation [ 43 , 44 ]:

where

and

represents the current and updated vectors, respectively.…”

Section: Improvement Of Controllers’ Performances Based On Pso Tecmentioning

confidence: 99%

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles

Humaidi

Ibraheem

Azar

et al. 2020

Entropy

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This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. However, in real PAM-actuated systems, the uncertainties are inherited features in their parameters and hence an adaptive synergetic control algorithm is proposed and synthesized for a PAM-actuated robot arm subjected to perturbation in its parameters. The adaptive synergetic laws are developed to estimate the uncertainties and to guarantee the asymptotic stability of the adaptive synergetic controlled PAM-actuated system. The work has also presented an improvement in the performance of proposed synergetic controllers (classical and adaptive) by applying a modern optimization technique based on Particle Swarm Optimization (PSO) to tune their design parameters towards optimal dynamic performance. The effectiveness of the proposed classical and adaptive synergetic controllers has been verified via computer simulation and it has been shown that the adaptive controller could cope with uncertainties and keep the controlled system stable. The proposed optimal Adaptive Synergetic Controller (ASC) has been validated with a previous adaptive controller with the same robot structure and actuation, and it has been shown that the optimal ASC outperforms its opponent in terms of tracking speed and error.

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“…The local path‐planning problem calculates the path while the environment of the mobile robot is continuously changing due to its motion. Whereas, in the global path planning, the environment is entirely known in advance and the terrain must be stationary [11, 12]. Many researchers have investigated the path‐planning problem in dynamic environments, for instance, authors of [13] suggested a novel scheme to choose the best route of the mobile robot by the ant colony optimization (ACO) algorithm in an unknown dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

A novel path planning algorithm for mobile robot in dynamic environments using modified bat swarm optimization

Ajeil

Ibraheem

Humaidi

et al. 2021

The Journal of Engineering

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This paper describes the path planning of an autonomous mobile robot using an advanced version of the swarm bat algorithm in a time-varying situation. The foremost objective of this present contribution is to acquire the shortest path between the initial and endpoint avoiding dynamic obstacles. In this work, an adaptation of the frequency parameter of the standard bat algorithm to develop an improved version known as the modified frequency bat (MFB) algorithm is proposed. The modified frequency bat algorithm works in two steps, in the first step, the robot path is generated when there are no obstacles in the environment using the proposed method. As soon as an obstacle is detected near the robot, the second mode is initiated to avoid any obstacle. Extensive simulations are performed to validate the effectiveness of the proposed modified frequency bat algorithm by implementing it on standard benchmark functions and path planning problems and simultaneously matching its performance with the original bat algorithm. It has been concluded that the modified frequency bat algorithm shows superior performance as compared to the standard bat algorithm by drawing a shorter and collision-free path for the mobile robot path-planning problem.

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Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment

Cited by 72 publications

References 44 publications

Hierarchical fuzzy obstacle avoidance control of omnidirectional mobile robot for substation inspection

Hierarchical fuzzy obstacle avoidance control of omnidirectional mobile robot for substation inspection

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles

A novel path planning algorithm for mobile robot in dynamic environments using modified bat swarm optimization

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