Development of Path Planning Algorithm Using Probabilistic Roadmap Based on Ant Colony Optimization

Abdulakareem, Mohammed I.; Raheem, Firas A.

doi:10.30684/etj.v38i3a.389

Cited by 13 publications

(18 citation statements)

References 12 publications

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“…dRO and dRG are used to calculate the space between the robot and the obstacle, and the space between the robot and the target point, respectively using the Euclidean distance formula (4) and (5). Moreover, θ in the corner of the difference is required by the robot to detect the next iteration location in the environment.…”

Section: Objective Function (Cost Function)mentioning

confidence: 99%

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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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Section: Objective Function (Cost Function)mentioning

confidence: 99%

“…Generally, planning the movement of a mobile robot in an unfamiliar environment has been divided into three categories. The first one is based on information about the obstacles that might be known or unknown [5]. The second category is according to time, including online and offline planning [6].…”

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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“…α3 represents parameter of path smoothing for avoiding the sharp turning [15]. dRO and dRG are utilized for the calculation of space between robot and obstacle, and space between robot and its point of destination, respectively with the use of distance formulas (4) and (5). Moreover, θ in the corner of the variation is wanted by robot for the detection of the next iteration location in an environment.…”

Section: Objective Function (Cost Function)mentioning

confidence: 99%

“…Planning the mobile robot's locomotion in an uncommon environment is typically divided into three categories. The first depends on information regarding potential obstacles, which may or may not be known [5]. The second is a reference to time [6], [7], which includes both offline and online planning.…”

Section: Introductionmentioning

confidence: 99%

Adaptive weight grey wolf algorithm application on path planning in unknown environments

Abed

Lutfy

Al-Doori

2022

IJEECS

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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 route to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The Modified Grey Wolf Optimization (MGWO) is demonstrated in this work using two approaches: first, the Adaptive Adjustment Approach of the Control Parameters, and second, the Adaptive Variable Weights method. Those two methods are utilized for updating the wolf position, accelerate convergence, and cut down on time. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, dynamic obstacles, and an environment with a dynamic target. The online optimization method is performed using two phases which are the sensors reading phase and the path calculation phase. The proposed approach can solve a local minima problem in the static obstacles. A comparison study result between the proposed method and two other algorithms revealed that the proposed algorithm performed better in avoiding obstacles, which include the situation with the local minima. Finally, when put to comparison with Hybrid Fuzzy-Wind Driven Optimization and Adaptive Particle Swarm Optimization the average improvement rates in route length are 2.86% and 4.70391%, respectively.

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“…The researchers considered that trajectory planning is very substantial to the process of the robotic arm and the prominent target of trajectory planning is the generation of a path planning from the beginning to the target review for the fully or partially automated process [3], [5]. Discussion and analysis of motion techniques are investigated to find the path joint space and path planning selection approaches such as kinematics techniques.…”

Section: Introductionmentioning

confidence: 99%

An optimal motion path planning control of a robotic manipulator based on the hybrid PI-sliding mode controller

2023

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This paper proposes a hybrid proportional-integral (PI-sliding) mode controller to improve and adjust the point-to-point path planning of a three-link robotic arm with three degrees of freedom. The main objectives of the proposed control unit are to control the tracking process to reach the desired path handle the outgoing vibrations, and dampen them in the links of the robotic arm during its movement to ensure accuracy in completing the work. Seventh-degree polynomial paths represented the segments of locomotion connecting the first, middle, and last points at the combined space through predefined route points via minimal travel time. While not exceeding a predetermined maximum torque, without hitting any obstacle in the robot's workspace. The results showed that the proposed control design provides a robust control performance and fast response corresponding with conventional sliding mode controller (SMC) and PI controller. Then the outcomes provide the best results for the demanded mission according to the whished intakes with minimal error. The system equations are solved using the techniques available in MATLAB software then the results of the model are validated by the results of simulations.

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Development of Path Planning Algorithm Using Probabilistic Roadmap Based on Ant Colony Optimization

Cited by 13 publications

References 12 publications

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

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

Adaptive weight grey wolf algorithm application on path planning in unknown environments

An optimal motion path planning control of a robotic manipulator based on the hybrid PI-sliding mode controller

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