Meta-heuristic techniques for path planning: recent trends and advancements

Sood, Monica; Panchal, Vinod Kumar

doi:10.1504/ijista.2020.105177

Cited by 5 publications

(3 citation statements)

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“…The 𝑎 vector is calculated for each GWO iteration, and a coefficient vector is generated for each individual by equations 10-15. The distance vector and trial vectors are calculated by equations (3)(4)(5) and (6)(7)(8), respectively; then, the new location that is furthest from the obstacle is calculated based on equation (9). If the wolf's new location is better than the fox's current location, the new location is valid.…”

Section: Figure 2: Rfo-gwo Path Planningmentioning

confidence: 99%

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Path Planning for Autonomous Mobile Robots Using the RFO-GWO Optimization Algorithm

Ketafa,

Al-Darraji

2024

Iraqi Journal of Science

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Path planning is a challenging navigation problem that can be handled using multi-objective methods. This paper, present a thre Path planning is a challenging navigation problem that can be handled using multi-objective methods. This paper presents a three-stage multi-objective path-planning method. The first stage is to locate the best or near-best solution path and avoid detected obstacles using a hybrid of the red fox–gray wolf optimizer (RFO–GWO), which finds a route from the start position to the target position. In the second step, a mutation operation using an evolutionary algorithm is utilized to enhance the length, integrity, and smoothness of the route generated by the RFO–GWO algorithm. The final step of the suggested method is refined further using a multiphase technique. By integrating the real sizes of the mobile robots and the size of the barriers and phrasing the issue as a traveling object in the available area, the suggested path-planning method resembles the actual world. The simulation results indicate that this strategy creates the most viable path even in complicated surroundings, overcoming the disadvantages of traditional approaches. Furthermore, when compared to prior path-planning methods, the simulation’s outcomes indicate that the suggested RFO–GWO method is effective in terms of the route, and the strategy is extremely competitive. The results showed a significant improvement, where the total percentage convergence time (in seconds) for RFO–GWO for the three maps was 15%, 12%, and 10%, respectively, whereas it was 35%, 41%, and 43% seconds in GWO and 34%, 35%, and 37% seconds in RFO. There was also a significant improvement in the number of nodes for RFO-GWO (2%, 3%, and 2%) compared to GWO nodes (64%, 65%, and 62%), and RFO nodes (32%, 30%, and 35%) for the same three maps. Subsequently, the smoothness of the path formed by the recommended approach was enhanced using the evolutionary algorithm (EA), where the total percentage length of the path in the worst scenario for GWO was 28% and for RFO was 26% in units, but after improvement with the RFO-GWO with EA, it became 22% in units. stages multi-objective path planning method: The first stage is to locate the best or near-best solution path and avoid the detected obstacles using a hybrid of the Red Fox-Grey Wolf optimizer (RFO-GWO) method, which finds a rout from the start position to the target position. In the second step, a mutation operation by evolutionary, are utilized to enhance the length, integrity, and smooth of the rout generated by the RFO-GWO method. the final step the suggested method is refined further by using the multiphase technique. By integrating both the real sizes of the mobile robots and the size of the barriers and phrasing the issue as a traveling object in the available area, the suggested path planning method resembles the actual world. The simulation results indicate that this strategy creates the best viable path even in complicated surroundings, overcoming the disadvantages of traditional approaches. Furthermore, when contrasted with prior path-planning methods, simulation outcomes indicate that the suggested RFO-GWO in terms of route effectivity, the strategy is extremely competitive.

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Section: Figure 2: Rfo-gwo Path Planningmentioning

confidence: 99%

“…Based on the nature of the environment, it may be classified into two types: static path planning, in which barriers do not change position over time, and dynamic path planning, in which the location and orientation of obstacles change over time. [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Autonomous Mobile Robots Using the RFO-GWO Optimization Algorithm

Ketafa,

Al-Darraji

2024

Iraqi Journal of Science

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“…So, the movement of the robots must be more efficient [4]. Path planning (PP) can be classified into dynamic and static path planning based on the environment [5]. The environment that includes moving obstacles is the dynamic environment, while the static environment objectes does not change with time [6].…”

Section: Introductionmentioning

confidence: 99%

Hybrid metaheuristic approach for robot path planning in dynamic environment

Amar

Jasim

2021

Bulletin EEI

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Recently robots have gained great attention due to their ability to operate in dynamic and complex environments with moving obstacles. The path planning of a moving robot in a dynamic environment is to find the shortest and safe possible path from the starting point towards the desired target point. A dynamic environment is a robot's environment that consists of some static and moving obstacles. Therefore, this problem can be considered as an optimization problem and thus it is solved via optimization algorithms. In this paper, three approaches for determining the optimal pathway of a robot in a dynamic environment were proposed. These approaches are; the particle swarming optimization (PSO), ant colony optimization (ACO), and hybrid PSO and ACO. These used to carry out the path planning tasks effectively. A set of certain constraints must be met simultaneously to achieve the goals; the shortest path, the least time, and free from collisions. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The effectiveness and superiority of the hybrid algorithm were verified on both PSO and ACO algorithms.

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Hybrid metaheuristic approach for robot path planning in static environment

Amar¹,

Jasim²

2022

The 2ND UNIVERSITAS LAMPUNG INTERNATIONAL CONFERENCE ON SCIENCE, TECHNOLOGY, AND ENVIRONMENT (ULICoSTE) 2021

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Meta-heuristic techniques for path planning: recent trends and advancements

Cited by 5 publications

References 0 publications

Path Planning for Autonomous Mobile Robots Using the RFO-GWO Optimization Algorithm

Path Planning for Autonomous Mobile Robots Using the RFO-GWO Optimization Algorithm

Hybrid metaheuristic approach for robot path planning in dynamic environment

Hybrid metaheuristic approach for robot path planning in static environment

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