Simulation of Moving Obstacle Avoidance for Auto Guided Land Vehicle

Altaei, Mohammed Sahib Mahdi; Al-Ani, Laith A.; Khamis, Qudama

doi:10.22401/anjs.21.4.07

Cited by 2 publications

(4 citation statements)

References 4 publications

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“…The GWO algorithm is initialized around the current location of the fox to bypass the barrier in the path, as shown in algorithm (1). The current location of the fox and the current obstacle are given to the GWO algorithm.…”

Section: Figure 2: Rfo-gwo Path Planningmentioning

confidence: 99%

“…Scientists have recently focused their attention on the development of robotics using artificial intelligence to achieve the autonomy of mobile robots. Autonomous mobile robots can be seen in many fields such as space, industry, transportation, and definition, as well as other social areas, and their use is increasing day by day [1]. Motion planning became autonomous and programmable as digital electronics and computer technology advanced, as did their compatibility using artificial intelligence (AI) methodologies [2].…”

Section: Introductionmentioning

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%

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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“…The longitudinal and lateral distances from the center of mass of the vehicle to the j-th point of the obstacle are shown in formula (8).…”

Section: Obstacle Avoidance Path Planningmentioning

confidence: 99%

“…In the case of multiple obstacles, the layered control can achieve smooth obstacle avoidance and re‐track the original path, indicating that the controller has high accuracy, but the system has the problem that the control path does not match the expected path well under multiple operating conditions. Reference 8 aims at the typical scene of pedestrians crossing the road, and real‐time control of vehicle speed can effectively prevent the collision between vehicles and pedestrians. Reference 9 proposes a collision risk calculation method based on pedestrian speed, which can quantitatively analyze the collision probability, and the calculation results have a high degree of agreement with the actual safety assessment data.…”

Section: Introductionmentioning

confidence: 99%

Design of auto obstacle avoidance system based on machine learning under the background of intelligent transportation

Wang

2023

Adv Control Appl

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With the process of urbanization and the increase in car ownership, traffic problems are becoming increasingly prominent. In order to improve traffic mobility and improve traffic safety, a machine learning based autonomous obstacle avoidance system was studied and designed in the context of intelligent transportation. Design an obstacle avoidance hardware system consisting of a tracking sensor module, an intelligent patrol module, an obstacle avoidance sensor module, and a motor module. Through the coordination and cooperation of multiple modules, the adaptive ability of the obstacle avoidance system is improved. On the basis of hardware design, a road coordinate system is established, and the lane‐changing path is planned with the longitudinal, lateral distance and speed of the ego vehicle and the preceding vehicle as input, and the vehicle steering and lane‐changing control is completed using the front wheel angle of the ego vehicle as the control quantity. The model predictive control method is used for obstacle avoidance trajectory planning. Based on the obstacle avoidance path planning results, the reinforcement learning method is used to design the vehicle's autonomous obstacle avoidance early warning to improve the efficiency of obstacle avoidance. The experimental results show that the designed system can maintain the lateral stability of the vehicle under continuous steering conditions, and the fit between the path tracking and the reference path is better, that is, the vehicle obstacle avoidance control effect is better; the convergence speed is faster. The vehicle autonomous obstacle avoidance warning time is short, which can ensure the safety of the vehicle to the greatest extent. This research achievement will provide important support for the development and practical application of intelligent transportation systems, and promote innovation and progress in the transportation field.

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Simulation of Moving Obstacle Avoidance for Auto Guided Land Vehicle

Cited by 2 publications

References 4 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

Design of auto obstacle avoidance system based on machine learning under the background of intelligent transportation

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