Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

doi:10.22266/ijies2022.0831.16

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…However, adjusting the potential functions can be difficult, and there may be path oscillations and local minima. While in [11], the authors used a different conventional strategy by improving the RRT*(Rapid Random search Tree) algorithm to address the challenges of the winding path and the time-consuming service robot pathfinding.…”

Section: Conventional Methodsmentioning

confidence: 99%

“…As a result, one of the limitations of this strategy is that more iterations are required when the number of orbits is not optimal. Finally, the ABC-SPPSO algorithm was evaluated against RRT, RRT*, PSO, and the hybrid RRT*PSO algorithm proposed in [11]. The evaluation was conducted in a static environment with a workspace measuring [500×500] cm.…”

Section: Algorithmsmentioning

confidence: 99%

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Enhancement of a Path-Finding Algorithm for the Hovercraft System Based on Intelligent Hybrid Stochastic Methods

2024

IJIES

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As a result of the widespread adoption of hovercraft systems, the path finding of such systems has become an essential rule for locating the target with the shortest distance and avoiding collision between the starting point and the target locations. The purpose of this paper is to develop a method for path finding by proposing a hybrid method of intelligent optimization techniques, including two stochastic approaches. The first one is called the Artificial Bee Colony (ABC) algorithm, and it aims to direct the hovercraft toward the target utilizing the behaviour of honey bees to reduce the path length required to reach the target. The second approach makes use of the Self Perception Particle Swarm Optimization (SP-PSO) algorithm, which is designed to improve particle swarm optimization in order to obtain a path that is more effective, smoother, and shorter for the hovercraft to travel in a complicated environment. The developed hybrid method is called the Artificial Bee Colony Self Perception Particle Swarm Optimization (ABC-SPPSO), which enhances the convergence speed and achieves a trade-off between exploration and exploitation in order to generate the shortest path while simultaneously avoiding collisions. The simulation results indicate that the performance of the proposed hybrid algorithm surpasses those of the original techniques. Moreover, the results demonstrate that the proposed hybrid algorithm outperforms the hybrid Firefly Algorithm Modified Chaotic Particle Swarm (HFAMCPSO) by (11.90%) in terms of distance and (56%) in terms of iterations. In addition, it outperforms the Quarter Orbit combined with the Particle Swarm Optimization (QOPSO) algorithm by (1.89%) in terms of distance and (16.66%) in terms of iterations. Furthermore, compared to the Rapid Random Tree Star Particle Swarm Optimization (RRT*PSO), the proposed method achieved enhancement of (0.82%) in terms of distance and (33.33%) in terms of iterations in providing the shortest path while avoiding collisions and reducing the trajectory tracking error to approximately zero. All the considered approaches were simulated in a global environment, utilizing the MATLAB 2022 package.

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Section: Conventional Methodsmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

Enhancement of a Path-Finding Algorithm for the Hovercraft System Based on Intelligent Hybrid Stochastic Methods

2024

IJIES

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“…The input GoalDist in Fig 6 is the membership distance function from robot to target point, which can increase robot flexibility to avoid obstacles to distant targets. The input ObstacleDist avoids missing targets due to being close to the target node [ 19 ]. The output is the weighting coefficient α , β , and γ .…”

Section: Robot Obstacle Avoidance Path Planning With A* and Dwa Fusio...mentioning

confidence: 99%

Robot obstacle avoidance optimization by A* and DWA fusion algorithm

Li,

Hao,

Zhao

et al. 2024

PLoS ONE

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The current robot path planning methods only use global or local methods, which is difficult to meet the real-time and integrity requirements, and can not avoid dynamic obstacles. Based on this, this study will use the improved A-star global planning algorithm to design a hybrid robot obstacle avoidance path planning algorithm that integrates sliding window local planning methods to solve related problems. Specifically, A-star is optimized by evaluation function, sub node selection mode and path smoothness, and fuzzy control is introduced to optimize the sliding window algorithm. The study conducted algorithm validation on the TurtleBot3 mobile robot, with data sourced from experimental data from a certain college. The results showed that hybrid algorithm enabled the planned path to effectively navigate around dynamic obstacles and reach the target point accurately. When compared with traditional methods, path length reduced by 9.6%, path planning time decreased by 29% with an approximate 26.7% increase in the average speed of the robot. Compared with the traditional methods, the research algorithm has greatly improved in avoiding dynamic obstacles, path planning efficiency, model adaptability and so on, which has important value for relevant research. It can be seen that the algorithm proposed in the study has performance advantages, demonstrating the effectiveness and advantages of robot path planning, and can provide reference for robot obstacle avoidance optimization. Research can complete tasks for robots in practical environments, which has certain reference value for the research of robots in path planning and the development of path obstacle avoidance planning.

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“…Second Stage: hybrid (RRT*PSO) path planning algorithm, the main goal of this layer is to create the best path between the starting node and the goal node. The proposed hybrid path-planning algorithm is based on the RRT* and PSO algorithms [26]. Fig.…”

Section: The Client-server Modeldesignmentioning

confidence: 99%

Real-Time Multi-Agent Mobile Robot based on A Client-Server Model

2023

IJCCCE

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A client-server network is one of the most important topics in a computer network. In this work, a real-time computer control system is designed based on the Client-Server Model for a Multi-Agent Mobile Robot System (CSM-MAMRS) and is applied to a building that consists of (N) floors and uses one mobile robot on each floor that has specific actions in different types of environments. The new proposed CSM-MAMRS consists of four layers. The first stage manages the network communication for each agent. The second stage solves the major problems of path planning by using a proposed hybrid algorithm that combines the Rapidly Exploring Random Tree Star (RRT*) and the Particle Swarm Optimization (PSO) algorithms in order to provide the shortest and smoothest path with collision avoidance between the starting and the target points in a static and dynamic robot environments are used.. In the third stage, a velocity planner controller is based on an inverse kinematic mobile robot model. In the fourth stage, the Hypertext Transfer Protocol HTTP is used to send velocity values to real mobile robots via the Wireless Network Control Administration. The simulation results and experimental works successfully achieved to significant improvement in real-time when using three missions of three mobile robots on different static map floors in the building. The maximum tracking pose errors for three robots in the static enviormnets are 0.39 cm, 0.02 cm, 0.32 cm respectively, but the maximum tracking pose error in the dynamic environments are 2.94 cm and 2.8 cm only for two mobile robots along the maximum distance of 250 cm. Index Terms— Real-time computer control, Mobile robot, Multi-agent system, Path planning algorithms.

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Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

Cited by 9 publications

References 29 publications

Enhancement of a Path-Finding Algorithm for the Hovercraft System Based on Intelligent Hybrid Stochastic Methods

Enhancement of a Path-Finding Algorithm for the Hovercraft System Based on Intelligent Hybrid Stochastic Methods

Robot obstacle avoidance optimization by A* and DWA fusion algorithm

Real-Time Multi-Agent Mobile Robot based on A Client-Server Model

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