Path Planning Method for Mobile Robot Based on a Hybrid Algorithm

Jiang, Zhaozhen; Wang, Wenlong; Sun, Wenqi; Da, Lianglong

doi:10.1007/s10846-023-01985-1

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…The existing PSO variant universality, or their capacity to perform well across a range of various fitness environments, remains an issue [24,39]. The state-of-the-art PSO variants, including Chaotic Cooperative Particle Swarm Optimization (CCPSO) proposed by Liu et al [25], are an advanced variant of the PSO algorithm that incorporates chaotic dynamics to enhance its performance, Comprehensive Learning Particle Swarm Optimization (CLPSO) proposed by Zhao et al [26] is developed to enhance the search and optimization capabilities of PSO, particularly in complex and high-dimensional problem spaces, Local Search Strategy Particle Swarm Optimization (LPSO) proposed by Jiang et al [27,28] enhances the exploration and exploitation capabilities of PSO by introducing a local search mechanism. LPSO retains the fundamental components of PSO, including the concept of particles, their positions, velocities, and the social learning process.…”

Section: Improved Self-adaptive Learning (Salpso) Methodologymentioning

confidence: 99%

Self-adaptive learning particle swarm optimization-based path planning of mobile robot using 2D Lidar environment

S.,

2024

Robotica

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The loading and unloading operations of smart logistic application robots depend largely on their perception system. However, there is a paucity of study on the evaluation of Lidar maps and their SLAM algorithms in complex environment navigation system. In the proposed work, the Lidar information is finetuned using binary occupancy grid approach and implemented Improved Self-Adaptive Learning Particle Swarm Optimization (ISALPSO) algorithm for path prediction. The approach makes use of 2D Lidar mapping to determine the most efficient route for a mobile robot in logistical applications. The Hector SLAM method is used in the Robot Operating System (ROS) platform to implement mobile robot real-time location and map building, which is subsequently transformed into a binary occupancy grid. To show the path navigation findings of the proposed methodologies, a navigational model has been created in the MATLAB 2D virtual environment using 2D Lidar mapping point data. The ISALPSO algorithm adapts its parameters inertia weight, acceleration coefficients, learning coefficients, mutation factor, and swarm size, based on the performance of the generated path. In comparison to the other five PSO variants, the ISALPSO algorithm has a considerably shorter path, a quick convergence rate, and requires less time to compute the distance between the locations of transporting and unloading environments, based on the simulation results that was generated and its validation using a 2D Lidar environment. The efficiency and effectiveness of path planning for mobile robots in logistic applications are validated using Quanser hardware interfaced with 2D Lidar and operated in environment 3 using proposed algorithm for production of optimal path.

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Section: Improved Self-adaptive Learning (Salpso) Methodologymentioning

confidence: 99%

Self-adaptive learning particle swarm optimization-based path planning of mobile robot using 2D Lidar environment

S.,

2024

Robotica

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“…So this paper combines a multi-parameter optimization ant colony algorithm under improved versions of the different scenes' grid maps with the dynamic window algorithm (DWA), adds the Bessel smoothing strategy to optimize the paths, and proposes a fused optimized ant colony algorithm, Opt Ant-DWA (Jiang et al, 2023;F. Li et al, 2022).…”

mentioning

confidence: 99%

Intelligent Logistics Vehicle Path Planning Using Fused Optimization Ant Colony Algorithm With Grid

Chu,

Guo,

Meng

2024

International Journal of Information Technologies and Systems Approach

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Aiming at the problem that the environmental planning path of intelligent logistics vehicles on urban roads and remote mountainous areas cannot fit the actual driving scene well. This study creates the algorithm model that combines an ant colony algorithm with a dynamic window algorithm and a Bessel smoothing strategy. Compared to the traditional colony algorithm with the same parameters, this fusion algorithm makes the path smoother by 72.2% when used on an urban highway. It also follows the right-hand rule for right-turn intersections. When the vehicle's height is determined in a mountain environment, this fusion algorithm reduces the driving's mean square deviation of height by 81.5% and shortens the path distance by 38.7%. The fusion algorithm can plan the target path of intelligent logistics vehicles and has the characteristics of scenarios available, multiple factors coordinated, and driving safety. It has provided certain research value and ideas for the digital transformation of the logistics industry.

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Improved ACO algorithm fused with improved Q-Learning algorithm for Bessel curve global path planning of search and rescue robots

Fang,

Liao,

Bai

2024

Robotics and Autonomous Systems

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Path Planning Method for Mobile Robot Based on a Hybrid Algorithm

Cited by 4 publications

References 26 publications

Self-adaptive learning particle swarm optimization-based path planning of mobile robot using 2D Lidar environment

Self-adaptive learning particle swarm optimization-based path planning of mobile robot using 2D Lidar environment

Intelligent Logistics Vehicle Path Planning Using Fused Optimization Ant Colony Algorithm With Grid

Improved ACO algorithm fused with improved Q-Learning algorithm for Bessel curve global path planning of search and rescue robots

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