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

Published online by Cambridge University Press:  26 January 2024

Julius Fusic S. Open the ORCID record for Julius Fusic S. [Opens in a new window]  and
Sitharthan R.
Julius Fusic S.*
Affiliation:
Department of Mechatronics Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India
Sitharthan R.
Affiliation:
School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
*
Corresponding author: Julius Fusic S.; Email: erjuliusfusic@gmail.com
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Abstract

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.

Keywords

Self-adaptive learning particle swarm optimization2D LidarHector SLAMlogistic applicationmobile robot
Type
Research Article
Information
Robotica , Volume 42 , Issue 4 , April 2024 , pp. 977 - 1000
Copyright
© Thiagarajar College of Engineering, 2024. Published by Cambridge University Press

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