AN IMPROVED METHOD OF PARTICLE SWARM OPTIMIZATION FOR PATH PLANNING OF MOBILE ROBOT
Objective
Improved PSO enhances mobile robot path planning, addressing slow convergence with refinements like cubic spline interpolation, uniform distribution, and exponential attenuation. IPSO achieves shorter paths and reduced iteration time.
Abstract
The current particle swarm optimization (PSO) approach suffers from two limitations when it comes to handling mobile robot path planning problems: its poor convergence speed and restricted application. In this study, an improved PSO integration strategy based on refined details is proposed, integrating the cubic spline interpolation function, uniform distribution, exponential attenuation inertia weight, and learning factor of increased control. In comparison with other standard functions, our improved PSO (IPSO) can provide optimal results with fewer iteration steps than the four path planning algorithms developed in the body of existing literature. IPSO finds the ideal path length in fewer than 20 iteration steps, cutting down on simulation time and path length by 2.8% and 1.1 seconds, respectively.
Keywords: Mobile Robot Path Planning, Particle Swarm Optimization (PSO), Improved Particle Swarm Optimization (IPSO), Path Length, Path Nodes.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GBLearning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Communication?
· About Communication
· Introduction to Communication
· How Communication Works?
· Importing the System Design, Characterization and Visualization
· Analyzing of BER tool
· Analyzing of Error Rate Test Console
· Generation of WSN
· WSN network creation
· Nodes Communication
· Clustering
· Routing
· Convolutional
· Equalization and Synchronization etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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