Pony: Leveraging m-Graphs and Pruned-BFS Algorithm to Elevate AI-Powered Low-Cost Self-Driving Robotics

In industrial environments, efficient indoor transportation is a cornerstone of streamlined operations. However, the availability of high-end robotic transportation systems often poses a challenge for small-scale manufacturers due to their prohibitive costs. Addressing this disparity, this research...

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Bibliographic Details
Published in:IEEE access Vol. 12; pp. 134185 - 134197
Main Authors: Godara, Samarth, Sikka, Geeta, Parsad, Rajender, Marwaha, Sudeep, Faiz, Mukhtar Ahmad, Swaroop Bana, Ram
Format: Journal Article
Language:English
Published: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and theory
Algorithms
Artificial intelligence
Automation
Convergence
Factorial design
Graph theory
Graphs
Harnesses
Heuristic algorithms
Indoor environments
Industrial robots
Low cost
Navigation
Nodes
Performance evaluation
Random walk
Robot sensing systems
Robotics
Robots
Service robots
Transportation
Transportation systems
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:In industrial environments, efficient indoor transportation is a cornerstone of streamlined operations. However, the availability of high-end robotic transportation systems often poses a challenge for small-scale manufacturers due to their prohibitive costs. Addressing this disparity, this research introduces "Pony", an innovative and cost-effective semi-autonomous self-driven robotic system tailored for indoor transportation purposes. Built upon a microcontroller-based platform, Pony harnesses low-cost technology to create and store m-graphs effectively, facilitating seamless navigation within indoor facilities. Moreover, the study presents a novel Pruned-BFS (P-BFS) algorithm designed to efficiently traverse m-graphs, outperforming conventional graph-traversal approaches. Furthermore, the experimental validation in the study encompasses a comprehensive evaluation of Pony's performance across a range of scenarios. Randomly generated graphs, varying in complexity from 26 to 200 nodes, serve as the testing ground. Notably, four distinct algorithms-Breadth First Seach (BFS), Depth First Search (DFS), Iterative DFS (ID), and P-BFS are put through their paces during numerous random walks on each graph. A meticulously executed full factorial design of the experiment demonstrates statistical significance in execution time, and the number of nodes traversed, further underscoring Pony's prowess. By converging affordability, AI-driven intelligence, and robust performance, Pony heralds a promising evolution in the landscape of indoor robotic transportation.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3462102