Low Latency Extended Dijkstra Algorithm with Multiple Linear Regression for Optimal Path Planning of Multiple AGVs Network

Abstract

Dijkstra algorithms are typically used to find the shortest path from a source node to a destination node. It is widely used in various applications due to its reliability and less complexity. This paper presents the extended Dijkstra Algorithm with lower latency and consumes less computing memory intended for implementation in many AGVs networks for effective decentralized task distribution path planning. This paper proposed linear regression normalization across the node network in Dijkstra architecture to reduce computing time and memory consumption. The issue addressed through this optimization focused on reducing the possibilities of collision between AGVs and deadlock. The extended Dijkstra algorithm significantly reduces computing time compared to the traditional Dijkstra algorithm. In addition, the proposed solutions suggest better AGV routing for collision avoidance and deadlock prevention possibilities.