A dynamic route-planning system for multi-vehicle distribution

Motivation- Nowadays, technology has become increasingly integrated into our daily lives, with advancements such as Artificial Intelligence (AI), Machine Learning, Automated Robotics, and Big Data Analysis. These innovations offer solutions to overcome human limitations, particularly in areas such as autonomous distribution systems for multiple vehicles, the more intelligent of route planning system in autonomous vehicles can potentially lead to reduced operating costs through decreased manpower. Problem statement- This research is specifically focused on route planning of automated vehicles for distribution systems. Most previous studies in the path-planning field have primarily concentrated on path-planning algorithms, emphasizing the movement between two points—origin and destination also in the Vehicle Route Problem (VRP) field which cannot cooperate in a dynamic environment. Therefore, this research aims to introduce a novel approach that addresses the complexities of route planning for multi-vehicle distribution, involving multiple stops, and can be applied to a dynamic environment. The primary objective of this research is to develop a route-planning system that optimizes for the shortest distance, optimal, considers real-time obstacle avoidance, and can adapt to dynamic environments. The vehicles under consideration in this study are Automated Guided Vehicles (AGVs) and Unmanned Aerial Vehicles (UAVs), both of which can play crucial roles in modern logistics. Methodology- To achieve this, two key components are integrated into a single system. The first component involves path planning algorithms for vehicles with obstacle avoidance functionality. The second component is a dynamic route-planning optimization model tailored for multi-vehicle distribution scenarios that uses the concept of Vehicle Route Problem (VRP). This innovative approach is implemented using a combination of EXCEL, CPLEX, and MATLAB programs. Moreover, this research also simulates the dynamic route-planning for a multi-vehicle distribution system in the Gazebo integrated with the Simulink model to ensure the ability of dynamic obstacle avoidance. The significance of this research lies in its contribution to a novel approach for efficiently managing route planning, which is a critical element within logistics management. Result- By addressing the challenges associated with multi-stop distribution systems and dynamic environments, this research successfully combines the path planning algorithm with multi-vehicle route optimization to enhance the overall efficiency and effectiveness of automated vehicle-based logistics. The system optimizes the shortest path while considering the real-time obstacle avoidance which can apply to real-world applications. Originality- This research introduces a new approach to overcoming limitations in traditional path planning and VRP paving the way for future research direction in automated distribution systems that can significantly impact the logistics sector as human consumption continues to rise.