Route Planning for an Autonomous Robotic Vehicle Employing a Weight-Controlled Particle Swarm-Optimized Dijkstra Algorithm

Abstract

An autonomous robotic vehicle (ARVs) is a self-driving vehicle that uses advanced technologies to navigate through the environment without human intervention. These vehicles can be used for various applications, including transportation, logistics, surveillance, and exploration. Route planning (RP) is the process of determining the most efficient and safe route for a vehicle, pedestrian, or any other mode of transportation to reach a destination. Route management is the process of selecting a collision-free path through an environment, which in practice is frequently crowded. Therefore, offering a RP solution for robotic systems is essential. The particle swarm optimization (PSO) method incorporates inertia weights and imitates the cooperative behavior of the flock’s population as well as its predatory nature to address route modeling issues. The Dijkstra algorithm (DA) works by determining the shortest path among the closest vertices between the source and destination. To choose the best path, inertia weight is also taken into account. By analyzing algorithms, we presented the combination technique for RP. In order to give a reliable route planning method, we suggested the weight-controlled particle swarm-optimized Dijkstra algorithm (WCPSODA). MATLAB was used to run the simulation, and conventional tools were used to evaluate the results. The findings of the study show that the suggested systems are capable of performing well.