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

Planning the path an autonomous robotic vehicle will take is essential part of developing and utilizing such a system. The task deciding on best route navigating technique to get its destination quickly safely. purpose planning select for that result in greatest fuel savings. planner aids accomplishing goals least amount time using by determining optimal considering variables including traffic, road conditions, distance. higher production lower operating expenses. An (ARVs) self-driving uses advanced technologies navigate through environment without human intervention. These vehicles can be used various applications, transportation, logistics, surveillance, exploration. Route (RP) process most efficient safe vehicle, pedestrian, or any other mode transportation reach destination. management selecting collision-free environment, which practice frequently crowded. Therefore, offering RP solution systems essential. particle swarm optimization (PSO) method incorporates inertia weights imitates cooperative behavior flock’s population as well predatory nature address modeling issues. Dijkstra algorithm (DA) works shortest among closest vertices between source To choose path, weight also taken into account. By analyzing algorithms, we presented combination RP. In order give reliable method, suggested weight-controlled swarm-optimized (WCPSODA). MATLAB was run simulation, conventional tools were evaluate results. findings study show are capable performing well.