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

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Sundarraj, Subaselvi, Vijaya Kumar Reddy, R., Mahesh Babu, B., Lokesh, Gururaj Harinahalli, Flammini, Francesco, Natarajan, Rajesh
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Apexes; Autonomous cars; Autonomous vehicles; Collision avoidance; collision-free; Dijkstra algorithm (DA); Dijkstra's algorithm; DijkstraAlgorithm (DA); Heuristic algorithms; Inertia; Mobile robots; Navigation; Optimization; Particle swarm optimization; particle swarm optimization (PSO); Planning; Robot sensing systems; Robotic systems; Robotic vehicles; Robotics; Robots; Route planning; Route planning (RP); Shortest-path problems; Transportation; weight-controlled particle swarm-optimized Dijkstra algorithm (WCPSODA)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3302698

Planning the path an autonomous robotic vehicle will take is an essential part of developing and utilizing such a system. The task is deciding on the best route and navigating technique to get the vehicle to its destination quickly and safely. The purpose of route planning is to select a route for the vehicle that will result in the greatest fuel savings. The planner aids the vehicle in accomplishing its goals in the least amount of time and using the least amount of fuel by determining the optimal route by considering variables including traffic, road conditions, and distance. The result is higher production and lower operating expenses. 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.