UAV Path Planning

Drones, often referred to as unmanned aerial vehicles, are being utilized more and more in vital fields including disaster relief, surveillance, and delivery. Efficient path planning, which entails determining the best and safest route through intricate surroundings, is a major operational difficulty. Four popular path-planning algorithms—Dijkstra, A Star, Rapidly Exploring Random Trees, and Rapidly Exploring Random Trees Star—were implemented and compared in this research using MATLAB. The objective was to assess their performance using three key criteria: battery usage, journey distance, and time efficiency. Although Dijkstra's method is highly dependable because it always finds the shortest path, it takes longer to calculate, particularly for larger or more complicated areas. This is enhanced by A Star, which use heuristics to more effectively direct the search. This ensures the path is ideal while making it significantly faster. In order to navigate high-dimensional and complicated landscapes, RRT and RRT Star employ a distinct strategy that involves random sampling. Finding the optimum path is not RRT's first priority, but it is quick to identify a feasible one. This is enhanced by RRT Star, which shortens and streamlines the route.