Shortest Path Trajectory System Based on Dijkstra Algorithm

In the master project, the researcher discussed the shortest path solution to a single source problem based on Dijkstra algorithm as resolving the basic concepts. Everybody can travel by different routes to reach a different destination point. This can be time consuming if they do not travel trough the best route. This project aims to determine locations of the node that reflect all the items in the list, build the route by connecting nodes and evaluate the proposed algorithm for the single source shortest path problem. This project includes the modification of main algorithm which has been implemented in the prototype development. This study discussed the emphasis on the single source shortest path at the location of specific studies. The study will produce a decision-makers prototype

Path Planning and Control of an Autonomous Quadrotor Testbed in a Cluttered Environment

A classical problem for robotic navigation is how to efficiently navigate from one point to another and what to do if obstacles are encountered along the way. Many map based path planning algorithms attempt to solve this problem, all with varying levels of optimality and complexity. This work shows a review of selected algorithms, and two of these are selected for simulation and testing using a quadrotor unmanned aerial vehicle (UAV) in a dynamic indoor environment which requires replanning capabilities. The Dynamic A* algorithm, or simply D*, and the Probabilistic Roadmap method (PRM) are used in a scenario designed to test their respective functionality and usefulness with the goal of determining the better algorithm for flight testing given a partially known or changing environment.;The development of the quadrotor platform hardware is discussed as well as the associated software and capabilities. Both algorithms are redesigned to fit this specific application and display their respective planned and replanned paths in an intuitive and comparable manner. Simulation is performed and an obstacle is added to the map during the quadrotor motion, requiring a replanned path. Results are compared for both computed path length and computational intensity. Flight testing is performed in an indoor environment, and during the flight an obstacle is inserted into the flight path, requiring detection and replanning. Results are compared for computed path length and intuitively analyzed to compare optimality and complexity