A Greedy Search Algorithm for Maneuver-Based Motion Planning of Agile Vehicles

• Main Author: Neas, Charles Bennett
• Other Authors: Aerospace and Ocean Engineering
• Format: Others
• Published: Virginia Tech 2014
• Subjects: Motion Primitives; A*; Motion Planning; Heuristic Search
• Online Access: http://hdl.handle.net/10919/36213; http://scholar.lib.vt.edu/theses/available/etd-12162010-145541/

This thesis presents a greedy search algorithm for maneuver-based motion planning of agile vehicles. In maneuver-based motion planning, vehicle maneuvers are solved offline and saved in a library to be used during motion planning. From this library, a tree of possible vehicle states can be generated through the search space. A depth-first, library-based algorithm called AD-Lib is developed and used to quickly provide feasible trajectories along the tree. AD-Lib combines greedy search techniques with hill climbing and effective backtracking to guide the search process rapidly towards the goal. Using simulations of a four-thruster hovercraft, AD-Lib is compared to existing suboptimal search algorithms in both known and unknown environments with static obstacles. AD-Lib is shown to be faster than existing techniques, at the expense of increased path cost. The motion planning strategy of AD-Lib along with a switching controller is also tested in an environment with dynamic obstacles. === Master of Science