Massachusetts Institute of Technology
Online trajectory planning for UAVs using mixed integer linear programming
Abstract
dc:description.abstractThis thesis presents a improved path planner using mixed-integer linear programming (MILP) to solve a receding horizon optimization problem for unmanned aerial vehicles (UAV's). Using MILP, hard constraints for obstacle and multi-vehicle avoidance as well as an approximation of vehicle dynamics are included into the formulation. The complete three dimensional formulation is described. The existing MILP framework has been modified to increase functionality, while also attempting to decrease solution time. A variable time step size, linear interpolation points, and horizon minimization techniques are used to enhance the capability of the online path planner. In this thesis, the concept of variable time steps is extended to the receding horizon, non-iterative MILP formulation. Variable time step sizing allows the simulation horizon time to be lengthened without increasing solve time too dramatically. Linear interpolation points are used to prevent solution trajectories from becoming overly conservative. Horizon minimization decreases solve time by removing unnecessary obstacle constraints from the the problem.
Degree
thesis:*- Department dc:contributor.department
- Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.
- Grantor dc:publisher
- Massachusetts Institute of Technology
- Year dc:date.issued
- 2006
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Culligan, Kieran Forbes
- Advisor dc:contributor.advisor
-
- Jonathan P. How.
Subjects
dc:subject × 1Rights
dc:rights- Statement dc:rights
-
- M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
- Licence dc:rights.uri
- Language dc:language.iso
- eng
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- http://hdl.handle.net/1721.1/37952
- OAI identifier oai:identifier
- oai:dspace.mit.edu:1721.1/37952