2026-09-19T05:51:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/379522022-01-13T07:54:11Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Jonathan P. How.
Culligan, Kieran Forbes
Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2007-07-18T13:14:29Z
2007-07-18T13:14:29Z
2006
2006
http://hdl.handle.net/1721.1/37952
144589286
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.
Includes bibliographical references (p. 95-100).
This 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.
(cont.) Computer simulations and test flights on an indoor quadrotor testbed shows that MILP can be used reliably as an online path planner, using a variety of different solution rates. Using the MILP path planner to create a plan ten seconds into the future, the quadrotor can navigate through an obstacle-rich field with MILP solve times under one second. Simple plans in obstacle-spare environments are solved in less than 50ms. A multi-vehicle test is also used to demostrate non-communicating deconfliction trajectory planning using MILP.
by Kieran Forbes Culligan.
S.M.
100 p.
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Aeronautics and Astronautics.
Online trajectory planning for UAVs using mixed integer linear programming
Online trajectory planning for unmanned aerial vehicles using MILP
Thesis
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<Abstract>This 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.</Abstract>
<Abstract>(cont.) Computer simulations and test flights on an indoor quadrotor testbed shows that MILP can be used reliably as an online path planner, using a variety of different solution rates. Using the MILP path planner to create a plan ten seconds into the future, the quadrotor can navigate through an obstacle-rich field with MILP solve times under one second. Simple plans in obstacle-spare environments are solved in less than 50ms. A multi-vehicle test is also used to demostrate non-communicating deconfliction trajectory planning using MILP.</Abstract>
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