TY - GEN
T1 - Mixed-integer nonlinear programming formulation of a UAV path optimization problem
AU - Ragi, Shankarachary
AU - Mittelmann, Hans
N1 - Publisher Copyright:
© 2017 American Automatic Control Council (AACC).
PY - 2017/6/29
Y1 - 2017/6/29
N2 - We present a mixed-integer nonlinear programming (MINLP) formulation of a UAV path optimization problem, and attempt to find the global optimum solution. As objective functions in UAV path optimization problems tend to be non-convex, traditional optimization solvers (typically local solvers) are prone to local optima, which lead to severely sub-optimal controls. For the purpose of this study, we choose a target tracking application, where the goal is to optimize the kinematic controls of UAVs while maximizing the target tracking performance. First, we compare the performance of two traditional solvers numerically - MATLAB's fmincon and knitro. Second, we formulate this UAV path optimization problem as a mixed-integer nonlinear program (MINLP). As this MINLP tends to be computationally expensive, we present two pruning methods to make this MINLP tractable. We also present numerical results to demonstrate the performance of these methods.
AB - We present a mixed-integer nonlinear programming (MINLP) formulation of a UAV path optimization problem, and attempt to find the global optimum solution. As objective functions in UAV path optimization problems tend to be non-convex, traditional optimization solvers (typically local solvers) are prone to local optima, which lead to severely sub-optimal controls. For the purpose of this study, we choose a target tracking application, where the goal is to optimize the kinematic controls of UAVs while maximizing the target tracking performance. First, we compare the performance of two traditional solvers numerically - MATLAB's fmincon and knitro. Second, we formulate this UAV path optimization problem as a mixed-integer nonlinear program (MINLP). As this MINLP tends to be computationally expensive, we present two pruning methods to make this MINLP tractable. We also present numerical results to demonstrate the performance of these methods.
KW - UAV path optimization
KW - fmincon
KW - knitro
KW - mixed-integer nonlinear programming
KW - target tracking
UR - http://www.scopus.com/inward/record.url?scp=85027076914&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027076914&partnerID=8YFLogxK
U2 - 10.23919/ACC.2017.7962987
DO - 10.23919/ACC.2017.7962987
M3 - Conference contribution
AN - SCOPUS:85027076914
T3 - Proceedings of the American Control Conference
SP - 406
EP - 411
BT - 2017 American Control Conference, ACC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 American Control Conference, ACC 2017
Y2 - 24 May 2017 through 26 May 2017
ER -