TY - GEN
T1 - Energy-efficient route planning for autonomous aerial vehicles based on graph signal recovery
AU - Ji, Tianxi
AU - Chen, Siheng
AU - Varma, Rohan
AU - Kovacevic, Jelena
N1 - Funding Information:
The authors gratefully acknowledge support from the NSF through awards 1130616,1421919, the University Transportation Center grant (DTRT12-GUTC11) from the US Department of Transportation
Publisher Copyright:
© 2015 IEEE.
PY - 2016/4/4
Y1 - 2016/4/4
N2 - We use graph signal sampling and recovery techniques to plan routes for autonomous aerial vehicles. We propose a novel method that plans an energy-efficient flight trajectory by considering the influence of wind. We model the weather stations as nodes on a graph and model wind velocity at each station as a graph signal. We observe that the wind velocities at two close stations are similar, that is, the graph signal of wind velocities is smooth. By taking advantages of the smoothness, we only query a small fraction of it and recover the rest by using a novel graph signal recovery algorithm, which solves an optimization problem. To validate the effectiveness of the proposed method, we first demonstrate the necessity to take wind into account when planning route for autonomous aerial vehicles, and then show that the proposed method produces a reliable and energy-efficient route.
AB - We use graph signal sampling and recovery techniques to plan routes for autonomous aerial vehicles. We propose a novel method that plans an energy-efficient flight trajectory by considering the influence of wind. We model the weather stations as nodes on a graph and model wind velocity at each station as a graph signal. We observe that the wind velocities at two close stations are similar, that is, the graph signal of wind velocities is smooth. By taking advantages of the smoothness, we only query a small fraction of it and recover the rest by using a novel graph signal recovery algorithm, which solves an optimization problem. To validate the effectiveness of the proposed method, we first demonstrate the necessity to take wind into account when planning route for autonomous aerial vehicles, and then show that the proposed method produces a reliable and energy-efficient route.
KW - Graph signal processing
KW - autonomous vehicle
KW - route planning
KW - sampling and recovery
UR - http://www.scopus.com/inward/record.url?scp=84969837123&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84969837123&partnerID=8YFLogxK
U2 - 10.1109/ALLERTON.2015.7447174
DO - 10.1109/ALLERTON.2015.7447174
M3 - Conference contribution
AN - SCOPUS:84969837123
T3 - 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
SP - 1414
EP - 1421
BT - 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
Y2 - 29 September 2015 through 2 October 2015
ER -