TY - GEN
T1 - Curvature-Constrained UAV Path Planning in Tracking a Moving Air Target
AU - Xu, Meng
AU - Dou, Lihua
AU - Xin, Bin
AU - Wang, Yipeng
AU - Fang, Hao
AU - Cai, Tao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/21
Y1 - 2018/8/21
N2 - This paper presents an online motion planning method for an unmanned aerial vehicle (UAV) tracking a moving air target (MAT). The detection range of a UAV is limited to a range of fan-shaped area which is located in the forward viewing angle of the UAV. A mathematical model for the MAT detection and tracking using UAV is built, taking into account the safety distance constraints and the sensor detection range constraints. A Differential Evolution (DE) based method is proposed to solve the above constrained optimization problem. Also, receding horizon control (RHC) is adopted to improve the efficiency of DE. Moreover, in order to ensure the generated paths smooth and flyable, Dubins path planning method is used, in consideration of curvature constrains. The impact of sensor detection error on path planning is considered and analyzed. Furthermore, we employ a widely used optimization method Genetic Algorithm (GA) as a competitor. The calculation and comparison results show that the DE performs better in detecting and tracking the MAT, in the constrast to GA, and it can generate feasible path much more efficiently.
AB - This paper presents an online motion planning method for an unmanned aerial vehicle (UAV) tracking a moving air target (MAT). The detection range of a UAV is limited to a range of fan-shaped area which is located in the forward viewing angle of the UAV. A mathematical model for the MAT detection and tracking using UAV is built, taking into account the safety distance constraints and the sensor detection range constraints. A Differential Evolution (DE) based method is proposed to solve the above constrained optimization problem. Also, receding horizon control (RHC) is adopted to improve the efficiency of DE. Moreover, in order to ensure the generated paths smooth and flyable, Dubins path planning method is used, in consideration of curvature constrains. The impact of sensor detection error on path planning is considered and analyzed. Furthermore, we employ a widely used optimization method Genetic Algorithm (GA) as a competitor. The calculation and comparison results show that the DE performs better in detecting and tracking the MAT, in the constrast to GA, and it can generate feasible path much more efficiently.
UR - http://www.scopus.com/inward/record.url?scp=85053140348&partnerID=8YFLogxK
U2 - 10.1109/ICCA.2018.8444321
DO - 10.1109/ICCA.2018.8444321
M3 - Conference contribution
AN - SCOPUS:85053140348
SN - 9781538660898
T3 - IEEE International Conference on Control and Automation, ICCA
SP - 582
EP - 587
BT - 2018 IEEE 14th International Conference on Control and Automation, ICCA 2018
PB - IEEE Computer Society
T2 - 14th IEEE International Conference on Control and Automation, ICCA 2018
Y2 - 12 June 2018 through 15 June 2018
ER -