TY - GEN
T1 - Real-time model predictive control for keeping a quadrotor visible on the camera field-of-view of a ground robot
AU - Ding, Wei
AU - Ganesh, Madan Ravi
AU - Severinghaus, Robert N.
AU - Corso, Jason J.
AU - Panagou, Dimitra
N1 - Publisher Copyright:
© 2016 American Automatic Control Council (AACC).
PY - 2016/7/28
Y1 - 2016/7/28
N2 - This paper considers a cooperative control design for an aerial/ground robot system, and addresses the problem of maintaining visibility of a quadrotor within the camera field-of-view of a ground robot in the presence of external disturbances. The quadrotor needs to be tracked by the ground robot with a monocular camera, and hence its motion should facilitate the ground vision-based tracking process by remaining in the effective camera sensing area. We design a model predictive controller (MPC) strategy where the visibility constraints of the camera and the control input constraints of the quadrotor are encoded into the cost function via barrier functions, and we adopt a fast MPC solver that is able to solve the optimization problem in real time. We also propose a method to enhance the robustness of the algorithm by suitably defining a restart method for the MPC solver. The applicability of the proposed algorithm is demonstrated through simulations and experimental results on real setups.
AB - This paper considers a cooperative control design for an aerial/ground robot system, and addresses the problem of maintaining visibility of a quadrotor within the camera field-of-view of a ground robot in the presence of external disturbances. The quadrotor needs to be tracked by the ground robot with a monocular camera, and hence its motion should facilitate the ground vision-based tracking process by remaining in the effective camera sensing area. We design a model predictive controller (MPC) strategy where the visibility constraints of the camera and the control input constraints of the quadrotor are encoded into the cost function via barrier functions, and we adopt a fast MPC solver that is able to solve the optimization problem in real time. We also propose a method to enhance the robustness of the algorithm by suitably defining a restart method for the MPC solver. The applicability of the proposed algorithm is demonstrated through simulations and experimental results on real setups.
UR - http://www.scopus.com/inward/record.url?scp=84992151590&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992151590&partnerID=8YFLogxK
U2 - 10.1109/ACC.2016.7525254
DO - 10.1109/ACC.2016.7525254
M3 - Conference contribution
AN - SCOPUS:84992151590
T3 - Proceedings of the American Control Conference
SP - 2259
EP - 2264
BT - 2016 American Control Conference, ACC 2016
T2 - 2016 American Control Conference, ACC 2016
Y2 - 6 July 2016 through 8 July 2016
ER -