TY - GEN
T1 - Online route planning for UAV based on model predictive control and particle swarm optimization algorithm
AU - Peng, Zhihong
AU - Li, Bo
AU - Chen, Xiaotian
AU - Wu, Jinping
PY - 2012
Y1 - 2012
N2 - Based on the model predictive control (MPC) and particle swarm optimization (PSO) algorithm, an online three-dimension route planning algorithm is proposed in this paper for UAV under the partially known task environment with appearing threats. By using the preplanning-online route tracking pattern, a reference route is planned in advance according to the known environment information. During the flight, the UAV tracks the reference route and detects the information of the environment and threats. Based on the MPC and PSO algorithm, the online route planning can be achieved by means of route prediction and receding horizon optimization. In such a case, UAV can avoid the known and appearing threats successfully. Compared to the traditional online route planning algorithm, the proposed method, by making use of the partially known information, can reduce the complexity, and meanwhile improve the real-time and the feasibility of the planning route. Simulation results demonstrate the effectiveness of the proposed algorithm.
AB - Based on the model predictive control (MPC) and particle swarm optimization (PSO) algorithm, an online three-dimension route planning algorithm is proposed in this paper for UAV under the partially known task environment with appearing threats. By using the preplanning-online route tracking pattern, a reference route is planned in advance according to the known environment information. During the flight, the UAV tracks the reference route and detects the information of the environment and threats. Based on the MPC and PSO algorithm, the online route planning can be achieved by means of route prediction and receding horizon optimization. In such a case, UAV can avoid the known and appearing threats successfully. Compared to the traditional online route planning algorithm, the proposed method, by making use of the partially known information, can reduce the complexity, and meanwhile improve the real-time and the feasibility of the planning route. Simulation results demonstrate the effectiveness of the proposed algorithm.
KW - model predictive control
KW - online route planning
KW - particle swarm optimization
KW - unmanned aerial vehicle (UAV)
UR - https://www.scopus.com/pages/publications/84872361196
U2 - 10.1109/WCICA.2012.6357907
DO - 10.1109/WCICA.2012.6357907
M3 - Conference contribution
AN - SCOPUS:84872361196
SN - 9781467313988
T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
SP - 397
EP - 401
BT - WCICA 2012 - Proceedings of the 10th World Congress on Intelligent Control and Automation
T2 - 10th World Congress on Intelligent Control and Automation, WCICA 2012
Y2 - 6 July 2012 through 8 July 2012
ER -