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Online route planning for UAV based on model predictive control and particle swarm optimization algorithm

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4 Author(s)
Zhihong Peng ; Sch. of Autom., Beijing Inst. of Technol., Beijing, China ; Bo Li ; Xiaotian Chen ; Jinping Wu

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.

Published in:

Intelligent Control and Automation (WCICA), 2012 10th World Congress on

Date of Conference:

6-8 July 2012