Differential Evolution Based Receding Horizon Control for UAV Motion Planning in Dynamic Environments
ZHANG Xing1,2, BAI YongQiang1,2, XIN Bin2,3, CHEN Jie1,2
1. School of Automation, Beijing Institute of Technology, Beijing 100081, China;
2. Key Laboratory of Complex System Intelligent Control and Decision, Ministry of Education, Beijing 100081, China;
3. Decision and Cognitive Sciences Research Centre, Manchester Business School, University of Manchester, Manchester M15 6PB, UK
Differential Evolution Based Receding Horizon Control for UAV Motion Planning in Dynamic Environments
ZHANG Xing1,2, BAI YongQiang1,2, XIN Bin2,3, CHEN Jie1,2
1. School of Automation, Beijing Institute of Technology, Beijing 100081, China；
2. Key Laboratory of Complex System Intelligent Control and Decision, Ministry of Education, Beijing 100081, China；
3. Decision and Cognitive Sciences Research Centre, Manchester Business School, University of Manchester, Manchester M15 6PB, UK
ZHANG Xing, BAI YongQiang, XIN Bin, CHEN Jie. Differential Evolution Based Receding Horizon Control for UAV Motion Planning in Dynamic Environments[J]. 机器人, 2013, 35(1): 107-114.DOI: 10.3724/SP.J.1218.2013.00107.
ZHANG Xing, BAI YongQiang, XIN Bin, CHEN Jie. Differential Evolution Based Receding Horizon Control for UAV Motion Planning in Dynamic Environments. ROBOT, 2013, 35(1): 107-114. DOI: 10.3724/SP.J.1218.2013.00107.
摘要
This paper presents online motion planning for UAV (unmanned aerial vehicle) in complex threat field, including both static threats and moving threats, which can be formulated as a dynamic constrained optimal control problem. Receding horizon control (RHC) based on differential evolution (DE) algorithm is adopted. A location-predicting model of moving threats is established to assess the value of threat that UAV faces in flight. Then flyable paths can be generated by the control inputs which are optimized by DE under the guidance of the objective function. Simulation results demonstrate that the proposed method not only generates smooth and flyable paths, but also enables UAV to avoid threats efficiently and arrive at destination safely.
This paper presents online motion planning for UAV (unmanned aerial vehicle) in complex threat field, including both static threats and moving threats, which can be formulated as a dynamic constrained optimal control problem. Receding horizon control (RHC) based on differential evolution (DE) algorithm is adopted. A location-predicting model of moving threats is established to assess the value of threat that UAV faces in flight. Then flyable paths can be generated by the control inputs which are optimized by DE under the guidance of the objective function. Simulation results demonstrate that the proposed method not only generates smooth and flyable paths, but also enables UAV to avoid threats efficiently and arrive at destination safely.
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