Trajectory generation for quadrotor while tracking a moving target in cluttered environment

被引:0
作者
Xi, Lele [1 ,2 ,3 ]
Peng, Zhihong [1 ,2 ]
Jiao, Lei [1 ,2 ]
机构
[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
[2] State Key Lab Intelligent Control & Decis Complex, Beijing 100081, Peoples R China
[3] Peng Cheng Lab, Shenzhen 518055, Peoples R China
来源
PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE | 2020年
基金
美国国家科学基金会;
关键词
Target tracking; Trajectory generation; Waypoints constraint; Convex optimization;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we present a trajectory generation method for quadrotor while tracking a moving target with relative pattern. Compared to existing methods, safe flying zone, vehicle's physical limits and smoothness are considered to ensure obstacles avoidance and real tracking flight. To tackle with the cluttered environment, a parallel Particle Swarm Optimization algorithm is applied to calculate the safe feasible waypoints for quadrotor with consideration of target's predicted state and the realistic tracking pattern, as well as safety. Then, a multiple waypoints constraints motion planning method is applied and embed into a cost function for solving the problem of trajectories generation and robust tracking for quadrotor via convex optimization approach, ensuring the real-time planning, with the geometrical constraint of safe flight corridor for quadrotor. The effectiveness of proposed method is verified by numerical simulation experiments.
引用
收藏
页码:6792 / 6797
页数:6
相关论文
共 18 条
[1]  
Chen J, 2016, 2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016), P446, DOI 10.1109/IROS.2016.7759092
[2]  
Chen J, 2016, IEEE INT CONF ROBOT, P1476, DOI 10.1109/ICRA.2016.7487283
[3]   Single Camera Structure and Motion [J].
Dani, Ashwin P. ;
Fischer, Nicholas R. ;
Dixon, Warren E. .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2012, 57 (01) :241-246
[4]  
Gao F, 2018, IEEE INT CONF ROBOT, P344
[5]   A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots [J].
Giusti, Alessandro ;
Guzzi, Jerome ;
Ciresan, Dan C. ;
He, Fang-Lin ;
Rodriguez, Juan P. ;
Fontana, Flavio ;
Faessler, Matthias ;
Forster, Christian ;
Schmidhuber, Jurgen ;
Di Caro, Gianni ;
Scaramuzza, Davide ;
Gambardella, Luca M. .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2016, 1 (02) :661-667
[6]   Real-Time Trajectory Generation for Quadrocopters [J].
Hehn, Markus ;
D'Andrea, Raffaello .
IEEE TRANSACTIONS ON ROBOTICS, 2015, 31 (04) :877-892
[7]  
Lai SP, 2018, CHIN CONTR CONF, P10065, DOI 10.23919/ChiCC.2018.8483213
[8]  
Landry B, 2016, IEEE INT CONF ROBOT, P1469, DOI 10.1109/ICRA.2016.7487282
[9]   Policy iteration based Q-learning for linear nonzero-sum quadratic differential games [J].
Li, Xinxing ;
Peng, Zhihong ;
Liang, Li ;
Zha, Wenzhong .
SCIENCE CHINA-INFORMATION SCIENCES, 2019, 62 (05)
[10]   Planning Dynamically Feasible Trajectories for Quadrotors Using Safe Flight Corridors in 3-D Complex Environments [J].
Liu, Sikang ;
Watterson, Michael ;
Mohta, Kartik ;
Sun, Ke ;
Bhattacharya, Subhrajit ;
Taylor, Camillo J. ;
Kumar, Vijay .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2017, 2 (03) :1688-1695