Motion planning for heterogeneous unmanned systems under partial observation from UAV

Chen，Ci1; Wan，Yuanfang4; Li，Baowei3; Wang，Chen2,3; Xie，Guangming3,5; Jiang，Huanyu1

10.1109/IROS45743.2020.9341326

2020-10-24

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2153-0858

2153-0866

978-1-7281-6213-3

IEEE International Conference on Intelligent Robots and Systems

1474-1479

OCT 24-JAN 24, 2020-2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

For heterogeneous unmanned systems composed of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), using UAVs serve as eyes to assist UGVs in motion planning is a promising research direction due to the UAVs' vast view scope. However, its limitations on flight altitude prevent the UAVs from observing the global map. Thus motion planning in the local map becomes a Partially Observable Markov Decision Process (POMDP) problem. This paper proposes a motion planning algorithm for heterogeneous unmanned systems under partial observation from UAV without reconstruction of global maps. Our algorithm consists of two parts designed for perception and decision-making, respectively. For the perception part, we propose the Grid Map Generation Network (GMGN), which is used to perceive scenes from UAV's perspective and classify the pathways and obstacles. For the decision-making part, we propose the Motion Command Generation Network (MCGN). Due to the addition of the memory mechanism, MCGN has planning and reasoning abilities under partial observation from UAVs. We evaluate our proposed algorithm by comparing it with baseline algorithms. The results show that our method effectively plans the motion of heterogeneous unmanned systems and achieves a relatively high success rate.

Decision making Markov processes Unmanned aerial vehicles Land vehicles Planning Classification algorithms Intelligent robots

其他

英语

EI ; CPCI-S

National Natural Science Foundation of China (NSFC)[61973007,61633002]

Automation & Control Systems ; Computer Science ; Engineering ; Robotics

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Robotics

WOS:000714033800030

20211110064007

Agricultural robots ; Antennas ; Behavioral research ; Decision making ; Ground vehicles ; Intelligent robots ; Markov processes ; Motion planning

Aircraft, General:652.1 ; Robot Applications:731.6 ; Management:912.2 ; Probability Theory:922.1 ; Social Sciences:971

2-s2.0-85102411830

Scopus

1.Zhejiang University,College of Biosystems Engineering and Food Science,Hangzhou,310058,China
2.Peking University,National Engineering Research Center for Software Engineering,Beijing,100871,China
3.Peking University,The State Key Laboratory of Turbulence and Complex Systems,Intelligent Biomimetic Design Lab,College of Engineering,Beijing,100871,China
4.Southern University of Science and Technology,Department of Ocean Science and Engineering,Shenzhen,518055,China
5.Peng Cheng Laboratory,Shenzhen,518055,China

Chen，Ci,Wan，Yuanfang,Li，Baowei,et al. Motion planning for heterogeneous unmanned systems under partial observation from UAV[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2020:1474-1479.