Paper Info
Title
Motion Planning for Heterogeneous Unmanned Systems under Partial Observation from UAV
Abstract
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.
Year
DOI
Venue
2020
10.1109/IROS45743.2020.9341326
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Keywords
DocType
ISSN
heterogeneous unmanned systems,partial observation,unmanned ground vehicles,UAV,partially observable Markov decision process,motion planning,grid map generation network,motion command generation network,MCGN,GMGN
Conference
2153-0858
ISBN
Citations 
PageRank 
978-1-7281-6213-3
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Chen Ci1276.10
Yuanfang Wan200.34
Baowei Li300.68
Chen Wang413516.47
Guangming Xie5127696.56
Huanyu Jiang601.01