Aerial Wilderness Search and Rescue with Ground Support

被引:33
作者
Kashino, Zendai [1 ]
Nejat, Goldie [1 ]
Benhabib, Beno [1 ]
机构
[1] Univ Toronto, 5 Kings Coll Rd, Toronto, ON M5S 3G8, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Autonomous mobile-target search; UAV-UGV Cooperative search planning; Iso-probability curves; Wilderness search and rescue; ROBOTIC INTERCEPTION; TARGET DETECTION; MOVING-OBJECTS; UAV; SURVEILLANCE; SYSTEM; AIR; COVERAGE; TRACKING; STRATEGY;
D O I
10.1007/s10846-019-01105-y
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Unmanned aerial vehicles (UAVs) have been proposed for a wide range of applications. Their use in wilderness search and rescue (WiSAR), in particular, has been investigated for fast search-area coverage from a high vantage point. The probability of success in such searches, however, can be further improved utilizing cooperative systems that employ both UAVs and unmanned ground vehicles (UGVs). In this paper, we present a new coordinated-search planning method, for collaborative UAV-UGV teams. The proposed method, particularly developed for WiSAR, considers the search area to be continuously growing and that the search is sparse. It is also assumed that targets detected by UAVs must be identified by a ground-level searcher. The UAV/UGV motion-planning method presented herein, therefore, has two major components: (i) coordinated search and (ii) joint target identification. The novelty of the proposed method lies in its use of (i) time-dependent target-location iso-probability curves, and (ii) an effective and efficient coordinated target-identification algorithm. The method has been validated via numerous simulated WiSAR searches for varying scenarios. Furthermore, extensive comparative experiments with other methods have shown that our method has higher rates of target detection and shorter search times, significantly outperforming alternative techniques by 75% - 255% in terms of target detection probability.
引用
收藏
页码:147 / 163
页数:17
相关论文
共 65 条
[11]   Radiation search operations using scene understanding with autonomous UAV and UGV [J].
Christie, Gordon ;
Shoemaker, Adam ;
Kochersberger, Kevin ;
Tokekar, Pratap ;
McLean, Lance ;
Leonessa, Alexander .
JOURNAL OF FIELD ROBOTICS, 2017, 34 (08) :1450-1468
[12]   NEAR-TIME OPTIMAL ROBOT MOTION PLANNING FOR ONLINE APPLICATIONS [J].
CROFT, EA ;
BENHABIB, B ;
FENTON, RG .
JOURNAL OF ROBOTIC SYSTEMS, 1995, 12 (08) :553-567
[13]   Active Autonomous Aerial Exploration for Ground Robot Path Planning [J].
Delmerico, Jeffrey ;
Mueggler, Elias ;
Nitsch, Julia ;
Scaramuzza, Davide .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2017, 2 (02) :664-671
[14]  
Dinnbier NM, 2017, INT CONF UNMAN AIRCR, P1418
[15]   An analysis of probability of area techniques for missing persons in Yosemite National Park [J].
Doherty, Paul J. ;
Guo, Quinghua ;
Doke, Jared ;
Ferguson, Don .
APPLIED GEOGRAPHY, 2014, 47 :99-110
[16]   Unmanned air/ground vehicles heterogeneous cooperative techniques: Current status and prospects [J].
Duan HaiBin ;
Liu SenQi .
SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2010, 53 (05) :1349-1355
[17]  
Flushing E.F., 2013 IEEE INT S SAFE, V2013, DOI [DOI 10.1109/SSRR.2013.6719370, 10.1109/SSRR.2013.6719370]
[18]   Toward a systems- and control-oriented agent framework [J].
Fregene, K ;
Kennedy, DC ;
Wang, DWL .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2005, 35 (05) :999-1012
[19]   Recursive Bayesian search-and-tracking using coordinated UAVs for lost targets [J].
Furukawa, Tomonari ;
Bourgault, Frederic ;
Lavis, Benjamin ;
Durrant-Whyte, Hugh F. .
2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), VOLS 1-10, 2006, :2521-+
[20]   Supporting wilderness search and rescue using a camera-equipped mini UAV [J].
Goodrich, Michael A. ;
Morse, Bryan S. ;
Gerhardt, Damon ;
Cooper, Joseph L. ;
Quigley, Morgan ;
Adams, Julie A. ;
Humphrey, Curtis .
JOURNAL OF FIELD ROBOTICS, 2008, 25 (1-2) :89-110