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Multistage bayesian autonomy for high‐precision operation in a large field
Author(s) -
Hodges Jonathan,
Attia Tamer,
Arukgoda Janindu,
Kang Changkoo,
Cowden Mickey,
Doan Luan,
Ranasinghe Ravindra,
Abdelatty Karim,
Dissanayake Gamini,
Furukawa Tomonari
Publication year - 2019
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.21829
Subject(s) - computer science , robot , unmanned ground vehicle , metric (unit) , artificial intelligence , bayesian probability , field (mathematics) , computer vision , real time computing , engineering , mathematics , pure mathematics , operations management
This paper presents a generalized multistage bayesian framework to enable an autonomous robot to complete high‐precision operations on a static target in a large field. The proposed framework consists of two multistage approaches, capable of dealing with the complexity of high‐precision operation in a large field to detect and localize the target. In the multistage localization, locations of the robot and the target are estimated sequentially when the target is far away from the robot, whereas these locations are estimated simultaneously when the target is close. A level of confidence (LOC) for each detection criterion of a sensor and the associated probability of detection (POD) of the sensor are defined to make the target detectable with different LOCs at varying distances. Differential entropies of the robot and target are used as a precision metric for evaluating the performance of the proposed approach. The proposed multistage observation and localization approaches were applied to scenarios using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Results with the UGV in simulated environments and then real environments show the effectiveness of the proposed approaches to real‐world problems. A successful demonstration using the UAV is also presented.