TY - JOUR
T1 - Swarm Signal Temporal Logic Inference for Swarm Behavior Analysis
AU - Yan, Ruixuan
AU - Xu, Zhe
AU - Julius, Agung
N1 - Funding Information:
This work was supported by the National Science Foundation through under Grant CNS-1618369
Funding Information:
Manuscript received February 2, 2019; accepted June 10, 2019. Date of publication June 24, 2019; date of current version July 1, 2019. This letter was recommended for publication by Associate Editor Y. Amirat and Editor D. Song upon evaluation of the reviewers’ comments. This work was supported by the National Science Foundation through under Grant CNS-1618369. (Corresponding author: Ruixuan Yan.) R. Yan and A. Julius are with the Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA (e-mail: yanr5@rpi.edu; julia2@rpi.edu).
Publisher Copyright:
© 2016 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - In this letter, we propose methods to perform swarm behavior analysis with a novel swarm signal temporal logic (SwarmSTL). We define generalized moments to describe swarm features and propose a logical proposition to represent an event, where the Boolean value of the logical proposition at a certain time is known a priori. We develop methods for SwarmSTL monitoring and inference. As the swarm size can be large, we also propose methods to perform the above tasks by sampling. The methods are applied to three case studies that aim to monitor swarm 'maneuver' behavior, infer a SwarmSTL formula to describe the cause of split, and explore a SwarmSTL formula to describe 'mixed-species foraging flock' of birds.
AB - In this letter, we propose methods to perform swarm behavior analysis with a novel swarm signal temporal logic (SwarmSTL). We define generalized moments to describe swarm features and propose a logical proposition to represent an event, where the Boolean value of the logical proposition at a certain time is known a priori. We develop methods for SwarmSTL monitoring and inference. As the swarm size can be large, we also propose methods to perform the above tasks by sampling. The methods are applied to three case studies that aim to monitor swarm 'maneuver' behavior, infer a SwarmSTL formula to describe the cause of split, and explore a SwarmSTL formula to describe 'mixed-species foraging flock' of birds.
KW - Formal methods in robotics and automation
KW - Optimization and optimal control
KW - Swarms
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U2 - 10.1109/LRA.2019.2924843
DO - 10.1109/LRA.2019.2924843
M3 - Article
AN - SCOPUS:85068728883
VL - 4
SP - 3021
EP - 3028
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 3
M1 - 8744570
ER -