Measurement Maximizing Adaptive Sampling with Risk Bounding Functions
© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. In autonomous exploration a mobile agent must adapt to new measurements to seek high reward, but disturbances cause a probability of collision that must be traded off against expected reward. This...
Main Authors: | Ayton, Benjamin James, Williams, Brian C, Camilli, Richard |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | English |
Published: |
Association for the Advancement of Artificial Intelligence (AAAI)
2021
|
Online Access: | https://hdl.handle.net/1721.1/137367 |
Similar Items
-
Toward Information-Driven and Risk-Bounded Autonomy for Adaptive Science and Exploration
by: Ayton, Benjamin J, et al.
Published: (2022) -
Query-Driven Adaptive Sampling
by: Ayton, Benjamin James
Published: (2023) -
Risk-bounded autonomous information gathering for localization of phenomena in hazardous environments
by: Ayton, Benjamin James
Published: (2018) -
Information-Driven and Risk-Bounded Autonomy for Scientist Avatars
by: Timmons, Eric M, et al.
Published: (2022) -
Lp bounds for rough parabolic maximal operators
by: Mohammed Ali, et al.
Published: (2020-10-01)