Closed-Loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
| Autor: | Luke Burks, Jeremy Muesing, Nisar Ahmed, Ian Loefgren, Luke Barbier, Jamison McGinley, Sousheel Vunnam |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer Science - Artificial Intelligence Computer science Systems and Control (eess.SY) 02 engineering and technology Machine learning computer.software_genre Semantic data model Computer Science - Robotics 020901 industrial engineering & automation FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering business.industry Testbed Partially observable Markov decision process Mixture model Artificial Intelligence (cs.AI) Scalability Computer Science - Systems and Control 020201 artificial intelligence & image processing State (computer science) Artificial intelligence business Robotics (cs.RO) computer Natural language |
| Zdroj: | FUSION |
| DOI: | 10.23919/icif.2018.8455634 |
| Popis: | In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged -- including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed. Comment: Final version accepted and submitted to 2018 FUSION Conference (Cambridge, UK, July 2018) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|