Gaussian process regression for radiological contamination mapping- Applied to optimal motion planning for mobile sensor platforms [Slides]
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Want to achieve best representative characterization of the entire area efficiently and accurately-Unmanned aerial/ground vehicles (UAV/UGVs) for contamination mapping. Some major challenges include: Limited battery life (move smart), Human operated (fully autonomous controls) and Many measurements (predictive mapping capabilities). The objective: Develop fully autonomous controls for mobile sensor platforms to improve efficiency and maintain performance.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- 89233218CNA000001
- OSTI ID:
- 1822694
- Report Number(s):
- LA-UR-21-29400
- Country of Publication:
- United States
- Language:
- English
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