1.3.2: Ergodic Control for Optimal Information Acquisition
Abstract
The objective of the proposed research is to develop an active search process that incorporates information measures into trajectory design to estimate the state of continuous, and potentially time-varying fields. Nonlinear optimal control theory plays a central role, and we are developing the tools necessary to select continuous-time trajectories for systems with nontrivial dynamics---for instance, systems with nonholonomic constraints, significant inertial effects,nonsmooth behavior---and often in high dimensions. The developed techniques use measures of ergodicity and the associated ergodic control to robustly explore for information while ensuring coverage of the informative subset of the state space (as indicated by prior measurements or even the lack thereof). The developed methods provide a synthesis technique for active sensing so that autonomous system scan optimize knowledge of continuous phenomena such as fluid or airflow fields, chemical dispersion, magnetic fields, electric fields, and other continuum phenomena. Further, by developing trajectories that enable fast and accurate estimation of the state and evolution of such fields in local regions, the research can support forecasting and prediction. Lastly, the outcomes of this work were tested both in computation and in experimental testbeds.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 22, 2018
- Accession Number
- AD1064964
Entities
People
- Todd D Murphey
Organizations
- Northwestern University