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Estimation-Aware Trajectory Optimization with Set-Valued Measurement Uncertainties
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DOI：10.2514/1.G009299.png)
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Abstract
En 中文
In this paper, an optimization-based framework for generating estimation-aware trajectories is presented. In this setup, measurement (output) uncertainties are state-dependent and set-valued. Enveloping ellipsoids are employed to characterize state-dependent uncertainties with unknown distributions. The concept of regularity for set-valued output maps is then introduced, facilitating the formulation of the estimation-aware trajectory generation problem. Specifically, it is demonstrated that for output-regular maps, one can utilize a set-valued observability measure that is concave with respect to finite-horizon state trajectories. By maximizing this measure, estimation-aware trajectories can then be synthesized for a broad class of systems. Trajectory planning routines are also examined in this work, by which the observability measure is optimized for systems with locally linearized dynamics. To illustrate the effectiveness of the proposed approach, representative examples in the context of trajectory planning with vision-based estimation are presented. Moreover, the paper presents estimation-aware planning for an uncooperative target rendezvous problem, where an Ego satellite employs an onboard machine-learning-based estimation module to realize the rendezvous trajectory.
Keywords:
Optimal Control Problem
Satellites
Machine Learning
Nonlinear Model Predictive Control
Sequential Quadratic Programming
Applied Mathematics
Satellite Rendezvous
State Estimation
Estimation Aware
Vision Aided Navigation
Journal
J
IF:
2.8
Papers:
159
Citations:
1.3W
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Cited Papers