Estimation-Aware Trajectory Optimization with Set-Valued Measurement Uncertainties

2025-01-15

AI Summary

This work addresses mobile trajectory prediction under state-dependent, set-valued measurement uncertainty. Method: We propose an estimation-aware trajectory optimization framework that integrates set-valued analysis, local linearization modeling, and a machine learning (ML)-driven visual estimation module. Its core innovation is the first formulation of a concave observability metric based on output-regular set-valued mappings—capable of characterizing non-Gaussian, state-dependent set uncertainties—and embedding this metric directly into nonlinear trajectory optimization. Results: Evaluated on cooperative-free satellite tracking, the optimized trajectories significantly improve both localization accuracy and robustness of the ML-based estimator, thereby enabling reliable perception–control closed-loop operation in realistic scenarios.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this paper, we present an optimization-based framework for generating estimation-aware trajectories in scenarios where measurement (output) uncertainties are state-dependent and set-valued. The framework leverages the concept of regularity for set-valued output maps. Specifically, we demonstrate 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, optimized estimation-aware trajectories can be designed for a broad class of systems, including those with locally linearized dynamics. To illustrate the effectiveness of the proposed approach, we provide a representative example in the context of trajectory planning for vision-based estimation. We present an estimation-aware trajectory for an uncooperative target-tracking problem that uses a machine learning (ML)-based estimation module on an ego-satellite.

Problem

• Optimal Path Prediction
• Complex Measurement Errors
• Dynamic Systems

Innovation

• Optimal Path Prediction
• State-dependent Multi-valued Measurements
• AI-based Prediction Techniques