Belief-Space Planning with Planner-Conditioned Estimator Error under Intermittent Observations

2026-10-06

AI Summary

This study addresses the non-zero-mean estimation errors induced by planner information under intermittent observations, where conventional zero-mean assumptions neglect correction uncertainty. To this end, we propose a belief-space planning method that introduces a novel planner-conditioned model integrating affine error dynamics with moment recursion to preserve uncertainties arising from stochastic corrections. By predicting and penalizing conditioned estimation errors, the approach optimizes a quadratic risk objective and achieves closed-loop decision-making via receding horizon control. Experimental results demonstrate that the proposed method reduces error prediction loss by 13.4% and decreases the median terminal task metric for VTOL landing from 1.60 to 0.99, significantly enhancing system accuracy and robustness.

Abstract

Belief-space planners with separately designed or off-the-shelf estimators may have access to state-relevant information the estimator does not observe. Consequently, even an estimator that minimizes mean-squared error under its own information can have a nonzero error mean when conditioned on planner information. When future corrections are intermittent and stochastic, differences between accepted and rejected error means introduce additional uncertainty terms to correction events that zero-mean assumptions ignore. In this paper, we propose a belief-space planning approach that predicts, propagates, and penalizes planner-conditioned estimator error along evaluated trajectories. A planner-conditioned estimator-error model combines affine error dynamics with a moment recursion that preserves stochastically-induced correction uncertainty. We describe a method by which to predict estimator error dynamics within specified operating regimes and incorporate predicted error moments into a task-weighted quadratic risk objective. We evaluate the approach in simulation for a tilt-rotor VTOL landing on a ship deck using receding-horizon planning. We find that conditioning on planner information reduced error-prediction loss for a command-blind EKF by 13.4% relative to a planner-ignorant model, with strongly regime and horizon-dependent forecasting abilities. In a small set of 16 paired closed-loop trials, the planner lowered the median terminal task gauge from 1.60 to 0.99, and predicted estimator error beyond 2s within a factor of 1.3, versus a 5.7-fold underprediction by covariance-only planning.

Problem

• Belief-Space Planning
• Estimator Error
• Intermittent Observations
• Planner-Conditioned Information
• Uncertainty

Innovation

• Belief-Space Planning
• Planner-Conditioned Estimator Error
• Intermittent Observations
• Moment Recursion
• Quadratic Risk Objective