Provably Safe Stein Variational Clarity-Aware Informative Planning

TL;DR

This paper introduces a novel planning framework for autonomous robots that models spatially varying information decay and enforces safety through filtering, enabling safer and more informative navigation in dynamic environments.

Contribution

It presents Stein Variational Clarity-Aware Planning, integrating clarity dynamics into trajectory optimization and safety filtering, advancing prior methods by handling non-uniform information decay and safety guarantees.

Findings

Demonstrates safety and reduced information deficits in simulations.

Validates approach through hardware experiments in complex environments.

Effectively models spatially varying information decay.

Abstract

Autonomous robots are increasingly deployed for information-gathering tasks in environments that vary across space and time. Planning informative and safe trajectories in such settings is challenging because information decays when regions are not revisited. Most existing planners model information as static or uniformly decaying, ignoring environments where the decay rate varies spatially; those that model non-uniform decay often overlook how it evolves along the robot's motion, and almost all treat safety as a soft penalty. In this paper, we address these challenges. We model uncertainty in the environment using clarity, a normalized representation of differential entropy from our earlier work that captures how information improves through new measurements and decays over time when regions are not revisited. Building on this, we present Stein Variational Clarity-Aware Informative Planning, a framework that embeds clarity dynamics within trajectory optimization and enforces safety through a low-level filtering mechanism based on our earlier gatekeeper framework for safety verification. The planner performs Bayesian inference-based learning via Stein variational inference, refining a distribution over informative trajectories while filtering each nominal Stein informative trajectory to ensure safety. Hardware experiments and simulations across environments with varying decay rates and obstacles demonstrate consistent safety and reduced information deficits.