AWARE: Adaptive Whole-body Active Rotating Control for Enhanced LiDAR-Inertial Odometry under Human-in-the-Loop Interaction

2026-04-12

AI Summary

This work addresses the challenge of degraded observability in LiDAR-inertial odometry (LIO) during human–drone collaborative operations, where limited field-of-view LiDAR sensors yield insufficient geometric features in sparse or degenerate environments, leading to pose drift and estimation instability. To mitigate this, the authors propose a biologically inspired whole-body active yaw control framework that autonomously rotates the drone to expand its perceptual coverage, thereby enhancing LIO observability without additional mechanical complexity. The approach integrates differentiable model predictive control with lightweight reinforcement learning to maximize information gain in an environment-adaptive manner, while a safety-aware flight corridor mechanism decouples the operator’s navigation intent from autonomous yaw optimization to ensure safe collaboration. Experiments demonstrate significant improvements in state estimation accuracy, stability, and human–drone cooperative efficiency in both simulated and real-world scenarios.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Human-in-the-loop (HITL) UAV operation is essential in complex and safety-critical aerial surveying environments, where human operators provide navigation intent while onboard autonomy must maintain accurate and robust state estimation. A key challenge in this setting is that resource-constrained UAV platforms are often limited to narrow-field-of-view LiDAR sensors. In geometrically degenerate or feature-sparse scenes, limited sensing coverage often weakens LiDAR Inertial Odometry (LIO)'s observability, causing drift accumulation, degraded geometric accuracy, and unstable state estimation, which directly compromise safe and effective HITL operation and the reliability of downstream surveying products. To overcome this limitation, we present AWARE, a bio-inspired whole-body active yawing framework that exploits the UAV's own rotational agility to extend the effective sensor horizon and improve LIO's observability without additional mechanical actuation. The core of AWARE is a differentiable Model Predictive Control (MPC) framework embedded in a Reinforcement Learning (RL) loop. It first identifies the viewing direction that maximizes information gain across the full yaw space, and a lightweight RL agent then adjusts the MPC cost weights online according to the current environmental context, enabling an adaptive balance between estimation accuracy and flight stability. A Safe Flight Corridor mechanism further ensures operational safety within this HITL paradigm by decoupling the operator's navigational intent from autonomous yaw optimization to enable safe and efficient cooperative control. We validate AWARE through extensive experiments in diverse simulated and real-world environments.

Problem

• Human-in-the-loop
• LiDAR-Inertial Odometry
• observability
• narrow-field-of-view LiDAR
• state estimation

Innovation

• Adaptive Active Control
• LiDAR-Inertial Odometry
• Human-in-the-Loop
• Differentiable MPC
• Reinforcement Learning