The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy

TL;DR

The paper presents the open-source Unified Autonomy Stack, a comprehensive system enabling resilient, multi-modal perception, planning, and safe navigation for diverse robots in challenging environments, validated through extensive field tests.

Contribution

It introduces a novel, integrated autonomy architecture with multi-modal sensing and planning modules, validated on aerial and ground robots in complex scenarios.

Findings

Demonstrated resilient navigation in GNSS-denied and perceptually-degraded environments.

Validated the stack on aerial and ground robots in demanding conditions.

Open-sourced the implementation with datasets and documentation.

Abstract

We introduce and open-source the Unified Autonomy Stack, a system-level solution that enables resilient autonomy across diverse aerial and ground robot morphologies. The architecture centers on three synergistic modules -- multi-modal perception, multi-behavior planning, and multi-layered safe navigation -- that together deliver comprehensive mission autonomy. The stack fuses data from LiDAR, radar, vision, and inertial sensing, enabling (a) robust localization and mapping through factor graph-based fusion, (b) semantic scene understanding, (c) motion and informative path planning through sampling-based techniques adaptive across spatial scales, as well as (d) multi-layered safe navigation both through planning on the online reconstructed map and deep learning-driven exteroceptive policies alongside last-resort safety filters using control barrier functions. The resulting behaviors include safe GNSS-denied navigation into unknown and perceptually-degraded regions, exploration of complex environments, object discovery, and efficient inspection planning. The stack has been field-tested and validated on both aerial (rotorcraft) and ground (legged) robots operating in a host of demanding environments, including self-similar and smoke-filled settings, with complex geometries and high obstacle clutter. These tests demonstrate resilient performance in challenging conditions. To facilitate ease of adoption, we open-source the implementation alongside supporting documentation, validation, and evaluation datasets https://github.com/ntnu-arl/unified_autonomy_stack. A video giving the overview of the paper and the field experiments is available at https://youtu.be/l8Su8OXsM-E.