Resilient Autonomy for Subterranean Robotics

Resilient autonomy for subterranean robotics has been an area of intense study from our lab. 

CERBERUS: Deployment at the DARPA Subterranean Challenge Urban Circuit

This paper reports the technological progress and performance of team “CERBERUS” after participating in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge. The SubT Challenge is an international robotics competition organized by the Defense Advanced Research Projects Agency to inspire advances in resilient robotic autonomy in subterranean settings. 

Hypergame-based Adaptive Behavior Path Planning for Combined Exploration and Visual Search

In this work we present an adaptive behavior path planning method for autonomous exploration and visual search of unknown environments. As volumetric exploration and visual coverage of unknown environments, with possibly different sensors, are non-identical objectives, a principled combination of the two is proposed. In particular, the method involves three distinct planning policies, namely exploration, and sparse or dense visual coverage. 

Autonomous Aerial Robotic Subterranean Exploration inside the Moaning Caverns

In this video we present the autonomous exploration and mapping of a section of the Moaning Caverns subterranean environment. The Charlie aerial robotic scout, integrating and fusing LiDAR, visual, thermal and inertial data, localizes itself and maps the environment in a multi-modal fashion. Given the real-time built volumetric representation of the environment, a graph-based exploration path planning method facilitates autonomous path planning optimizing for exploration. 

DARPA SubT Urban Circuit: Autonomous Exploration in the Satsop Abandoned Power Plant

In this video, a mission of the Alpha Aerial Scout of Team CERBERUS during the DARPA Subterranean Challenge Urban Circuit event is presented. The Alpha Robot operates inside the Satsop Abandoned Power Plant and performs autonomous exploration. The robot autonomy relies on a resilient multi-modal localization and mapping solution fusing LiDAR, vision and inertial sensing, combined with a graph-based exploration path planner.

DARPA SubT Urban Circuit: Collision-tolerant Exploration of Staircases using Aerial Robots

In this video we present the autonomous exploration of a staircase with four sub-levels and the transition between two floors of the Satsop Nuclear Power Plant during the DARPA Subterranean Challenge Urban Circuit. The utilized system is a collision-tolerant flying robot capable of multi-modal Localization And Mapping fusing LiDAR, vision and inertial sensing. Autonomous exploration and navigation through the staircase is enabled through a Graph-based Exploration Planner.

Graph-based Exploration Path Planning - Aerial Robot inside an Underground Mine

​In this video we present results on autonomous subterranean exploration inside an abandoned underground mine using an aerial robot. The aerial robot is utilizing the proposed Graph-based Exploration Path Planner which ensures the efficient exploration of the complex underground environment, while simultaneously avoiding obstacles.

Graph-based Path Planner: ANYmal Quadruped Robot Exploring Gonzen Mine

In this video we present results on autonomous subterranean exploration inside an abandoned underground mine using the ANYmal legged robot. ANYmal is utilizing the proposed Graph-based Exploration Path Planner which ensures the efficient exploration of the complex underground environment, while simultaneously avoiding obstacles and respecting traversability constraints.
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Aerial Robotic Graph-based Exploration Path Planning during the DARPA SubT Challenge Tunnel Circuit

In this video we present an autonomous exploration deployment of our aerial robotic scouts inside an underground mine in Pittsburgh. The robot is utilizing the proposed Graph-based Exploration Planner to guide its motion so as to explore a first long segment inside this subterranean environment. The presented result took place in the framework of the Tunnel Circuit competition phase of the DARPA SubT Challenge.

Motion Primitives-based Path Planning for Fast and Agile Exploration using Aerial Robots

This work presents a novel path planning strategy for fast and agile exploration using aerial robots. Tailored to the combined need for large-scale exploration of challenging and confined environments, despite the limited endurance of micro aerial vehicles, the proposed planner employs motion primitives to identify admissible paths that search the configuration space, while exploiting the dynamic flight properties of small aerial robots. ​​

Learning-based Path Planning for Autonomous Exploration of Subterranean Environments

In this work we present a new methodology on learning-based path planning for autonomous exploration of subterranean environments using aerial robots. Utilizing a recently proposed graph-based path planner as a "training expert" and following an approach relying on the concepts of imitation learning, we derive a trained policy capable of guiding the robot to autonomously explore underground mine drifts and tunnels.

Keyframe-based Thermal-Inertial Odometry

This work presents a keyframe-based thermal-inertial odometry estimation framework tailored to the exact data and concepts of operation of thermal cameras which provide a potential solution to penetrate and overcome conditions of darkness and strong presence of obscurants. The developed framework was verified with respect to its resilience, performance and ability to enable autonomous navigation in an extensive set of experimental studies including multiple field deployments in severely degraded, dark and obscurants-filled underground mines.

Subterranean Exploration: When the going gets tough ...

... the robots get going! 

The presented results are from a series of deployments in underground environments conducted in the framework of the DARPA Subterranean Challenge - Team CERBERUS.

Autonomous Exploration and Mapping in Underground Mines using Aerial Robots

In this work we present a comprehensive solution towards autonomous exploration and mapping in underground mine environments. The work relies on a set of contributions in sensing-degraded localization and mapping, as well as exploration path planning

Graph-based Path Planning for Autonomous Robotic Exploration in Subterranean Environments

This work presents a new strategy for autonomous graph-based exploration path planning in subterranean environments. Tailored to the fact that subterranean settings such as underground mines are often large-scale networks of narrow tunnel-like and multi-branched topologies, the proposed planner is structured around a bifurcated local- and global-planner architecture. ​​

Graph-based Path Planning for Autonomous Subterranean Exploration

In this work we present new results on autonomous exploration and mapping of underground mines using aerial robots. A flying robot capable of sensing-degraded localization and mapping utilizes a new graph search-based planning algorithm to ensure efficient and smooth exploration even in narrow subterranean settings. 

Graph-based Path Planning for Autonomous Subterranean Exploration

In this work we present new results on autonomous exploration and mapping of underground mines using aerial robots. A flying robot capable of sensing-degraded localization and mapping utilizes a new graph search-based planning algorithm to ensure efficient and smooth exploration even in narrow subterranean settings. 

Autonomous Exploration and Mapping in Underground Mines using Aerial Robots

In this work we present a comprehensive solution towards autonomous exploration and mapping in underground mine environments. The work relies on a set of contributions in sensing-degraded localization and mapping, as well as exploration path planning

Contact-based Navigation Path Planning for Aerial Robots

In this work, we present a path planning method for exploiting contact by aerial robots to enable the traversal of highly anomalous surfaces. Apart from sliding in contact, the proposed strategy introduces a new locomotion modality of azimuth rotations perpendicular to the surface, dubbed the flying cartwheel mode.

Keyframe-based Direct Thermal-Inertial Odometry

In this work we present a method for fusion of direct radiometric data from a thermal camera with inertial measurements to enable pose estimation of aerial robots. LWIR thermal vision is not affected by the lack of illumination and the presence of obscurants such as fog and dust, rendering it suitable for GPS-denied, dark and obscurants-filled environments. In contrast to previous approaches, which use 8-bit re-scaled thermal imagery as a complementary sensing modality to visual image data, our approach makes use of the full  14 bit - radiometric data making it generalizable to a variety of environments. Furthermore, our approach implements a key-frame based joint optimization scheme, making odometry estimates robust against image data interruption, which is common during the operation of thermal cameras due to the application of flat field corrections.

Visual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments

In this work we present a multi--sensor fusion algorithm for reliable odometry estimation in GPS--denied and degraded visual environments. The proposed method utilizes information from both the visible and thermal spectra for landmark selection and prioritizes feature extraction from informative image regions based on a metric over spatial entropy. Furthermore, inertial sensing cues are integrated to improve the robustness of the odometry estimation process.

Field Deployment of Autonomous Aerial Robots in Underground Mines

In this video we present results from a field deployment of robotic systems inside an underground mine. The deployment involved the autonomous operation, exploration and mapping of underground mine drifts and headings using two aerial robots. The first robot based its operation on the fusion of visible-light and thermal camera data alongside IMU cues, while the second robot employed LiDAR as a prime sensing modality. Both robotic systems demonstrated the ability to operate in the challenging underground environment. In addition, we evaluated the ability of a comprehensive LiDAR, visual- and thermal-inertial sensor system to provide persistent localization when ferried onboard a truck navigating across the mine drifts.