Autonomous exploration of subterranean environments remains a major challenge for robotic systems. In response, this paper contributes a novel graph-based subterranean exploration path planning method that is attuned to key topological properties of subterranean settings, such as large-scale tunnel-like networks and complex multibranched topologies. Designed both for aerial and legged robots, the proposed method is structured around a bifurcated local-and global-planner architecture. The local planner utilizes a rapidly exploring random graph to reliably and efficiently identify paths that optimize an exploration gain within a local subspace, while simultaneously avoiding obstacles, respecting applicable traversability constraints and honoring dynamic limitations of the robots. Reflecting the fact that multibranched and tunnel-like networks of underground environments can often lead to dead-ends and accounting for the robot endurance, the global planning layer works in conjunction with the local planner to incrementally build a sparse global graph and is engaged when the system must be repositioned to a previously identified frontier of the exploration space, or commanded to return-to-home. The designed planner is detailed with respect to its computational complexity and compared against state-of-the-art approaches. Emphasizing field experimentation, the method is evaluated within multiple real-life deployments using aerial robots and the ANYmal legged system inside both long-wall and room-and-pillar underground mines in the United States and in Switzerland, as well as inside an underground bunker. The presented results further include missions conducted within the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge, a relevant competition on underground exploration.
This paper provides insight into the application of the quadrupedal robot ANYmal in outdoor missions of industrial inspection (autonomous robot for gas and oil sites[ARGOS] challenge) and search and rescue (European Robotics League (ERL) Emergency Robots). In both competitions, the legged robot had to autonomously and semiautonomously navigate in real-world scenarios to complete high-level tasks such as inspection and payload delivery. In the ARGOS competition, ANYmal used a rotating light detection and ranging sensor to localize on the industrial site and map the terrain and obstacles around the robot. In the ERL competition, additional realtime kinematic-global positioning system was used to colocalize the legged robot with respect to a micro aerial vehicle that creates maps from the aerial view. The high mobility of legged robots allows overcoming large obstacles, for example, steps and stairs, with statically and dynamically stable gaits. Moreover, the versatile machine can adapt its posture for inspection and payload delivery. The paper concludes with insight into the general learnings from the ARGOS and ERL challenges.
This article presents the core technologies and deployment strategies of Team CERBERUS that enabled our winning run in the DARPA Subterranean Challenge finals. CERBERUS is a robotic system-of-systems involving walking and flying robots presenting resilient autonomy, as well as mapping and navigation capabilities to explore complex underground environments.
Autonomous exploration of subterranean environments constitutes a major frontier for robotic systems, as underground settings present key challenges that can render robot autonomy hard to achieve. This problem has motivated the DARPA Subterranean Challenge, where teams of robots search for objects of interest in various underground environments. In response, we present the CERBERUS system-of-systems, as a unified strategy for subterranean exploration using legged and flying robots. Our proposed approach relies on ANYmal quadraped as primary robots, exploiting their endurance and ability to traverse challenging terrain. For aerial robots, we use both conventional and collision-tolerant multirotors to explore spaces too narrow or otherwise unreachable by ground systems. Anticipating degraded sensing conditions, we developed a complementary multimodal sensor-fusion approach, utilizing camera, LiDAR, and inertial data for resilient robot pose estimation. Individual robot pose estimates are refined by a centralized multi-robot map-optimization approach to improve the reported location accuracy of detected objects of interest in the DARPA-defined coordinate frame. Furthermore, a unified exploration path-planning policy is presented to facilitate the autonomous operation of both legged and aerial robots in complex underground networks. Finally, to enable communication among team agents and the base station, CERBERUS utilizes a ground rover with a high-gain antenna and an optical fiber connection to the base station and wireless “breadcrumb” nodes deployed by the legged robots. We report results from the CERBERUS system-of-systems deployment at the DARPA Subterranean Challenge’s Tunnel and Urban Circuit events, along with the current limitations and the lessons learned for the benefit of the community.
This study describes the hardware and software systems of the Micro Aerial Vehicle (MAV) platforms used by the ETH Zurich team in the 2017 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The aim was to develop robust outdoor platforms with the autonomous capabilities required for the competition, by applying and integrating knowledge from various fields, including computer vision, sensor fusion, optimal control, and probabilistic robotics. This paper presents the major components and structures of the system architectures and reports on experimental findings for the MAV‐based challenges in the competition. Main highlights include securing the second place both in the individual search, pick, and place the task of Challenge 3 and the Grand Challenge, with autonomous landing executed in less than 1 min and a visual servoing success rate of over 90% for object pickups.
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