CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge

Abstract

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.

Learning robust perceptive locomotion for quadrupedal robots in the wild

“…Navigation planners no longer need to identify ground type or to switch modes during autonomous operation. Our con troller was used as the default controller in the Defense Ad vanced Research Pro jects Agency Subterranean Challenge missions of team Cerberus (50,51), which has won the first prize in the finals (52). In this challenge, our con troller drove ANYmals to oper ate autonomously over extended periods of time in underground environments with rough terrain, obstructions, and degraded sens ing in the presence of dust, fog, water, and smoke (53).…”

Autonomous Exploration of Unknown Indoor Environments for High-Quality Mapping Using Feature-Based RGB-D SLAM

“…On the other hand, high-end sensors and robots are utilized in extreme conditions, such as the exploration of the underground environments in the competitions funded by the Defense Advanced Research Projects Agency (DARPA), called the “DARPA Subterranean Challenge”. The last winning team [ 25 ] developed a team of robots to explore the three challenges of the competition: the “Tunnel Circuit”, the “Urban Circuit”, and the “Cave Circuit”, using their robot and exploration systems [ 26 , 27 , 28 ].…”

Elevation Mapping for Locomotion and Navigation using GPU

“…Our implementation supported legged locomotion and navigation research and formed the basis for perceiving surrounding terrains used by learning-based controllers [30], [38], [29], model-based controllers [23], [22], [16], or others such as navigation [45], [47] or learning occlusion filling [40]. In addition, the proposed elevation mapping solution was successfully used for underground exploration missions during Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge [9] where team CERBERUS [8], [42] deployed our mapping on four quadrupedal robots, ANYmal [21].…”