CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge
ETH Zurich（苏黎世联邦理工学院）；University of Nevada, Reno（内华达大学雷诺分校）；Norwegian University of Science and Technology（挪威科技大学）；Flyability（Flyability公司）；Oxford Robotics Institute, University of Oxford（牛津大学牛津机器人研究所）
Marco Tranzatto, Frank Mascarich, Lukas Bernreiter, Carolina Godinho, Marco Camurri, Shehryar Khattak, Tung Dang, Victor Reijgwart, Johannes Loeje, David Wisth,… 展开作者
Marco Tranzatto, Frank Mascarich, Lukas Bernreiter, Carolina Godinho, Marco Camurri, Shehryar Khattak, Tung Dang, Victor Reijgwart, Johannes Loeje, David Wisth, Samuel Zimmermann, Huan Nguyen, Marius Fehr, Lukas Solanka, Russell Buchanan, Marko Bjelonic, Nikhil Khedekar, Mathieu Valceschini, Fabian Jenelten, Mihir Dharmadhikari, Timon Homberger, Paolo De Petris, Lorenz Wellhausen, Mihir Kulkarni, Takahiro Miki, Satchel Hirsch, Markus Montenegro, Christos Papachristos, Fabian Tresoldi, Jan Carius, Giorgio Valsecchi, Joonho Lee, Konrad Meyer, Xiangyu Wu, Juan Nieto, Andy Smith, Marco Hutter, Roland Siegwart, Mark Mueller, Maurice Fallon, Kostas Alexis
备注：
50 pages, 25 figures. Accepted at Field Robotics, 2021
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 has motivated the DARPA Subterranean Challenge, where teams of robots search for objects of interest in various underground environments. In response, the CERBERUS system-of-systems is presented as a unified strategy towards subterranean exploration using legged and flying robots. As primary robots, ANYmal quadruped systems are deployed considering their endurance and potential to traverse challenging terrain. For aerial robots, both conventional and collision-tolerant multirotors are utilized to explore spaces too narrow or otherwise unreachable by ground systems. Anticipating degraded sensing conditions, a complementary multi-modal sensor fusion approach utilizing camera, LiDAR, and inertial data for resilient robot pose estimation is proposed. 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 between the robots and the base station, CERBERUS utilizes a ground rover with a high-gain antenna and an optical fiber connection to the base station, alongside breadcrumbing of wireless nodes by our legged robots. We report results from the CERBERUS system-of-systems deployment at the DARPA Subterranean Challenge Tunnel and Urban Circuits, along with the current limitations and the lessons learned for the benefit of the community.
NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；California Institute of Technology（加州理工学院）；Massachusetts Institute of Technology（麻省理工学院）；University of Southern California（南加州大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an u…
ETH Zurich（苏黎世联邦理工学院）；Norwegian University of Science and Technology（挪威科技大学）；University of Nevada, Reno（内华达大学雷诺分校）；University of Oxford（牛津大学）
This article presents the CERBERUS robotic system-of-systems, which won the DARPA Subterranean Challenge Final Event in 2021. The Subterranean Challenge was organized by DARPA with the vision to facilitate the novel technologies necessary to reliably explore diverse underground environments despite the grueling challenges they present for robotic autonomy. Due to their geometric complexity, degraded perceptual conditions combined with lack of GPS support, austere navigation conditions, and denied communications, subterranean settings render autonomous operations particularly demanding. In resp…
Faculty of Electrical Engineering, Czech Technical University（捷克技术大学电气工程学院）
We present a field report of CTU-CRAS-NORLAB team from the Subterranean Challenge (SubT) organised by the Defense Advanced Research Projects Agency (DARPA). The contest seeks to advance technologies that would improve the safety and efficiency of search-and-rescue operations in GPS-denied environments. During the contest rounds, teams of mobile robots have to find specific objects while operating in environments with limited radio communication, e.g. mining tunnels, underground stations or natural caverns. We present a heterogeneous exploration robotic system of the CTU-CRAS-NORLAB team, which…
Subterranean environments with lots of obstacles, including narrow passages, large voids, rock falls and absence of illumination were always challenging for control, navigation, and perception of mobile robots. The limited availability and access to such environments restricts the development pace of capabilities for robotic platforms to autonomously accomplish tasks in such challenging areas. The Subterranean Challenge is a competition focusing on bringing robotic exploration a step closer to real life applications for man-made underground tunnels, urban areas and natural cave networks, envis…
Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 hrs) were conducted based on the DARPA Subterranean Challenge. These missions were to deploy a heterogeneous team of robots for a search task to locate and identify artifacts such as climbing rope, drills and mannequins representing human survivors. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Tea…
University of Colorado Boulder（科罗拉多大学博尔德分校）；University of California Santa Cruz（加州大学圣克鲁兹分校）
Implementing a wireless mesh network in a real-life scenario requires a significant systems engineering effort to turn a network concept into a complete system. This paper presents an evaluation of a fielded system within the DARPA Subterranean (SubT) Challenge Final Event that contributed to a 3rd place finish. Our system included a team of air and ground robots, deployable mesh extender nodes, and a human operator base station. This paper presents a real-world evaluation of a stack optimized for air and ground robotic exploration in a RF-limited environment under practical system design limi…
Heterogeneous teams of robots, leveraging a balance between autonomy and human interaction, bring powerful capabilities to the problem of exploring dangerous, unstructured subterranean environments. Here we describe the solution developed by Team CSIRO Data61, consisting of CSIRO, Emesent and Georgia Tech, during the DARPA Subterranean Challenge. These presented systems were fielded in the Tunnel Circuit in August 2019, the Urban Circuit in February 2020, and in our own Cave event, conducted in September 2020. A unique capability of the fielded team is the homogeneous sensing of the platforms …
The DARPA Subterranean Challenge was designed for competitors to develop and deploy teams of autonomous robots to explore difficult unknown underground environments. Categorised in to human-made tunnels, underground urban infrastructure and natural caves, each of these subdomains had many challenging elements for robot perception, locomotion, navigation and autonomy. These included degraded wireless communication, poor visibility due to smoke, narrow passages and doorways, clutter, uneven ground, slippery and loose terrain, stairs, ledges, overhangs, dripping water, and dynamic obstacles that …
NASA Jet Propulsion Laboratory（NASA喷气推进实验室）；California Institute of Technology（加州理工学院）
This paper serves as one of the first efforts to enable large-scale and long-duration autonomy using the Boston Dynamics Spot robot. Motivated by exploring extreme environments, particularly those involved in the DARPA Subterranean Challenge, this paper pushes the boundaries of the state-of-practice in enabling legged robotic systems to accomplish real-world complex missions in relevant scenarios. In particular, we discuss the behaviors and capabilities which emerge from the integration of the autonomy architecture NeBula (Networked Belief-aware Perceptual Autonomy) with next-generation mobili…
ETRI（韩国电子通信研究院）；KAIST（韩国科学技术院）；NASA-JPL（美国国家航空航天局喷气推进实验室）；California Institute of Technology（加州理工学院）
Collision-free path planning is an essential requirement for autonomous exploration in unknown environments, especially when operating in confined spaces or near obstacles. This study presents an autonomous exploration technique using a small drone. A local end-point selection method is designed using LiDAR range measurement and then generates the path from the current position to the selected end-point. The generated path shows the consistent collision-free path in real-time by adopting the Euclidean signed distance field-based grid-search method. The simulation results consistently showed th…
California Institute of Technology（加州理工学院）；Stanford University（斯坦福大学）；NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；University of California Los Angeles（加州大学洛杉矶分校）
We present a method for solving the coverage problem with the objective of autonomously exploring an unknown environment under mission time constraints. Here, the robot is tasked with planning a path over a horizon such that the accumulated area swept out by its sensor footprint is maximized. Because this problem exhibits a diminishing returns property known as submodularity, we choose to formulate it as a tree-based sequential decision making process. This formulation allows us to evaluate the effects of the robot's actions on future world coverage states, while simultaneously accounting for …
Due to the highly complex environment present during the DARPA Subterranean Challenge, all six funded teams relied on legged robots as part of their robotic team. Their unique locomotion skills of being able to step over obstacles require special considerations for navigation planning. In this work, we present and examine ArtPlanner, the navigation planner used by team CERBERUS during the Finals. It is based on a sampling-based method that determines valid poses with a reachability abstraction and uses learned foothold scores to restrict areas considered safe for stepping. The resulting planni…
An Addendum to NeBula: Towards Extending TEAM CoSTAR's Solution to Larger Scale Environments
NASA Jet Propulsion Laboratory, California Institute of Technology（美国国家航空航天局喷气推进实验室、加州理工学院）；California Institute of Technology（加州理工学院）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Massachusetts Institute of Technology（麻省理工学院）；Stanford University（斯坦福大学）；California State University, Northridge（加州州立大学北岭分校）；Polytechnic University of Bari（巴里理工大学）；Cornell University（康奈尔大学）；McGill University（麦吉尔大学）；Blue Marble Space Institute of Science（蓝 marble 太空科学研究所）
There are several challenges for search and rescue robots: mobility, perception, autonomy, and communication. Inspired by the DARPA Subterranean (SubT) Challenge, we propose an autonomous blimp robot, which has the advantages of low power consumption and collision-tolerance compared to other aerial vehicles like drones. This is important for search and rescue tasks that usually last for one or more hours. However, the underground constrained passages limit the size of blimp envelope and its payload, making the proposed system resource-constrained. Therefore, a careful design consideration is n…
NASA Jet Propulsion Laboratory - California Institute of Technology（美国国家航空航天局喷气推进实验室 - 加州理工学院）；Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（麻省理工学院航空航天系）；Department of Computer Science, University of Southern California（南加州大学计算机科学系）；Aptiv（阿普提夫公司 (Aptiv)）
Simultaneous Localization and Mapping (SLAM) in large-scale, unknown, and complex subterranean environments is a challenging problem. Sensors must operate in off-nominal conditions; uneven and slippery terrains make wheel odometry inaccurate, while long corridors without salient features make exteroceptive sensing ambiguous and prone to drift; finally, spurious loop closures that are frequent in environments with repetitive appearance, such as tunnels and mines, could result in a significant distortion of the entire map. These challenges are in stark contrast with the need to build highly-accu…
This paper presents a novel approach for autonomous cooperating UAVs in search and rescue operations in subterranean domains with complex topology. The proposed system was ranked second in the Virtual Track of the DARPA SubT Finals as part of the team CTU-CRAS-NORLAB. In contrast to the winning solution that was developed specifically for the Virtual Track, the proposed solution also proved to be a robust system for deployment onboard physical UAVs flying in the extremely harsh and confined environment of the real-world competition. The proposed approach enables fully autonomous and decentrali…
ETH Zürich（苏黎世联邦理工学院）；Delft University of Technology（代尔夫特理工大学）；Boston Dynamics（波士顿动力公司）
Wheeled-legged robots have the potential for highly agile and versatile locomotion. The combination of legs and wheels might be a solution for any real-world application requiring rapid, and long-distance mobility skills on challenging terrain. In this paper, we present an online trajectory optimization framework for wheeled quadrupedal robots capable of executing hybrid walking-driving locomotion strategies. By breaking down the optimization problem into a wheel and base trajectory planning, locomotion planning for high dimensional wheeled-legged robots becomes more tractable, can be solved i…
University of Colorado Boulder（科罗拉多大学博尔德分校）；University of California Santa Cruz（加利福尼亚大学圣克鲁兹分校）；Aerospace Engineering Sciences（航空航天工程科学（注：此处为系/专业，按要求不提取，仅保留大学/学院级别机构））；Mechanical Engineering（机械工程（注：此处为系/专业，按要求不提取，仅保留大学/学院级别机构））；Computer Science（计算机科学（注：此处为系/专业，按要求不提取，仅保留大学/学院级别机构））；Electrical & Computer Engineering（电气与计算机工程（注：此处为系/专业，按要求不提取，仅保留大学/学院级别机构））
Artificial intelligence has undergone immense growth and maturation in recent years, though autonomous systems have traditionally struggled when fielded in diverse and previously unknown environments. DARPA is seeking to change that with the Subterranean Challenge, by providing roboticists the opportunity to support civilian and military first responders in complex and high-risk underground scenarios. The subterranean domain presents a handful of challenges, such as limited communication, diverse topology and terrain, and degraded sensing. Team MARBLE proposes a solution for autonomous explora…
CIRS Underwater Vision and Robotics Research Centre（CIRS水下视觉与机器人研究中心）；Universitat de Girona（赫罗纳大学）；VICOROB VIsió per COmputador i ROBòtica（VICOROB计算机视觉与机器人实验室）
Safe autonomous navigation is an essential and challenging problem for robots operating in highly unstructured or completely unknown environments. Under these conditions, not only robotic systems must deal with limited localisation information, but also their manoeuvrability is constrained by their dynamics and often suffer from uncertainty. In order to cope with these constraints, this manuscript proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety-guarantees. The proposed approach deals with the motion, probabilistic safety, and o…
Detecting objects of interest, such as human survivors, safety equipment, and structure access points, is critical to any search-and-rescue operation. Robots deployed for such time-sensitive efforts rely on their onboard sensors to perform their designated tasks. However, as disaster response operations are predominantly conducted under perceptually degraded conditions, commonly utilized sensors such as visual cameras and LiDARs suffer in terms of performance degradation. In response, this work presents a method that utilizes the complementary nature of vision and depth sensors to leverage mul…
CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments
Jet Propulsion Lab, California Institute of Technology, USA（喷气推进实验室，加州理工学院，美国）；Division of Robotics, Perception, and Learning, KTH Royal Institute of Technology, Sweden（机器人、感知与学习系，皇家理工学院，瑞典）；Autonomous Systems Lab, ETH Zürich, Switzerland（自主系统实验室，苏黎世联邦理工学院，瑞士）；Robotic Systems Lab, ETH Zürich, Switzerland（机器人系统实验室，苏黎世联邦理工学院，瑞士）；Autonomous Robots Lab, NTNU, Trondheim, Norway（自主机器人实验室，挪威特罗姆瑟大学，挪威）
Exploration and mapping of unknown environments is a fundamental task in applications for autonomous robots. In this article, we present a complete framework for deploying MAVs in autonomous exploration missions in unknown subterranean areas. The main motive of exploration algorithms is to depict the next best frontier for the robot such that new ground can be covered in a fast, safe yet efficient manner. The proposed framework uses a novel frontier selection method that also contributes to the safe navigation of autonomous robots in obstructed areas such as subterranean caves, mines, and urba…
University of Southern California（南加州大学）；NASA Jet Propulsion Laboratory, California Institute of Technology（加州理工学院美国国家航空航天局喷气推进实验室）
Multi-robot exploration of complex, unknown environments benefits from the collaboration and cooperation offered by inter-robot communication. Accurate radio signal strength prediction enables communication-aware exploration. Models which ignore the effect of the environment on signal propagation or rely on a priori maps suffer in unknown, communication-restricted (e.g. subterranean) environments. In this work, we present Propagation Environment Modeling and Learning (PropEM-L), a framework which leverages real-time sensor-derived 3D geometric representations of an environment to extract infor…
Autonomous Exploration Development Environment is an open-source repository released to facilitate the development of high-level planning algorithms and integration of complete autonomous navigation systems. The repository contains representative simulation environment models, fundamental navigation modules, e.g., local planner, terrain traversability analysis, waypoint following, and visualization tools. Together with two of our high-level planner releases -- TARE planner for exploration and FAR planner for route planning, we detail usage of the three open-source repositories and share experi…
NASA-Jet Propulsion Laboratory, California Institute of Technology（美国国家航空航天局喷气推进实验室，加州理工学院）
Robots and particularly drones are especially useful in exploring extreme environments that pose hazards to humans. To ensure safe operations in these situations, usually perceptually degraded and without good GNSS, it is critical to have a reliable and robust state estimation solution. The main body of literature in robot state estimation focuses on developing complex algorithms favoring accuracy. Typically, these approaches rely on a strong underlying assumption: the main estimation engine will not fail during operation. In contrast, we propose an architecture that pursues robustness in stat…
University of Pennsylvania（宾夕法尼亚大学）；GRASP Laboratory（GRASP实验室）
Robotic exploration of underground environments is a particularly challenging problem due to communication, endurance, and traversability constraints which necessitate high degrees of autonomy and agility. These challenges are further exacerbated by the need to minimize human intervention for practical applications. While legged robots have the ability to traverse extremely challenging terrain, they also engender new challenges for planning, estimation, and control. In this work, we describe a fully autonomous system for multi-robot mine exploration and mapping using legged quadrupeds, as well…
Czech Technical University in Prague（捷克布拉格技术大学）；Brno University of Technology（布尔诺理工大学）
This paper presents a self-contained system for the robust utilization of aerial robots in the autonomous exploration of cave environments to help human explorers, first responders, and speleologists. The proposed system is generally applicable to an arbitrary exploration task within an unknown and unstructured subterranean environment and interconnects crucial robotic subsystems to provide full autonomy of the robots. Such subsystems primarily include mapping, path and trajectory planning, localization, control, and decision making. Due to the diversity, complexity, and structural uncertainty…
Polytechnique Montreal（蒙特利尔理工学院）；NASA Jet Propulsion Laboratory, California Institute of Technology（美国国家航空航天局喷气推进实验室，加州理工学院）；University of Southern California（南加州大学）；University of California, Riverside（加州大学河滨分校）
Real-world deployment of new technology and capabilities can be daunting. The recent DARPA Subterranean (SubT) Challenge, for instance, aimed at the advancement of robotic platforms and autonomy capabilities in three one-year development pushes. While multi-agent systems are traditionally deployed in controlled and structured environments that allow for controlled testing (e.g., warehouses), the SubT challenge targeted various types of unknown underground environments that imposed the risk of robot loss in the case of failure. In this work, we introduce a video game-inspired interface, an auto…
Massachusetts Institute of Technology（麻省理工学院）；NASA Jet Propulsion Laboratory – California Institute of Technology（美国国家航空航天局喷气推进实验室——加州理工学院）；University of Southern California（南加州大学）；University of Bonn（波恩大学）；Polytechnic University of Bari（巴里理工大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
Search and rescue with a team of heterogeneous mobile robots in unknown and large-scale underground environments requires high-precision localization and mapping. This crucial requirement is faced with many challenges in complex and perceptually-degraded subterranean environments, as the onboard perception system is required to operate in off-nominal conditions (poor visibility due to darkness and dust, rugged and muddy terrain, and the presence of self-similar and ambiguous scenes). In a disaster response scenario and in the absence of prior information about the environment, robots must rely…
