We present a waypoint planning algorithm for an unmanned aerial vehicle (UAV) that is teamed with an unmanned ground vehicle (UGV) for the task of search and rescue in a subterranean environment. The UAV and UGV are teamed such that the localization of the UAV is conducted on the UGV via the multi-sensor fusion of a fish-eye camera, 3D LIDAR, ranging radio, and a laser altimeter. Likewise, the trajectory planning of the UAV is conducted on the UGV, which is assumed to have a 3D map of the environment (e.g., from Simultaneous Localization and Mapping). The goal of the planning algorithm is to satisfy the mission's exploration criteria while reducing the localization error of the UAV by evaluating the belief space for potential exploration routes. The presented algorithm is evaluated in a relevant simulation environment where the planning algorithm is shown to be effective at reducing the localization errors of the UAV.
This paper proposes a novel cooperative system for an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a flying sensor but also as a tether attachment device. Two robots are connected with a tether, allowing the UAV to anchor the tether to a structure located at the top of a steep terrain, impossible to reach for UGVs. Thus, enhancing the poor traversability of the UGV by not only providing a wider range of scanning and mapping from the air, but also by allowing the UGV to climb steep terrains with the winding of the tether. In addition, we …
Université de Technologie de Compiègne（贡比涅技术大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Université de Lorraine（洛林大学）；Aix Marseille Université（艾克斯-马赛大学）
Miniature multi-rotors are promising robots for navigating subterranean networks, but maintaining a radio connection underground is challenging. In this paper, we introduce a distributed algorithm, called U-Chain (for Underground-chain), that coordinates a chain of flying robots between an exploration drone and an operator. Our algorithm only uses the measurement of the signal quality between two successive robots as well as an estimate of the ground speed based on an optic flow sensor. We evaluate our approach formally and in simulation, and we describe experimental results with a chain of 3 …
University of Maryland（马里兰大学）；DEVCOM Army Research Laboratory（美国陆军作战能力发展司令部陆军研究实验室）
We study a resource allocation problem for the cooperative aerial-ground vehicle routing application, in which multiple Unmanned Aerial Vehicles (UAVs) with limited battery capacity and multiple Unmanned Ground Vehicles (UGVs) that can also act as a mobile recharging stations need to jointly accomplish a mission such as persistently monitoring a set of points. Due to the limited battery capacity of the UAVs, they sometimes have to deviate from their task to rendezvous with the UGVs and get recharged. Each UGV can serve a limited number of UAVs at a time. In contrast to prior work on determinis…
Fly, Track, Land: Infrastructure-less Magnetic Localization for Heterogeneous UAV-UGV Teaming
Department of Engineering, University of Perugia（工程系，佩鲁吉亚大学）；Multi-Robot Systems Group, Faculty of Electrical Engineering, Czech Technical University in Prague（多机器人系统组，电气工程系，布拉格捷克技术大学）；D-ITET Department, ETH Zürich（D-ITET部门，苏黎世联邦理工学院）
Constructing large structures with robots is a challenging task with many potential applications that requires mobile manipulation capabilities. We present two systems for autonomous wall building that we developed for the Mohamed Bin Zayed International Robotics Challenge 2020. Both systems autonomously perceive their environment, find bricks, and build a predefined wall structure. While the UGV uses a 3D LiDAR-based perception system which measures brick poses with high precision, the UAV employs a real-time camera-based system for visual servoing. We report results and insights from our suc…
Collaborative Planning with Concurrent Synchronization for Operationally Constrained UAV-UGV Teams
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；North Carolina State University（北卡罗来纳州立大学）；U.S. Army DEVCOM Army Research Laboratory (ARL)（美国陆军 DEVCOM 军事研究实验室 (ARL)）
Virginia Tech（弗吉尼亚理工大学）；University of South Carolina（南卡罗来纳大学）；University of Maryland（马里兰大学）
We study the problem of covering an environment using an Unmanned Aerial Vehicle (UAV) with limited battery capacity. We consider a scenario where the UAV can land on an Unmanned Ground Vehicle (UGV) and recharge the onboard battery. The UGV can also recharge the UAV while transporting the UAV to the next take-off site. We present an algorithm to solve a new variant of the area coverage problem that takes into account this symbiotic UAV and UGV system. The input consists of a set of boustrophedon cells -- rectangular strips whose width is equal to the field-of-view of the sensor on the UAV. Th…
National Robotics Engineering Center, the Robotics Institute of Carnegie Mellon University（卡内基梅隆大学机器人研究所国家机器人工程中心）
A robotic system of multiple unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) has the potential for advancing autonomous object geolocation performance. Much research has focused on algorithmic improvements on individual components, such as navigation, motion planning, and perception. In this paper, we present a UGV-UAV object detection and geolocation system, which performs perception, navigation, and planning autonomously in real scale in unstructured environment. We designed novel sensor pods equipped with multispectral (visible, near-infrared, thermal), high resolution (…
Every day, burning buildings threaten the lives of occupants and first responders trying to save them. Quick action is of essence, but some areas might not be accessible or too dangerous to enter. Robotic systems have become a promising addition to firefighting, but at this stage, they are mostly manually controlled, which is error-prone and requires specially trained personal. We present two systems for autonomous firefighting from air and ground we developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The systems use LiDAR for reliable localization within narrow…
BIM-Discrepancy-Driven Active Sensing for Risk-Aware UAV-UGV Navigation
Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA（电气与计算机工程系，加州大学圣地亚哥分校）；Department of Civil, Construction, and Environmental Engineering, San Diego State University, San Diego, CA, USA（土木、建设与环境工程系，圣地亚哥州立大学）
Virginia Tech（弗吉尼亚理工大学）；U.S. Army Research Laboratory（美国陆军研究实验室）
Localization in a global map is critical to success in many autonomous robot missions. This is particularly challenging for multi-robot operations in unknown and adverse environments. Here, we are concerned with providing a small unmanned ground vehicle (UGV) the ability to localize itself within a 2.5D aerial map generated from imagery captured by a low-flying unmanned aerial vehicle (UAV). We consider the scenario where GPS is unavailable and appearance-based scene changes may have occurred between the UAV's flight and the start of the UGV's mission. We present a GPS-free solution to this lo…
Energy-Aware Collaborative Exploration for a UAV-UGV Team
Department of Mechanical and Industrial Engineering at Northeastern University（东北大学机械与工业工程系）；Departments of Mechanical and Industrial Engineering and Electrical and Computer Engineering at Northeastern University（东北大学机械与工业工程系和电气与计算机工程系）
In apprenticeship learning (AL), agents learn by watching or acquiring human demonstrations on some tasks of interest. However, the lack of human demonstrations in novel tasks where they may not be a human expert yet, or when it is too expensive and/or time consuming to acquire human demonstrations motivated a new algorithm: Apprenticeship bootstrapping (ABS). The basic idea is to learn from demonstrations on sub-tasks then autonomously bootstrap a model on the main, more complex, task. The original ABS used inverse reinforcement learning (ABS-IRL). However, the approach is not suitable for co…
University of Maryland, College Park（马里兰大学帕克分校）；University of Toronto（多伦多大学）；DEVCOM Army Research Laboratory（美国陆军作战能力发展司令部陆军研究实验室）
We study a chance-constrained variant of the cooperative aerial-ground vehicle routing problem, in which an Unmanned Aerial Vehicle (UAV) with limited battery capacity and an Unmanned Ground Vehicle (UGV) that can also act as a mobile recharging station need to jointly accomplish a mission such as monitoring a set of points. Due to the limited battery capacity of the UAV, two vehicles sometimes have to deviate from their task to rendezvous and recharge the UAV\@. Unlike prior work that has focused on the deterministic case, we address the challenge of stochastic energy consumption of the UAV\@…
A mixed aerial and ground robot team, which includes both unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), is widely used for disaster rescue, social security, precision agriculture, and military missions. However, team capability and corresponding configuration vary since robots have different motion speeds, perceiving ranges, reaching areas, and resilient capabilities to the dynamic environment. Due to heterogeneous robots inside a team and the resilient capabilities of robots, it is challenging to perform a task with an optimal balance between reasonable task allocations…
PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments
Khoury Department of Computer Sciences at Northeastern University（东北大学Khoury计算机科学系）；The Charles Stark Draper Laboratory, Inc.（查尔斯·斯塔克·德雷珀实验室公司）；Department of Mechanical and Industrial Engineering at Northeastern University（东北大学机械与工业工程系）；Departments of Mechanical and Industrial Engineering and Electrical and Computer Engineering at Northeastern University（东北大学机械与工业工程系和电气与计算机工程系）
Virginia Tech（弗吉尼亚理工大学）；Remote Sensing Laboratory（遥感实验室）
Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting. In this paper, we present systems, algorithms, and experiments to perform radiation search using unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) by employing semantic scene segmentation. The aerial data is used to identify radiological points of interest, generate an orthophoto along with a digital elevation model (DEM) of the scene, and perform semantic segmentation to assign a category (e.g. road, grass) to each pixel in the…
This paper focuses on the control of a cooperative system composed of an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) manipulating an object. The two units are subject to input saturations and collaborate to move the object to a desired pose characterized by its position and inclination. The dynamics are derived using Euler-Lagrange method. A pre-stabilizing control law is proposed where the UGV is tasked to deploy the object to a certain position whereas the UAV adjusts its inclination. In particular, a proportional-derivative control law is proposed for the UGV, and a c…
Veermata Jijabai Technological Institute (VJTI)（维尔玛塔·吉贾拜技术学院）
The necessity of maintaining a robust antiterrorist task force has become imperative in recent times with resurgence of rogue element in the society. A well equipped combat force warrants the safety and security of citizens and the integrity of the sovereign state. In this paper we propose a novel teleoperating robot which can play a major role in combat, rescue and reconnaissance missions by substantially reducing loss of human soldiers in such hostile environments. The proposed robotic solution consists of an unmanned ground vehicle equipped with an IP camera visual system broadcasting real-…
UAV See, UGV Do: Aerial Imagery and Virtual Teach Enabling Zero-Shot Ground Vehicle Repeat
University of Toronto Robotics Institute（多伦多大学机器人研究所）；University of Toronto Institute of Aerospace Studies（多伦多大学航空航天研究学院）；Ingenuity Labs（Ingenuity实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）
We study the problem of planning a tour for an energy-limited Unmanned Aerial Vehicle (UAV) to visit a set of sites in the least amount of time. We envision scenarios where the UAV can be recharged along the way either by landing on stationary recharging stations or on Unmanned Ground Vehicles (UGVs) acting as mobile recharging stations. This leads to a new variant of the Traveling Salesperson Problem (TSP) with mobile recharging stations. We present an algorithm that finds not only the order in which to visit the sites but also when and where to land on the charging stations to recharge. Our …
A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives
Interdisciplinary Center for Security Reliability and Trust (SnT)（安全、可靠性与跨学科研究中心（SnT））；University of Luxembourg（卢森堡大学）；Faculty of Science, Technology, and Medicine (FSTM)（科学、技术与医学学院（FSTM））
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…
We address the problem of achieving persistent surveillance over an environment by using energy-constrained unmanned aerial vehicles (UAVs), which are supported by unmanned ground vehicles (UGVs) serving as mobile charging stations. Specifically, we plan the trajectories of all vehicles and the charging schedule of UAVs to minimize the long-term maximum age, where age is defined as the time between two consecutive visits to regions of interest in a partitioned environment. We introduce a scalable planning strategy based on 1) creating UAV- UGV teams, 2) decomposing the environment into optimal…
University of Applied Science Gelsenkirchen（盖尔森基兴应用科学大学）；Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS（弗劳恩霍夫智能分析与信息系统研究所 IAIS）
This paper presents a novel approach to build consistent 3D maps for multi robot cooperation in USAR environments. The sensor streams from unmanned aerial vehicles (UAVs) and ground robots (UGV) are fused in one consistent map. The UAV camera data are used to generate 3D point clouds that are fused with the 3D point clouds generated by a rolling 2D laser scanner at the UGV. The registration method is based on the matching of corresponding planar segments that are extracted from the point clouds. Based on the registration, an approach for a globally optimized localization is presented. Apart fr…
Visual Heading Prediction for Autonomous Aerial Vehicles
Department of Computer Science at North Carolina A&T State University（北卡罗来纳A&T州立大学计算机科学系）；Department of Electrical and Computer Engineering at North Carolina A&T State University（北卡罗来纳A&T州立大学电气与计算机工程系）
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…
A flexible topological representation consisting of a two-layer graph structure built on-board an Unmanned Aerial Vehicle (UAV) by continuously filling the free space of an occupancy map with intersecting spheres is proposed in this \paper{}. Most state-of-the-art planning methods find the shortest paths while keeping the UAV at a pre-defined distance from obstacles. Planning over the proposed structure reaches this pre-defined distance only when necessary, maintaining a safer distance otherwise, while also being orders of magnitude faster than other state-of-the-art methods. Furthermore, we d…
This paper details the algorithms involved and task planner for vehicle collaboration in building a structure. This is the problem defined in challenge 2 of Mohammed Bin Zayed International Robotic Challenge 2020 (MBZIRC). The work addresses various aspects of the challenge for Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicle (UGV). The challenge involves repeated pick and place operations using UAVs and UGV to build two structures of different shape and sizes. The algorithms are implemented using the Robot Operating System (ROS) framework and visualised in Gazebo. The whole develop…
Fast moving but power hungry unmanned aerial vehicles (UAVs) can recharge on slow-moving unmanned ground vehicles (UGVs) to survey large areas in an effective and efficient manner. In order to solve this computationally challenging problem in a reasonable time, we created a two-level optimization heuristics. At the outer level, the UGV route is parameterized by few free parameters and at the inner level, the UAV route is solved by formulating and solving a vehicle routing problem with capacity constraints, time windows, and dropped visits. The UGV free parameters need to be optimized judicious…
In this paper we present a cooperative multi-robot strategy to adaptively explore and sample environments that are unfavorable for humans. We propose a methodology for a team of heterogeneous robots to collaborate on information based planning for applications like sampling thermal imagery in a wildfire affected site to assist with detecting spot fires and areas of residual fires, fire mapping and monitoring fire progression or applications in marine domain for coral reef monitoring and survey. We use Gabor filter based texture classifier on aerial images from an Unmanned Aerial Vehicle (UAV) …
Large-Scale Tunnel Air-Ground Collaboration With FLISP: Fast LiDAR-IMU Synchronized Path Planner
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海自主智能无人系统科学中心）；State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University（同济大学自主智能无人系统全国重点实验室）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）
