With the deepening of research on the SLAM system, the possibility of cooperative SLAM with multi-robots has been proposed. This paper presents a map matching and localization approach considering the cooperative SLAM of an aerial-ground system. The proposed approach aims to help precisely matching the map constructed by two independent systems that have large scale variance of viewpoints of the same route and eventually enables the ground mobile robot to localize itself in the global map given by the drone. It contains dense mapping with Elevation Map and software "Metashape", map matching with a proposed template matching algorithm, weighted normalized cross-correlation (WNCC) and localization with particle filter. The approach enables map matching for cooperative SLAM with the feasibility of multiple scene sensors, varies from stereo cameras to lidars, and is insensitive to the synchronization of the two systems. We demonstrate the accuracy, robustness, and the speed of the approach under experiments of the Aero-Ground Dataset.
We present a novel weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in hi…
Safety-Aware Optimal Scheduling for Autonomous Masonry Construction using Collaborative Heterogeneous Aerial Robots
Robotics and Artificial Intelligence Group, Department of Computer, Electrical and Space Engineering, Luleå University of Technology（机器人与人工智能组，计算机、电气与空间工程系，卢勒奥技术大学）
Guidance Control and Decision Systems Laboratory Department of Aerospace Engineering Indian institute of Science（印度科学研究所航空航天工程系制导控制与决策系统实验室）；Robert Bosch Center for Cyber Physical Systems Indian Institute of Science（印度科学研究所罗伯特·博世网络物理系统中心）
This work details the problem of aerial target capture using multiple UAVs. This problem is motivated from the challenge 1 of Mohammed Bin Zayed International Robotic Challenge 2020. The UAVs utilise visual feedback to autonomously detect target, approach it and capture without disturbing the vehicle which carries the target. Multi-UAV collaboration improves the efficiency of the system and increases the chance of capturing the ball robustly in short span of time. In this paper, the proposed architecture is validated through simulation in ROS-Gazebo environment and is further implemented on ha…
BGC-Nav: Cross-Altitude Multi-UAV Collaborative Navigation via Belief-Guided Coupling
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Small unmanned aerial vehicles (UAVs) have generally little capacity to carry payloads. Through collaboration, the UAVs can increase their joint payload capacity and carry more significant loads. For maximum flexibility to dynamic and unstructured environments and task demands, we propose a fully decentralized control infrastructure based on a swarm-specific scripting language, Buzz. In this paper, we describe the control infrastructure and use it to compare two algorithms for collaborative transport: field potentials and spring-damper. We test the performance of our approach with a fleet of m…
Sapienza University of Rome（罗马第一大学）；ETH Zurich（苏黎世联邦理工学院）
The combination of aerial survey capabilities of Unmanned Aerial Vehicles with targeted intervention abilities of agricultural Unmanned Ground Vehicles can significantly improve the effectiveness of robotic systems applied to precision agriculture. In this context, building and updating a common map of the field is an essential but challenging task. The maps built using robots of different types show differences in size, resolution and scale, the associated geolocation data may be inaccurate and biased, while the repetitiveness of both visual appearance and geometric structures found within ag…
Hong Kong University of Science and Technology（香港科技大学）
Although the use of multiple Unmanned Aerial Vehicles (UAVs) has great potential for fast autonomous exploration, it has received far too little attention. In this paper, we present RACER, a RApid Collaborative ExploRation approach using a fleet of decentralized UAVs. To effectively dispatch the UAVs, a pairwise interaction based on an online hgrid space decomposition is used. It ensures that all UAVs simultaneously explore distinct regions, using only asynchronous and limited communication. Further, we optimize the coverage paths of unknown space and balance the workloads partitioned to each …
AirSimAG: A High-Fidelity Simulation Platform for Air-Ground Collaborative Robotics
School of Aeronautic Science and Engineering, Beihang University（北京航空航天大学航空科学与工程学院）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；Institution of Unmanned System, Beihang University（北京航空航天大学无人系统研究院）
Australian National University（澳大利亚国立大学）；Defence Science and Technology Group（国防科学技术集团）
Collaborative Localisation has been studied extensively in recent years as a way to improve pose estimation of unmanned aerial vehicles in challenging environments. However little attention has been paid toward advancing the underlying filter design beyond standard Extended Kalman Filter-based approaches. In this paper, we detail a discrete-time collaborative localisation filter using the deterministic minimum-energy framework. The filter incorporates measurements from an inertial measurement unit and models the effects of sensor bias and gravitational acceleration. We present a simulation bas…
The Hong Kong University of Science and Technology（香港科技大学）；Beihang University（北京航空航天大学）
Autonomous exploration of unknown environments has been widely applied in inspection, surveillance, and search and rescue. In exploration task, the basic requirement for robots is to detect the unknown space as fast as possible. In this paper, we propose an autonomous collaborative system consists of an aerial robot and a ground vehicle to explore in unknown environments. We combine the frontier based method and the harmonic field to generate a path. Then, For the ground robot, a minimum jerk piecewise Bezier curve which can guarantee safety and dynamical feasibility is generated amid obstacle…
Sapienza University of Rome（罗马大学）；IT+Robotics Srl（IT+Robotics有限责任公司）；International Research Lab 2958 Georgia Tech-CNRS（佐治亚理工-法国国家科学研究中心2958国际研究实验室）；University of Freiburg（弗莱堡大学）；Toyota Research Institute（丰田研究院）；University of Bonn（波恩大学）；Robert Bosch GmbH（罗伯特·博世有限公司）；Agency for Agro-food Sector Services of the Marche Region (ASSAM)（马尔凯大区农业食品部门服务局）；ETH Zurich（苏黎世联邦理工学院）
The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, and the optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview of …
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)）
In this paper, we present a vision based collaborative localization framework for groups of micro aerial vehicles (MAV). The vehicles are each assumed to be equipped with a forward-facing monocular camera, and to be capable of communicating with each other. This collaborative localization approach is built upon a distributed algorithm where individual and relative pose estimation techniques are combined for the group to localize against surrounding environments. The MAVs initially detect and match salient features between each other to create a sparse reconstruction of the observed environment…
Visibility-Aware Cooperative Tracking with Decentralized LiDAR-Based Aerial Swarms
Department of Mechanical Engineering, The University of Hong Kong（香港大学机械工程系）；School of Intelligent System Engineering, Sun Yat-sen University（中山大学智能系统工程学院）
University of New South Wales（新南威尔士大学）；Australian National University（澳大利亚国立大学）
The movement of cooperative robots in a densely cluttered environment may not be possible if the formation type is invariant. Hence, we investigate a new method for time-varying formation control for a group of heterogeneous autonomous vehicles, which may include Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAV). We have extended a Negative-Imaginary (NI) consensus control approach to switch the formation shape of the robots whilst only using the relative distance between agents and between agents and obstacles. All agents can automatically create a new safe formation to overco…
Institute for Systems and Robotics（系统与机器人研究所）；Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）
This paper presents a first contribution to the design of a small aerial robot for inhabited microgravity environments, such as orbiting space stations. In particular, we target a fleet of robots for collaborative tasks with humans, such as telepresence and cooperative mobile manipulation. We explore a propeller based propulsion system, arranged in such a way that the translational and the rotational components can be decoupled, resulting in an holonomic hexarotor. Since propellers have limited thrust, we employ an optimization approach to select the geometric configuration given a criteria of…
Rapid deployment and operation are key requirements in time critical application, such as Search and Rescue (SaR). Efficiently teleoperated ground robots can support first-responders in such situations. However, first-person view teleoperation is sub-optimal in difficult terrains, while a third-person perspective can drastically increase teleoperation performance. Here, we propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide third-person perspective to ground robots. While our approach is based on local visual servoing, it further leverages the global localization of…
Communication and Control in Collaborative UAVs: Recent Advances and Future Trends
Tongji University（同济大学）；Frontiers Science Center for Intelligent Autonomous Systems（智能自主系统前沿科学中心）；United Arab Emirates University (UAEU)（阿联酋大学）；Western Sydney University（西悉尼大学）；Embry-Riddle Aeronautical University（安柏瑞德航空大学）；Hankuk University of Foreign Studies（韩国外国语大学）
AirBender: Adaptive Transportation of Bendable Objects Using Dual UAVs
Autonomous and Intelligent Robotics Laboratory (AIRLab)（自主与智能机器人实验室）；Lehigh University（莱斯大学）；Electrical and Computer Engineering Department（电子与计算机工程系）；The University of New Mexico（新墨西哥大学）
Is Intermediate Fusion All You Need for UAV-based Collaborative Perception?
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所认知与决策智能复杂系统重点实验室）
A UAV-assisted Wireless Localization Challenge on AERPAW
School of Electrical and Computer Engineering, University of Georgia（佐治亚大学电气与计算机工程学院）；University of North Texas（北得克萨斯大学）；NYU Tandon School of Engineering（纽约大学坦登工程学院）；Delmont Systems LLC（德尔蒙特系统有限责任公司）；North Carolina State University（北卡罗来纳州立大学）
Ground robots always get collision in that only if they get close to the obstacles, can they sense the danger and take actions, which is usually too late to avoid the crash, causing severe damage to the robots. To address this issue, we present collaboration of aerial and ground robots in recognition of feasible region. Taking the aerial robots' advantages of having large scale variance of view points of the same route which the ground robots is on, the collaboration work provides global information of road segmentation for the ground robot, thus enabling it to obtain feasible region and adjus…
The Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
Aerial vehicles are revolutionizing applications that require capturing the 3D structure of dynamic targets in the wild, such as sports, medicine, and entertainment. The core challenges in developing a motion-capture system that operates in outdoors environments are: (1) 3D inference requires multiple simultaneous viewpoints of the target, (2) occlusion caused by obstacles is frequent when tracking moving targets, and (3) the camera and vehicle state estimation is noisy. We present a real-time aerial system for multi-camera control that can reconstruct human motions in natural environments wit…
In this paper, we present a framework for performing collaborative localization for groups of micro aerial vehicles (MAV) that use vision based sensing. The vehicles are each assumed to be equipped with a forward-facing monocular camera, and to be capable of communicating with each other. This collaborative localization approach is developed as a decentralized algorithm and built in a distributed fashion where individual and relative pose estimation techniques are combined for the group to localize against surrounding environments. The MAVs initially detect and match salient features between e…
Indian Institute of Science（印度科学理工学院）；Robert Bosch Center for Cyber-Physical Systems, Indian Institute of Science（罗伯特·博世信息物理系统中心，印度科学理工学院）
This paper presents the design, development, and testing of hardware-software systems by the IISc-TCS team for Challenge 1 of the Mohammed Bin Zayed International Robotics Challenge 2020. The goal of Challenge 1 was to grab a ball suspended from a moving and maneuvering UAV and pop balloons anchored to the ground, using suitable manipulators. The important tasks carried out to address this challenge include the design and development of a hardware system with efficient grabbing and popping mechanisms, considering the restrictions in volume and payload, design of accurate target interception al…
This work contributes a marsupial robotic system-of-systems involving a legged and an aerial robot capable of collaborative mapping and exploration path planning that exploits the heterogeneous properties of the two systems and the ability to selectively deploy the aerial system from the ground robot. Exploiting the dexterous locomotion capabilities and long endurance of quadruped robots, the marsupial combination can explore within large-scale and confined environments involving rough terrain. However, as certain types of terrain or vertical geometries can render any ground system unable to c…
Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains
Lab of Systems Engineering and Robotics (LASER), Department of Computer Systems, Universidade Federal da Paraiba（系统工程与机器人实验室（LASER），计算机系统系，巴伊帕巴联邦大学）；Department of Cybernetics, Czech Technical University in Prague（控制论系，布拉格捷克技术大学）
Collaborative target-tracking control using multiple autonomous fixed-wing UAVs with constant speeds
Department of Electrical Engineering, Eindhoven University of Technology（埃因霍温理工大学电子工程系）；Universidad Complutense de Madrid（马德里complutense大学）；Research School of Electrical, Energy and Material Engineering, Australian National University（澳大利亚国立大学电气、能源和材料工程研究学校）；Optus-Curtin Centre of Excellence in Artificial Intelligence, Curtin University（Curtin大学人工智能卓越中心）
We present a framework for a ground-aerial robotic team to explore large, unstructured, and unknown environments. In such exploration problems, the effectiveness of existing exploration-boosting heuristics often scales poorly with the environments' size and complexity. This work proposes a novel framework combining incremental frontier distribution, goal selection with Monte-Carlo view quality rendering, and an automatic-differentiable information gain measure to improve exploration efficiency. Simulated with multiple complex environments, we demonstrate that the proposed method effectively ut…
