Mobile robotics datasets are essential for research on robotics, for example for research on Simultaneous Localization and Mapping (SLAM). Therefore the ShanghaiTech Mapping Robot was constructed, that features a multitude high-performance sensors and a 16-node cluster to collect all this data. That robot is based on a Clearpath Husky mobile base with a maximum speed of 1 meter per second. This is fine for indoor datasets, but to collect large-scale outdoor datasets a faster platform is needed. This system paper introduces our high-speed mobile platform for data collection. The mapping robot is secured on the rear-steered flatbed car with maximum field of view. Additionally two encoders collect odometry data from two of the car wheels and an external sensor plate houses a downlooking RGB and event camera. With this setup a dataset of more than 10km in the underground parking garage and the outside of our campus was collected and is published with this paper.
Imperial College London（帝国理工学院）；AIOZ Ltd.（AIOZ有限公司）
Autonomous navigation is a long-standing field of robotics research, which provides an essential capability for mobile robots to execute a series of tasks on the same environments performed by human everyday. In this chapter, we present a set of algorithms to train and deploy deep networks for autonomous navigation of mobile robots using the Robot Operation System (ROS). We describe three main steps to tackle this problem: i) collecting data in simulation environments using ROS and Gazebo; ii) designing deep network for autonomous navigation, and iii) deploying the learned policy on mobile rob…
Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previously unseen environment while simultaneously localising itself with respect to the map. In particular, Visual-SLAM uses various sensors from the mobile robot for collecting and sensing a representation of the map. Traditionally, geometric model-based techniques were used to tackle the SLAM problem, which tends to be error-prone under challenging environments. Recent advancements in computer vision, such as deep learning techniques, have provided…
National Institute of Advanced Industrial Science and Technology (AIST)（国立先进工业科学技术研究所（AIST））；Osaka University（大阪大学）
In this paper, we address efficiently and robustly collecting objects stored in different trays using a mobile manipulator. A resolution complete method, based on precomputed reachability database, is proposed to explore collision-free inverse kinematics (IK) solutions and then a resolution complete set of feasible base positions can be determined. This method approximates a set of representative IK solutions that are especially helpful when solving IK and checking collision are treated separately. For real world applications, we take into account the base positioning uncertainty and plan a se…
Universiti Tun Hussein Onn Malaysia（马来西亚敦胡先翁大学）；Universiti Teknikal Malaysia Melaka（马来西亚马六甲技术大学）；Universiti Malaya（马来亚大学）
Researchers and robotic development groups have recently started paying special attention to autonomous mobile robot navigation in indoor environments using vision sensors. The required data is provided for robot navigation and object detection using a camera as a sensor. The aim of the project is to construct a mobile robot that has integrated vision system capability used by a webcam to locate, track and follow a moving object. To achieve this task, multiple image processing algorithms are implemented and processed in real-time. A mini-laptop was used for collecting the necessary data to be …
Stanford AI Lab., Computer Science, Stanford University（斯坦福大学计算机科学系斯坦福人工智能实验室）
It is important for robots to be able to decide whether they can go through a space or not, as they navigate through a dynamic environment. This capability can help them avoid injury or serious damage, e.g., as a result of running into people and obstacles, getting stuck, or falling off an edge. To this end, we propose an unsupervised and a near-unsupervised method based on Generative Adversarial Networks (GAN) to classify scenarios as traversable or not based on visual data. Our method is inspired by the recent success of data-driven approaches on computer vision problems and anomaly detectio…
Navigation in an unknown environment consists of multiple separable subtasks, such as collecting information about the surroundings and navigating to the current goal. In the case of pure visual navigation, all these subtasks need to utilize the same vision system, and therefore a way to optimally control the direction of focus is needed. We present a case study, where we model the active sensing problem of directing the gaze of a mobile robot with three machine vision cameras as a partially observable Markov decision process (POMDP) using a mutual information (MI) based reward function. The k…
This paper presents different possibilities of using mobile robots in education. Through the application of mobile mechatronic robotic system "Robotino" this paper shows the possibilities of developing interactive lectures and exercises in order to raise the quality of education and to provide new competencies for students. Application of robot as a real system supports strengthening specific areas of knowledge and skills that the students develop through design, creation, assembly and operating with the robot. This way of learning contains a very important element and that is "Playful learnin…
Hanoi University of Industry（河内工业大学）；University of Economics - Technology for Industries（经济与技术工业大学）
In recent years, the mobile robot has been considerable attention to researchers for its application in various environments. For a mobile robot navigating its way from starting point to a goal point while traversing through deterrents, needs to recognize the obstacles and generate new trajectories to reach the destination. This paper presents an obstacle avoidance method for mobile robots using an open-source in robot operation system (ROS) combining with the dynamic window approach (DWA) algorithm. The experiment is carried out using a mobile robot in which the navigation data is based on da…
Department of Computing and Software, McMaster University（麦克马斯特大学计算机与软件工程系）；McMaster University（麦克马斯特大学）；Department of Computing and Software（计算机与软件工程系）
Large-scale spatial data such as air quality, thermal conditions and location signatures play a vital role in a variety of applications. Collecting such data manually can be tedious and labour intensive. With the advancement of robotic technologies, it is feasible to automate such tasks using mobile robots with sensing and navigation capabilities. However, due to limited battery lifetime and scarcity of charging stations, it is important to plan paths for the robots that maximize the utility of data collection, also known as the informative path planning (IPP) problem. In this paper, we propos…
In mobile manipulation (MM), robots can both navigate within and interact with their environment and are thus able to complete many more tasks than robots only capable of navigation or manipulation. In this work, we explore how to apply imitation learning (IL) to learn continuous visuo-motor policies for MM tasks. Much prior work has shown that IL can train visuo-motor policies for either manipulation or navigation domains, but few works have applied IL to the MM domain. Doing this is challenging for two reasons: on the data side, current interfaces make collecting high-quality human demonstra…
Shenzhen Amigaga Technology Co. Ltd.（深圳阿米嘎嘎科技有限公司）；The University of Sydney（悉尼大学）；University of Edinburgh（爱丁堡大学）
To enable a mobile manipulator to perform human tasks from a single teaching demonstration is vital to flexible manufacturing. We call our proposed method MMPA (Mobile Manipulator Process Automation with One-shot Teaching). Currently, there is no effective and robust MMPA framework which is not influenced by harsh industrial environments and the mobile base's parking precision. The proposed MMPA framework consists of two stages: collecting data (mobile base's location, environment information, end-effector's path) in the teaching stage for robot learning; letting the end-effector repeat the ne…
Deep reinforcement learning algorithms require large and diverse datasets in order to learn successful policies for perception-based mobile navigation. However, gathering such datasets with a single robot can be prohibitively expensive. Collecting data with multiple different robotic platforms with possibly different dynamics is a more scalable approach to large-scale data collection. But how can deep reinforcement learning algorithms leverage such heterogeneous datasets? In this work, we propose a deep reinforcement learning algorithm with hierarchically integrated models (HInt). At training …
Social robots have been used to assist with mental well-being in various ways such as to help children with autism improve on their social skills and executive functioning such as joint attention and bodily awareness. They are also used to help older adults by reducing feelings of isolation and loneliness, as well as supporting mental well-being of teens and children. However, existing work in this sphere has only shown support for mental health through social robots by responding interactively to human activity to help them learn relevant skills. We hypothesize that humans can also get help f…
UniWAM Technical Report: Unified Mobile Manipulation via Mixed-Stream World-Action Modeling and Manipulation Anchor Pose Supervision
Physical Superintelligence Lab, Fysics AI（Fysics AI 物理超级智能实验室）；College of Intelligent Robotics and Advanced Manufacturing, Fudan University（复旦大学智能机器人与先进制造学院）
The Role of Embodiment in Intuitive Whole-Body Teleoperation for Mobile Manipulation
Computer Science Department, TU Darmstadt（图恩-达姆施塔特大学计算机科学系）；University of São Paulo（圣保罗大学）；Centre for Cognitive Science, TU Darmstadt（图恩-达姆施塔特大学认知科学中心）；Systems AI for Robot Learning, German Research Center for AI (DFKI)（德国人工智能研究中心（DFKI）机器人学习系统AI）
Pontificial Catholic University of Rio de Janeiro (PUC-Rio)（里约热内卢天主教大学（PUC-Rio））
In many kinds of research, collecting data is tailored to individual research. It is usual to use dedicated and not reusable software to collect data. GrADyS Ground Station framework (GrADyS-GS) aims to collect data in a reusable manner with dynamic background tools. This technical report describes GrADyS-GS, a ground station software designed to connect with various technologies to control, monitor, and store results of Mobile Internet of Things field experiments with Autonomous Vehicles (UAV) and Sensor Networks (WSN). In the GrADyS project GrADyS-GS is used with ESP32-based IoT devices on t…
Collecting Human Motion Data in Large and Occlusion-Prone Environments using Ultra-Wideband Localization
Chemnitz University of Technology, Chair for Circuit and System Design（Chemnitz技术大学，电路与系统设计教研室）；Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich（慕尼黑机器人与智能机器研究所（MIRMI），慕尼黑技术大学）；Center for Applied Autonomous Sensor Systems (AASS)（应用自主传感器系统中心）；Pinpoint GmbH（Pinpoint公司）；Bosch Corporate Research（博世企业研究）
