In the context of Intelligent Transportation Systems and the delivery of goods, new technology approaches need to be developed in order to cope with certain challenges that last mile delivery entails, such as navigation in an urban environment. Autonomous delivery robots can help overcome these challenges. We propose a method for performing mixed reality (MR) simulation with ROS-based robots using Unity, which synchronizes the real and virtual environment, and simultaneously uses the sensor information of the real robots to locate themselves and project them into the virtual environment, so that they can use their virtual doppelganger to perceive the virtual world. Using this method, real and virtual robots can perceive each other and the environment in which the other party is located, thereby enabling the exchange of information between virtual and real objects. Through this approach a more realistic and reliable simulation can be obtained. Results of the demonstrated use-cases verified the feasibility and efficiency as well as the stability of implementing MR using Unity for ROS-based robots.
Autonomous systems developed with the Belief-Desire-Intention (BDI) architecture are usually mostly implemented in simulated environments. In this project we sought to build a BDI agent for use in the real world for campus mail delivery in the tunnel system at Carleton University. Ideally, the robot should receive a delivery order via a mobile application, pick up the mail at a station, navigate the tunnels to the destination station, and notify the recipient. We linked the Robot Operating System (ROS) with a BDI reasoning system to achieve a subset of the required use cases. ROS handles the l…
Agri-Sim: Agricultural Simulation Platform for Embodied Intelligence Evaluation in Greenhouse Robotics
School of Mechatronic Engineering and Automation, Shanghai University（上海大学机电工程与自动化学院）；Sino-European School of Technology of Shanghai University（上海大学中欧工程技术学院）；School of Future Technology (Institute of Artificial Intelligence), Shanghai University（上海大学未来技术学院（人工智能研究院））
Maersk McKinney Moeller Institute, University of Southern Denmark（南丹麦大学马士基·麦克金尼·穆勒研究所）
The use of the ROS middleware is a growing trend in robotics in general, ROS and hard real-time embedded systems have however not been easily uniteable while retaining the same overall communication and processing methodology at all levels. In this paper we present an approach aimed at tackling the schism between high-level, flexible software and low-level, real-time software. The key idea of our approach is to enable software components written for a high-level publish-subscribe software architecture to be automatically migrated to a dedicated hardware architecture implemented using programma…
In this paper, we report on our use of cloud-robotics solutions to teach a Robotics Applications Programming course at Zurich University of Applied Sciences (ZHAW). The usage of Kubernetes based cloud computing environment combined with real robots -- turtlebots and Niryo arms -- allowed us to: 1) minimize the set up times required to provide a Robotic Operating System (ROS) simulation and development environment to all students independently of their laptop architecture and OS; 2) provide a seamless "simulation to real" experience preserving the exciting experience of writing software interac…
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
In this work, we introduce iviz, a mobile application for visualizing ROS data. In the last few years, the popularity of ROS has grown enormously, making it the standard platform for open source robotic programming. A key reason for this success is the availability of polished, general-purpose modules for many tasks, such as localization, mapping, path planning, and quite importantly, data visualization. However, the availability of the latter is generally restricted to PCs with the Linux operating system. Thus, users that want to see what is happening in the system with a smartphone or a tabl…
In this paper, we consider a large-scale instance of the classical Pickup-and-Delivery Vehicle Routing Problem (PDVRP) that must be solved by a network of mobile cooperating robots. Robots must self-coordinate and self-allocate a set of pickup/delivery tasks while minimizing a given cost figure. This results in a large, challenging Mixed-Integer Linear Problem that must be cooperatively solved % without a central coordinator. We propose a distributed algorithm based on a primal decomposition approach that provides a feasible solution to the problem in finite time. An interesting feature of the…
We introduce the Beam, a collaborative autonomous mobile service robot, based on SuitableTech's Beam telepresence system. We present a set of enhancements to the telepresence system, including autonomy, human awareness, increased computation and sensing capabilities, and integration with the popular Robot Operating System (ROS) framework. Together, our improvements transform the Beam into a low-cost platform for research on service robots. We examine the Beam on target search and object delivery tasks and demonstrate that the robot achieves a 100% success rate.
Coordinated Multi-Robot Navigation with Formation Adaptation
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）；University of Denver（丹佛大学）；U.S. Army DEVCOM Army Research Laboratory (ARL)（美国陆军DEVCOM陆军研究实验室）
SUNY Binghamton（纽约州立大学宾汉姆顿分校）；OPPO US Research Center（OPPO美国研究中心）
Human-robot collaboration frequently requires extensive communication, e.g., using natural language and gestures. Augmented reality (AR) has provided an alternative way of bridging the communication gap between robots and people. However, most current AR-based human-robot communication methods are unidirectional, focusing on how the human adapts to robot behaviors, and are limited to single-robot domains. In this paper, we develop AR for Robots Collaborating with a Human (ARROCH), a novel algorithm and system that supports bidirectional, multi-turn, human-multi-robot communication in indoor mu…
The State University of New York at Binghamton（纽约州立大学宾汉姆顿分校）
In human-robot collaboration domains, augmented reality (AR) technologies have enabled people to visualize the state of robots. Current AR-based visualization policies are designed manually, which requires a lot of human efforts and domain knowledge. When too little information is visualized, human users find the AR interface not useful; when too much information is visualized, they find it difficult to process the visualized information. In this paper, we develop a framework, called VARIL, that enables AR agents to learn visualization policies (what to visualize, when, and how) from demonstra…
School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）；Delft University of Technology（代尔夫特理工大学）；VORTEX CoLab（VORTEX联合实验室）；Fraunhofer Institute for Manufacturing Engineering and Automation IPA（弗劳恩霍夫制造工程与自动化研究所）；IT University of Copenhagen（哥本哈根IT大学）
Learning Multi-Agent Task Assignment and Navigation in the Factory: from Simulation to Real Robots
INIT Robots Lab, École de technologie supérieure（INIT机器人实验室，高等工程技术学院）；MISTLab, Department of Computer and Software Engineering, Polytechnique Montréal（MIST实验室，蒙特利尔理工学院计算机与软件工程系）
MRTA-Sim: A Modular Simulator for Multi-Robot Allocation, Planning, and Control in Open-World Environments
Department of Electrical Engineering and Computer Sciences, University of California at Berkeley（加州大学伯克利分校电子工程与计算机科学系）；Toyota Motor North America（丰田北美公司）
OpenRoIS: A Community-Driven Open-Source Middleware Implementing the Robotic Interaction Service (RoIS) Framework for Physical Robots and Virtual Agents
Coarobo GK（Coarobo 合同会社）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Advanced Telecommunications Research Institute International (ATR)（国际电气通信基础技术研究所（ATR））
