A wearable general-purpose solution for Human-Swarm Interaction

Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. Controlling the motion and behavior of these swarms presents unique challenges for human operators, who cannot yet effectively convey their high-level intentions to a group of robots in application. This work proposes a new human-swarm interface based on novel wearable gesture-control and haptic-feedback devices. This work seeks to combine a wearable gesture recognition device that can detect high-level intentions, a portable device that can detect Cartesian information and finger movements, and a wearable advanced haptic device that can provide real-time feedback. This project is the first to envisage a wearable Human-Swarm Interaction (HSI) interface that separates the input and feedback components of the classical control loop (input, output, feedback), as well as being the first of its kind suitable for both indoor and outdoor environments

RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction

Robots have potential to revolutionize the way we interact with the world around us. One of their largest potentials is in the domain of mobile health where they can be used to facilitate clinical interventions. However, to accomplish this, robots need to have access to our private data in order to learn from these data and improve their interaction capabilities. Furthermore, to enhance this learning process, the knowledge sharing among multiple robot units is the natural step forward. However, to date, there is no well-established framework which allows for such data sharing while preserving the privacy of the users (e.g., the hospital patients). To this end, we introduce RoboChain - the first learning framework for secure, decentralized and computationally efficient data and model sharing among multiple robot units installed at multiple sites (e.g., hospitals). RoboChain builds upon and combines the latest advances in open data access and blockchain technologies, as well as machine learning. We illustrate this framework using the example of a clinical intervention conducted in a private network of hospitals. Specifically, we lay down the system architecture that allows multiple robot units, conducting the interventions at different hospitals, to perform efficient learning without compromising the data privacy.Comment: 7 pages, 6 figure

Personal Food Computer: A new device for controlled-environment agriculture

Due to their interdisciplinary nature, devices for controlled-environment agriculture have the possibility to turn into ideal tools not only to conduct research on plant phenology but also to create curricula in a wide range of disciplines. Controlled-environment devices are increasing their functionalities as well as improving their accessibility. Traditionally, building one of these devices from scratch implies knowledge in fields such as mechanical engineering, digital electronics, programming, and energy management. However, the requirements of an effective controlled environment device for personal use brings new constraints and challenges. This paper presents the OpenAg Personal Food Computer (PFC); a low cost desktop size platform, which not only targets plant phenology researchers but also hobbyists, makers, and teachers from elementary to high-school levels (K-12). The PFC is completely open-source and it is intended to become a tool that can be used for collective data sharing and plant growth analysis. Thanks to its modular design, the PFC can be used in a large spectrum of activities.Comment: 9 pages, 11 figures, Accepted at the 2017 Future Technologies Conference (FTC

Secure Encoded Instruction Graphs for End-to-End Data Validation in Autonomous Robots

As autonomous robots become increasingly ubiquitous, more attention is being paid to the security of robotic operation. Autonomous robots can be seen as cyber-physical systems that transverse the virtual realm and operate in the human dimension. As a consequence, securing the operation of autonomous robots goes beyond securing data, from sensor input to mission instructions, towards securing the interaction with their environment. There is a lack of research towards methods that would allow a robot to ensure that both its sensors and actuators are operating correctly without external feedback. This paper introduces a robotic mission encoding method that serves as an end-to-end validation framework for autonomous robots. In particular, we put our framework into practice with a proof of concept describing a novel map encoding method that allows robots to navigate an objective environment with almost-zero a priori knowledge of it, and to validate operational instructions. We also demonstrate the applicability of our framework through experiments with real robots for two different map encoding methods. The encoded maps inherit all the advantages of traditional landmark-based navigation, with the addition of cryptographic hashes that enable end-to-end information validation. This end-to-end validation can be applied to virtually any aspect of robotic operation where there is a predefined set of operations or instructions given to the robot

Editorial: Proceedings of the First Symposium on Blockchain and Robotics, MIT Media Lab, 5 December 2018

An introductory statement by the editors of the present proceedings, detailing the symposium itself as well as its peer-review process and acceptance rate, a summary of the included papers, and details on the editors themselves

Grex: A Decentralized Hive Mind

Swarm Robotics (SR) faces a series of challenges impeding widespread adoption for real-world applications. Distributed Ledger Technology (DLT) has shown it can solve a number of these challenges. An experiment was conducted to showcase the resolution of these challenges. A search and rescue mission was simulated using drones coupled with single board computers and several simulated agents. Inter-agent communications were facilitated through DLT in a completely decentralized network. A frontend interface was built to demonstrate the ease with which information can be extracted from the system. This paper shows the feasibility of the application of DLT to SR-related challenges in a practical experiment. For future work, it is proposed to focus on more complex tasks through federated learning or inter-swarm communications, possibly through Cosmos

Robotic Services for New Paradigm Smart Cities Based on Decentralized Technologies

This article describes different methods of organizing robotic services for smart cities using secure encrypted decentralized technologies and market mechanisms—as opposed to models based on centralized solutions based (or not) on using cloud services and stripping citizens of the control of their own data. The basis of the proposed methods is the Ethereum decentralized computer with the mechanism of smart contracts. In this work, special attention is paid to the integration of technical and economic information into one network of transactions, which allows creating a unified way of interaction between robots—the robot economy. Three possible scenarios of robotic services for smart cities based on the economy of robots are presented: unmanned aerial vehicles (UAVs), environmental monitoring, and smart factories. In order to demonstrate the feasibility of the proposed scenarios, three experiments are presented and discussed. Our work shows that the Ethereum network can provide, through smart contracts and their ability to activate programs to interact with the physical world, an effective and practical way to manage robot services for smart cities

Self-employment for autonomous robots using smart contracts

The physical autonomy of robots is well understood both theoretically and practically. By contrast, there is almost no research exploring a robot's potential economic autonomy. In this paper, we present the first economically autonomous robot -- a robot able to produce marketable goods while having full control over the use of its generated income. In our proof-of-concept, the robot is self-employed as an artist. It produces physical artistic goods and uses blockchain-based smart contracts on the Ethereum network to autonomously list its goods for sale in online auctions. Using the blockchain-based smart contract, the robot then uses its income from sales to autonomously order more materials from an online shop, pay for its consumables such as network fees, and remunerate human assistance for support tasks. The robot also uses its income to repay investor loans that funded its initial phase of production. In these transactions, the robot interacts with humans as a peer, not as a tool. In other words, the robot makes peer financial transactions with humans in the same way that another human would, first as an investment vehicle, then as a seller at an auction, and then as a shop customer and a client. Our proof-of-concept is conducted as an in-lab experiment, but gives rise to an important discussion of the legal implications of economically autonomous robots, which under existing frameworks can already be embedded in corporate entities that are classed as artificial persons.Comment: Discussion extended with the legal implications subsectio

Urban Swarms: A new approach for autonomous waste management

Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of attention in the past years and they are expected to become one of the main driving factors for innovation in the field of robotics. The research presented in this paper explores the feasibility of a swarm robotics system in an urban environment. By using bio-inspired foraging methods such as multi-place foraging and stigmergy-based navigation, a swarm of robots is able to improve the efficiency and autonomy of the urban waste management system in a realistic scenario. To achieve this, a diverse set of simulation experiments was conducted using real-world GIS data and implementing different garbage collection scenarios driven by robot swarms. Results presented in this research show that the proposed system outperforms current approaches. Moreover, results not only show the efficiency of our solution, but also give insights about how to design and customize these systems.Comment: Manuscript accepted for publication in IEEE ICRA 201