Teams of mobile [aerial, ground, or aquatic] robots have applications in resource delivery, patrolling, information-gathering, agriculture, forest fire fighting, chemical plume source localization and mapping, and search-and-rescue. Robot teams traversing hazardous environments -- with e.g. rough terrain or seas, strong winds, or adversaries capable of attacking or capturing robots -- should plan and coordinate their trails in consideration of risks of disablement, destruction, or capture. Specifically, the robots should take the safest trails, coordinate their trails to cooperatively achieve the team-level objective with robustness to robot failures, and balance the reward from visiting locations against risks of robot losses. Herein, we consider bi-objective trail-planning for a mobile team of robots orienteering in a hazardous environment. The hazardous environment is abstracted as a directed graph whose arcs, when traversed by a robot, present known probabilities of survival. Each node of the graph offers a reward to the team if visited by a robot (which e.g. delivers a good to or images the node). We wish to search for the Pareto-optimal robot-team trail plans that maximize two [conflicting] team objectives: the expected (i) team reward and (ii) number of robots that survive the mission. A human decision-maker can then select trail plans that balance, according to their values, reward and robot survival. We implement ant colony optimization, guided by heuristics, to search for the Pareto-optimal set of robot team trail plans. As a case study, we illustrate with an information-gathering mission in an art museum.

In order to clear the world of the threat posed by landmines and other explosive devices, robotic systems can play an important role. However, the development of such field robots that need to operate in hazardous conditions requires the careful consideration of multiple aspects related to the perception, mobility, and collaboration capabilities of the system. In the framework of a European challenge, the Artificial Intelligence for Detection of Explosive Devices - eXtended (AIDEDeX) project proposes to design a heterogeneous multi-robot system with advanced sensor fusion algorithms. This system is specifically designed to detect and classify improvised explosive devices, explosive ordnances, and landmines. This project integrates specialised sensors, including electromagnetic induction, ground penetrating radar, X-Ray backscatter imaging, Raman spectrometers, and multimodal cameras, to achieve comprehensive threat identification and localisation. The proposed system comprises a fleet of unmanned ground vehicles and unmanned aerial vehicles. This article details the operational phases of the AIDEDeX system, from rapid terrain exploration using unmanned aerial vehicles to specialised detection and classification by unmanned ground vehicles equipped with a robotic manipulator. Initially focusing on a centralised approach, the project will also explore the potential of a decentralised control architecture, taking inspiration from swarm robotics to provide a robust, adaptable, and scalable solution for explosive detection.

The head of the UK's largest and most advanced robotics centre has said that society needs to prepare for the increased integration of robots but shouldn't fear the rise of artificial intelligence (AI). Stewart Miller, the chief executive of the National Robotarium, which opens today in Edinburgh, told Sky News that "Inevitably there will be more robots in everybody's life. They'll be helping you at home, when you go out shopping, when you go to a hotel, they'll be involved in hospitality, when you go to a theatre, everything. Internationally, some scientists have expressed concerns over rapid progress in the field of artificial intelligence. A new survey of researchers from the New York University Centre for Data Science found that more than a third (36%) of respondents that had published recent papers in the field thought that AI could produce catastrophic outcomes in this century, "on the level of all-out nuclear war". Mr Miller said that "the thing to remembers is that we, the humans, are in control.

The head of the UK's largest and most advanced robotics centre has said that society needs to prepare for the increased integration of robots but shouldn't fear the rise of artificial intelligence (AI). Stewart Miller, the chief executive of the National Robotarium, which opens today in Edinburgh, told Sky News that "Inevitably there will be more robots in everybody's life. They'll be helping you at home, when you go out shopping, when you go to a hotel, they'll be involved in hospitality, when you go to a theatre, everything. Internationally, some scientists have expressed concerns over rapid progress in the field of artificial intelligence. A new survey of researchers from the New York University Centre for Data Science found that more than a third (36%) of respondents that had published recent papers in the field thought that AI could produce catastrophic outcomes in this century, "on the level of all-out nuclear war". Mr Miller said that "the thing to remembers is that we, the humans, are in control.

There has been a plethora of work towards improving robot perception and navigation, yet their application in hazardous environments, like during a fire or an earthquake, is still at a nascent stage. We hypothesize two key challenges here: first, it is difficult to replicate such scenarios in the real world, which is necessary for training and testing purposes. Second, current systems are not fully able to take advantage of the rich multi-modal data available in such hazardous environments. To address the first challenge, we propose to harness the enormous amount of visual content available in the form of movies and TV shows, and develop a dataset that can represent hazardous environments encountered in the real world. The data is annotated with high-level danger ratings for realistic disaster images, and corresponding keywords are provided that summarize the content of the scene. In response to the second challenge, we propose a multi-modal danger estimation pipeline for collaborative human-robot escape scenarios. Our Bayesian framework improves danger estimation by fusing information from robot's camera sensor and language inputs from the human. Furthermore, we augment the estimation module with a risk-aware planner that helps in identifying safer paths out of the dangerous environment. Through extensive simulations, we exhibit the advantages of our multi-modal perception framework that gets translated into tangible benefits such as higher success rate in a collaborative human-robot mission.

The National Safety Council (NSC) reports that the top three leading causes of work-related injuries in the U.S. are: overexertion; slips, trips and falls; and contact with objects and equipment. But what if these incidents could be prevented before they occur? Emerging technologies like computer vision, advanced sensors, augmented reality and Artificial Intelligence (AI) are creating new opportunities to do just that. Working together as a connected IIoT system, these solutions provide the visibility and intelligence to identify hazards, prevent injury and reduce overall risk -- empowering organizations to build a virtual safety net for their employees. Let's examine a few ways the industrial Internet of Things (IoT) is helping to keep workers safe.

Public perceptions of Robotics and Artificial Intelligence (RAI) are important in the acceptance, uptake, government regulation and research funding of this technology. Recent research has shown that the public's understanding of RAI can be negative or inaccurate. We believe effective public engagement can help ensure that public opinion is better informed. In this paper, we describe our first iteration of a high throughput in-person public engagement activity. We describe the use of a light touch quiz-format survey instrument to integrate in-the-wild research participation into the engagement, allowing us to probe both the effectiveness of our engagement strategy, and public perceptions of the future roles of robots and humans working in dangerous settings, such as in the off-shore energy sector. We critique our methods and share interesting results into generational differences within the public's view of the future of Robotics and AI in hazardous environments. These findings include that older peoples' views about the future of robots in hazardous environments were not swayed by exposure to our exhibit, while the views of younger people were affected by our exhibit, leading us to consider carefully in future how to more effectively engage with and inform older people.

Researchers have developed stretchable, pocket-sized robots which could crawl up walls and across ceiling to clean them, for environmental monitoring and deployment in hazardous environments. Published in the journal Soft Robotics, the study from University of Bristol in the UK describe how a robot made from the skin, called "ElectroSkin", can be scrunched up, put in one's pocket and then later pulled out and thrown on a surface where it moves. ElectroSkin is a new fundamental building block for a range of soft next-generation robots. "ElectroSkin is an important step toward soft robots that can be easily transported, deployed and even worn. The combination of electrical artificial muscles and electrical gripping replicated the movements of animals like slugs and snails, and where they can go, so could our robots," said study researcher Jonathan Rossiter, Professor at University of Bristol.

A nature-inspired robot using water and combustible powder can launch itself from water like a flying fish. The device, which can travel 26 metres through the air after take-off, could potentially be used to collect water samples in hazardous environments, such as floods. Researchers at Imperial College London created the system, which weighs just 160 grams and can'jump' multiple times after refilling its water tank. Furthermore, while similar robots often require calm conditions to leap from the water, the team's invention generates a force 25 times the robot's weight, giving it a greater chance of overcoming choppy waves. The water and the calcium-carbide powder combine in a reaction chamber, producing a burnable acetylene gas.

ABSTRACT Vision-based navigation of modern autonomous vehicles primarily depends on Deep Neural Network (DNN) based systems in which the controller obtains input from sensors/detectors such as cameras, and produces an output such as a steering wheel angle to navigate the vehicle safely in roadway traffic. Typically, these DNN-based systems are trained through supervised and/or transfer learning; however, recent studies show that these systems can be compromised by perturbation or adversarial input features on the trained DNN-based models. Similarly, this perturbation can be introduced into the autonomous vehicle DNN-based system by roadway hazards such as debris and roadblocks. In this study, we first introduce a roadway hazardous environment (both intentional and unintentional) that can compromise the DNN-based system of an autonomous vehicle, producing an incorrect vehicle navigational output such as a steering wheel angle, which can cause crashes resulting in fatality and injury. Then, we develop an approach based on object detection and semantic segmentation to mitigate the adverse effect of this hazardous environment, one that helps the autonomous vehicle to navigate safely around such hazards. This study finds the DNN-based model with hazardous object detection, and semantic segmentation improves the ability of an autonomous vehicle to avoid potential crashes by 21% compared to the traditional DNN-based autonomous driving system.