Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A missing hiker's dead body was finally found in July in Italy's rugged Piedmont region after 10 months. The recovery team credited the breakthrough to an AI-powered drone that spotted a critical clue within hours. The same process would have taken weeks or even months if done by the human eye.

Air-ground collaborative robots have shown great potential in the field of fire and rescue, which can quickly respond to rescue needs and improve the efficiency of task execution. Mapping and navigation, as the key foundation for air-ground collaborative robots to achieve efficient task execution, have attracted a great deal of attention. This growing interest in collaborative robot mapping and navigation is conducive to improving the intelligence of fire and rescue task execution, but there has been no comprehensive investigation of this field to highlight their strengths. In this paper, we present a systematic review of the ground-to-ground cooperative robots for fire and rescue from a new perspective of mapping and navigation. First, an air-ground collaborative robots framework for fire and rescue missions based on unmanned aerial vehicle (UAV) mapping and unmanned ground vehicle (UGV) navigation is introduced. Then, the research progress of mapping and navigation under this framework is systematically summarized, including UAV mapping, UAV/UGV co-localization, and UGV navigation, with their main achievements and limitations. Based on the needs of fire and rescue missions, the collaborative robots with different numbers of UAVs and UGVs are classified, and their practicality in fire and rescue tasks is elaborated, with a focus on the discussion of their merits and demerits. In addition, the application examples of air-ground collaborative robots in various firefighting and rescue scenarios are given. Finally, this paper emphasizes the current challenges and potential research opportunities, rounding up references for practitioners and researchers willing to engage in this vibrant area of air-ground collaborative robots.

A new piece of technology has been engineered by Revolute Robotics that resembles a hamster ball, yet has the ability to fly. Our world is filled with many incredible inventions and feats of engineering. But, occasionally, something comes along that genuinely revolutionizes our perspective on what technology can do. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK TIPS, TECH REVIEWS AND EASY HOW-TO'S – SIGN UP FREE HERE It's a flying, crawling, autonomous robot that resembles a hamster ball. It sounds unbelievable, but this little sphere of technological wonder is already turning heads in the industry.

Drones have shown to be useful aerial vehicles for unmanned transport missions such as food and medical supply delivery. This can be leveraged to deliver life-saving nutrition and medicine for people in emergency situations. However, commercial drones can generally only carry 10 % - 30 % of their own mass as payload, which limits the amount of food delivery in a single flight. One novel solution to noticeably increase the food-carrying ratio of a drone, is recreating some structures of a drone, such as the wings, with edible materials. We thus propose a drone, which is no longer only a food transporting aircraft, but itself is partially edible, increasing its food-carrying mass ratio to 50 %, owing to its edible wings. Furthermore, should the edible drone be left behind in the environment after performing its task in an emergency situation, it will be more biodegradable than its non-edible counterpart, leaving less waste in the environment. Here we describe the choice of materials and scalable design of edible wings, and validate the method in a flight-capable prototype that can provide 300 kcal and carry a payload of 80 g of water.

On behalf of the Metropolis of Reno and the Reno Hearth Division (RFD), Iris Automation has been granted approval from the Federal Aviation Administration (FAA) to fly a small drone autonomously past the pilot's visible line of sight (BVLOS), with out the help of any observers or further ground-based detection gear. Testing will start over unpopulated areas earlier than shifting to city areas. The BVLOS waiver covers a rural, unpopulated space south of Reno and was submitted by Iris Automation for using its Casia X detect and keep away from resolution. "That is an thrilling venture, working with the BEYOND program and the most recent applied sciences to open the skies each for our group and the broader public," mentioned Reno Mayor Hillary Schieve. "It's a novel teaming of private and non-private pursuits to attain breakthrough operations for a variety of cost-effective, public-facing companies. Autonomous flying will profit each member of our group and drive long run financial advantages together with job creation, value financial savings and extra environment friendly companies. We intend this to be our first of many waivers as a part of this collaboration. We're proud to be main the way in which on this unbelievable area--and with a neighborhood BEYOND participant too--and excited to see our companions shifting to this subsequent step within the course of."

Cooperation in multi-agent and multi-robot systems can help agents build various formations, shapes, and patterns presenting corresponding functions and purposes adapting to different situations. Relationship between agents such as their spatial proximity and functional similarities could play a crucial role in cooperation between agents. Trust level between agents is an essential factor in evaluating their relationships' reliability and stability, much as people do. This paper proposes a new model called Relative Needs Entropy (RNE) to assess trust between robotic agents. RNE measures the distance of needs distribution between individual agents or groups of agents. To exemplify its utility, we implement and demonstrate our trust model through experiments simulating a heterogeneous multi-robot grouping task in a persistent urban search and rescue mission consisting of tasks at two levels of difficulty. The results suggest that RNE trust-Based grouping of robots can achieve better performance and adaptability for diverse task execution compared to the state-of-the-art energy-based or distance-based grouping models.

Researchers have developed the'RoboFly' to aid in rescue missions, detect gas leaks and pollinate crops. The penny-size robot, inspired by flying insects, can move through the air, on the ground and drift over water surfaces to carry out different tasks. It is fitted with thin hinges of plastic in its carbon fiber body that act as joints and sports balanced control system commands that provides rotational motions of the wings – each wing is controlled independently in real-time. The team believes its creation is far more effective than current models, because the Robofly is able to avoid obstacles with the help of its different modes of locomotion. Researchers have developed the'RoboFly' to aid in rescue missions, detect gas leaks and pollinate crops.

On Friday night in an old newspaper printing plant in Austin, the future of drone automation lifted off, accelerated and flew, nearly fast enough to beat one of the best drone pilots in the world. Gabriel Kocher, known in the professional Drone Racing League as Gab707, sat behind a net, wearing video goggles and steering his drone through five square gates on a short, curvy course. Next to him were four teammates from the MavLAB of the Delft University of Technology in the Netherlands. They had already programmed their automated drone, which resembled a mini Stealth Bomber. Now they were watching to see if their code had made the drone fast and accurate enough to defeat Kocher.

It is a challenging and complex task to acquire information from different regions of a disaster-affected area in a timely fashion. The extensive spread and reach of social media and networks allow people to share information in real-time. However, the processing of social media data and gathering of valuable information require a series of operations such as (1) processing each specific tweet for a text classification, (2) possible location determination of people needing help based on tweets, and (3) priority calculations of rescue tasks based on the classification of tweets. These are three primary challenges in developing an effective rescue scheduling operation using social media data. In this paper, first, we propose a deep learning model combining attention based Bi-directional Long Short-Term Memory (BLSTM) and Convolutional Neural Network (CNN) to classify the tweets under different categories. We use pre-trained crisis word vectors and global vectors for word representation (GLoVe) for capturing semantic meaning from tweets. Next, we perform feature engineering to create an auxiliary feature map which dramatically increases the model accuracy. In our experiments using real data sets from Hurricanes Harvey and Irma, it is observed that our proposed approach performs better compared to other classification methods based on Precision, Recall, F1-score, and Accuracy, and is highly effective to determine the correct priority of a tweet. Furthermore, to evaluate the effectiveness and robustness of the proposed classification model a merged dataset comprises of 4 different datasets from CrisisNLP and another 15 different disasters data from CrisisLex are used. Finally, we develop an adaptive multitask hybrid scheduling algorithm considering resource constraints to perform an effective rescue scheduling operation considering different rescue priorities.

A nimble robot inspired by bush babies can now bounce three times its own height in a single leap. SALTO (saltatorial locomotion terrain obstacles) was fist designed to jump at 4mph (1.75 m/s) but a host of new features have now been added to the nifty machine. A single leg, inspired by those of the galago, or Senegalese bush baby, propels the robot across a range of terrain and over various obstacles. Video footage of it in action reveals how it effortlessly navigates obstacle courses and bounces through the streets. Creators of SALTO hope the technology will one day aid the development of other robots which can assist in rescue missions by jumping over rubble.