This paper proposes a novel methodology for addressing the simulation-reality gap for multi-robot swarm systems. Rather than immediately try to shrink or `bridge the gap' anytime a real-world experiment failed that worked in simulation, we characterize conditions under which this is actually necessary. When these conditions are not satisfied, we show how very simple simulators can still be used to both (i) design new multi-robot systems, and (ii) guide real-world swarming experiments towards certain emergent behaviors when the gap is very large. The key ideas are an iterative simulator-in-the-design-loop in which real-world experiments, simulator modifications, and simulated experiments are intimately coupled in a way that minds the gap without needing to shrink it, as well as the use of minimally viable phase diagrams to guide real world experiments. We demonstrate the usefulness of our methods on deploying a real multi-robot swarm system to successfully exhibit an emergent milling behavior.

Earlier this month, Amazon confirmed plans to lay off around 18,000 workers. The move has hit certain divisions hard, including Comixology and Prime Air. The latter's drone delivery program was just starting to gain traction after commencing deliveries in test markets and unveiling a new model, but the layoffs have reportedly had a significant impact on that team. Prime Air employees learned about the cuts on Wednesday, according to CNBC. Employees in the drone delivery department's design, maintenance, systems engineering, flight testing and flight operations teams are said to have been laid off.

A drone attack hit a US-led coalition base in southern Syria, the US military's Central Command has said. "Three one-way attack drones attacked the al-Tanf Garrison in Syria," a CENTCOM statement said on Friday. Two of the drones were shot down by the coalition, but the third hit the compound, wounding two allied Syrian opposition fighters who received treatment, the statement added. "Attacks of this kind are unacceptable," CENTCOM spokesperson Joe Buccino said, without specifying who carried it out. "They place our troops and our partners at risk and jeopardise the fight against ISIL." There was no immediate claim of responsibility for the attack.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Three one-way drones, sometimes called "kamikaze" drones, targeted a U.S. garrison at an outpost in Syria's Al-Tanf region U.S. Central Command said Friday, noting that no Americans were injured in the attack. Two members of the Syrian Free Army received medical attention after they were injured in the strike when one of the drones hit the compound. The other two drones were shot down by Coalition Forces, the U.S. military confirmed.

Efficient exploration strategies are vital in tasks such as search-and-rescue missions and disaster surveying. Unmanned Aerial Vehicles (UAVs) have become particularly popular in such applications, promising to cover large areas at high speeds. Moreover, with the increasing maturity of onboard UAV perception, research focus has been shifting toward higher-level reasoning for single- and multi-robot missions. However, autonomous navigation and exploration of previously unknown large spaces still constitutes an open challenge, especially when the environment is cluttered and exhibits large and frequent occlusions due to high obstacle density, as is the case of forests. Moreover, the problem of long-distance wireless communication in such scenes can become a limiting factor, especially when automating the navigation of a UAV swarm. In this spirit, this work proposes an exploration strategy that enables UAVs, both individually and in small swarms, to quickly explore complex scenes in a decentralized fashion. By providing the decision-making capabilities to each UAV to switch between different execution modes, the proposed strategy strikes a great balance between cautious exploration of yet completely unknown regions and more aggressive exploration of smaller areas of unknown space. This results in full coverage of forest areas of variable density, consistently faster than the state of the art. Demonstrating successful deployment with a single UAV as well as a swarm of up to three UAVs, this work sets out the basic principles for multi-root exploration of cluttered scenes, with up to 65% speed up in the single UAV case and 40% increase in explored area for the same mission time in multi-UAV setups.

This report reviews all results derived from performance benchmarking conducted during Phase I of the Development and Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) project by the University of Massachusetts Lowell, using the test methods specified in the DECISIVE Test Methods Handbook v1.1 for evaluating small unmanned aerial systems (sUAS) performance in subterranean and constrained indoor environments, spanning communications, field readiness, interface, obstacle avoidance, navigation, mapping, autonomy, trust, and situation awareness. Using those 20 test methods, over 230 tests were conducted across 8 sUAS platforms: Cleo Robotics Dronut X1P (P = prototype), FLIR Black Hornet PRS, Flyability Elios 2 GOV, Lumenier Nighthawk V3, Parrot ANAFI USA GOV, Skydio X2D, Teal Golden Eagle, and Vantage Robotics Vesper. Best in class criteria is specified for each applicable test method and the sUAS that match this criteria are named for each test method, including a high-level executive summary of their performance.

This handbook outlines all test methods developed under the Development and Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) project by the University of Massachusetts Lowell for evaluating small unmanned aerial systems (sUAS) performance in subterranean and constrained indoor environments, spanning communications, field readiness, interface, obstacle avoidance, navigation, mapping, autonomy, trust, and situation awareness. For sUAS deployment in subterranean and constrained indoor environments, this puts forth two assumptions about applicable sUAS to be evaluated using these test methods: (1) able to operate without access to GPS signal, and (2) width from prop top to prop tip does not exceed 91 cm (36 in) wide (i.e., can physically fit through a typical doorway, although successful navigation through is not guaranteed). All test methods are specified using a common format: Purpose, Summary of Test Method, Apparatus and Artifacts, Equipment, Metrics, Procedure, and Example Data. All test methods are designed to be run in real-world environments (e.g., MOUT sites) or using fabricated apparatuses (e.g., test bays built from wood, or contained inside of one or more shipping containers).

"We have a tough situation here," Andriy acknowledged, before slipping into a well-disguised command bunker hidden amid the ruins. His team had just received detailed information about a Russian armoured personnel carrier (APC), spotted by a Ukrainian drone. Moments later, there were three loud outgoing blasts from a nearby UK-supplied light artillery piece, used here by Ukrainian forces, and aimed at the vehicle.

The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops, compromising agri-food production. Field monitoring procedures are fundamental to perform risk assessment operations, in order to promptly face crop infestations and avoid economical losses. To improve pest management, spectral cameras mounted on Unmanned Aerial Vehicles (UAVs) and other Internet of Things (IoT) devices, such as smart traps or unmanned ground vehicles, could be used as an innovative technology allowing fast, efficient and real-time monitoring of insect infestations. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens on different vegetal backgrounds, overcoming the problem of BMSB mimicry. Hyperspectral images of BMSB were acquired in the 980-1660 nm range, considering different vegetal backgrounds selected to mimic a real field application scene. Classification models were obtained following two different chemometric approaches. The first approach was focused on modelling spectral information and selecting relevant spectral regions for discrimination by means of sparse-based variable selection coupled with Soft Partial Least Squares Discriminant Analysis (s-Soft PLS-DA) classification algorithm. The second approach was based on modelling spatial and spectral features contained in the hyperspectral images using Convolutional Neural Networks (CNN). Finally, to further improve BMSB detection ability, the two strategies were merged, considering only the spectral regions selected by s-Soft PLS-DA for CNN modelling.

For safe and efficient operation, mobile robots need to perceive their environment, and in particular, perform tasks such as obstacle detection, localization, and mapping. Although robots are often equipped with microphones and speakers, the audio modality is rarely used for these tasks. Compared to the localization of sound sources, for which many practical solutions exist, algorithms for active echolocation are less developed and often rely on hardware requirements that are out of reach for small robots. We propose an end-to-end pipeline for sound-based localization and mapping that is targeted at, but not limited to, robots equipped with only simple buzzers and low-end microphones. The method is model-based, runs in real time, and requires no prior calibration or training. We successfully test the algorithm on the e-puck robot with its integrated audio hardware, and on the Crazyflie drone, for which we design a reproducible audio extension deck. We achieve centimeter-level wall localization on both platforms when the robots are static during the measurement process. Even in the more challenging setting of a flying drone, we can successfully localize walls, which we demonstrate in a proof-of-concept multi-wall localization and mapping demo.