Does gravity affect decision-making? This question comes into sharp focus as plans for interplanetary human space missions solidify. In the framework of Bayesian brain theories, gravity encapsulates a strong prior, anchoring agents to a reference frame via the vestibular system, informing their decisions and possibly their integration of uncertainty. What happens when such a strong prior is altered? We address this question using a self-motion estimation task in a space analog environment under conditions of altered gravity. Two participants were cast as remote drone operators orbiting Mars in a virtual reality environment on board a parabolic flight, where both hyper- and microgravity conditions were induced. From a first-person perspective, participants viewed a drone exiting a cave and had to first predict a collision and then provide a confidence estimate of their response. We evoked uncertainty in the task by manipulating the motion's trajectory angle. Post-decision subjective confidence reports were negatively predicted by stimulus uncertainty, as expected. Uncertainty alone did not impact overt behavioral responses (performance, choice) differentially across gravity conditions. However microgravity predicted higher subjective confidence, especially in interaction with stimulus uncertainty. These results suggest that variables relating to uncertainty affect decision-making distinctly in microgravity, highlighting the possible need for automatized, compensatory mechanisms when considering human factors in space research.

President Volodymyr Zelenskyy said Ukraine's forces were destroying Russian forces in the two main areas of the conflict in the east and south of Ukraine. Russia launched a major drone assault on Kyiv and other cities, Ukrainian officials said, with air defence systems shooting down 28 out of 30 Iranian-made Shahed drones. Ukraine's Environment Minister Ruslan Strilets said last week's collapse of the Nova Kakhovka hydroelectric dam caused 1.2 billion euros ($1.3bn) of damage and warned that mines unearthed by flooding could wash onto other European countries' shores. Andriy Yermak, head of the office of the Ukrainian president, said Russian forces shelled rescue workers clearing mud in the flood-affected regions of Kherson, killing one and injuring eight. Russian-appointed authorities in the occupied town of Nova Kakhovka said one woman was killed and four injured in a Ukrainian drone attack.

With the advent of Unmanned Aerial Vehicles (UAV) and Micro Aerial Vehicles (MAV) in commercial sectors, their application for transporting and manipulating payloads has attracted many research work. A swarm of agents, cooperatively working to transport and manipulate a payload can overcome the physical limitations of a single agent, adding redundancy and tolerance against failures. In this paper, the dynamics of a swarm connected to a payload via flexible cables are modeled, and a decentralized control is designed using Artificial Potential Field (APF). The swarm is able to transport the payload through an unknown environment to a goal position while avoiding obstacles from the local information received from the onboard sensors. The key contributions are (a) the cables are modelled more accurately using lumped mass model instead of geometric constraints, (b) a decentralized swarm control is designed using potential field approach to ensure hover stability of system without payload state information, (c) the manipulation of payload elevation and azimuth angles are controlled by APF, and (d) the trajectory of the payload for transportation is governed by potential fields generated by goal point and obstacles. The efficacy of the method proposed in this work are evaluated through numerical simulations under the influence of external disturbances and failure of agents.

Russia has accused Ukraine of planning to attack annexed Crimea with long-range United States and British missiles and warned it would retaliate if that happened. Russian Defence Minister Sergey Shoigu told a meeting of military officials on Tuesday that Moscow possesses information that Ukraine plans to strike Crimea with US-supplied HIMARS long-range rocket systems and British-supplied Storm Shadow cruise missiles. "The use of these missiles outside the zone of our special military operation would mean that the United States and Britain would be fully dragged into the conflict and would entail immediate strikes on decision-making centres in Ukraine," Shoigu said. Russia annexed Ukraine's Crimean Peninsula in 2014 and considers it to be outside the scope of its invasion – which is focused in eastern and southern Ukraine, where Ukraine is fighting to retake territory. Kyiv, which says it is battling for its survival in a war of colonial conquest, said it wants to reclaim all of its territory, including Crimea, the home of Russia's Black Sea naval base.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Turkish drone attack killed two Kurdish local officials and their driver in northeast Syria on Tuesday in the latest such strike in the war-torn country, officials said, as talks on Syria's conflict began in Kazakhstan. The Kurdish-led authority in northeast Syria said Tuesday's strike hit a car near the town of Qamishli, killing the co-chairperson of the town's council, Yusra Darwish, and her deputy, Liman Shweish, as well as their driver. An additional local officials was wounded in the attack.

An efficient characterization of scientifically significant locations is essential prior to the return of humans to the Moon. The highest resolution imagery acquired from orbit of south-polar shadowed regions and other relevant locations remains, at best, an order of magnitude larger than the characteristic length of most of the robotic systems to be deployed. This hinders the planning and successful implementation of prospecting missions and poses a high risk for the traverse of robots and humans, diminishing the potential overall scientific and commercial return of any mission. We herein present the design of a lightweight, compact, autonomous, and reusable lunar reconnaissance drone capable of assisting other ground-based robotic assets, and eventually humans, in the characterization and high-resolution mapping (~0.1 m/px) of particularly challenging and hard-to-access locations on the lunar surface. The proposed concept consists of two main subsystems: the drone and its service station. With a total combined wet mass of 100 kg, the system is capable of 11 flights without refueling the service station, enabling almost 9 km of accumulated flight distance. The deployment of such a system could significantly impact the efficiency of upcoming exploration missions, increasing the distance covered per day of exploration and significantly reducing the need for recurrent contacts with ground stations on Earth.

This paper studies the bearing-based time-varying formation control problem for unicycle-type agents without bearing rigidity conditions. In the considered problem, only a small set of agents, named as anchors, can obtain their global positions, and the other agents only have access to the bearing information relative to their neighbors. To address the problem, we propose a novel scheme integrating the distributed localization algorithm and the observer-based formation tracking controller. The designed localization algorithm estimates the global position by using inter-agent bearing measurements, and the observer-based controller tracks the desired formation with the estimated positions. A key distinction of our approach is extending the localization-and-tracking control scheme to the bearing-based coordination of nonholonomic systems, where the desired inter-agent bearings can be time-varying, instead of the constant ones assumed in most of the existing researches. The asymptotic stability of the coupled localization-and-tracking control system is proved, and simulations are carried out to validate the theoretical analysis.

Sensors are crucial for autonomous operation in robotic vehicles (RV). Unfortunately, RV sensors can be compromised by physical attacks such as tampering or spoofing, leading to a crash. In this paper, we present DeLorean, a modelfree recovery framework for recovering autonomous RVs from sensor deception attacks (SDA). DeLorean is designed to recover RVs even from a strong SDA in which the adversary targets multiple heterogeneous sensors simultaneously (even all the sensors). Under SDAs, DeLorean inspects the attack induced errors, identifies the targeted sensors, and prevents the erroneous sensor inputs from being used to derive actuator signals. DeLorean then replays historic state information in the RV's feedback control loop for a temporary mitigation and recovers the RV from SDA. Our evaluation on four real and two simulated RVs shows that DeLorean can recover RVs from SDAs, and ensure mission success in 90.7% of the cases on average.

This paper explores the potential of 5G new radio (NR) Time-of-Arrival (TOA) data for indoor drone localization under different scenarios and conditions when fused with inertial measurement unit (IMU) data. Our approach involves performing graph-based optimization to estimate the drone's position and orientation from the multiple sensor measurements. Due to the lack of real-world data, we use Matlab 5G toolbox and QuaDRiGa (quasi-deterministic radio channel generator) channel simulator to generate TOA measurements for the EuRoC MAV indoor dataset that provides IMU readings and ground truths 6DoF poses of a flying drone. Hence, we create twelve sequences combining three predefined indoor scenarios setups of QuaDRiGa with 2 to 5 base station antennas. Therefore, experimental results demonstrate that, for a sufficient number of base stations and a high bandwidth 5G configuration, the pose graph optimization approach achieves accurate drone localization, with an average error of less than 15 cm on the overall trajectory. Furthermore, the adopted graph-based optimization algorithm is fast and can be easily implemented for onboard real-time pose tracking on a micro aerial vehicle (MAV).

Texas DPS said the smuggler was smuggling two illegal immigrants from Mexico. Texas authorities announced Thursday that they have arrested a confirmed MS-13 gang member with a lengthy rap sheet, whose name is on an international watchlist, after he attempted to escape deeper into the U.S. in a grain hauler on a train. Delmar Sanchez Zuniga is a previously deported Honduran national with a lengthy rap sheet, authorities said. Texas Department of Public Safety said in a statement on Thursday that he is a confirmed MS-13 gang member on the Transnational Criminal Organization Watchlist. The agency said that Texas Rangers, using drone technology, observed a number of illegal immigrants attempting to board a train in Maverick County on June 3.