Many scenarios in mobility and traffic involve multiple different agents that need to cooperate to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning to find effective and performant behavior strategies. However, as autonomous vehicles and vehicle-to-X communications become more mature, solutions that only utilize single, independent agents leave potential performance gains on the road. Multi-Agent Reinforcement Learning (MARL) is a research field that aims to find optimal solutions for multiple agents that interact with each other. This work aims to give an overview of the field to researchers in autonomous mobility. We first explain MARL and introduce important concepts. Then, we discuss the central paradigms that underlie MARL algorithms, and give an overview of state-of-the-art methods and ideas in each paradigm. With this background, we survey applications of MARL in autonomous mobility scenarios and give an overview of existing scenarios and implementations.

Al-Qaeda leader Ayman al-Zawahiri has been killed in a CIA drone strike in Afghanistan, United States President Joe Biden has said. Al-Zawahiri was killed on Sunday in the biggest blow to the group since its founder Osama bin Laden was killed in 2011. "Justice has been delivered and this terrorist leader is no more," Biden said in a special address from the White House. Intelligence had located al-Zawahiri's family in Kabul earlier this year, Biden said, adding that no civilians or members of Al-Zawahiri's family had been killed in the attack. An Egyptian surgeon with a $25m reward on his head, al-Zawahiri helped coordinate the September 11, 2001 attacks on the US that killed nearly 3,000 people.

Ayman Al Zawahiri, the terrorist killed in a U.S. drone strike in Afghanistan Monday, was a top deputy to al Qaeda leader Usama bin Laden before taking the helm of the organization after his predecessor's death in 2011. A drone strike on a Kabul home took him out over the weekend, Fox News reported earlier. Taliban spokesman Zabihullah Mujahid confirmed and condemned the attack on Twitter, calling it "a clear violation of international principles," according to a translation of the thread. However, the 2020 Doha Agreement, which preceded the Biden administration's highly criticized withdrawal of U.S. troops from Afghanistan last year, called for the Taliban to combat terrorism within the country. Al Zawahiri was also a doctor and founder of the Egyptian Islamic Jihad terror group, which later merged with al-Qaeda, according to authorities.

"Such actions are a repetition of the failed experiences of the past 20 years and are against the interests of the United States of America, Afghanistan and the region," the spokesman added.

Uncontrolled emissions of gases from industrial accidents and disasters result in huge loss of life and property. Such extreme events require a quick and reliable survey of the site for effective rescue strategy planning. To achieve these goals, a network of unmanned aerial vehicles can be deployed that survey the affected region and identify safe and danger zones. Although single UAV-based systems for gas sensing applications are well-studied in literature, research on the deployment of a UAV network for such applications, which is more robust and fault tolerant, is still in infancy. The objective of this project is to design a system that can be deployed in emergency situations to provide a quick survey and identification of safe and dangerous zones in a given region that contains a toxic plume without making any assumptions about plume location. We focus on an end-to-end solution and formulate a two-phase strategy that can not only guarantee detection/acquisition of plume but also its characterization with high spatial resolution. To guarantee coverage of the region with a certain spatial resolution, we set up a vehicle routing problem. To overcome the limitations imposed by limited range of sensors and drone resources, we estimate the concentration map by using Gaussian kernel extrapolation. Finally, we evaluate the suggested framework in simulations. Our results suggest that this two-phase strategy not only gives better error performance but is also more efficient in terms of mission time. Moreover, the comparison between 2-phase random search and 2-phase uniform coverage suggest that the latter is better for single drone systems whereas for multiple drones the former gives reasonable performance at low computational cost.

"The fact that newer drones, such as the Mohajer-6, are now being seen in places like the Horn of Africa shows that countries see them as a potential game-changer," he added, referring to an advanced Iranian drone claimed to have a range of about 125 miles and the ability to carry precision-guided munitions. "It's amazing warfare on the cheap," said Mr. Frantzman, adding that Iranian drones cost less than other models on the market but were growing in sophistication, and had proved their worth on battlefields across the Middle East. Tehran began drone development in the 1980s during the Iran-Iraq war. Despite crippling sanctions imposed on Iran over its nuclear and missile programs in recent years, it has managed to produce and field a vast array of military drones, used for both surveillance and attack, according to analysis by experts. That program has become a major concern for Israel and the United States in recent years.

It is a gripping scene of an orca viciously ripping out the liver of a nine-foot-long great white shark, as two other killer whales excitedly watch the once blue waters of South Africa's Mossel Bay turn blood red before the shark sinks to a the bottom of the sea – never to be seen again. The wild story was captured by a drone camera soaring above and now gives scientists a better understanding about why these apex-predators seem to be fleeing from this regions that was once the shark capital of the world. Orcas are known to feast on a great white shark liver, as to organ is are large, fatty and has become the whale's favorite dish – eight shark carcasses washing ashore the Western Cape in 2017 and all were missing their liver. The footage is part of marine biologist Alison Towner's long-term work with great whites. She shared on her Instagram page that the clip is'one of the most incredible pieces of natural history ever captured on film. The clip which is the first to show an orca eating a great white, is set to air on Discovery's Shark House Thursday night at 9pm ET, which is a day before the highly anticipated Shark Week begins.

Research attention on natural user interfaces (NUIs) for drone flights are rising. Nevertheless, NUIs are highly diversified, and primarily evaluated by different physical environments leading to hard-to-compare performance between such solutions. We propose a virtual environment, namely VRFlightSim, enabling comparative evaluations with enriched drone flight details to address this issue. We first replicated a state-of-the-art (SOTA) interface and designed two tasks (crossing and pointing) in our virtual environment. Then, two user studies with 13 participants demonstrate the necessity of VRFlightSim and further highlight the potential of open-data interface designs.

Rendezvous is an old problem of assuring that two or more parties, initially separated, not knowing the position of each other, and not allowed to communicate, meet without pre-agreement on the meeting point. This problem has been extensively studied in classical computer science and has vivid importance to modern applications like coordinating a fleet of drones in an enemy's territory. Quantum non-locality, like Bell inequality violation, has shown that in many cases quantum entanglement allows for improved coordination of two separated parties compared to classical sources. The non-signaling correlations in many cases even strengthened such phenomena. In this work, we analyze, how Bell non-locality can be used by asymmetric location-aware agents trying to rendezvous on a finite network with a limited number of steps. We provide the optimal solution to this problem for both agents using quantum resources, and agents with only ``classical'' computing power. Our results show that for cubic graphs and cycles it is possible to gain an advantage by allowing the agents to use assistance of entangled quantum states.

In autonomous and mobile robotics, one of the main challenges is the robust on-the-fly perception of the environment, which is often unknown and dynamic, like in autonomous drone racing. In this work, we propose a novel deep neural network-based perception method for racing gate detection -- PencilNet -- which relies on a lightweight neural network backbone on top of a pencil filter. This approach unifies predictions of the gates' 2D position, distance, and orientation in a single pose tuple. We show that our method is effective for zero-shot sim-to-real transfer learning that does not need any real-world training samples. Moreover, our framework is highly robust to illumination changes commonly seen under rapid flight compared to state-of-art methods. A thorough set of experiments demonstrates the effectiveness of this approach in multiple challenging scenarios, where the drone completes various tracks under different lighting conditions.