This study proposes an approach for establishing an optimal multihop ad-hoc network using multiple unmanned aerial vehicles (UAVs) to provide emergency communication in disaster areas. The approach includes two stages, one uses particle swarm optimization (PSO) to find optimal positions to deploy UAVs, and the other uses a behavior-based controller to navigate the UAVs to their assigned positions without colliding with obstacles in an unknown environment. Several constraints related to the UAVs' sensing and communication ranges have been imposed to ensure the applicability of the proposed approach in real-world scenarios. A number of simulation experiments with data loaded from real environments have been conducted. The results show that our proposed approach is not only successful in establishing multihop ad-hoc routes but also meets the requirements for real-time deployment of UAVs.

Unmanned Aerial Vehicles (UAVs) dynamic encirclement is an emerging field with great potential. Researchers often get inspiration from biological systems, either from macro-world like fish schools or bird flocks etc, or from micro-world like gene regulatory networks (GRN). However, most swarm control algorithms rely on centralized control, global information acquisition, and communications among neighboring agents. In this work, we propose a distributed swarm control method based purely on vision and GRN without any direct communications, in which swarm agents of e.g. UAVs can generate an entrapping pattern to encircle an escaping target of UAV based purely on their installed omnidirectional vision sensors. A finite-state-machine (FSM) describing the behavioral model of each drone is also designed so that a swarm of drones can accomplish searching and entrapping of the target collectively in an integrated way. We verify the effectiveness and efficiency of the proposed method in various simulation and real-world experiments.

Many aerial robotic applications require the ability to land on moving platforms, such as delivery trucks and marine research boats. We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle. A visual servoing controller approaches the ground vehicle using velocity commands calculated directly in image space. The control laws generate velocity commands in all three dimensions, eliminating the need for a separate height controller. The method has shown the ability to approach and land on the moving deck in simulation, indoor and outdoor environments, and compared to the other available methods, it has provided the fastest landing approach. Unlike many existing methods for landing on fast-moving platforms, this method does not rely on additional external setups, such as RTK, motion capture system, ground station, offboard processing, or communication with the vehicle, and it requires only the minimal set of hardware and localization sensors. The videos and source codes are also provided.

With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving people's work efficiency. Specifically, the IoT can replace humans in jobs that they cannot perform. As a new type of IoT vehicle, the current status and trend of research on Unmanned Aerial Vehicle(UAV) is gratifying, and the development prospect is very promising. However, privacy and communication are still very serious issues in drone applications. This is because most drones still use centralized cloud-based data processing, which may lead to leakage of data collected by drones. At the same time, the large amount of data collected by drones may incur greater communication overhead when transferred to the cloud. Federated learning as a means of privacy protection can effectively solve the above two problems. However, federated learning when applied to UAV networks also needs to consider the heterogeneity of data, which is caused by regional differences in UAV regulation. In response, this paper proposes a new algorithm FedBA to optimize the global model and solves the data heterogeneity problem. In addition, we apply the algorithm to some real datasets, and the experimental results show that the algorithm outperforms other algorithms and improves the accuracy of the local model for UAVs.

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed.

Russia has stepped up satellite navigation jamming, especially around Moscow, in an apparent attempt to ward off any long-range strikes by Ukrainian drones. Russia is known for interfering with global navigation satellite systems, in particular GPS, which is operated by the US military and is ubiquitous in smartphones, car satnavs and other devices. Updates from the monitoring site GPSJam show that Russian jamming activity has increased sharply within Russia (see map, above).

Abstract--This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access applications. For the purpose, a single neural network is utilized in centralized training for cooperation among multiple agents while maximizing the total quality of service (QoS) in mobile access applications. In order to provide seamless network services in crowded, wild, or extreme areas, which is one of the potential scenarios in 6G networks, the use of unmanned aerial vehicles (UAVs) is widely considered where the UAVs are autonomously operated with deep learning algorithms [1]. In this paper, a multi-agent deep reinforcement learning (MADRL) algorithm is designed and evaluated for autonomous is good enough to utilize the desired performance of multiagent aerial mobile base-station (BS) network coordination cooperation and coordination. In order to neural network, a cost function is required, and the function is achieve our desired goal, one of the promising approaches is designed to maximize the quality of services (QoS) in mobile centralized training and distributed execution (CTDE) where access applications.

For guiding the UAV swarm to pass through narrow openings, a trapezoid virtual tube is designed in our previous work. In this paper, we generalize its application range to the condition that there exist obstacles inside the trapezoid virtual tube and UAVs have strict speed constraints. First, a distributed vector field controller is proposed for the trapezoid virtual tube with no obstacle inside. The relationship between the trapezoid virtual tube and the speed constraints is also presented. Then, a switching logic for the obstacle avoidance is put forward. The key point is to divide the trapezoid virtual tube containing obstacles into several sub trapezoid virtual tubes with no obstacle inside. Formal analyses and proofs are made to show that all UAVs are able to pass through the trapezoid virtual tube safely. Besides, the effectiveness of the proposed method is validated by numerical simulations and real experiments.

The U.S. is solidifying a defense package to Ukraine, which would help assist Ukraine with shooting down Russian drone strikes on civilian targets. North Korea is supplying arms to a Russian mercenary group and could continue to deliver military equipment to support the Kremlin's war against Ukraine, the Biden administration said Thursday. The White House said the weapons "will not change battlefield dynamics," however, the private entity receiving the equipment, Wagner Group, is committing atrocities and human rights abuses across Ukraine. "Because the Russian military is struggling in Ukraine, President [Vladimir] Putin has increasingly been turning to Wagner, which is owned by Yevgeny Prigozhin, for military support," White House National Security Council spokesman John Kirby said Thursday. Kirby said Prigozhin has been spending more than $100 million per month to fund Wagner's efforts inside Ukraine.

National security analyst Dr. Rebecca Grant joined'Fox & Friends First' to discuss Zelenskyy's visit to the White House and his request for additional aid in the war against Russia. Iran on Thursday took a swing at Ukrainian President Volodymyr Zelenskyy over comments he made to Congress this week and denied accusations that Tehran has supplied Russia with drones. Zelenskyy had better know that Iran's strategic patience over such unfounded accusations is not endless," Iranian Foreign Ministry spokesman Nasser Kanaani said in a threatening message posted to the ministry's website. Kanaani also advised Zelenskyy "to draw a lesson from the fate of some other political leaders who contented themselves with the US support." Volodymyr Zelenskyy, Ukraine's president, arrives to speak during a joint meeting of Congress at the U.S. Capitol on Wednesday, Dec. 21, 2022. The spokesman's comments came one day after Zelenskky addressed the U.S. Congress in an appeal for additional aid – a plea aimed at GOP lawmakers who are divided on whether providing support to Kyiv is a matter of national security. "When Russia cannot reach our cities by its artillery, it tries to destroy them with missile attacks," he said. "More than that, Russia found an ally in its genocidal policy – Iran.