With the development of industry, drones are appearing in various field. In recent years, deep reinforcement learning has made impressive gains in games, and we are committed to applying deep reinforcement learning algorithms to the field of robotics, moving reinforcement learning algorithms from game scenarios to real-world application scenarios. We are inspired by the LunarLander of OpenAI Gym, we decided to make a bold attempt in the field of reinforcement learning to control drones. At present, there is still a lack of work applying reinforcement learning algorithms to robot control, the physical simulation platform related to robot control is only suitable for the verification of classical algorithms, and is not suitable for accessing reinforcement learning algorithms for the training. In this paper, we will face this problem, bridging the gap between physical simulation platforms and intelligent agent, connecting intelligent agents to a physical simulation platform, allowing agents to learn and complete drone flight tasks in a simulator that approximates the real world. We proposed a reinforcement learning framework based on Gazebo that is a kind of physical simulation platform (ROS-RL), and used three continuous action space reinforcement learning algorithms in the framework to dealing with the problem of autonomous landing of drones. Experiments show the effectiveness of the algorithm, the task of autonomous landing of drones based on reinforcement learning achieved full success.

Climate change is leading to more and more extreme weather events such as heavy rainfall and flooding. This technical report deals with the question of how rescue commanders can be better and faster provided with current information during flood disasters using Unmanned Aerial Vehicles (UAVs), i.e. during the flood in July 2021 in Central Europe, more specifically in Erftstadt / Blessem. The UAVs were used for live observation and regular inspections of the flood edge on the one hand, and on the other hand for the systematic data acquisition in order to calculate 3D models using Structure from Motion and MultiView Stereo. The 3D models embedded in a GIS application serve as a planning basis for the systematic exploration and decision support for the deployment of additional smaller UAVs but also rescue forces. The systematic data acquisition of the UAVs by means of autonomous meander flights provides high-resolution images which are computed to a georeferenced 3D model of the surrounding area within 15 minutes in a specially equipped robotic command vehicle (RobLW). From the comparison of high-resolution elevation profiles extracted from the 3D model on successive days, changes in the water level become visible. This information enables the emergency management to plan further inspections of the buildings and to search for missing persons on site.

LAS VEGAS--(BUSINESS WIRE)--MORAI, a leading developer of full-stack autonomous vehicle simulation technology in Korea, announced today that it is launching a new simulation platform for aircraft, MORAI SIM Air, at Commercial UAV Expo 2022, held in Las Vegas from September 6 to September 8, 2022. Urban Air Mobility (UAM) is getting attention as a next-generation urban transport system that can solve problems such as increasing urban population and traffic congestion. However, it is essential to establish a safe and stable operating environment as it may create hazards to persons or property in the event of a crash or accident compared to a car. To handle such challenges, MORAI offers simulation tools and solutions for aircraft. The MORAI SIM Air is a simulation solution designed for aircraft such as UAM and UAVs (unmanned aerial vehicles) to verify the system safety of aircraft in realistic virtual spaces.

On August 9, at least a dozen explosions rocked a Russian military base in Crimea. Russia's defence ministry avoided assigning blame – saying the "detonation of several aviation ammunition stores" caused the blast – while, for its part, Ukraine's military played it coy. It did not claim responsibility for the damaged combat planes, nor for a subsequent drone strike on the navy headquarters in the Russian-occupied area. Instead, Ukraine's defence ministry mockingly warned on Twitter about the dangers of smoking – sardonically suggesting that Russian soldiers caused the explosions themselves by tossing lit cigarettes. Unless they want an unpleasantly hot summer break, we advise our valued russian guests not to visit Ukrainian Crimea.

Factor graph is a graph representing the factorization of a probability distribution function, and has been utilized in many autonomous machine computing tasks, such as localization, tracking, planning and control etc. We are developing an architecture with the goal of using factor graph as a common abstraction for most, if not, all autonomous machine computing tasks. If successful, the architecture would provide a very simple interface of mapping autonomous machine functions to the underlying compute hardware. As a first step of such an attempt, this paper presents our most recent work of developing a factor graph accelerator for LiDAR-Inertial Odometry (LIO), an essential task in many autonomous machines, such as autonomous vehicles and mobile robots. By modeling LIO as a factor graph, the proposed accelerator not only supports multi-sensor fusion such as LiDAR, inertial measurement unit (IMU), GPS, etc., but solves the global optimization problem of robot navigation in batch or incremental modes. Our evaluation demonstrates that the proposed design significantly improves the real-time performance and energy efficiency of autonomous machine navigation systems. The initial success suggests the potential of generalizing the factor graph architecture as a common abstraction for autonomous machine computing, including tracking, planning, and control etc.

Multi-agent reinforcement learning was performed in this study for indoor path planning of two unmanned aerial vehicles (UAVs). Each UAV performed the task of moving as fast as possible from a randomly paired initial position to a goal position in an environment with obstacles. To minimize training time and prevent the damage of UAVs, learning was performed by simulation. Considering the non-stationary characteristics of the multi-agent environment wherein the optimal behavior varies based on the actions of other agents, the action of the other UAV was also included in the state space of each UAV. Curriculum learning was performed in two stages to increase learning efficiency. A goal rate of 89.0% was obtained compared with other learning strategies that obtained goal rates of 73.6% and 79.9%.

In this paper, we analyze the effect of time delay dynamics on controller design for Unmanned Aerial Vehicles (UAVs) with vision based navigation. Time delay is an inevitable phenomenon in cyber-physical systems, and has important implications on controller design and trajectory generation for UAVs. The impact of time delay on UAV dynamics increases with the use of the slower vision based navigation stack. We show that the existing models in the literature, which exclude time delay, are unsuitable for controller tuning since a trivial solution for minimizing an error cost functional always exists. The trivial solution that we identify suggests use of infinite controller gains to achieve optimal performance, which contradicts practical findings. We avoid such shortcomings by introducing a novel nonlinear time delay model for UAVs, and then obtain a set of linear decoupled models corresponding to each of the UAV control loops. The cost functional of the linearized time delay model of angular and altitude dynamics is analyzed, and in contrast to the delay-free models, we show the existence of finite optimal controller parameters. Due to the use of time delay models, we experimentally show that the proposed model accurately represents system stability limits. Due to time delay consideration, we achieved a tracking results of RMSE 5.01 cm when tracking a lemniscate trajectory with a peak velocity of 2.09 m/s using visual odometry (VO) based UAV navigation, which is on par with the state-of-the-art.

Africa led the world in medical drone delivery. Now, instant logistics leader Zipline announced a partnership with African e-commerce platform Jumia that will see the integration of Zipline's delivery system with Jumia's distribution network for the deployment of automated, on-demand delivery for e-commerce in Africa. "Using the latest instant logistics technology will allow Jumia to offer our consumers on-demand delivery of the products they need – instantly," said Apoorva Kumar, EVP Jumia, Group COO. "Whether they're ordering electronics, fashion, health, and beauty, or other categories, Zipline's instant logistics system will provide fast and convenient access. This will support Jumia's commitment to sustainability and innovation and provide much-needed access to rural and remote areas where conventional delivery services have challenges." A trial period was conducted across a variety of use cases with a range of assorted products, covering up to 2,500km in testing.

Examples of these applications include public security, remote sensing, surveillance, photography, and delivery of goods [2]. The continual growth of e-commerce, especially during the COVID-19 pandemic, has revolutionized the way customers acquire goods and services [3]. The ubiquity of drones in the sky has prompted an increasing interest of several e-commerce companies such as UPS, Flytrex, and Amazon Prime Air to use drones for package delivery [4]. Several countries have used drones for safe and contactless deliveries during the pandemic lockdowns [5]. Drone delivery is highly desired in urban areas to reduce delivery time and traffic congestion on roads by utilizing urban airspace [6]. The recent developments in drone technology show that drones can carry multiple packages [7]. Therefore, a drone can serve more than one customer in one trip.

Fox News chief Washington correspondent Mike Emanuel reports on an Iranian warship intercepting American drones only to return them the following morning on'Special Report.' Iran has armed 51 cities and towns with a civil defense system aimed to respond to any foreign attack as tensions with the U.S. have mounted in recent weeks. The defenses will enable Iran's arms forces to "identify and monitor threats by using round-the-clock software according to the type of the threat and risk," Deputy Defense Minister General Mehdi Farahi said Saturday, according to a Reuters report. "These days, depending on the strength of countries, the form of battles has become more complicated," he added. Farahi did not name any specific countries Tehran could be targeted by but noted that conventional warfare has largely been replaced by cyber, biological and radioactive attack tactics.