This article establishes the novel D$^*_+$, a risk-aware and platform-agnostic heterogeneous global path planner for robotic navigation in complex environments. The proposed planner addresses a fundamental bottleneck of occupancy-based path planners related to their dependency on accurate and dense maps. More specifically, their performance is highly affected by poorly reconstructed or sparse areas (e.g. holes in the walls or ceilings) leading to faulty generated paths outside the physical boundaries of the 3-dimensional space. As it will be presented, D$^*_+$ addresses this challenge with three novel contributions, integrated into one solution, namely: a) the proximity risk, b) the modeling of the unknown space, and c) the map updates. By adding a risk layer to spaces that are closer to the occupied ones, some holes are filled, and thus the problematic short-cutting through them to the final goal is prevented. The novel established D$^*_+$ also provides safety marginals to the walls and other obstacles, a property that results in paths that do not cut the corners that could potentially disrupt the platform operation. D$^*_+$ has also the capability to model the unknown space as risk-free areas that should keep the paths inside, e.g in a tunnel environment, and thus heavily reducing the risk of larger shortcuts through openings in the walls. D$^*_+$ is also introducing a dynamic map handling capability that continuously updates with the latest information acquired during the map building process, allowing the planner to use constant map growth and resolve cases of planning over outdated sparser map reconstructions...

The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets.

The increase in the number of unmanned aerial vehicles a.k.a. drones pose several threats to public privacy, critical infrastructure and cyber security. Hence, detecting unauthorized drones is a significant problem which received attention in the last few years. In this paper, we present our experimental work on three drone detection methods (i.e., acoustic detection, radio frequency (RF) detection, and visual detection) to evaluate their efficacy in both indoor and outdoor environments. Owing to the limitations of these schemes, we present a novel encryption-based drone detection scheme that uses a two-stage verification of the drone's received signal strength indicator (RSSI) and the encryption key generated from the drone's position coordinates to reliably detect an unauthorized drone in the presence of authorized drones.

This book provides a solution to the control and motion planning design for an octocopter system. It includes a particular choice of control and motion planning algorithms which is based on the authors' previous research work, so it can be used as a reference design guidance for students, researchers as well as autonomous vehicles hobbyists. The control is constructed based on a fault tolerant approach aiming to increase the chances of the system to detect and isolate a potential failure in order to produce feasible control signals to the remaining active motors. The used motion planning algorithm is risk-aware by means that it takes into account the constraints related to the fault-dependant and mission-related maneuverability analysis of the octocopter system during the planning stage. Such a planner generates only those reference trajectories along which the octocopter system would be safe and capable of good tracking in case of a single motor fault and of majority of double motor fault scenarios. The control and motion planning algorithms presented in the book aim to increase the overall reliability of the system for completing the mission.

The emergence of low-cost, small form factor and light-weight solid-state LiDAR sensors have brought new opportunities for autonomous unmanned aerial vehicles (UAVs) by advancing navigation safety and computation efficiency. Yet the successful developments of LiDAR-based UAVs must rely on extensive simulations. Existing simulators can hardly perform simulations of real-world environments due to the requirements of dense mesh maps that are difficult to obtain. In this paper, we develop a point-realistic simulator of real-world scenes for LiDAR-based UAVs. The key idea is the underlying point rendering method, where we construct a depth image directly from the point cloud map and interpolate it to obtain realistic LiDAR point measurements. Our developed simulator is able to run on a light-weight computing platform and supports the simulation of LiDARs with different resolution and scanning patterns, dynamic obstacles, and multi-UAV systems. Developed in the ROS framework, the simulator can easily communicate with other key modules of an autonomous robot, such as perception, state estimation, planning, and control. Finally, the simulator provides 10 high-resolution point cloud maps of various real-world environments, including forests of different densities, historic building, office, parking garage, and various complex indoor environments. These realistic maps provide diverse testing scenarios for an autonomous UAV. Evaluation results show that the developed simulator achieves superior performance in terms of time and memory consumption against Gazebo and that the simulated UAV flights highly match the actual one in real-world environments. We believe such a point-realistic and light-weight simulator is crucial to bridge the gap between UAV simulation and experiments and will significantly facilitate the research of LiDAR-based autonomous UAVs in the future.

The same applies to typical aviation instruments, such as the gyro stabilizer, also known as a gyroscope, which enables aircraft to orient themselves in the air. The mechanical gyroscope CAR found in a Mohajer 6 drone resembles one previously documented by the group in a Qasef 1 drone. That model is similar to the Shahed 136 and 131 and also belongs to the loitering munitions category, known casually as kamikaze drones. "The data from the report seems valid and very detailed," Ulrike Franke, a drone expert at the European Council of Foreign Relations in London told DER SPIEGEL. At the same time, the experts also discovered differences to older models. According to the report, some of the drones found in Ukraine had been fitted with more modern technology, such as a software-defined radio that was in a Shahad 136.

On a cloudy Christmas morning last year, a rocket carrying the most powerful space telescope ever built blasted off from a launchpad in French Guiana. After reaching its destination in space about a month later, the James Webb Space Telescope (JWST) began sending back sparkling presents to humanity--jaw-dropping images that are revealing our universe in stunning new ways. Every year since 1988, Popular Science has highlighted the innovations that make living on Earth even a tiny bit better. And this year--our 35th--has been remarkable, thanks to the successful deployment of the JWST, which earned our highest honor as the Innovation of the Year. But it's just one item out of the 100 stellar technological accomplishments our editors have selected to recognize. The list below represents months of research, testing, discussion, and debate. It celebrates exciting inventions that are improving our lives in ways both big and small. These technologies and discoveries are teaching us about the ...

In recent years, unmanned aerial vehicle (UAV) technology has been steadily gaining momentum. With technological advancements, UAVs are proving to be extremely useful in a variety of application scenarios. These include monitoring and inspection of large infrastructures and energy facilities such as offshore wind farms, power lines, roads, oil and gas pipelines [1]-[4], monitoring of cultivated land and forests [4], [5], in the mining industry [6], layout planning and digital reconstruction in construction [7] and for damage assessment after disaster events [8], [9]. To perform aerial flights in the above-mentioned use cases, two aspects are of crucial importance. On the one hand, the individual waypoints of a mission have to be put in a suitable order to minimize unnecessary time and energy consumption. This aspect is covered by solving route planning problems such as the NP-hard Traveling Salesman Problem (TSP), which is discussed in detail in [10].

In many unmanned aerial vehicle (UAV) applications for surveillance and data collection, it is not possible to reach all requested locations due to the given maximum flight time. Hence, the requested locations must be prioritized and the problem of selecting the most important locations is modeled as an Orienteering Problem (OP). To fully exploit the kinematic properties of the UAV in such scenarios, we combine the OP with the generation of time-optimal trajectories with bounds on velocity and acceleration. We define the resulting problem as the Kinematic Orienteering Problem (KOP) and propose an exact mixed-integer formulation together with a Large Neighborhood Search (LNS) as a heuristic solution method. We demonstrate the effectiveness of our approach based on Orienteering instances from the literature and benchmark against optimal solutions of the Dubins Orienteering Problem (DOP) as the state-of-the-art. Additionally, we show by simulation \color{black} that the resulting solutions can be tracked precisely by a modern MPC-based flight controller. Since we demonstrate that the state-of-the-art in generating time-optimal trajectories in multiple dimensions is not generally correct, we further present an improved analytical method for time-optimal trajectory generation.

Founded in 2017, Bionaut Labs arrived out of stealth in March 2021, with plans to commercialize longstanding research around drug delivery robots. The Los Angeles-based startup today followed up its initial $20 million funding announcement with a $43.2 million Series B, bringing its total raise up to – you guessed it -- $63.2 million. This round was led by Khosla Ventures and featured new investors, Deep Insight, OurCrowd, PSPRS, Sixty Degree Capital, Dolby Family Ventures, GISEV Family Ventures, What if Ventures, Tintah Grace and Gaingels. If you've followed the robots space, you're likely familiar with the research that gone into these tiny, remote controlled medical robots. Bionaut's own work now has a couple of deadlines in place, including 2023 pre-clinical studies, followed by clinical trials with human patients the follow years.