Landing an unmanned aerial vehicle unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to land autonomously on a USV in these harsh conditions. The MPC employs a novel objective function and an online decomposition of the oscillatory motion of the vessel to predict, attempt, and accomplish the landing during near-zero tilt of the landing platform. The nonlinear prediction of the motion of the vessel is performed using visual data from an onboard camera. Therefore, the system does not require any communication with the USV or a control station. The proposed method was analyzed in numerous robotics simulations in harsh and extreme conditions and further validated in various real-world scenarios.

This paper presents a novel approach for autonomous cooperating UAVs in search and rescue operations in subterranean domains with complex topology. The proposed system was ranked second in the Virtual Track of the DARPA SubT Finals as part of the team CTU-CRAS-NORLAB. In contrast to the winning solution that was developed specifically for the Virtual Track, the proposed solution also proved to be a robust system for deployment onboard physical UAVs flying in the extremely harsh and confined environment of the real-world competition. The proposed approach enables fully autonomous and decentralized deployment of a UAV team with seamless simulation-to-world transfer, and proves its advantage over less mobile UGV teams in the flyable space of diverse environments. The main contributions of the paper are present in the mapping and navigation pipelines. The mapping approach employs novel map representations -- SphereMap for efficient risk-aware long-distance planning, FacetMap for surface coverage, and the compressed topological-volumetric LTVMap for allowing multi-robot cooperation under low-bandwidth communication. These representations are used in navigation together with novel methods for visibility-constrained informed search in a general 3D environment with no assumptions about the environment structure, while balancing deep exploration with sensor-coverage exploitation. The proposed solution also includes a visual-perception pipeline for on-board detection and localization of objects of interest in four RGB stream at 5 Hz each without a dedicated GPU. Apart from participation in the DARPA SubT, the performance of the UAV system is supported by extensive experimental verification in diverse environments with both qualitative and quantitative evaluation.

Integration of Visual Inertial Odometry (VIO) methods into a modular control system designed for deployment of Unmanned Aerial Vehicles (UAVs) and teams of cooperating UAVs in real-world conditions are presented in this paper. Reliability analysis and fair performance comparison of several methods integrated into a control pipeline for achieving full autonomy in real conditions is provided. Although most VIO algorithms achieve excellent localization precision and negligible drift on artificially created datasets, the aspects of reliability in non-ideal situations, robustness to degraded sensor data, and the effects of external disturbances and feedback control coupling are not well studied. These imperfections, which are inherently present in cases of real-world deployment of UAVs, negatively affect the ability of the most used VIO approaches to output a sensible pose estimation. We identify the conditions that are critical for a reliable flight under VIO localization and propose workarounds and compensations for situations in which such conditions cannot be achieved. The performance of the UAV system with integrated VIO methods is quantitatively analyzed w.r.t. RTK ground truth and the ability to provide reliable pose estimation for the feedback control is demonstrated onboard a UAV that is tracking dynamic trajectories under challenging illumination.

Autonomous operation of UAVs in a closed environment requires precise and reliable pose estimate that can stabilize the UAV without using external localization systems such as GNSS. In this work, we are concerned with estimating the pose from laser scans generated by an inexpensive and lightweight LIDAR. We propose a localization system for lightweight (under 200g) LIDAR sensors with high reliability in arbitrary environments, where other methods fail. The general nature of the proposed method allows deployment in wide array of applications. Moreover, seamless transitioning between different kinds of environments is possible. The advantage of LIDAR localization is that it is robust to poor illumination, which is often challenging for camera-based solutions in dark indoor environments and in the case of the transition between indoor and outdoor environment. Our approach allows executing tasks in poorly-illuminated indoor locations such as historic buildings and warehouses, as well as in the tight outdoor environment, such as forest, where vision-based approaches fail due to large contrast of the scene, and where large well-equipped UAVs cannot be deployed due to the constrained space.

A flexible topological representation consisting of a two-layer graph structure built on-board an Unmanned Aerial Vehicle (UAV) by continuously filling the free space of an occupancy map with intersecting spheres is proposed in this \paper{}. Most state-of-the-art planning methods find the shortest paths while keeping the UAV at a pre-defined distance from obstacles. Planning over the proposed structure reaches this pre-defined distance only when necessary, maintaining a safer distance otherwise, while also being orders of magnitude faster than other state-of-the-art methods. Furthermore, we demonstrate how this graph representation can be converted into a lightweight shareable topological-volumetric map of the environment, which enables decentralized multi-robot cooperation. The proposed approach was successfully validated in several kilometers of real subterranean environments, such as caves, devastated industrial buildings, and in the harsh and complex setting of the final event of the DARPA SubT Challenge, which aims to mimic the conditions of real search and rescue missions as closely as possible, and where our approach achieved the \nth{2} place in the virtual track.

Amazon's drone delivery program doesn't seem to be off to a great start. The Prime Air division was said to be hit hard by recent, widespread layoffs. Now, a new report indicates that Amazon's drones have made just a handful of deliveries in their first few weeks of operation. After nearly a decade of working on the program, Amazon said in December that it would start making deliveries by drone in Lockeford, California, and College Station, Texas. However, by the middle of January, as few as seven houses had received Amazon packages by drone, according to The Information: two in California and five in Texas.

An Iranian military facility was hit with a drone strike Jan. 29, 2023. Iran on Thursday blamed Israel for a drone strike that hit a military factory near the city of Isfahan over the weekend and threatened revenge, saying it "reserves its legitimate and inherent right" to respond. Reports surfaced earlier this week citing a U.S. official who attributed the attack to Israel, but Tehran's accusation could prolong what appears to have become a covert war between the Middle Eastern nations. "Early investigations suggest that the Israeli regime was responsible for this attempted act of aggression," Iranian Ambassador Amir Saeid Iravani said in a letter to the United Nations, though he did not cite the evidence Tehran has to back its accusations. Eyewitness footage shows what is said to be the moment of an explosion at a military industry factory in Isfahan, Iran, Jan. 29, 2023, in this still image obtained from a video.

A new Pentagon project envisages automated, coordinated attacks by swarms of many types of drones that operate in the air, on the ground and in the water. The idea is raising concerns about whether human oversight of such a "swarm of swarms" would be possible. The Autonomous Multi-Domain Adaptive Swarms-of-Swarms (AMASS) is a project from US defence research agency DARPA.

A drone can guide itself and map environments via echolocation, using a simple buzzer and microphone set-up, much like a bat uses sound to see in the dark. For robots to be able to move autonomously, they need to determine where they are in space and whether any obstacles lie in their path.

Iran has blamed Israel for last week's drone attack on a military factory near the central city of Isfahan, promising revenge for what appeared to be the latest episode in a long-running covert war. The Iranian claim, carried by the semi-official ISNA news agency on Thursday, corroborates remarks made by United States officials following the attack. The attack came amid tension between Iran and the West over Tehran's nuclear activity and its supply of arms – including long-range "suicide drones" – for Russia's war in Ukraine, as well as months of anti-government demonstrations at home. In a letter to the United Nations chief, Iran's UN envoy, Amir Saeid Iravani, said "primary investigation suggested Israel was responsible" for Saturday night's attack, which Tehran had said caused no casualties or serious damage. "Iran reserves its legitimate and inherent right to defend its national security and firmly respond to any threat or wrongdoing of the Zionist regime [Israel] wherever and whenever it deems necessary," Iravani said in the letter.