In this paper, we present our proposed approach for active tracking to increase the autonomy of Unmanned Aerial Vehicles (UAVs) using event cameras, low-energy imaging sensors that offer significant advantages in speed and dynamic range. The proposed tracking controller is designed to respond to visual feedback from the mounted event sensor, adjusting the drone movements to follow the target. To leverage the full motion capabilities of a quadrotor and the unique properties of event sensors, we propose an end-to-end deep-reinforcement learning (DRL) framework that maps raw sensor data from event streams directly to control actions for the UAV. To learn an optimal policy under highly variable and challenging conditions, we opt for a simulation environment with domain randomization for effective transfer to real-world environments. We demonstrate the effectiveness of our approach through experiments in challenging scenarios, including fast-moving targets and changing lighting conditions, which result in improved generalization capabilities.

As both sides of the ongoing Russia-Ukraine war attempt to claim technological supremacy, drone versus drone combat has become routine – and thanks to the ubiquity of cameras, this new kind of warfare is being documented in real time across air, land and sea. Russia's invasion of Ukraine has already been dubbed the "first drone war", with some sources suggesting that small drones are inflicting as much as 80 per cent of the damage to military hardware and personnel.

A new joint venture between Serve Robotics sidewalk delivery robots and Alphabet's Wing flying drone service will do a dual test run. Both tech companies hope that flying and sidewalk drones can cover areas its counterpart can't and speed up delivery times. TechCrunch reported that Serve Robotics and Wing will start making deliveries in Dallas sometime in the coming months. The test will include a select number of customer orders being delivered by a combination of sidewalk robots and flying drones. One of the biggest challenges for drone delivery is coverage.

A new joint venture between Uber's Serve Robotics sidewalk delivery drones and Alphabet's Wing flying drone service will do a dual test run. Both tech companies hope that flying and sidewalk drones can cover areas its counterpart can't and speed up delivery times. TechCrunch reported that Serve Robotics and Wing will start making deliveries in Dallas, Texas sometime in the coming months. The test will include a select number of customer orders being delivered by a combination of sidewalk and flying drones. One of the biggest challenges for drone delivery is coverage.

Bats are amongst the animal kingdom's most unorthodox fliers. Unlike birds, the furry, flying mammals can dynamically reshape and morph their wings to achieve maximum force and hover in place. The soft membrane of their wings, which more closely resembles a human arm than a bird's wing, is also extremely flexible, which means bats can contour themselves to squeeze into tiny corridors. Now, researchers from Northeastern University are leaning on those unique elements and applying them to a fully autonomous flying drone called "Aerobat." Eventually, they believe this bat-inspired robot could be used to navigate sewer tunnels, caves, and other tight corridors largely off-limits to current flying robots.

The Pentagon has said the mass production and deployment of systems to detect, track and neutralize small drones will be the next focus of its Replicator initiative as it seeks to better protect the "most critical" U.S. installations and forces around the globe. "My expectation is that Replicator 2 will deliver meaningfully improved C-sUAS (counter-small unmanned aerial system) protection to critical assets within 24 months of Congress approving funding," Defense Secretary Lloyd Austin wrote in a memorandum published Monday in which he also charged his deputy, Kathleen Hicks, with spearheading the new effort. "I am confident the Replicator Initiative will complement and advance the significant C-sUAS work already underway in the DoD (Department of Defense)," Austin said in the memo.

We perform uncertainty propagation on an event manifold for Guidance & Control Networks (G&CNETs), aiming to enhance the certification tools for neural networks in this field. This work utilizes three previously solved optimal control problems with varying levels of dynamics nonlinearity and event manifold complexity. The G&CNETs are trained to represent the optimal control policies of a time-optimal interplanetary transfer, a mass-optimal landing on an asteroid and energy-optimal drone racing, respectively. For each of these problems, we describe analytically the terminal conditions on an event manifold with respect to initial state uncertainties. Crucially, this expansion does not depend on time but solely on the initial conditions of the system, thereby making it possible to study the robustness of the G&CNET at any specific stage of a mission defined by the event manifold. Once this analytical expression is found, we provide confidence bounds by applying the Cauchy-Hadamard theorem and perform uncertainty propagation using moment generating functions. While Monte Carlo-based (MC) methods can yield the results we present, this work is driven by the recognition that MC simulations alone may be insufficient for future certification of neural networks in guidance and control applications.

The next ubiquitous computing platform, following personal computers and smartphones, is poised to be inherently autonomous, encompassing technologies like drones, robots, and self-driving cars. Ensuring reliability for these autonomous machines is critical. However, current resiliency solutions make fundamental trade-offs between reliability and cost, resulting in significant overhead in performance, energy consumption, and chip area. This is due to the "one-size-fits-all" approach commonly used, where the same protection scheme is applied throughout the entire software computing stack. This paper presents the key insight that to achieve high protection coverage with minimal cost, we must leverage the inherent variations in robustness across different layers of the autonomous machine software stack. Specifically, we demonstrate that various nodes in this complex stack exhibit different levels of robustness against hardware faults. Our findings reveal that the front-end of an autonomous machine's software stack tends to be more robust, whereas the back-end is generally more vulnerable. Building on these inherent robustness differences, we propose a Vulnerability-Adaptive Protection (VAP) design paradigm. In this paradigm, the allocation of protection resources - whether spatially (e.g., through modular redundancy) or temporally (e.g., via re-execution) - is made inversely proportional to the inherent robustness of tasks or algorithms within the autonomous machine system. Experimental results show that VAP provides high protection coverage while maintaining low overhead in both autonomous vehicle and drone systems.

At least eight people have died in two consecutive Russian drone attacks on a medical centre in the northeast Ukrainian city of Sumy, Ukrainian officials have said. The first attack on Saturday morning killed one person, and it was followed by another attack while patients and staff were evacuating, Ukraine's Interior Minister Ihor Klymenko said. Ukraine's President Volodymyr Zelenskyy said on his Telegram channel that Russia had hit the hospital using Shahed drones, stating that eleven people were injured. Sumy lies just across the border from Russia's Kursk region where Kyiv launched a shock offensive on August 6, which it says is aimed partly at creating a "buffer zone" inside Russia. Regional prosecutors said the first attack in Sumy on Saturday took place at about 7:35am (04:35 GMT), hitting the hospital where there were 86 patients and 38 staff.

We address the challenge of real-time planning of minimum-time trajectories over multiple waypoints, onboard multirotor UAVs. Previous works demonstrated that achieving a truly time-optimal trajectory is computationally too demanding to enable frequent replanning during agile flight, especially on less powerful flight computers. Our approach overcomes this stumbling block by utilizing a point-mass model with a novel iterative thrust decomposition algorithm, enabling the UAV to use all of its collective thrust, something previous point-mass approaches could not achieve. The approach enables gravity and drag modeling integration, significantly reducing tracking errors in high-speed trajectories, which is proven through an ablation study. When combined with a new multi-waypoint optimization algorithm, which uses a gradient-based method to converge to optimal velocities in waypoints, the proposed method generates minimum-time multi-waypoint trajectories within milliseconds. The proposed approach, which we provide as open-source package, is validated both in simulation and in real-world, using Nonlinear Model Predictive Control. With accelerations of up to 3.5g and speeds over 100 km/h, trajectories generated by the proposed method yield similar or even smaller tracking errors than the trajectories generated for a full multirotor model.