Existing nighttime unmanned aerial vehicle (UAV) trackers follow an "Enhance-then-Track" architecture - first using a light enhancer to brighten the nighttime video, then employing a daytime tracker to locate the object. This separate enhancement and tracking fails to build an end-to-end trainable vision system. To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts. Without a separate enhancer, DCPT directly encodes anti-dark capabilities into prompts using a darkness clue prompter (DCP). Specifically, DCP iteratively learns emphasizing and undermining projections for darkness clues. It then injects these learned visual prompts into a daytime tracker with fixed parameters across transformer layers. Moreover, a gated feature aggregation mechanism enables adaptive fusion between prompts and between prompts and the base model. Extensive experiments show state-of-the-art performance for DCPT on multiple dark scenario benchmarks. The unified end-to-end learning of enhancement and tracking in DCPT enables a more trainable system. The darkness clue prompting efficiently injects anti-dark knowledge without extra modules. Code is available at https://github.com/bearyi26/DCPT.

Amazon has some big plans for its drone delivery program, including an international expansion to the UK and Italy in 2024. The company also aims to start drone operations in a third US city next year, following existing efforts in College Station, Texas (where it just started offering prescription medication delivery by drone) and Lockeford, California. Drone deliveries in the UK and Italy will start at one site each before expanding to more locations over time. Amazon says it will announce the specific locations for the US, UK and Italy expansion in the coming months. Moreover, Amazon will integrate the Prime Air program into its delivery network.

Frederic Laugere, head of innovation advisory services at the UK Civil Aviation Authority (CAA) said projects like this one were "vital to feed into the overall knowledge and experiences that will soon enable drones to be operating beyond the line of sight of their pilot on a day-to-day basis, while also still allowing safe and equitable use of the air by other users."

Amazon is now offering drone prescription deliveries in College Station, TX. Customers will be eligible for aerial deliveries of "more than 500 medications" for common conditions like the flu, asthma and pneumonia. The home of Texas A&M has enjoyed Prime Air drone deliveries of (non-medical) Amazon shipments since 2022. The company says medications will arrive within an hour of placing their order, and there won't be an additional fee to use the service. The drones fly at 40 to 120 meters, an altitude Amazon says presents minimal obstacles. After arriving at the customer's home, the drone "slowly and safely" lowers itself to a delivery marker.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Two U.S. defense officials have confirmed to Fox News that the U.S. intercepted two one-way attack drones targeting Iraq's al-Asad air base where American troops are located. The incident happened early Wednesday morning local time. No injuries have been reported.

An ever increasing number of applications can employ aerial unmanned vehicles, or so-called drones, to perform different sensing and possibly also actuation tasks from the air. In some cases, the data that is captured at a given point has to be processed before moving to the next one. Drones can exploit nearby edge servers to offload the computation instead of performing it locally. However, doing this in a naive way can be suboptimal if servers have limited computing resources and drones have limited energy resources. In this paper, we propose a protocol and resource reservation scheme for each drone and edge server to decide, in a dynamic and fully decentralized way, whether to offload the computation and respectively whether to accept such an offloading requests, with the objective to evenly reduce the drones' mission times. We evaluate our approach through extensive simulation experiments, showing that it can significantly reduce the mission times compared to a no-offloading scenario by up to 26.2%, while outperforming an offloading schedule that has been computed offline by up to 7.4% as well as a purely opportunistic approach by up to 23.9%.

A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its objective better but that it optimizes a better objective. OC decomposes the problem into planning and control with an explicit intermediate representation, such as a trajectory, that serves as an interface. This decomposition limits the range of behaviors that can be expressed by the controller, leading to inferior control performance when facing unmodeled effects. In contrast, RL can directly optimize a task-level objective and can leverage domain randomization to cope with model uncertainty, allowing the discovery of more robust control responses. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour. Our policy achieved superhuman control within minutes of training on a standard workstation. This work presents a milestone in agile robotics and sheds light on the role of RL and OC in robot control.

Trajectory visualization and animation play critical roles in robotics research. However, existing data visualization and animation tools often lack flexibility, scalability, and versatility, resulting in limited capability to fully explore and analyze flight data. To address this limitation, we introduce Flymation, a new flight trajectory visualization and animation tool. Built on the Unity3D engine, Flymation is an intuitive and interactive tool that allows users to visualize and analyze flight data in real time. Users can import data from various sources, including flight simulators and real-world data, and create customized visualizations with high-quality rendering. With Flymation, users can choose between trajectory snapshot and animation; both provide valuable insights into the behavior of the underlying autonomous system. Flymation represents an exciting step toward visualizing and interacting with large-scale data in robotics research.

Artificial intelligence has the potential to make valuable contributions to wildlife management through cost-effective methods for the collection and interpretation of wildlife data. Recent advances in remotely piloted aircraft systems (RPAS or ``drones'') and thermal imaging technology have created new approaches to collect wildlife data. These emerging technologies could provide promising alternatives to standard labourious field techniques as well as cover much larger areas. In this study, we conduct a comprehensive review and empirical study of drone-based wildlife detection. Specifically, we collect a realistic dataset of drone-derived wildlife thermal detections. Wildlife detections, including arboreal (for instance, koalas, phascolarctos cinereus) and ground dwelling species in our collected data are annotated via bounding boxes by experts. We then benchmark state-of-the-art object detection algorithms on our collected dataset. We use these experimental results to identify issues and discuss future directions in automatic animal monitoring using drones.

Drones will revolutionize 3D modeling. A 3D model represents an accurate reconstruction of an object or structure. This paper explores the design and implementation of ARES, which provides near real-time, accurate reconstruction of 3D models using a drone-mounted LiDAR; such a capability can be useful to document construction or check aircraft integrity between flights. Accurate reconstruction requires high drone positioning accuracy, and, because GPS can be in accurate, ARES uses SLAM. However, in doing so it must deal with several competing constraints: drone battery and compute resources, SLAM error accumulation, and LiDAR resolution. ARES uses careful trajectory design to find a sweet spot in this constraint space, a fast reconnaissance flight to narrow the search area for structures, and offloads expensive computations to the cloud by streaming compressed LiDAR data over LTE. ARES reconstructs large structures to within 10s of cms and incurs less than 100 ms compute latency.