Space situational awareness (SSA) plays an imperative role in maintaining safe space operations, especially given the increasingly congested space traffic around the Earth. Space-based SSA offers a flexible and lightweight solution compared to traditional ground-based SSA. With advanced machine learning approaches, space-based SSA can extract features from high-resolution images in space to detect and track resident space objects (RSOs). However, existing spacecraft image datasets, such as SPARK, fall short of providing realistic camera observations, rendering the derived algorithms unsuitable for real SSA systems. In this work, we introduce SpaceDet, a large-scale realistic space-based image dataset for SSA. We consider accurate space orbit dynamics and a physical camera model with various noise distributions, generating images at the photon level. To extend the available observation window, four overlapping cameras are simulated with a fixed rotation angle. SpaceDet includes images of RSOs observed from 19 km to 63,000 km, captured by a tracker operating in LEO, MEO, and GEO orbits over a period of 5,000 seconds. Each image has a resolution of 4418 x 4418 pixels, providing detailed features for developing advanced SSA approaches. We split the dataset into three subsets: SpaceDet-100, SpaceDet-5000, and SpaceDet-full, catering to various image processing applications. The SpaceDet-full corpus includes a comprehensive dataloader with 781.5 GB of images and 25.9 MB of ground truth labels. Furthermore, we adapted detection and tracking algorithms on the collected dataset using a specified splitting method to accelerate the training process. The trained model can detect RSOs from real-world space observations with zero-shot capability.

Multidrone systems offer notable advantages in various missions, such as search and rescue, environmental surveillance, and industrial inspection, providing enhanced efficiency and redundancy over single-drone operations. However, ensuring resilient multidrone formation in dynamic and adversarial environments, such as during communication loss or cyberattacks, remains a significant challenge. Traditional approaches often struggle with complex modeling requirements and scalability issues. Among them, leader-follower methods rely heavily on predefined hierarchies, making them vulnerable to single-point failures, while distributed methods incur high communication costs and lack efficient mechanisms to dynamically adapt to changing environments. This article proposes a novel learning-based formation control method to enhance the scalability and resilience of multidrone formations. First, a graph attention network (GAT) is leveraged to dynamically model interagent relationships and prioritize critical interactions among variable neighbors via attention mechanisms with bounded communication overhead. Second, a dual-mode control strategy is designed, integrating leader-follower and distributed control approaches to optimize communication costs while maintaining formation performance. Third, deep reinforcement learning is utilized to train the GAT-based controller, achieving objectives, such as maintaining formation tightness, avoiding collisions, and ensuring resilience against Denial-of-Service (DoS) attacks. Extensive simulations demonstrate superior performance of our method over baseline controllers under normal and adversarial conditions. Furthermore, real-world flight experiments validate the effectiveness and generalizability of the trained policy.

Equipping drones with target search capabilities is highly desirable for applications in disaster rescue and smart warehouse delivery systems. Multiple intelligent drones that can collaborate with each other and maneuver among obstacles show more effectiveness in accomplishing tasks in a shorter amount of time. However, carrying out collaborative target search (CTS) without prior target information is extremely challenging, especially with a visual drone swarm. In this work, we propose a novel data-efficient deep reinforcement learning (DRL) approach called adaptive curriculum embedded multistage learning (ACEMSL) to address these challenges, mainly 3-D sparse reward space exploration with limited visual perception and collaborative behavior requirements. Specifically, we decompose the CTS task into several subtasks including individual obstacle avoidance, target search, and inter-agent collaboration, and progressively train the agents with multistage learning. Meanwhile, an adaptive embedded curriculum (AEC) is designed, where the task difficulty level (TDL) can be adaptively adjusted based on the success rate (SR) achieved in training. ACEMSL allows data-efficient training and individual-team reward allocation for the visual drone swarm. Furthermore, we deploy the trained model over a real visual drone swarm and perform CTS operations without fine-tuning. Extensive simulations and real-world flight tests validate the effectiveness and generalizability of ACEMSL. The project is available at https://github.com/NTU-UAVG/CTS-visual-drone-swarm.git.

Drones, as advanced cyber-physical systems (CPSs), are undergoing a transformative shift with the advent of vision-based learning, a field that is rapidly gaining prominence due to its profound impact on drone autonomy and functionality. Unlike existing task-specific surveys, this work offers a comprehensive overview of vision-based learning for drones, emphasizing its pivotal role in enhancing their operational capabilities across various scenarios. First, the fundamental principles of vision-based learning are elucidated, demonstrating how it significantly improves drones' visual perception and decision-making processes. Vision-based control methods are then categorized into indirect, semidirect, and end-to-end approaches from the perception-control perspective. Various applications of vision-based drones with learning capabilities are further explored, ranging from single-agent systems to more complex multiagent and heterogeneous system scenarios, while highlighting the challenges and innovations characterizing each domain. Finally, open questions and potential solutions are discussed to guide future research and development in this dynamic and rapidly evolving field. With the growth of large language models (LLMs) and embodied intelligence, vision-based learning for drones provides a promising yet challenging road toward achieving artificial general intelligence (AGI) in the 3-D physical world.

Unmanned aerial vehicles (UAVs) play an important role in advancing fire prevention technology. However, existing research usually assumes that the firefighting equipment carried by UAVs, such as fire extinguishing bombs and water, can significantly satisfy actual requirements. However, in many practical scenarios, the firefighting resources available on UAVs are limited, necessitating an assessment and prioritization of affected areas. This article proposes a group decision-making framework for uncertain environments, employing Z-numbers and q-rung orthopair fuzzy sets to address this challenge. Specifically, a polar coordinate system is employed to transform the parameters in Z-numbers into the corresponding membership and nonmembership. Meanwhile, the potential probability distribution of Z-numbers is calculated based on an optimization model. To combine multiple sets of uncertain information into a final overall assessment, we propose an aggregation operator and provide strict proof using mathematical induction. Furthermore, a new weight calculation method is introduced to determine the weights of multiple pieces of information based on the potential probability distribution of Z-numbers and an improved golden rule representative value. Based on the sigmoid function and generalized knowledge measure, a novel score function is defined to rank different Z-information. In addition, a distance measure between Z-information is proposed and rigorously proved. Finally, the proposed method is validated through practical flight tests. The results and comparative analysis illustrate that our proposed method effectively mitigates other existing approaches' computational and informational loss limitations.

Various quadrotor drones have been developed in recent years, mainly focusing on either improving maximum thrust per platform area or flight maneuverability. Evidently, achieving both advantages simultaneously is a challenging task, since they call for opposing rotor requirements. Specifically, improving the drone's maximum thrust per platform area mainly requires reducing the number of rotors to make way for larger and more powerful rotors. While this can be an effective method to increase overall thrust, improving flight maneuverability requires a greater number of rotors to generate larger rotating torques or to increase the thrust vectoring capability. To address this challenge, we design a novel coaxial drone with two contra-rotating rotors for high thrust efficiency while enabling independent dual-axis rotor rotation to maintain maneuverability along the roll and pitch axes. The thrust vectoring capability is provided by two dedicated servo motors connected vertically in series with the coaxial propellers to produce a compact and elongated fuselage frame. A nonlinear flight model in six degrees of freedom is developed for the underactuated system, incorporating four control inputs from the two propellers and servos respectively. Consequently, a nonlinear control allocation approach is proposed such that the drone can produce a desired control force and yaw torque to stabilize the drone's position and yaw angle. For the uncontrolled roll and pitch dynamics, a damping component is added such that the roll and pitch angular velocities can also be stabilized. Both numerical simulations and real experiments are conducted to validate the design of the drone and the effectiveness of the proposed control strategy.