Vision-based object tracking is a critical component for achieving autonomous aerial navigation, particularly for obstacle avoidance. Neuromorphic Dynamic Vision Sensors (DVS) or event cameras, inspired by biological vision, offer a promising alternative to conventional frame-based cameras. These cameras can detect changes in intensity asynchronously, even in challenging lighting conditions, with a high dynamic range and resistance to motion blur. Spiking neural networks (SNNs) are increasingly used to process these event-based signals efficiently and asynchronously. Meanwhile, physics-based artificial intelligence (AI) provides a means to incorporate system-level knowledge into neural networks via physical modeling. This enhances robustness, energy efficiency, and provides symbolic explainability. In this work, we present a neuromorphic navigation framework for autonomous drone navigation. The focus is on detecting and navigating through moving gates while avoiding collisions. We use event cameras for detecting moving objects through a shallow SNN architecture in an unsupervised manner. This is combined with a lightweight energy-aware physics-guided neural network (PgNN) trained with depth inputs to predict optimal flight times, generating near-minimum energy paths. The system is implemented in the Gazebo simulator and integrates a sensor-fused vision-to-planning neuro-symbolic framework built with the Robot Operating System (ROS) middleware. This work highlights the future potential of integrating event-based vision with physics-guided planning for energy-efficient autonomous navigation, particularly for low-latency decision-making.

This paper investigates the application of Deep Reinforcement (DRL) Learning to address motion control challenges in drones for additive manufacturing (AM). Drone-based additive manufacturing promises flexible and autonomous material deposition in large-scale or hazardous environments. However, achieving robust real-time control of a multi-rotor aerial robot under varying payloads and potential disturbances remains challenging. Traditional controllers like PID often require frequent parameter re-tuning, limiting their applicability in dynamic scenarios. We propose a DRL framework that learns adaptable control policies for multi-rotor drones performing waypoint navigation in AM tasks. We compare Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) within a curriculum learning scheme designed to handle increasing complexity. Our experiments show TD3 consistently balances training stability, accuracy, and success, particularly when mass variability is introduced. These findings provide a scalable path toward robust, autonomous drone control in additive manufacturing.

Bennett's tree kangaroos, one of Australia's most mysterious marsupials, have long eluded researchers. Our new study, published in Australian Mammalogy today, has achieved a breakthrough: using thermal drones to detect these rare animals with unprecedented efficiency. Tree kangaroos are found only in the tropical rainforests of Australia and New Guinea. Unlike their ground-dwelling relatives, they spend their lives in treetops, feeding on leaves and vines. Their dependence on rainforest trees makes them vulnerable to deforestation and climate change.

The Israeli army has demolished several Palestinian homes in the Jenin refugee camp as it continues the deadly raids across the occupied West Bank that it launched on January 21. Explosions echoed throughout the camp overnight as Israeli forces demolished the civilian homes, Wafa, the official Palestinian news agency, reported on Friday. Witnesses said Israeli forces reinforced their presence around the camp and conducted intensive drone surveillance. The army also continues to besiege Jenin Governmental Hospital, having bulldozed the main entrance and the main road leading to it earlier in its raids. Earlier this week, it carried out the demolition of residential blocks in Jenin for the first time since 2002, as reported by Jenin Governor Kamal Abu al-Rub.

Three people, including a minor, were killed in a Ukrainian drone attack that hit a car in Logachyovka village in the Russian border region of Belgorod. Governor Vyacheslav Gladkov said a man, an 18-year-old woman and 14-year-old girl, all passengers, died in the attack. Ukraine's military said Kyiv's forces struck an airfield overnight in Russia's Krasnodar region, which sits on the Black Sea and Sea of Azov, resulting in explosions and a fire. According to the military, Moscow's forces use the airfield to store and launch drones to attack Ukraine and maintain aircraft that carry out missions in southern Ukraine. The military also said its army shot down 56 of 77 Russian drones launched at Ukraine overnight while 18 did not reach their targets.

Robotic simulators play a crucial role in the development and testing of autonomous systems, particularly in the realm of Uncrewed Aerial Vehicles (UAV). However, existing simulators often lack high-level autonomy, hindering their immediate applicability to complex tasks such as autonomous navigation in unknown environments. This limitation stems from the challenge of integrating realistic physics, photorealistic rendering, and diverse sensor modalities into a single simulation environment. At the same time, the existing photorealistic UAV simulators use mostly hand-crafted environments with limited environment sizes, which prevents the testing of long-range missions. This restricts the usage of existing simulators to only low-level tasks such as control and collision avoidance. To this end, we propose the novel FlightForge UAV open-source simulator. FlightForge offers advanced rendering capabilities, diverse control modalities, and, foremost, procedural generation of environments. Moreover, the simulator is already integrated with a fully autonomous UAV system capable of long-range flights in cluttered unknown environments. The key innovation lies in novel procedural environment generation and seamless integration of high-level autonomy into the simulation environment. Experimental results demonstrate superior sensor rendering capability compared to existing simulators, and also the ability of autonomous navigation in almost infinite environments.

Drones or unmanned aerial vehicles are traditionally used for military missions, warfare, and espionage. However, the usage of drones has significantly increased due to multiple industrial applications involving security and inspection, transportation, research purposes, and recreational drone flying. Such an increased volume of drone activity in public spaces requires regulatory actions for purposes of privacy protection and safety. Hence, detection of illegal drone activities such as boundary encroachment becomes a necessity. Such detection tasks are usually automated and performed by deep learning models which are trained on annotated image datasets. This paper builds on a previous work and extends an already published open source dataset. A description and analysis of the entire dataset is provided. The dataset is used to train the YOLOv7 deep learning model and some of its minor variants and the results are provided. Since the detection models are based on a single image input, a simple cross-correlation based tracker is used to reduce detection drops and improve tracking performance in videos. Finally, the entire drone detection system is summarized.

Armed with drones, long-distance camera lenses and metal detectors, a hillside in Eaton Canyon has become the focus of intense scrutiny over the last month by teams of private investigators now seeking clues on whether Southern California Edison equipment caused the massive fire that destroyed large swaths of Altadena. Some of the findings and theories of these privately hired teams of fire investigators and electrical engineers have emerged in more than 40 lawsuits that residents have filed against the utility. Much of the focus has been centered on a group of transmission towers where the first flames were seen just as the Eaton fire exploded. Earlier this week, a new lawsuit alleged that an idle transmission tower on the hillside -- one that has not been in use for more than 50 years -- might have sparked the devastating blaze. With more than 9,000 homes lost and 17 people killed, liability is going to be a costly question that could affect how Altadena is rebuilt.

Abstract-- In this paper, we present an approach for controlling a team of agile fixed-wing aerial vehicles in close proximity to one another. Our approach relies on recedinghorizon nonlinear model predictive control (NMPC) to plan maneuvers across an expanded flight envelope to enable interagent collision avoidance. To facilitate robust collision avoidance and characterize the likelihood of inter-agent collisions, we compute a statistical bound on the probability of the system leaving a tube around the planned nominal trajectory. Finally, we propose a metric for evaluating highly dynamic swarms and use this metric to evaluate our approach. We successfully demonstrated our approach through both simulation and hardware experiments, and to our knowledge, this the first time close-quarters swarming has been achieved with physical aerobatic fixed-wing vehicles.

Accurate people localisation using drones is crucial for effective crowd management, not only during massive events and public gatherings but also for monitoring daily urban crowd flow. Traditional methods for tiny object localisation using high-resolution drone imagery often face limitations in precision and efficiency, primarily due to constraints in image scaling and sliding window techniques. To address these challenges, a novel approach dedicated to point-oriented object localisation is proposed. Along with this approach, the Pixel Distill module is introduced to enhance the processing of high-definition images by extracting spatial information from individual pixels at once. Additionally, a new dataset named UP-COUNT, tailored to contemporary drone applications, is shared. It addresses a wide range of challenges in drone imagery, such as simultaneous camera and object movement during the image acquisition process, pushing forward the capabilities of crowd management applications. A comprehensive evaluation of the proposed method on the proposed dataset and the commonly used DroneCrowd dataset demonstrates the superiority of our approach over existing methods and highlights its efficacy in drone-based crowd object localisation tasks. These improvements markedly increase the algorithm's applicability to operate in real-world scenarios, enabling more reliable localisation and counting of individuals in dynamic environments.