Biological sensing and processing is asynchronous and sparse, leading to low-latency and energy-efficient perception and action. In robotics, neuromorphic hardware for event-based vision and spiking neural networks promises to exhibit similar characteristics. However, robotic implementations have been limited to basic tasks with low-dimensional sensory inputs and motor actions due to the restricted network size in current embedded neuromorphic processors and the difficulties of training spiking neural networks. Here, we present the first fully neuromorphic vision-to-control pipeline for controlling a freely flying drone. Specifically, we train a spiking neural network that accepts high-dimensional raw event-based camera data and outputs low-level control actions for performing autonomous vision-based flight. The vision part of the network, consisting of five layers and 28.8k neurons, maps incoming raw events to ego-motion estimates and is trained with self-supervised learning on real event data. The control part consists of a single decoding layer and is learned with an evolutionary algorithm in a drone simulator. Robotic experiments show a successful sim-to-real transfer of the fully learned neuromorphic pipeline. The drone can accurately follow different ego-motion setpoints, allowing for hovering, landing, and maneuvering sideways$\unicode{x2014}$even while yawing at the same time. The neuromorphic pipeline runs on board on Intel's Loihi neuromorphic processor with an execution frequency of 200 Hz, spending only 27 $\unicode{x00b5}$J per inference. These results illustrate the potential of neuromorphic sensing and processing for enabling smaller, more intelligent robots.

The U.S. military said a Russian fighter plane clipped the propeller of one its spy drones and made it crash into the Black Sea on Tuesday in the first such direct encounter between the two world powers since Russia invaded Ukraine over a year ago. The Russian Defense Ministry offered a different account, and Moscow's ambassador to Washington said his country "views this incident as a provocation" involving a U.S. MQ-9 drone and Russian Su-27 fighter jet. The United States, which has provided tens of billions of dollars in military aid to Ukraine, has not become directly engaged in the war but it does conduct regular surveillance flights in the region. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

"If the message is that they want to deter or dissuade us from flying or operating in international airspace over the Black Sea," Mr Kirby told broadcaster VOA, "then that message will fail because that is not going to happen".

A Russian fighter jet has hit a US military drone over international waters, crashing the drone. The MQ-9 Reaper drone and two SU-27 craft were all flying above the Black Sea, and according to the US military's European Command, the Russian planes dumped fuel on the drone and flew in front of it dangerously, and eventually one of them hit the drone's propeller, forcing the US to bring down the drone. "This unsafe and unprofessional act by the Russians nearly caused both aircraft to crash," said US Air Force commander James B. Hecker in a press release. He also stated that the drone was "conducting routine operations" and that this incident will not stop US aircraft from operating in international airspace. Drones like this one have been operating over the Black Sea since well before the beginning of the Russia-Ukraine war to monitor the situation in Ukraine.

Former U.S. Ambassador to Ukraine John Herbst discusses massive missile attacks launched by Russia as the battle for city of Bakhmut rages on. A Russian Su-27 jet collided with a U.S. MQ-9 Reaper drone over the Black Sea Tuesday, a U.S. defense official told Fox News. It was one of two Su-27's flying. This happened in international airspace over international waters. The propeller to the drone was damaged and the drone landed in the Black Sea, west of Crimea, the U.S. defense official says.

TAICHUNG, Taiwan – Taiwan's top military research unit has unveiled a series of locally made attack and surveillance drones -- including a loitering munition similar in appearance to the U.S.-made AeroVironment Switchblade 300 drone deployed by Ukraine -- as the self-ruled island focuses on asymmetric capabilities to defend against the much larger Chinese military. The move by the state-owned National Chung-Shan Institute of Science and Technology is part of Taipei's efforts to speed up research, development and production of military drones as it draws lessons from Ukraine's use of these relatively low-cost assets in its fight against Russia. To achieve this, NCSIST has been teaming up with private companies that provide key components and sensitive technologies for the unmanned systems. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

This work aims at showing that it is feasible and safe to use a swarm of Unmanned Aerial Vehicles (UAVs) indoors alongside humans. UAVs are increasingly being integrated under the Industry 4.0 framework. UAV swarms are primarily deployed outdoors in civil and military applications, but the opportunities for using them in manufacturing and supply chain management are immense. There is extensive research on UAV technology, e.g., localization, control, and computer vision, but less research on the practical application of UAVs in industry. UAV technology could improve data collection and monitoring, enhance decision-making in an Internet of Things framework and automate time-consuming and redundant tasks in the industry. However, there is a gap between the technological developments of UAVs and their integration into the supply chain. Therefore, this work focuses on automating the task of transporting packages utilizing a swarm of small UAVs operating alongside humans. MoCap system, ROS, and unity are used for localization, inter-process communication and visualization. Multiple experiments are performed with the UAVs in wander and swarm mode in a warehouse like environment.

Perching on {the surface} of moving objects, like vehicles, could extend the flight {time} and range of quadrotors. Suction cups are usually adopted for {surface attachment} due to their durability and large adhesive force. To seal on {a surfaces}, suction cups {must} be aligned with {the surface} and {possess proper relative tangential velocity}. {However, quadrotors' attitude and relative velocity errors would become significant when the object surface is moving and inclined. To address this problem, we proposed a real-time trajectory planning algorithm. The time-optimal aggressive trajectory is efficiently generated through multimodal search in a dynamic time-domain. The velocity errors relative to the moving surface are alleviated.} To further adapt to the residual errors, we design a compliant gripper using self-sealing cups. Multiple cups in different directions are integrated into a wheel-like mechanism to increase the tolerance to attitude errors. The wheel mechanism also eliminates the requirement of matching the attitude and tangential velocity. {Extensive tests are conducted to perch on static and moving surfaces at various inclinations.} Results demonstrate that our proposed system enables a quadrotor to reliably perch on moving inclined surfaces (up to $1.07m/s$ and $90^\circ$) with a success rate of $70\%$ or higher. {The efficacy of the trajectory planner is also validated. Our gripper has larger adaptability to attitude errors and tangential velocities than conventional suction cup grippers.} The success rate increases by 45\% in dynamic perches.

Deep Reinforcement Learning is quickly becoming a popular method for training autonomous Unmanned Aerial Vehicles (UAVs). Our work analyzes the effects of measurement uncertainty on the performance of Deep Reinforcement Learning (DRL) based waypoint navigation and obstacle avoidance for UAVs. Measurement uncertainty originates from noise in the sensors used for localization and detecting obstacles. Measurement uncertainty/noise is considered to follow a Gaussian probability distribution with unknown non-zero mean and variance. We evaluate the performance of a DRL agent trained using the Proximal Policy Optimization (PPO) algorithm in an environment with continuous state and action spaces. The environment is randomized with different numbers of obstacles for each simulation episode in the presence of varying degrees of noise, to capture the effects of realistic sensor measurements. Denoising techniques like the low pass filter and Kalman filter improve performance in the presence of unbiased noise. Moreover, we show that artificially injecting noise into the measurements during evaluation actually improves performance in certain scenarios. Extensive training and testing of the DRL agent under various UAV navigation scenarios are performed in the PyBullet physics simulator. To evaluate the practical validity of our method, we port the policy trained in simulation onto a real UAV without any further modifications and verify the results in a real-world environment.

This paper contributes a novel strategy for semantics-aware autonomous exploration and inspection path planning. Attuned to the fact that environments that need to be explored often involve a sparse set of semantic entities of particular interest, the proposed method offers volumetric exploration combined with two new planning behaviors that together ensure that a complete mesh model is reconstructed for each semantic, while its surfaces are observed at appropriate resolution and through suitable viewing angles. Evaluated in extensive simulation studies and experimental results using a flying robot, the planner delivers efficient combined exploration and high-fidelity inspection planning that is focused on the semantics of interest. Comparisons against relevant methods of the state-of-the-art are further presented.