Multi-robot system for manufacturing is an Industry Internet of Things (IIoT) paradigm with significant operational cost savings and productivity improvement, where Unmanned Aerial Vehicles (UAVs) are employed to control and implement collaborative productions without human intervention. This mission-critical system relies on 3-Dimension (3-D) scene recognition to improve operation accuracy in the production line and autonomous piloting. However, implementing 3-D point cloud learning, such as Pointnet, is challenging due to limited sensing and computing resources equipped with UAVs. Therefore, we propose a Digital Twin (DT) empowered Knowledge Distillation (KD) method to generate several lightweight learning models and select the optimal model to deploy on UAVs. With a digital replica of the UAVs preserved at the edge server, the DT system controls the model sharing network topology and learning model structure to improve recognition accuracy further. Moreover, we employ network calculus to formulate and solve the model sharing configuration problem toward minimal resource consumption, as well as convergence. Simulation experiments are conducted over a popular point cloud dataset to evaluate the proposed scheme. Experiment results show that the proposed model sharing scheme outperforms the individual model in terms of computing resource consumption and recognition accuracy. Index Terms Digital Twin, Distributed Model Sharing, Knowledge Distillation, Network Calculus, Multi-Robot System. HE advances in wireless communication, and machine learning technologies have boosted the research and development of the Industrial Internet of Things (IIoT). A multi-robot system is a typical IIoT paradigm, in which Unmanned Aerial Vehicles (UAVs) are employed to implement auto-production collaboratively without human intervention. It can significantly save operation costs and improve productivity [1].

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This paper describes a methodology for learning flight control systems from human demonstrations and interventions while considering the estimated uncertainty in the learned models. The proposed approach uses human demonstrations to train an initial model via imitation learning and then iteratively, improve its performance by using real-time human interventions. The aim of the interventions is to correct undesired behaviors and adapt the model to changes in the task dynamics. The learned model uncertainty is estimated in real-time via Monte Carlo Dropout and the human supervisor is cued for intervention via an audiovisual signal when this uncertainty exceeds a predefined threshold. This proposed approach is validated in an autonomous quadrotor landing task on both fixed and moving platforms. It is shown that with this algorithm, a human can rapidly teach a flight task to an unmanned aerial vehicle via demonstrating expert trajectories and then adapt the learned model by intervening when the learned controller performs any undesired maneuver, the task changes, and/or the model uncertainty exceeds a threshold

In this paper an autonomous system to detect and combat Rumex obtusifolius leveraging autonomous unmanned aerial vehicles (UAV), small autonomous sprayer robots and 5G SA connectivity is presented. Rumex obtusifolius is a plant found on grassland that drains nutrients from surrounding plants and has lower nutritive value than the surrounding grass. High concentrations of it have to be combated in order to use the grass as feed for livestock. One or more UAV are controlled through 5G to survey the current working area and send back high-definition photos of the ground to an edge cloud server. There an AI algorithm using neural networks detects the Rumex obtusifolius and calculates its position using the UAVs position data. When plants are detected an optimal path is calculated and sent via 5G to the sprayer robot to get to them in minimal time. It will then move to the position of the broad-leafed dock and use an on-board camera and the edge cloud to verify the position of the plant and precisely spray crop protection only where the target plant is. The spraying robot and UAV are already operational, the training of the detection algorithm is still ongoing. The described system is being tested with a fixed private 5G SA network and a nomadic 5G SA network as public cellular networks are not performant enough in regards to low latency and upload bandwidth.

Bryan Stern and Mark Geist discusses helping Americans out of a war zone after being left behind by the Biden admin. The U.S. has evacuated its first group of American citizens and permanent residents from Sudan since war broke out in the capital weeks ago. The land evacuation started Friday with efforts to bus a large group of Americans to the Red Sea via Port Sudan. Officials revealed Saturday that unmanned aircraft provided armed overwatch as a bus convoy carried 200 to 300 Americans over 500 miles. Smoke is seen in Khartoum, Sudan, Wednesday, April 19, 2023.

A massive Crimea oil reservoir fire broke out after the site was hit by a drone, according to video posted Saturday. A Ukrainian drone strike caused a massive fire to erupt at an oil depot in Crimea, a Russia-appointed official reported Saturday. Mikhail Razvozhayev, Russia's selected governor of Sevastopol, said that authorities had spotted two "enemy drones" that attacked the depot, with four tanks burned down as a result. Local forces were able to shoot down a third drone and disable a fourth through radio-electronic means. Razvozhayev assigned the fire the highest level of difficulty to extinguish, but he claimed the fire had at least been contained.

Turkish defence firm Baykar aims to begin production of its new unmanned combat aerial vehicle next year which is already attracting international interest, its chairman Selcuk Bayraktar said. Named "Kizilelma", the drone expands the company's product range from slow, ground attack drones to fast and agile autonomous ones that work alongside fighter jets. "It is designed to be a highly autonomous, under human purview of course, air-to-air combat vehicle," said Bayraktar, who led the design of the 15-metre-long (49 feet) jet-powered weapon. "In a sense, the Kizilelma expresses a whole new future for combat aviation." Baykar has come to prominence internationally in recent years because of the company's light drone TB-2, which has been used in Ukraine, Azerbaijan, and North Africa and has been a huge export success, catapulting the firm to becoming one of the largest Turkish defence exporters.

A massive fire was ignited in the Crimean port city of Sevastopol following a suspected drone attack on a fuel storage tank. The blaze was assigned the highest ranking in terms of how complicated it will be to extinguish, Mikhail Razvozhayev, the Russian-installed governor, wrote on Telegram on Saturday. The fire was still burning but it had been contained and no one was injured. The oil reservoir fire did not cause any casualties and would not hinder fuel supplies in Sevastopol, he said. "The four fuel tanks that were hit, they are practically burnt out already," said Razvozhayev, adding an area of 1,000 square metres (11,000 square feet) had been engulfed in flames.

In this paper, we address the problem of safe trajectory planning for autonomous search and exploration in constrained, cluttered environments. Guaranteeing safe (collision-free) trajectories is a challenging problem that has garnered significant due to its importance in the successful utilization of robots in search and exploration tasks. This work contributes a method that generates guaranteed safety-critical search trajectories in a cluttered environment. Our approach integrates safety-critical constraints using discrete control barrier functions (DCBFs) with ergodic trajectory optimization to enable safe exploration. Ergodic trajectory optimization plans continuous exploratory trajectories that guarantee complete coverage of a space. We demonstrate through simulated and experimental results on a drone that our approach is able to generate trajectories that enable safe and effective exploration. Furthermore, we show the efficacy of our approach for safe exploration using real-world single- and multi- drone platforms.

Abstract--We propose a novel context-aware drone delivery framework for optimizing package delivery through skyway networks in smart cities. In this respect, we propose a novel line-of-sight heuristic-based context-aware composition algorithm that selects and composes near-optimal drone delivery services. We conducted an extensive experiment using a real dataset to show the robustness of our proposed approach. A skyway network is defined is a popular type of UAV that offers potential as a set of skyway segments that directly connect benefits in smart city applications [2]. Drones two nodes representing take-off and landing are increasingly becoming pervasive in their use, stations [8]. Take-off and landing stations (aka including surveillance, agriculture, and delivery network nodes) are typically from and to building of goods [3].