In April, as COVID-19 cases exploded across the U.S. and local officials scrambled for solutions, a police department in Connecticut tried a new way to monitor the spread of the virus. One morning, as masked shoppers lined up 6 feet apart outside Trader Joe's in Westport, the police department flew a drone overhead to observe their social distancing and detect potential coronavirus symptoms, such as high temperature and increased heart rate. According to internal emails, the captain flying the mission wanted to "take advantage" of the store's line. But the store had no heads-up about the flight, and neither did the customers on their grocery runs, even though the drone technology managed to track figures both inside and outside. The drone program was unveiled a week later when the department announced its "Flatten the Curve Pilot Program" in collaboration with the Canadian drone company Draganfly, which was due to last through the summer. But less than 48 hours later after the program's public unveiling, the police department was forced to dump it amid intense backlash from Westport residents.

Building domain-specific architectures for autonomous aerial robots is challenging due to a lack of systematic methodology for designing onboard compute. We introduce a novel performance model called the F-1 roofline to help architects understand how to build a balanced computing system for autonomous aerial robots considering both its cyber (sensor rate, compute performance) and physical components (body-dynamics) that affect the performance of the machine. We use F-1 to characterize commonly used learning-based autonomy algorithms with onboard platforms to demonstrate the need for cyber-physical co-design. To navigate the cyber-physical design space automatically, we subsequently introduce AutoPilot. This push-button framework automates the co-design of cyber-physical components for aerial robots from a high-level specification guided by the F-1 model. AutoPilot uses Bayesian optimization to automatically co-design the autonomy algorithm and hardware accelerator while considering various cyber-physical parameters to generate an optimal design under different task level complexities for different robots and sensor framerates. As a result, designs generated by AutoPilot, on average, lower mission time up to 2x over baseline approaches, conserving battery energy.

The aircraft carrier Nimitz is finally going home. The Pentagon last month ordered the warship to remain in the Middle East because of Iranian threats against President Donald J. Trump and other American officials, just three days after announcing the ship was returning home as a signal to de-escalate rising tensions with Tehran. With those immediate tensions seeming to ease a bit, and President Biden looking to renew discussions with Iran on the 2015 nuclear accord that Mr. Trump withdrew from, three Defense Department officials said on Monday that the Nimitz and its 5,000-member crew were ordered on Sunday to return to the ship's home port of Bremerton, Wash., after a longer-than-usual 10-month deployment. The Pentagon for weeks had been engaged in a muscle-flexing strategy aimed at deterring Iran and its Shia proxies in Iraq from attacking American personnel in the Persian Gulf to avenge the death of Maj. General Suleimani, the commander of Iran's elite Quds Force of the Islamic Revolutionary Guards Corps, was killed in an American drone strike in January 2020.

Honeybees move quickly from flower to flower, landing easily on each one in turn – but a study involving small drones suggests that the undertaking is more difficult than it looks, implying the bees rely on learning as well as hardwired instinct. Bees and other insects judge movement using what is called "optical flow" – basically the rate at which things are moving through the field of view. Optical flow is useful during landing too, particularly to help a bee decelerate.

New drone video shows the recent damage wrought on California's iconic Highway 1, where part of the road collapsed after heavy rains washed it into the ocean last week. The video, released by the Monterey County Sheriff's Office, shows a large part of the highway still flooded and covered with debris from recent rainfall and mudslides. At the point of collapse, about 45 miles south of Carmel in the Big Sur area, both lanes of the road are completely gone, with a massive hole sloping toward the Pacific Ocean in its place. The sheriff's office video shows water running through the collapsed part of the road, which by Friday had fallen into the sea. California has been plagued by extensive mudslides, largely in areas burned out during the previous season's wildfires.

To reduce Operation and Maintenance (O&M) costs on offshore wind farms, wherein 80% of the O&M cost relates to deploying personnel, the offshore wind sector looks to robotics and Artificial Intelligence (AI) for solutions. Barriers to Beyond Visual Line of Sight (BVLOS) robotics include operational safety compliance and resilience, inhibiting the commercialization of autonomous services offshore. To address safety and resilience challenges we propose a symbiotic system; reflecting the lifecycle learning and co-evolution with knowledge sharing for mutual gain of robotic platforms and remote human operators. Our methodology enables the run-time verification of safety, reliability and resilience during autonomous missions. We synchronize digital models of the robot, environment and infrastructure and integrate front-end analytics and bidirectional communication for autonomous adaptive mission planning and situation reporting to a remote operator. A reliability ontology for the deployed robot, based on our holistic hierarchical-relational model, supports computationally efficient platform data analysis. We analyze the mission status and diagnostics of critical sub-systems within the robot to provide automatic updates to our run-time reliability ontology, enabling faults to be translated into failure modes for decision making during the mission. We demonstrate an asset inspection mission within a confined space and employ millimeter-wave sensing to enhance situational awareness to detect the presence of obscured personnel to mitigate risk. Our results demonstrate a symbiotic system provides an enhanced resilience capability to BVLOS missions. A symbiotic system addresses the operational challenges and reprioritization of autonomous mission objectives. This advances the technology required to achieve fully trustworthy autonomous systems.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Pizza Hut is reaching new heights with its latest delivery experiment. Tech company Dragontail Systems Limited announced this week that it has deployed drones for restaurants to carry meals to delivery drivers in remote landing zones. Those drones will be flying pizzas from a Pizza Hut location in northern Israel starting in June, The Wall Street Journal reported.

In this period where the number of systems developed by utilizing unmanned aerial technology is increasing day by day, unmanned aerial vehicles will be used to achieve the targets of minimizing the destruction of our forests which are the lungs of the world and optimizing the usage of workforce and time resources [1, 2, 3]. As a result of the application carried out in line with the subject of this paper, it is proposed that the system based on the detection of smoke image with unmanned aerial vehicle can provide a great benefit in reducing the error rate occurring in fire detection. The microprocessor in the system has been trained with deep learning methods and has been given the ability to recognize smoke image, which is the earliest sign of fire diagnosis. The most fundamental problem in the common algorithms used in fire detection is the high level of false alarm and overlook rate [4,5].Confirming the result obtained from the detection and defining an additional proof will increase the reliability of the system as well as the accuracy. Since the drones provide a mobile vision, the point of view can be controlled by the ground station can manipulate it for the sake of the accuracy of the result. The application developed in line with the subject of the paper was implemented in both simulation and physical environments and the advantages of early fire detection system and analysis results are discussed in the conclusion section of the article.

Developing the collision-free flocking behavior for a dynamic squad of fixed-wing UAVs is still a challenge due to kinematic complexity and environmental uncertainty. In this paper, we deal with the decentralized leader-follower flocking control problem through deep reinforcement learning (DRL). Specifically, we formulate a decentralized DRL-based decision making framework from the perspective of every follower, where a collision avoidance mechanism is integrated into the flocking controller. Then, we propose a novel reinforcement learning algorithm CACER-II for training a shared control policy for all the followers. Besides, we design a plug-n-play embedding module based on convolutional neural networks and the attention mechanism. As a result, the variable-length system state can be encoded into a fixed-length embedding vector, which makes the learned DRL policies independent with the number or the order of followers. Finally, numerical simulation results demonstrate the effectiveness of the proposed method, and the learned policies can be directly transferred to semiphysical simulation without any parameter finetuning.

How do honeybees land on flowers or avoid obstacles? One would expect such questions to be mostly of interest to biologists. However, the rise of small electronics and robotic systems has also made them relevant to robotics and Artificial Intelligence (AI). For example, small flying robots are extremely restricted in terms of the sensors and processing that they can carry onboard. If these robots are to be as autonomous as the much larger self-driving cars, they will have to use an extremely efficient type of artificial intelligence – similar to the highly developed intelligence possessed by flying insects.