A robot with wings that move like a hawk's can fly more stably and nimbly than other flying robots – and it uses less power, extending flight time. Enrico Ajanic at the Swiss Federal Institute of Technology Lausanne and his colleagues borrowed from the biology of the northern goshawk (Accipiter gentilis) to make a 284-gram drone with a maximum wingspan of 1.05 metres. The craft includes 27 feather-like plates – nine on each wing and a further nine on the tail – so that it moves through air as a goshawk does. The goal was to develop a drone that can fly long distances across cities, but manoeuvre around buildings and objects that it is likely to encounter. "Multicopter drones can hover and move well, but can't fly long distances," says Ajanic.

Drones of all sizes are being used by environmental advocates to monitor deforestation, by conservationists to track poachers, and by journalists and activists to document large protests. As a political sociologist who studies social movements and drones, I document a wide range of nonviolent and pro-social drone uses in my new book, "The Good Drone." I show that these efforts have the potential to democratize surveillance. But when the Department of Homeland Security redirects large, fixed-wing drones from the U.S.-Mexico border to monitor protests, and when towns experiment with using drones to test people for fevers, it's time to think about how many eyes are in the sky and how to avoid unwanted aerial surveillance. One way that's within reach of nearly everyone is learning how to simply disappear from view.

The challenge of establishing assurance in autonomy is rapidly attracting increasing interest in the industry, government, and academia. Autonomy is a broad and expansive capability that enables systems to behave without direct control by a human operator. To that end, it is expected to be present in a wide variety of systems and applications. A vast range of industrial sectors, including (but by no means limited to) defense, mobility, health care, manufacturing, and civilian infrastructure, are embracing the opportunities in autonomy yet face the similar barriers toward establishing the necessary level of assurance sooner or later. Numerous government agencies are poised to tackle the challenges in assured autonomy. Given the already immense interest and investment in autonomy, a series of workshops on Assured Autonomy was convened to facilitate dialogs and increase awareness among the stakeholders in the academia, industry, and government. This series of three workshops aimed to help create a unified understanding of the goals for assured autonomy, the research trends and needs, and a strategy that will facilitate sustained progress in autonomy. The first workshop, held in October 2019, focused on current and anticipated challenges and problems in assuring autonomous systems within and across applications and sectors. The second workshop held in February 2020, focused on existing capabilities, current research, and research trends that could address the challenges and problems identified in workshop. The third event was dedicated to a discussion of a draft of the major findings from the previous two workshops and the recommendations.

Surveillance is becoming an increasingly controversial application given the rapid pace at which AI systems are being developed and deployed worldwide. While protestors marched through the city demanding justice for George Floyd and an end to police brutality, Minneapolis police trained surveillance tools to identify them. With just hours to sift through thousands of CCTV camera feeds and other dragnet data streams, the police turned to a range of automated systems for help, reaching for information collected by automated license plate readers, CCTV-video analysis software, open-source geolocation tools, and Clearview AI's controversial facial recognition system. High above the city, an unarmed Predator drone flew in circles, outfitted with a specialized camera first pioneered by the police in Baltimore that is capable of identifying individuals from 10,000 feet in the air, providing real-time surveillance of protestors across the city. But Minneapolis is not an isolated case of excessive policing and technology run amok. Instead, it is part of a larger strategy by the state, local, and federal government to build surveillance dragnets that pull in people's emails, texts, bank records, and smartphone location as well as their faces, movements, and physical whereabouts to equip law enforcement with unprecedented tools to search for and identify Americans without a warrant.

Salah Sukkarieh is Professor of Robotics and Intelligent Systems at the University of Sydney, and Director of Research and Innovation at the Australian Centre for Field Robotics. He has worked on autonomous systems for ports, mines, aerospace, and, most recently, agriculture. He recalls that when he started working on drone technology there were not many aerospace companies in Australia working on drones, and those that were were not interested in drones for agriculture or the environment as the business case didn't stack up financially. Australia's size and the remoteness of many rural areas have also been deterrents. There is strong interest from the agriculture industry in the use of robotics and automation to support farmers, and he is surprised by the number of students who are interested in working on these projects.

Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods. We propose a new end-to-end reinforcement learning (RL) approach to UAV-enabled data collection from Internet of Things (IoT) devices in an urban environment. An autonomous drone is tasked with gathering data from distributed sensor nodes subject to limited flying time and obstacle avoidance. While previous approaches, learning and non-learning based, must perform expensive recomputations or relearn a behavior when important scenario parameters such as the number of sensors, sensor positions, or maximum flying time, change, we train a double deep Q-network (DDQN) with combined experience replay to learn a UAV control policy that generalizes over changing scenario parameters. By exploiting a multi-layer map of the environment fed through convolutional network layers to the agent, we show that our proposed network architecture enables the agent to make movement decisions for a variety of scenario parameters that balance the data collection goal with flight time efficiency and safety constraints. Considerable advantages in learning efficiency from using a map centered on the UAV's position over a non-centered map are also illustrated.

Next-generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes. Despite having advantages of using UAV-BSs, their dependence on the on-board, limited-capacity battery hinders their service continuity. Shorter trajectories can save flying energy, however, UAV-BSs must also serve nodes based on their service priority since nodes' service requirements are not always the same. In this paper, we present an energy-efficient trajectory optimization for a UAV assisted IoT system in which the UAV-BS considers the IoT nodes' service priorities in making its movement decisions. We solve the trajectory optimization problem using Double Q-Learning algorithm. Simulation results reveal that the Q-Learning based optimized trajectory outperforms a benchmark algorithm, namely Greedily-served algorithm, in terms of reducing the average energy consumption of the UAV-BS as well as the service delay for high priority nodes.

A lasting legacy of the COVID-19 pandemic is that work will never be the same. Thanks to the two As, automation and artificial intelligence (AI), expect to see significant growth and changes in 2021. "The'great lockdown' of 2020 will make the drive for automation in 2021 both inevitable and irreversible,'' according to Forrester's Predictions 2021. "Remote work, new digital muscles, and pandemic constraints will create millions of pragmatic automations in 2021; document extraction, RPA (robotic process automation) from anywhere, drones, and various employee robots will proliferate; and, as expected, the mad dash to automate will bring trouble." At the same time, while AI didn't predict the pandemic, it will help businesses rethink the future of work; drive more efficiency, elasticity, and scale in operations; and reimagine customer and employee experiences, Forrester said. AI is driving the growth of automated processes, helping them become smarter. Companies that adopt machine learning, a subset of AI, "will massively multiply their number of AI use cases, including for employee augmentation and automation,'' the firm said.

Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect the most relevant recent discoveries and papers -- particularly in but not limited to artificial intelligence -- and explain why they matter. This week, a startup that's using UAV drones for mapping forests, a look at how machine learning can map social media networks and predict Alzheimer's, improving computer vision for space-based sensors and other news regarding recent technological advances. Machine learning tools are being used to aid diagnosis in many ways, since they're sensitive to patterns that humans find difficult to detect. IBM researchers have potentially found such patterns in speech that are predictive of the speaker developing Alzheimer's disease.

This column aims to collect the most relevant recent discoveries and papers — particularly in but not limited to artificial intelligence — and explain why …