An autonomous drone with wheels can roll along the ground, only flying when needed to clear obstacles, which helps its battery last seven times longer, according to its developers. Rolling robots are efficient and can travel long distances, but cannot traverse big obstacles, while flying drones can get past large obstructions, but have limited range.

Commanding a drone with a natural language is not only user-friendly but also opens the door for emerging language agents to control the drone. Emerging large language models (LLMs) provide a previously impossible opportunity to automatically translate a task description in a natural language to a program that can be executed by the drone. However, powerful LLMs and their vision counterparts are limited in three important ways. First, they are only available as cloud-based services. Sending images to the cloud raises privacy concerns. Second, they are expensive, costing proportionally to the request size. Finally, without expensive fine-tuning, existing LLMs are quite limited in their capability of writing a program for specialized systems like drones. In this paper, we present a system called TypeFly that tackles the above three problems using a combination of edge-based vision intelligence, novel programming language design, and prompt engineering. Instead of the familiar Python, TypeFly gets a cloud-based LLM service to write a program in a small, custom language called MiniSpec, based on task and scene descriptions in English. Such MiniSpec programs are not only succinct (and therefore efficient) but also able to consult the LLM during their execution using a special skill called query. Using a set of increasingly challenging drone tasks, we show that design choices made by TypeFly can reduce both the cost of LLM service and the task execution time by more than 2x. More importantly, query and prompt engineering techniques contributed by TypeFly significantly improve the chance of success of complex tasks.

There is a lot of buzz these days around autonomous aerial vehicles (AAV) and all of the ways that they can benefit us in our everyday lives. From express deliveries to disaster management, search and rescue operations, and mapping of inaccessible locations, the list of potential applications goes on and on. But when was the last time a drone dropped off an online order at your home? If you are like most people, the answer is "never." While the potential of UAVs to transform our lives in many ways is real, the reason that relatively few of us have experienced that stems from a number of problems that have yet to be solved. One of these problems is the difficulty of executing safe and precise flight maneuvers under windy conditions.

In a new short video that has surfaced on TikTok, apes have been spotted flying drones. The drone is an Autel Robotics Evo and the apes are located in a Myrtle Beach Safari in South Carolina. The video was taken by photographer Nick B. and shows two apes flying a drone. One is standing up using the drone's controller while the other sits beside him holding the drone's case. The video is particularly impressive as the ape seems very much in control of the drone.

"I expect the first things that we'll do will not appear as sexy as what you might imagine in a movie, but will be completely game-changing," Roper explained at the conference on Mar. 13, 2019. He did not elaborate, but one of the immediate benefits of an AI or AI-enabled system is the increased speed of decision-making. At first, a drone with Skyborg might not necessarily do anything a manned aircraft can't do already, but it will be able to perform those tasks, such as aerial combat maneuvers or weapons employment, faster based on its set parameters. Where a human might be distracted or confused by the chaos of an aerial engagement, an autonomous unmanned aircraft would simply act. The drone would be able to make its decision quickly, but also based on an immense amount of situational data that would take an actual pilot much longer to process.

In 2017, two foreign journalists for Turkish state TV station TRT World and their interpreter spent two months in a Myanmar jail for attempting to fly a drone near the legislature. The three pleaded guilty to breaching the colonial-era Aircraft Act.

The offense would be a misdemeanor if the drone operator takes photos or videos or gathers other information without permission. Republican Rep. Diane St. Onge said her bill would not affect news media using drones to cover emergencies, and that a separate bill under consideration would set out permitted uses of drones by the media and others.

When the milk had to sit quiet for about an hour to allow the culture to work, Shari served a couple of bowls of cereal, one with Honey's milk and the other with Honey's yogurt. Dane Lambson walked to an adjacent room where the couple keeps a large refrigerator full of cheese. He talked about the different types of cheese he has made over the years. While his mother made cheese when he was growing up, he did not learn until 10 years after he and Shari got married. They got a milk cow and fed their five children homemade dairy produce.

The U.S. Drug Enforcement Administration said in a recent annual report that drones are not often used to smuggle drugs from Mexico because they can only carry small loads, though it said they may become more common. In 2015, two people pleaded guilty to dropping 28 pounds (62 kilograms) of heroin from a drone in the border town of Calexico, California. That same year, Border Patrol agents in San Luis, Arizona, spotted a drone dropping bundles with 30 pounds (66 kilograms) of marijuana.

The dawn of autonomous robots brings a question of automated modeling of robot behavior such that the learned robot capabilities can be used to plan robot activities. To bridge the continuous world of sensor readings and control signals with the symbolic world of planning, one needs to identify robot activities as somehow compact behaviors that can be repeated later when a given activity is planned to be performed. In this paper we focus on identifying activities from a sequence of sensor reading and corresponding control signals by using the methods of machine learning, both supervised and unsupervised. The methods are experimentally evaluated using data from a flying drone.