On September 28, Hubsan will launch a modified drone “Black Hawk 1 Obstacle Avoidance Edition”, priced at RMB 1,899 for the dual-battery standard version and RMB 2,099 for the dual-battery high-end version.

According to reports, the Black Hawk 1 obstacle avoidance version is equipped with the second generation of visual perception obstacle avoidance, equipped with forward and bottom two-way obstacle avoidance, and supports obstacle avoidance, flying around, and obstacle avoidance and braking.

In terms of other configurations, the Habson Black Hawk 1 obstacle avoidance version is basically the same as the original version. The drone adopts a simple black design and is equipped with a single camera. The image transmission uses Habson Syncleas 2.0 digital image transmission, supports 4G, and is said to be able to do 9km video transmission.

The drone is equipped with a 1/2.6-inch 12MP image sensor that supports up to 4K 30-frame shooting; it is equipped with a “three-axis mechanical stabilization gimbal”; it has a slow battery life of 37 minutes and a hover battery life of 33 minutes. It comes standard with two pieces 3200mAh lithium battery.

In addition, users can choose a 5000mAh long-lasting battery, which is said to hover for 45 minutes and cruise at slow speed for 53 minutes.

The prices of the various versions of the Hubsan Blackhawk 1 UAV attached are as follows:

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At present, drones have been widely used in firefighting and rescue scenarios, such as taking aerial photos, hoisting fire hoses to skyscrapers, or dropping fire extinguishing agents in remote areas to prevent forest fires from spreading. However, these drones can only be flown within a safe distance of the fire. Inside a burning building, temperatures can reach as high as 1,000 degrees Celsius, and such extreme heat can cause the frame of an ordinary drone to melt and electronic components to fail. Commercial drones can only take pictures of the fire from a distance and cannot provide more information.

To allow drones to get closer to fire, researchers at the Swiss Federal Laboratory for Materials Science and Technology (Empa) and Imperial College London have developed a high-temperature-resistant drone called the FireDrone. They draw inspiration from nature and animals such as penguins and arctic foxes can survive extreme temperatures because they have a layer of blubber or fur to insulate them from the cold.

The heat-resistant drone uses a type of airgel, an ultra-light material that consists almost entirely of air-filled pores surrounded by a polymer substance, for its insulation. The materials researchers chose an aerogel based on polyimide plastic, which is also used by NASA to insulate space suits. For the drone itself, the scientists created a composite material composed of polyimide and silica, reinforced with glass fibers.

A prototype of the FireDrone has already undergone preliminary tests at Empa’s flying field in Dübendorf near Zurich. Empa says the flight characteristics and controllability of the roughly 50-centimeter-tall drone remain excellent after adding an airgel heat shield, an additional built-in cooling system, and an aluminum shell that reflects heat. The FireDrone has also undergone several successful tests at its training center in Andelfingen in Zurich, with drone operators flying the device directly into a gas flame in a large metal bowl. “Even after numerous flights, the FireDrone’s electronics, thermal imaging camera and CO2 sensor were undamaged and further tests could continue,” said David Häusermann from Empa.

Next, the researchers will further test the FireDrone in other fire situations. In theory, the drone could also be used in extremely cold environments, such as the polar regions and glaciers. The research team has already tested the flying device in a glacier tunnel in Switzerland to study how the system behaves at low temperatures. Currently, some potential industry partners are already in discussions with the research team to further develop the prototype.

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Each agent broadcasts its trajectory so that the other drones know where it intends to go. The agents then consider each other’s trajectories as they optimize their own to ensure they do not collide.

But when the team tested the system on real drones, they found that if the drone didn’t have up-to-date information about its partner’s trajectory, it could inadvertently choose a path that led to a collision. The researchers modified their system and are now rolling out Robust MADER, a multi-agent trajectory planner that generates collision-free trajectories even when communication between agents is delayed.

“MADER works well in simulations, but it has not been tested in hardware. Therefore, we built a batch of drones and started flying them. The drones need to talk to each other to share trajectories, but once you start flying, you quickly realize that there are always communication delays that introduce some glitches,” said Kota Kondo, a graduate student in aeronautics and astronautics.

Making multiple drones work together

When multiple drones are working together in the same airspace, they run the risk of colliding. But now researchers at AeroAstro have created a trajectory planning system that allows drones in the same airspace to always choose a safe path forward. Credit: Courtesy of the researchers

The algorithm includes a delay-checking step, during which the drone waits a certain amount of time before committing to a new, optimized trajectory. If it receives additional trajectory information from other drones during the delay, it may abandon its new trajectory and restart the optimization process.

When Kondo and his collaborators tested Robust MADER in flight experiments with simulated and real drones, it achieved a 100 percent success rate in generating collision-free trajectories. While the drones’ flight times were somewhat slower than some other methods, no other baseline could guarantee safety.

“If you want to fly safer, you have to be careful, so it makes sense that if you don’t want to collide with an obstacle, it will take longer to get to your destination.” Kondo said, “If you collide with something, it doesn’t really matter how fast you go, because you won’t get to your destination.”

Kondo, along with postdoctoral fellow Jesus Tordesillas, graduate student Parker C. Lusk, MIT undergraduates Reinaldo Figueroa, Juan Rached and Joseph Merkel, and senior author Richard C. Maclaurin Professor of Aeronautics and Astronautics and Information and Decision Systems Laboratory (LIDS) principal investigator and member of the MIT-IBM Watson AI Lab, Jonathan P. How, co-authored the paper. The research will be presented at the International Conference on Robotics and Automation.

Planning trajectories

MADER is an asynchronous, decentralized, multi-agent trajectory planner. This means that each UAV develops its own trajectory, and while all agents must agree on each new trajectory, they do not need to agree at the same time. This makes MADER more scalable than other approaches, since it is difficult for thousands of UAVs to agree on a trajectory at the same time. Because of its decentralized nature, the system will also work better in realistic environments where drones may fly far from a central computer.

With MADER, each drone optimizes a new trajectory using an algorithm that incorporates the trajectories it receives from other agents. By constantly optimizing and broadcasting their new trajectories, drones can avoid collisions.

However, perhaps an agent shared its new trajectory a few seconds ago, but its companion did not receive it immediately due to communication delays. In real-world environments, signals are often delayed by environmental factors such as interference from other devices or stormy weather. Due to this inevitable delay, a drone may inadvertently submit a new trajectory that puts it on a collision course.

Robust MADER completely prevents such collisions because each agent has two available trajectories. It keeps one trajectory that it knows is safe and that it has checked for potential collisions. While flying along the original trajectory, the drone optimizes a new trajectory, but it will not commit to the new trajectory until it has completed the delayed check step.

During the delayed check, the drone spends a fixed amount of time double-checking communications from other agents to see if its new trajectory is safe. If it detects a potential collision, it abandons the new trajectory and starts the optimization process again. The length of the delayed checking period depends on the distance between agents and environmental factors that may impede communication. For example, if the agents are miles apart, then the delayed check period needs to be longer.

Eliminating collisions completely

The researchers tested their new approach by running hundreds of simulations in which they artificially introduced communication delays. In each simulation, Robust MADER was 100% successful in generating collision-free trajectories, while all baselines caused collisions.

The researchers also built six drones and two aerial obstacles and tested Robust MADER in a multi-agent flight environment. they found that while using the original version of MADER in this environment resulted in seven collisions, Robust MADER did not cause a single crash in any of the hardware experiments.

“Until you actually fly the hardware, you don’t know what might cause problems. Because we know there’s a difference between simulation and hardware, we made the algorithm robust so it works in real drones, and it’s very rewarding to see that in practice,” Kondo said.

Using Robust MADER, the drones were able to fly 3.4 meters per second, although their average travel time was slightly longer than some baselines. But no method has been completely collision-free in every experiment.

In the future, Kondo and his collaborators hope to put Robust MADER to the test outdoors, where there are many obstacles and types of noise that can interfere with communication. They also want to equip the drones with vision sensors so that they can detect other agents or obstacles, predict their movements, and incorporate that information into trajectory optimization.

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