Lifeboat Foundation

COPENHAGEN, June 14 (Reuters) — Swedish electric self-driving truck company Einride expects to reduce CO₂ emissions in Norway by 2,100 tonnes over the coming three years as it partners up with Scandinavia’s leading postal service, PostNord, the company said on Wednesday.

Norway has the world’s highest number of electric vehicles per head of population and aims for all heavy vehicles to be zero-emission by 2040, potentially cutting CO₂ emissions by 4.4 million tonnes or nearly 9% of the country’s annual emissions.

“Given Norway’s pioneering work in electrifying passenger vehicles, it’s only logical that they should take a leading role in the electrification of heavy-duty freight as well,” Einride CEO Robert Falck said.

The fascinating collective behaviors of biological systems have inspired extensive studies on shape assembly of robot swarms^6,7,8,9. One class of strategies widely studied in the literature are based on goal assignment in either centralized or distributed ways^10,11,12. Once a swarm of robots are assigned unique goal locations in a desired shape, the consequent task is simply to plan collision-free trajectories for the robots to reach their goal locations¹⁰ or conduct distributed formation control based on locally sensed information^6,13,14. It is notable that centralized goal assignment is inefficient to support large-scale swarms since the computational complexity increases rapidly as the number of robots increases^15,16. Moreover, when robots fail to function normally, additional algorithms for fault-tolerant detection and goal re-assignment are required to handle such situations¹⁷. As a comparison, distributed goal assignment can support large-scale swarms by decomposing the centralized assignment into multiple local ones^11,12. It also exhibits better robustness to robot faults. However, since distributed goal assignments are based on locally sensed information, conflicts among local assignments are inevitable and must be resolved by sophisticated algorithms such as local task swapping^11,12.

Another class of strategies for shape assembly that have also attracted extensive research attention are free of goal assignment^18,19,20,21. For instance, the method proposed in ref. ¹⁸ can assemble complex shapes using thousands of homogeneous robots. An interesting feature of this method is that it does not rely on external global positioning systems. Instead, it establishes a local positioning system based on a small number of pre-localized seed robots. As a consequence of the local positioning system, the proposed edge-following control method requires that only the robots on the edge of a swarm can move while those inside must stay stationary. The method in ref. ¹⁹ can generate swarm shapes spontaneously from a reaction-diffusion network similar to embryogenesis in nature. However, this method is not able to generate user-specified shapes precisely. The method in ref. ²¹ can aggregate robots on the frontier of shapes based on saliency detection. The user-defined shape is specified by a digital light projector. An interesting feature of this method is that it does not require centralized edge detectors. Instead, edge detection is realized in a distributed manner by fusing the beliefs of a robot with its neighbors. However, since the robots cannot self-localize themselves relative to the desired shape, they make use of random walks to search for the edges, which would lead to random trajectories. Another class of methods that do not require goal assignment is based on artificial potential fields^22,23,24,25. One limitation of this class of methods is that robots may easily get trapped in local minima, making it difficult to assemble nonconvex complex shapes.

Here, we propose a strategy for shape assembly of robot swarms based on the idea of mean-shift exploration: when a robot is surrounded by neighboring robots and unoccupied locations, it would actively give up its current location by exploring the highest density of nearby unoccupied locations in the desired shape. This idea does not rely on goal assignment. It is realized by adapting the mean-shift algorithm^26,27,28, which is an optimization technique widely used in machine learning for locating the maxima of a density function. Moreover, a distributed negotiation mechanism is designed to allow robots to negotiate the final desired shape with their neighbors in a distributed manner. This negotiation mechanism enables the swarm to maneuver while maintaining a desired shape based on a small number of informed robots. The proposed strategy empowers robot swarms to assemble nonconvex complex shapes with strong adaptability and high efficiency, as verified by numerical simulation results and real-world experiments with swarms of 50 ground robots. The strategy can be adapted to generate interesting behaviors including shape regeneration, cooperative cargo transportation, and complex environment exploration.

Researchers claim the new electronic chip can mimic human vision and memory, which could help make self-driving cars smarter.

Researchers at the Royal Melbourne Institute of Technology (RMIT) have successfully developed a tiny electronic device that, they claim, can mimic human vision and memory. This could be a promising step to one-day developing sophisticated ways to make rapid decision-making in self-driving cars.

Continue reading “Innovative human eye-mimicking chip has been developed at RMIT” »

Engineers at MIT say they have developed a new motor that could be used to electrify large aircraft, significantly reducing their carbon footprint with the help of innovative new electric propulsion technology.

The 1-megawatt motor has already undergone design and testing of its primary components, which the MIT team says helps demonstrate that its power generation is comparable to current small aircraft engines.

Every year, pollution from carbon dioxide in excess of 850 million tons is produced by the aviation industry. If left unmitigated, those levels could increase by as much as three times by mid-century, concerns that have prompted caps on the carbon dioxide emissions of international flights that have been instituted in recent years.

Sphere Studios has developed a brand new type of cinema camera called The Big Sky. It features a single 316-megapixel HDR image sensor that the company says is a 40x resolution increase over existing 4K cameras and PetaPixel was given an exclusive look at the incredible technology.

The Big Sky cameras are not up for sale (yet) but they are meeting with film companies and filmmakers to find ways to bring the technology to the home-entertainment world. A discussion we had on-site revolved around gimbals mounted on helicopters, airplanes, and automobiles and how those systems, even “the best” still experience some jitter/vibration which is often stabilized which causes the footage to be cropped in.

Continue reading “Sphere Studios’ Big Sky Cinema Camera Features an Insane 18K Sensor” »

Researchers led by the University of California San Diego have developed a new model that trains four-legged robots to see more clearly in 3D. The advance enabled a robot to autonomously cross challenging terrain with ease—including stairs, rocky ground and gap-filled paths—while clearing obstacles in its way.

The researchers will present their work at the 2023 Conference on Computer Vision and Pattern Recognition (CVPR), which will take place from June 18 to 22 in Vancouver, Canada.

Continue reading “Four-legged robot traverses tricky terrains thanks to improved 3D vision” »

In the film “Top Gun: Maverick,” Maverick, played by Tom Cruise, is charged with training young pilots to complete a seemingly impossible mission—to fly their jets deep into a rocky canyon, staying so low to the ground they cannot be detected by radar, then rapidly climb out of the canyon at an extreme angle, avoiding the rock walls. Spoiler alert: With Maverick’s help, these human pilots accomplish their mission.

A machine, on the other hand, would struggle to complete the same pulse-pounding task. To an autonomous aircraft, for instance, the most straightforward path toward the target is in conflict with what the machine needs to do to avoid colliding with the canyon walls or staying undetected. Many existing AI methods aren’t able to overcome this conflict, known as the stabilize-avoid problem, and would be unable to reach their goal safely.

Continue reading “A new AI-based approach for controlling autonomous robots” »

The disposal of tires represents a significant burden on the environment, so companies like Marangoni developed methods to recycle and reuse old tires. Watch how retreading machines make old tires usable again.

Following is a transcript of the video.

Narrator: When your tire wears out, you take it to a shop where it’s tossed out for a new one. The discarded tire is typically recycled — ground up and chemically broken down to use as a building material in streets or parks. Some companies hope to recycle differently. For years, companies like Marangoni have been saving tire casings, replacing the old tread (the rubber that touches the ground) with new tread in a process called “retreading.” These tires are not only easier to make — they typically take 20% of the energy of creating a new tire — they perform well too, standing up to the same tests that one-use tires are subjected to.

Tesla has unveiled a new retail concept called ‘Giga Laboratory’, which appears to be a showcase of its manufacturing capacity.

While Tesla announced a move to online sales and moved back from physical retail back in 2019, the automaker has, in practice, continued to rely heavily on retail locations as part of its marketing effort.

In fact, Tesla’s number of physical locations (retail and service) has more than doubled to now 1,000 locations around the work over the last 3 years.