Lifeboat Foundation

Tesla has added a discount to the new inventory of Model S and Model X vehicles and three years of free Supercharging for deliveries by the end of the quarter.

With the end of the quarter approaching, Tesla is looking to deliver better-looking financial results by not ending it with many vehicles in inventory.

To achieve this, Tesla has regularly applied special discounts or incentives to take delivery of new inventory vehicles by the end of the quarter.

Einride is trying to change how the world moves goods. The Sweden-based company with a growing U.S. presence combines battery-electric power with automation and data to develop freight’s future.

The company’s futuristic-looking equipment became a tourist attraction in New York City last week when it parked an electric truck and a cab-less Autonomous Electric Transport vehicle on West 23rd Street in Chelsea, where passersby stopped to take pictures with the electric freight movers.

While some companies focused on the future of transportation are taking more measured approaches, Einride’s leaders told FleetOwner that it is ready to move the freight world into the future now.

A team of researchers at Stanford University recently created Sonicverse, a simulated environment that includes both visual and auditory elements to train robots more realistically.

The Tesla and LVMH chiefs were joined by Maye Musk and Alexandre and Antoine Arnault at the ritzy Cheval Blanc hotel in the French capital.

Tesla CEO Elon Musk thinks the automaker’s market capitalization is directly tied to whether the automaker is able to solve autonomous driving.

Superfast, subatomic-sized particles called muons have been used to wirelessly navigate underground for the first time. By using muon-detecting ground stations synchronized with an underground muon-detecting receiver, researchers at the University of Tokyo were able to calculate the receiver’s position in the basement of a six-story building.

As GPS cannot penetrate rock or water, this new technology could be used in future search and rescue efforts, to monitor undersea volcanoes, and guide autonomous vehicles underground and underwater. The findings are published in the journal iScience.

GPS, the global positioning system, is a well-established navigation tool and offers an extensive list of positive applications, from safer air travel to real-time location mapping. However, it has some limitations. GPS signals are weaker at higher latitudes and can be jammed or spoofed (where a counterfeit signal replaces an authentic one). Signals can also be reflected off surfaces like walls, interfered with by trees, and can’t pass through buildings, rock or water.

Mercedes owners in the U.S. will soon add a new luxury to their already luxurious vehicles: ChatGPT. The automaker is adding OpenAI’s conversational AI agent to its MBUX infotainment system, though what it could possibly be needed for is hard to say.

U.S. owners of models that use MBUX will be able to opt into a beta program starting tomorrow, June 16, activating ChatGPT functionality. This will enable the highly versatile large language model to augment the car’s conversation skills. You can join up simply by telling your car “Hey Mercedes, I want to join the beta program.”

It’s not really clear what for, though. After all, a car is a pretty well constrained environment. People need to drive, navigate, and control their media and the car’s basic functions, and certainly a voice interface is sometimes the safest or best option for doing so without taking their eyes off the road.

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.