An efficient characterization of scientifically significant locations is essential prior to the return of humans to the Moon. The highest resolution imagery acquired from orbit of south-polar shadowed regions and other relevant locations remains, at best, an order of magnitude larger than the characteristic length of most of the robotic systems to be deployed. This hinders the planning and successful implementation of prospecting missions and poses a high risk for the traverse of robots and humans, diminishing the potential overall scientific and commercial return of any mission. We herein present the design of a lightweight, compact, autonomous, and reusable lunar reconnaissance drone capable of assisting other ground-based robotic assets, and eventually humans, in the characterization and high-resolution mapping (~0.1 m/px) of particularly challenging and hard-to-access locations on the lunar surface. The proposed concept consists of two main subsystems: the drone and its service station. With a total combined wet mass of 100 kg, the system is capable of 11 flights without refueling the service station, enabling almost 9 km of accumulated flight distance. The deployment of such a system could significantly impact the efficiency of upcoming exploration missions, increasing the distance covered per day of exploration and significantly reducing the need for recurrent contacts with ground stations on Earth.

We study the problem of optimally routing plug-in electric and conventional fuel vehicles on a city level. In our model, commuters selfishly aim to minimize a local cost that combines travel time, from a fixed origin to a desired destination, and the monetary cost of using city facilities, parking or service stations. The traffic authority can influence the commuters' preferred routing choice by means of personalized discounts on parking tickets and on the energy price at service stations. We formalize the problem of designing these monetary incentives optimally as a large-scale bilevel game, where constraints arise at both levels due to the finite capacities of city facilities and incentives budget. Then, we develop an efficient decentralized solution scheme with convergence guarantees based on BIG Hype, a recently-proposed hypergradient-based algorithm for hierarchical games. Finally, we validate our model via numerical simulations over the Anaheim's network, and show that the proposed approach produces sensible results in terms of traffic decongestion and it is able to solve in minutes problems with more than 48000 variables and 110000 constraints.

This paper analyzes how the presence of service stations on highways affects traffic congestion. We focus on the problem of optimally designing a service station to achieve beneficial effects in terms of total traffic congestion and peak traffic reduction. Microsimulators cannot be used for this task due to their computational inefficiency. We propose a genetic algorithm based on the recently proposed CTMs, that efficiently describes the dynamics of a service station. Then, we leverage the algorithm to train a neural network capable of solving the same problem, avoiding implementing the CTMs. Finally, we examine two case studies to validate the capabilities and performance of our algorithms. In these simulations, we use real data extracted from Dutch highways.

Despite widespread use of artificial intelligence, only 11% of companies say they see a significant financial return on their investment, according a new study by Boston Consulting Group in partnership with MIT Sloan Management Review. The low yield was revealed in a global survey of more than 3,000 managers with 57% saying they have piloted or deployed AI, a significant increase over three years ago. The authors of the study reported that companies can get the basics of AI right with the right data, technology, talent and strategy, but still see low ROI. "Only when organizations add the ability to learn with AI do significant benefits become likely," the authors said. The elements of learning with AI require companies to have a combination of machines learning autonomously, humans teaching machines and machines teaching humans.

Royal Dutch Shell is heavily investing in research and development of artificial intelligence (AI), which it hopes will provide solutions to some of its most pressing challenges. From meeting the demands of a transitioning energy market, urgently in need of cleaner and more efficient power, to improving safety on the forecourts of its service stations, AI is at the top of the agenda. I have been working with Shell over the past months to help create a data strategy, which gave me a thorough insight into Shell's AI priorities and initiatives. Current initiatives include deploying reinforcement learning in its exploration and drilling program, to reduce the cost of extracting the gas that still drives a significant proportion of its revenues. Elsewhere across its global business, Shell is rolling out AI at its public electric car charging stations, to manage the shifting demand for power throughout a day.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. The Gecko Gripper uses the same adhesive system for gripping as the feet of a gecko, with millions of fine fibers that adhere to the surface of the workpiece and generate strong van der Waals forces. And then Endeavor spoils everything by reminding us that "this is humor. Snow clearing is not the robot's primary mission."

Royal Dutch Shell is heavily investing in research and development of artificial intelligence (AI), which it hopes will provide solutions to some of its most pressing challenges. From meeting the demands of a transitioning energy market, urgently in need of cleaner and more efficient power, to improving safety on the forecourts of its service stations, AI is at the top of the agenda. I have been working with Shell over the past months to help create a data strategy, which gave me a thorough insight into Shell's AI priorities and initiatives. Current initiatives include deploying reinforcement learning in its exploration and drilling program, to reduce the cost of extracting the gas that still drives a significant proportion of its revenues. Elsewhere across its global business, Shell is rolling out AI at its public electric car charging stations, to manage the shifting demand for power throughout a day.

Royal Dutch Shell is heavily investing in research and development of artificial intelligence (AI), which it hopes will provide solutions to some of its most pressing challenges. From meeting the demands of a transitioning energy market, urgently in need of cleaner and more efficient power, to improving safety on the forecourts of its service stations, AI is at the top of the agenda. I have been working with Shell over the past months to help create a data strategy, which gave me a thorough insight into Shell's AI priorities and initiatives. Current initiatives include deploying reinforcement learning in its exploration and drilling program, to reduce the cost of extracting the gas that still drives a significant proportion of its revenues. Elsewhere across its global business, Shell is rolling out AI at its public electric car charging stations, to manage the shifting demand for power throughout a day.