Large efforts have focused on ensuring that the controllers for mobile service robots follow proxemics and other social rules to ensure both safe and socially acceptable distance to pedestrians. Nonetheless, involuntary contact may be unavoidable when the robot travels in crowded areas or when encountering adversarial pedestrians. Freezing the robot in response to contact might be detrimental to bystanders' safety and prevents it from achieving its task. Unavoidable contacts must hence be controlled to ensure the safe and smooth travelling of robots in pedestrian alleys. We present a force-limited and obstacle avoidance controller integrated into a time-invariant dynamical system (DS) in a closed-loop force controller that let the robot react instantaneously to contact or to the sudden appearance of pedestrians. Mitigating the risk of collision is done by modulating the velocity commands upon detecting a contact and by absorbing part of the contact force through active compliant control when the robot bumps inadvertently against a pedestrian. We evaluated our method with a personal mobility robot -- Qolo -- showing contact mitigation with passive and active compliance. We showed the robot able to overcome an adversarial pedestrian within 9 N of the set limit contact force for speeds under 1 m/s. Moreover, we evaluated integrated obstacle avoidance proving the ability to advance without incurring any other collision.

Accelerating their net zero ambitions has been top of retailer's agendas for a number of years, with the sector considering itself one of the most ambitious when it comes to tackling climate change, but the real picture is alarming. According to research from Microsoft, only 28 per cent of UK retail organizations are currently on course to be net zero by 2050 – in stark contrast to the national average of 41 per cent. Furthermore, as 74 per cent of organizations within all sectors describe a'one foot in, one foot out' approach on sustainability, whereby strong ambitions have yet to be translated into meaningful action, retailers can't afford to be left behind. Harnessing available technology, such as data analytics and AI, to accurately record their progress is one of the fundamental steps to securing their continued success, in an ever more competitive marketplace. Contributing to the protection of the future of the planet is obviously the core reason for improving the sustainability performance of any business, but aside from that, retailers should view boosting their sustainability credentials as a way of remaining competitive and relevant in a fast-changing world.

Neuromorphic vision sensors have been extremely beneficial in developing energy-efficient intelligent systems for robotics and privacy-preserving security applications. There is an extreme need for devices to mimic the retina's photoreceptors that encode the light illumination into a sequence of spikes to develop such sensors. KAUST researchers have built an artificial electronic retina that can "see" in a similar way to the human vision system and can recognize handwritten digits. They have designed and fabricated an array of perovskite-based flexible photoreceptors that detect the visible light intensity via a change in electrical capacitance, mimicking the behavior of the eye's rod retina cells. Perovskite is very efficient at absorbing light and is already of great interest in solar cell research, while terpolymer has a high dielectric constant.

At the beginning of everything, there was energy. Energy is one of the most important things in the universe . Without usable energy, you could not read this article. Fusion power gives life to earth. Sun is a very big fusion reactor, without it earth would be frozen.

To be useful tools in real scenarios, humanoid robots must realize tasks dynamically. This means that they must be capable of applying substantial forces, rapidly swinging their limbs, and also mitigating impacts that may occur during the motion. Towards creating capable humanoids, this letter presents the leg of the robot TELLO and demonstrates how it embodies two new fundamental design concepts for dynamic legged robots. The limbs follows the principles of: (i) Cooperative Actuation (CA), by combining motors in differential configurations to increase the force capability of the limb. We demonstrate that the CA configuration requires half the motor torque to perform a jump in comparison to conventional serial design configurations. And (ii) proximal actuation, by placing heavy motors near the body to reduce the inertia of the limb. To quantify the effect of motor placement on the robot's dynamics, we introduce a novel metric entitle Centroidal Inertia Isotropy (CII). We show that the design of state-of-the-art dynamic legged robots empirically increase the CII to improve agility and facilitate model-based control. We hope this metric will enable a quantifiable way to design these machines in the future.

Hydropower has been stirring up controversies since the early 2000s. Despite being promoted as a solution to mitigate climate change, the hydropower bubble burst when researchers discovered in 2005 that hydropower dams are responsible for huge amounts of greenhouse gas emissions. Hydropower dams' walls restrict the flow of rivers and turn them into pools of stagnant water. Reservoir surfaces and turbines then release methane into the atmosphere. Methane makes up approximately 80 percent of the greenhouse gases emitted from hydropower dams, peaking in the first decade of the dams lifecycle.

Next time your power stays on during a severe weather event, you may have a machine learning model to thank. Researchers at the MIT-IBM Watson AI Lab are using artificial intelligence to solve power grid failures. The manager of the MIT-IBM Watson AI Lab, Jie Chen, and his colleagues have developed a machine learning model that works to analyze data collected from hundreds of thousands of sensors located across the U.S. power grid. The sensors, components of what is known as synchrophasor technology, compile vast amounts of real-time data related to electric current and voltage in order to monitor the health of the grid and locate anomalies that could cause outages. Synchrophasor analysis requires intensive computational resources due to the size and real-time nature of the data streams the sensors produce.

Listed below are the key technology trends impacting the smart grid in power theme, as identified by GlobalData. By clicking the Download Free Report button, you accept the terms and conditions and acknowledge that your data will be used as described in the GlobalData Privacy Policy By downloading this Report, you acknowledge that we may share your information with our white paper partners/sponsors who may contact you directly with information on their products and services. Visit our privacy policy for more information about our services, how we may use, process and share your personal data, including information on your rights in respect of your personal data and how you can unsubscribe from future marketing communications. Our services are intended for corporate subscribers and you warrant that the email address submitted is your corporate email address. AI has an important role to play in understanding demand, and generating predictions from non-dispatchable resources such as wind and solar and from wholesale prices.

The Global Machine Learning in Utilities Market report from MarketQuest.biz on the most recent survey report offers information and vital …

A new machine-learning technique could pinpoint potential power grid failures or cascading traffic bottlenecks in real time. Identifying a malfunction in the nation's power grid can be like trying to find a needle in an enormous haystack. Hundreds of thousands of interrelated sensors spread across the U.S. capture data on electric current, voltage, and other critical information in real time, often taking multiple recordings per second. Researchers at the MIT-IBM Watson AI Lab have devised a computationally efficient method that can automatically pinpoint anomalies in those data streams in real time. They demonstrated that their artificial intelligence method, which learns to model the interconnectedness of the power grid, is much better at detecting these glitches than some other popular techniques.