The battery is a key component of autonomous robots. Its performance limits the robot's safety and reliability. Unlike liquid-fuel, a battery, as a chemical device, exhibits complicated features, including (i) capacity fade over successive recharges and (ii) increasing discharge rate as the state of charge (SOC) goes down for a given power demand. Existing formal verification studies of autonomous robots, when considering energy constraints, formalise the energy component in a generic manner such that the battery features are overlooked. In this paper, we model an unmanned aerial vehicle (UA V) inspection mission on a wind farm and via probabilistic model checking in PRISM show (i) how the battery features may affect the verification results significantly in practical cases; and (ii) how the battery features, together with dynamic environments and battery safety strategies, jointly affect the verification results. Potential solutions to explicitly integrate battery prognostics and health management (PHM) with formal verification of autonomous robots are also discussed to motivate future work. Keywords: Formal verification · Probabilistic model checking · PRISM · Autonomous systems · Unmanned aerial vehicle · Battery PHM. 1 Introduction Autonomous robots, such as unmanned aerial vehicles (UA V) (commonly termed drones 3), unmanned underwater vehicles (UUV), self-driving cars and legged-robots, obtain increasingly widespread applications in many domains [14].

A latent function decomposition method is proposed for forecasting the capacity of lithium-ion battery cells. The method uses the Multi-Output Gaussian Process, a generative machine learning framework for multi-task and transfer learning. The MCGP decomposes the available capacity trends from multiple battery cells into latent functions. The latent functions are then convolved over kernel smoothers to reconstruct and/or forecast capacity trends of the battery cells. Besides the high prediction accuracy the proposed method possesses, it provides uncertainty information for the predictions and captures nontrivial cross-correlations between capacity trends of different battery cells. These two merits make the proposed MCGP a very reliable and practical solution for applications that use battery cell packs. The MCGP is derived and compared to benchmark methods on an experimental lithium-ion battery cells data. The results show the effectiveness of the proposed method.

Alexa-enabled devices like the Echo or Echo Dot can be used to control smart home products such as the Ring Alarm Kit. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. Our picks and opinions are independent from USA TODAY's newsroom and any business incentives. Prime Day is a great opportunity to find money-saving deals on smart home products that can be controlled using Amazon Alexa on your Echo device. From smart doorbells to pressure cookers, here are the best Alexa-compatible Prime Day deals of 2019.

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

The aluminum-bodied robot weighs in at 16.5 kg (36 lb), and can run for a claimed five hours on one eight-hour charge of its user-swappable 28.8-V/7.2-Ah And while the prototype that we saw in Montreal at the 2019 International Conference on Robotics and Automation had six dual-purpose composite "legs," more swimming-efficient vinyl/steel-spring flippers can be substituted if it's going to only be used underwater – a place where it should be more eco-friendly than traditional remote-operated vehicles (ROVs).

Last year RISC-V cores made it into low-cost hardware with neural network and audio accelerator to speed up artificial intelligence workloads at the edge such as object recognition, and speech processing. More precisely, Kendryte K210 dual-core RISC-V processor was found in Sipeed MAIX modules and boards going for $5 and up. Since then a few other variants and kits have been made available including Seeed Studio Grove AI HAT that works connected to a Raspberry Pi or in standalone mode. Seeed Studio has now released another board with Kendryte K210 RISC-V AI processor, but based on Arduino UNO form factor and equipped with an ESP32 module for WiFi and Bluetooth connectivity. Typical applications would include smart home (robot cleaners or smart speakers), medical devices, factory 4.0 (intelligent sorting or monitoring of electrical equipment), as well as agriculture, and education.

Visual inspection of underwater structures by vehicles, e.g. remotely operated vehicles (ROVs), plays an important role in scientific, military, and commercial sectors. However, the automatic extraction of information using software tools is hindered by the characteristics of water which degrade the quality of captured videos. As a contribution for restoring the color of underwater images, Underwater Denoising Autoencoder (UDAE) model is developed using a denoising autoencoder with U-Net architecture. The proposed network takes into consideration the accuracy and the computation cost to enable real-time implementation on underwater visual tasks using end-to-end autoencoder network. Underwater vehicles perception is improved by reconstructing captured frames; hence obtaining better performance in underwater tasks. Related learning methods use generative adversarial networks (GANs) to generate color corrected underwater images, and to our knowledge this paper is the first to deal with a single autoencoder capable of producing same or better results. Moreover, image pairs are constructed for training the proposed network, where it is hard to obtain such dataset from underwater scenery. At the end, the proposed model is compared to a state-of-the-art method.

This post was done in partnership with Wirecutter. When readers choose to buy Wirecutter's independently chosen editorial picks, Wirecutter and Engadget may earn affiliate commission. If you want to see who's on the other side of your door without having to get up and look yourself, then the Ring Video Doorbell 2 is the best choice for most everyone. It lets you screen (and record) visitors and keep an eye out for package deliveries. Motion and ring alerts to a smartphone are typically fast, audio and 1080p video are clear, and the Ring 2 can be powered by either standard doorbell wiring or a removable rechargeable battery. The Ring Video Doorbell 2 performs like a cross between a modestly aggressive guard dog and a trusty digital butler. In addition to notifying you--audibly and via smartphone--of activity, it records all motion events to the cloud, letting you view those recordings (as well as live video) on your phone or computer any time. It's also compatible with a good number of smart-home devices, platforms, and monitored security systems. Though video recording and storage require a subscription, the $30 annual fee (a mere 8¢ per day) for 60 days of unlimited video storage is downright cheap compared with the competition. We like the Ring Video Doorbell Pro for all the reasons we like the Ring 2. Additionally, it has a much slimmer and sleeker design that will fit in more doorframes and includes the option for customized motion-detection zones.

In this paper, we develop a multi-agent reinforcement learning (MARL) framework to obtain online power control policies for a large energy harvesting (EH) multiple access channel, when only the causal information about the EH process and wireless channel is available. In the proposed framework, we model the online power control problem as a discrete-time mean-field game (MFG), and leverage the deep reinforcement learning technique to learn the stationary solution of the game in a distributed fashion. We analytically show that the proposed procedure converges to the unique stationary solution of the MFG. Using the proposed framework, the power control policies are learned in a completely distributed fashion. In order to benchmark the performance of the distributed policies, we also develop a deep neural network (DNN) based centralized as well as distributed online power control schemes. Our simulation results show the efficacy of the proposed power control policies. In particular, the DNN based centralized power control policies provide a very good performance for large EH networks for which the design of optimal policies is intractable using the conventional methods such as Markov decision processes. Further, performance of both the distributed policies is close to the throughput achieved by the centralized policies. The work in this paper will appear in part at IEEE ICASSP 2019 [1] and IEEE WiOpt 2019 [2]. This research has been partly supported by the ERC-PoC 727682 CacheMire project. I. INTRODUCTION Internet-of-things (IoT) [3] networks connect a large number of low power sensors whose lifespan is typically limited by the energy that can be stored in their batteries. In this context, the advent of the energy harvesting (EH) technology [4] promises to prolong the lifespan of IoT networks by enabling the nodes to operate by harvesting energy from environmental sources, e.g., the sun, the wind, etc.

This post was done in partnership with Wirecutter. When readers choose to buy Wirecutter's independently chosen editorial picks, Wirecutter and Engadget may earn affiliate commission. Wireless TV headphones allow you to enjoy TV shows, movies, and video games without disturbing people around you. After spending dozens of hours researching the available options and testing 20 systems, we're confident that the Sennheiser RS 165 is the best one available today. It's easy to set up, sounds much better than the competition, and produces almost no latency between the audio and video (a major problem with many systems). The Sennheiser RS 165 is the best-sounding wireless TV headphone system we tested, and unlike with most of the competition, we didn't detect any noticeable delay between audio and the video we watched, making for the best experience. The lightweight headphones are comfortable to wear, easy to charge, and easy to add to most existing TVs or home theater setups. The rechargeable batteries last long enough to make it through several movies.