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A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

#artificialintelligence

Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed.


MIT's Cheetah 3 robot is effectively blind

ZDNet

Chipmakers like Nvidia and Qualcomm have been busy building products to bring vision intelligence to robots, but in some scenarios, robots may be better off relying on other capabilities to navigate their surroundings. That's why the latest version of MIT's Cheetah robot, the Cheetah 3, is designed to move across rough terrain and through obstacles without relying on vision. "Vision can be noisy, slightly inaccurate, and sometimes not available, and if you rely too much on vision, your robot has to be very accurate in position and eventually will be slow," the robot's designer, MIT Associate Prof. Sangbae Kim, said in a release. "So we want the robot to rely more on tactile information. That way, it can handle unexpected obstacles while moving fast."


In US, a Virus-Era Ramadan Presents Obstacles, Opportunities

U.S. News

For Muslims in the United States, there is no other time more centered around gathering in congregation than the holy month of Ramadan. In every corner of the country, believers attend community iftar meals to break the fast and then pack neatly into tight rows for nightly prayers at the mosque. On weekends, especially, some may linger longer as they catch up, share in the pre-dawn suhoor meal and line up again for the fajr, dawn, prayers.


Pothole Detection for the Visually Impaired

IEEE Spectrum Robotics

Over the years, researchers and companies have invented plenty of devices to help people with visual impairments avoid objects such as a desk or chair. Many of these gadgets used ultrasonic sensors to detect such hazards. Just to name a few, there was the discontinued Pathsounder (which hung around a person's neck), the cumbersome NavBelt (worn around the waist), and the wheeled GuideCane. However, there's another type of obstacle that lurks underfoot--slight depressions in the ground such as steps, curbs, or divots that can cause a person to stumble or a wheelchair to suddenly turn awry. For these subtle features, most high-tech detection systems don't work very well.


Increasing Solar Energy Adoption Through AI Roof Detection

#artificialintelligence

Solar AI, a Singapore based startup incubated as a part of ENGIE Factory, collaborated with Omdena, to hyper-scale the deployment of distributed solar and the transition towards 100% renewables by modernizing the way rooftop solar is sold. Solar energy is a promising and freely available resource for managing the forthcoming energy crisis, without hurting the environment. There's enough solar energy hitting the Earth every hour to meet all of humanity's power needs for an entire year. The rooftop solar assessment process can be time consuming and expensive, taking anywhere between 1 hour to 2 full days to calculate the solar potential of each rooftop. In the solar industry, this has resulted in the cost of sales taking up to 30–40% of total project costs, significantly worsening the unit economics of solar projects.