Calspan’s Post

Get ready for part 3 of the Autonomous Dictionary by Volvo! In this series, we help you better understand autonomy-related words and terms, one post at a time. Today, let’s look at our acronyms GNSS and H2H, and discuss what we mean when we talk about infrastructure. Do you have any words you’d like us to cover? Let us know in the comments! #VolvoAutonomousSolutions #GuidedBySafety #AutonomousDictionary

Testing with DanaDynamics

Learn how to deploy autonomous navigation on #ROSbot XL with our new open source #ROS 2 project 👇 https://lnkd.in/dhSKBx7g Mapping and autonomous navigation are central subjects in mobile robotics and play a crucial role in many projects. Thanks to our #Docker images, you can now start to navigate autonomously with just a few commands, enjoying all the benefits of using #Nav2, #MPPI controller, and the ready-made Foxglove setup as a web user interface.

👏🏻 Once again, Festo surprises us with this amazing project from the Bionic Learning Network 🤖 Developments for digitalized, efficient and sustainable automation 🦾 #festobionic #bionicbee 🐝 #hannovermesse #automation #automatizacion #automacion #industrytrends #tendenciasindustriales #industry #industria #festo #engineering #ingenieria

Optimal Trajectory Planning for Autonomous Parking

【Efficient Vision Transformers for Autonomous Off-Road Perception Systems】 Full article: https://lnkd.in/g5_kiYMM (Authored by Max H. Faykus III, et al., from Clemson University, USA.) In autonomous vehicle research, the environment perception system is one of the key components of development. Environment perception systems allow the vehicle to understand its surroundings. Deep learning computer vision algorithms have been shown to be the strongest tool for translating camera data into accurate and safe traversability decisions regarding the environment surrounding a vehicle. This work evaluates the viability of multiple deep learning architectures for use in off-road Unmanned Ground Vehicles (UGVs). The results demonstrate viability of Vision #Transformer (ViT) architectures for off-road perception systems, having a strong segmentation accuracy, lower inference speed and memory footprint compared to previous results with #CNN architectures. #Semantic_Segmentation #Off_Road_Vision

“For autonomous technology to move forward, developers must be able to ensure and demonstrate safety and reliability.” Pilotless flight is one of the more interesting and debated topics in the #aerospace industry that is experiencing surges in innovation due to the injection of human-machine interface, AI, machine learning, and other (literally) space age technologies and technical implementations. Recognizing that consumer adoption of autonomous #aircraft and #VTOL involves overcoming skepticism and fear, developers are fielding incremental solutions to build confidence. Developers plan to extensively verify that the underlying machine learning systems and mechanical architectures can make the right decisions and act in synchronization, at the correct times, to produce the desired #flightsafety (and potentially, survival) outcomes. They understand success must be demonstrated over thousands of hours peppered with hundreds of emergency procedures. They’re doing this because they know consumers will not be “theorized” by aviation experts into blindly accepting safety in these new platforms – especially when safety is arguably the top condition of all phases of flight. Moreover, just because AI allows a computer to process inputs at super-speed, and just because that computer is joined to a flight control system, doesn’t mean that computer can necessarily create equal or better outcomes than its competitive “computer” – that is, the brain in a highly trained aviator. (Think “Sully” and ‘The Miracle on the Hudson’.) Acknowledging those factors, a practice of “human-supervised autonomy” using a remote operator is now being demonstrated as one of the next steps to building confidence that autonomous platforms can overcome the many challenges a skilled human pilot might face in aircraft operation in controlled and uncontrolled airspace. Further, as new and more miniaturized systems are developed to support autonomous operation, #AS9100 quality fabricators like #KLINGERIGI are providing precision parts for electronics #thermalmanagement, actuator #sealing solutions, and more to help developers design components that transform the idea of autonomy into reality. #klinger https://lnkd.in/gqTvbcv4

This comparison is the kind of things we like to see for any new product... Sensor Fusion will be a thing on many fields (e.g. autonomous navigation). https://lnkd.in/dijVFgdn

Robot Autonomous Navigation using ROS

2 Pillars Of An Autonomous Navigation Partnership: Electrification & Autonomy https://lnkd.in/eZjgbC8V