Deepak Gaur’s Post

Squeezed Edge YOLO was built for fast, energy-efficient object detection on edge computing platforms where autonomous navigation is needed.

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.

Elevate Your Innovation with Calspan Flight Testing Discover the future of autonomous flight with Calspan's Experimentation Platform for Enhanced Research and Testing (ExPERT). • Open System Architecture • Run-Time Assurance • Surrogate Test Platforms • Live-Virtual-Constructive

I invite to read below article: "Exploring the Feasibility of Autonomous Lighting Systems for Pedestrian Crossings in Off-Grid Areas". Link WEB: https://lnkd.in/dewPxBqD

Global autonomous navigation market size is expected to reach $10.89 Bn by 2028 at a rate of 19.6%, segmented as by solution, sensing system, software, processing unit For more Info: https://lnkd.in/gP-MEQiD #marketresearch #marketintelligence #marketreport #industryanalysis #TheBusinessResearchCompany #TBRC #GlobalMarketModel

Read a paper about a framework for planning and executing autonomous parking maneuvers in complex parking scenarios. Some advantages of autonomous parking include the ability to move and park in tighter spaces than a human driver, automatically finding the fastest and shortest parking path, and allowing passengers to exit the vehicle and let it look for parking itself. More here: 🔗 https://loom.ly/guHd7SM

Urban Computing Lab has published a new paper in ACM Transactions on Software Engineering and Methodology 2024. This work is co-authored by Dr. Neelofar, Aldeida Aleti Title: Identifying and Explaining Safety-critical Scenarios for Autonomous Vehicles via Key Features Abstract: Ensuring the safety of autonomous vehicles (AVs) is of utmost importance, and testing them in simulated environments is a safer option than conducting in-field operational tests. However, generating an exhaustive test suite to identify critical test scenarios is computationally expensive, as the representation of each test is complex and contains various dynamic and static features, such as the AV under test, road participants (vehicles, pedestrians, and static obstacles), environmental factors (weather and light), and the road’s structural features (lanes, turns, road speed, etc.). In this article, we present a systematic technique that uses Instance Space Analysis (ISA) to identify the significant features of test scenarios that affect their ability to reveal the unsafe behaviour of AVs. ISA identifies the features that best differentiate safety-critical scenarios from normal driving and visualises the impact of these features on test scenario outcomes (safe/unsafe) in two dimensions. This visualisation helps to identify untested regions of the instance space and provides an indicator of the quality of the test suite in terms of the percentage of feature space covered by testing. To test the predictive ability of the identified features, we train five Machine Learning classifiers to classify test scenarios as safe or unsafe. The high precision, recall, and F1 scores indicate that our proposed approach is effective in predicting the outcome of a test scenario without executing it and can be used for test generation, selection, and prioritisation. The paper is available at https://lnkd.in/e4-x6fNg #AutonomousVehicles #InstanceSpaceAnalysis

Our Autonomous Systems and Robotics project, where we have developed advanced software for the TurtleBot3 robot using the Robot Operating System (ROS). In this project, we focused on creating a fully autonomous robot capable of navigating and mapping its environment in real-time. Implemented lane detection and computer vision techniques for analyzing camera images to identify lane markings.Developed sign detection algorithms for recognizing and responding to traffic signs using camera and LiDAR data Autonomous Driving of TurtleBot3 robot | SLAM | Lane & sign detection Robot Operating System (ROS).

Testing with DanaDynamics

Solito navigates pre-programmed routes and avoids obstacles. Autonomous Navigation Technologies (ANTs). Learn more @ sheyene.tech