Rerun’s Post

Yes, you have a good way to transmit/process data by using advanced technologies. But we need metadata to show the power of data. Can your product, or any other AI products you know, interpret and answer the following Chinese-English multilingual questions of BI? "Who, in the Ontario province of Canada, has new US patents granted on the nearest Tuesday, when the USPTO releases the newly granted US patents on a weekly basis?" "Who, in the "江蘇‘’ province of China, has new US patents granted on the nearest Tuesday, when the USPTO releases the newly granted US patents on a weekly basis?" With our intellectual property (IP), we can answer. Do you or any of your contacts need our expertise and our IP? We're selling, NOT just talking. Thanks. Here is our pitch deck: "Experiment results showed that with our intellectual property (IP), a copyrighted multilingual metadata, we are doing what AI, like ChatGPT, can't do in data analytics. Without metadata, NO data can be found/retrieved, even by the most advanced technologies, like AI, supercomputers, etc. https://lnkd.in/g-aJFnXR Our IP can also make your information service UNIQUE in the world." P.S. We wrote this post manually, without using any AI tool.

Path planning is categorized as a decision-making step in the Autonomous Robotic domain. To find the optimal path, the A*search algorithm with its heuristic property is used. Heuristic value can be anything that indicates which of the potential next moves is the most promising to explore (It makes a search more reliable to move closer to the goal). It significantly outperforms Breath First search (BFS). Two examples of implementing a path-finding algorithm on Montreal and Manhattan Maps are shown below. The red line indicates the optimal from the start location to the goal location. #robotics #control

Hello robot enthusiasts 🚀!. The new episode of the series Building an Autonomous Robot from Scratch 🤖 has just been released. In this episode, we look into developing our first ROS 2 package for our robot. We develop a ROS driver which communicates with the Arduino motor controller that we have developed over the previous episodes. With this driver, we can now control our robot using velocity commands via any computer that is connected to the same network as the robot. 🎥 Check out the episode here: https://lnkd.in/e9vPwRbk 🔗 Perceptron GitHub Repo: https://lnkd.in/e7Tpz3Wr Join me on this exciting adventure! 💡🔧🤖 #robotics #diy #autonomousrobots #techprojects #PerceptronBot

Time of Flight (ToF) cameras operate by transmitting the light source and receiving the reflected light source. It ensures they calculate the depth based on the reflected light intensity and the time taken for the light to return to the camera. So, they can empower embedded vision applications to leverage the real-time depth and visual information to capture and use high-quality imaging data. These cameras have seen wide adoption in recent years, especially in the industrial sector, with systems like Modern Automated Guided Vehicles and Autonomous Mobile Robots playing significant roles. However, one of the biggest challenges Time-of-Flight (ToF) faces is multi-camera interference (MCI). In this blog, you’ll discover why MCI is such a big thorn in the side of ToF technology – and how you can overcome it. https://hubs.li/Q02lG6vX0

Autonomous robots are becoming a more common sight in workplaces and urban centers for delivering and retrieving assets normally handled by humans. The efficiency and effectiveness of this robotic work force is far from perfected and requires accurate positioning in a multitude of environments.     Autonomous robots must be able to read and react to any environment if tasked with making outdoor deliveries. AUTO provides decimeter-level accuracy even in challenging situations for vision systems, such as tunnels or weather conditions causing poor visibility.     Learn more about AUTO here: https://lnkd.in/gPqw3pVk #TDK #TDKTrustedPositioning #TrustedPositioning  #sensors #PositioningSoftware  #INS #GNSS #InertialNavigation #LocationSystems #RTLS #AUTO #Robotics 

The latest in underwater robots: remote and autonomous machines making waves🌊 Continuing advancement in the underwater robotics sector is enabling an ever-growing range of complex operations to be conducted more efficiently, safely and sustainably than by traditional methods. Full article👉https://ow.ly/jBYi50SUFtE

With the advent of Autonomous Mobile Robots (AMRs) in public areas such as malls and airports, their harmonious coexistence with humans is crucial. AMRs must operate in a manner that ensures human safety, comfort, and acceptability to reduce stress. This is called Human Aware Navigation. This study introduces a framework for AMR navigation that prioritizes safety and human comfort in such environments, utilizing an enhanced Potential Field approach augmented by Fuzzy Inference Systems. To achieve a smooth AMR trajectory, the framework employs these systems based on AMR, human, and obstacle information. The proposed approach is tested across various scenarios, including complex, cluttered environments that mimic practical situations. Simulation results demonstrate that AMRs using the proposed method navigate human-rich environments safely and comfortably while mitigating common issues with Potential Field-based approaches, such as local minima and obstacles near the goal.

Robot Autonomous Navigation using ROS