@MrStickman

We put together a robotics overview for business leaders, operators, procurement teams, investors, and executives who want to understand which robots are actually being deployed, which are still early, and where the industry is heading. The goal is not to make a technical ranking or a hype list. It is to explain the major categories of real-world robotics in a way that can be shared with people outside the robotics field. The overview covers: Boston Dynamics Spot — industrial inspection quadrupeds ANYbotics ANYmal — rugged inspection robots for energy, mining, chemicals, and heavy industry Agility Robotics Digit — logistics humanoids Figure 03 — general-purpose humanoids and embodied AI Boston Dynamics Atlas — all-electric humanoid mobility and manipulation Tesla Optimus — vertically integrated humanoid robotics strategy Unitree G1 — lower-cost humanoid research and education platform Universal Robots UR Series — collaborative robot arms for machine tending, packaging, assembly, and small manufacturers Amazon Proteus — autonomous mobile warehouse robots for logistics facilities Intuitive da Vinci 5 — surgical robotics and robotic-assisted surgery The main article is the general overview, and we are also building individual deep dives for each robot so non-technical readers can understand the business case, deployment maturity, pricing context, use cases, risks, and hardware/software stack behind each system. The audience is intentionally non-technical. It is meant to be something robotics professionals, engineers, founders, or operators can share with leadership teams, clients, or colleagues who need a grounded introduction without reading a robotics textbook. Disclosure: I’m affiliated with Black Scarab, where the article is published. The article is free to read and does not require signup. Most of the deep dives are already live. The Intuitive da Vinci 5 deep dive is still in progress and will complete the series. Full overview: https://www.blackscarab.ai/insights/top-10-robots-edge-ai-automation-humanoid-robotics submitted by /u/rgc4444 [link] [Kommentare]

I've recently completed the assembly of a SunFounder PiCar-X and am currently running it on a legacy Raspberry Pi B. I have the base movement and motor control working and am currently prepping to get it chasing ArUco/AprilTags this coming week. I'm looking to evolve this platform into something capable of SLAM and eventually Structure from Motion (SfM). I'd love to get some community advice on the best way to handle these upgrades: Traction The stock wheels are quite slippery. Has anyone found direct-fit replacement tires or wheels that offer better grip on smooth indoor surfaces? Odometry Since the stock motors lack encoders, my dead reckoning is non-existent. Should I attempt to mount external encoders to these motors, or is it better to swap out the motor/gearbox assembly entirely for something with integrated feedback? IMU for SLAM I'm planning to add an accelerometer/gyroscope. Any specific sensors (such as the BNO055 vs. MPU6050) that are currently considered the "gold standard" for stability and ease of integration on a Raspberry Pi? Computer Vision The current camera resolution is limiting for SfM. Any recommendations for a higher-resolution CSI or USB camera that fits well within the PiCar's chassis? ROS 2 / Distributed Computing A specific question on the software side: I'm planning to move this platform to ROS 2. Given that I'm working with a legacy Raspberry Pi B, is this a lost cause, or should I keep the Pi as a low-level hardware node and offload the heavy ROS 2 processing, SLAM, and visualization tasks to a more powerful machine on my network? If a distributed setup is the preferred approach, what does the typical workflow look like? For example: Pi handles motor control, sensors, and camera acquisition ROS 2 nodes run on a desktop/laptop workstation Visualization and mapping performed via RViz on the workstation Communication over Wi-Fi using DDS Is this the recommended architecture, or are there better approaches for a platform like the PiCar-X? General Advice Any feedback on the hardware upgrade path, software architecture, or general "gotchas" with this kit would be greatly appreciated. Thanks in advance! submitted by /u/okineedaplan [link] [Kommentare]

This machine takes around four seconds for each solve. To reach that speed I had to use the kociemba algorithm, which can find a solution of around 20 moves for all scrambles. It took me a really long time to complete this so I would appreciate it if you show it some love! I made this when I was around 15. Please ask questions! submitted by /u/Henry517 [link] [Kommentare]