Miniature dual camera Full HD or 4K encoder boards support RGB and thermal cameras

Dual camera video encoder board

Z3 Technology has introduced several miniature dual-camera encoder boards with the PoE-capable FV2K-13A and FV4K-13A boards capable of handling two visible and/or thermal cameras at Full HD or 4K resolution respectively, along with the FV2K-15A and FV4K-15A variants equipped with low-profile connectors. All four models enable H.265/H.264 video streaming of a single or dual camera system, support ONVIF Profile S and T profile, and comply with the NDAA law in the US. This set of features as well as low power consumption and the lightweight designs make the boards suitable for UAV, inspection, surveillance, and other weight-constrained applications. Z3 Technology FV2K-13A and FV4K-13A dual-camera encoder boards specifications: SoC – Ambarella CV22 quad-core Cortex-A53 processor with 4Kp60 encoder (see PDF product brief) Storage – MicroSD, USB, or NFS Camera interfaces FV2K-13A Visible Camera Formats – Sony FCB-EV7520(A) and FCB-EV9500L HD and SD IR Formats – RS170, NTSC/PAL, and LVDS FV4K-13A Visible […]

$199+ NVIDIA Jetson Orin Nano system-on-module delivers up to 40 TOPS

NVIDIA Jetson Orin Nano

NVIDIA Jetson Orin Nano system-on-module (SoM) is an update to the Jetson Nano entry-level Edge AI and robotics module that delivers up to 40 TOPS of AI performance, meaning it’s up to 80 times faster than the original module. The new SoM features an hexa-core Arm Cortex-A78AE processor, an up to 1024-core NVIDIA Ampere architecture GPU with 32 Tensor cores, up to 8GB RAM, and the same 260-pin SO-DIMM connector found in the Jetson Orin NX modules. Two versions are offered with the following specifications: That means the Jetson Orin family has now six modules ranging from 20 TOPS to 275 TOPS. There’s no specific development kit for the Jetson Orin Nano SoM since it can be emulated on the NVIDIA Jetson AGX Orin developer kit, and supported by the JetPack 5.0.2 SDK based on Ubuntu 20.04. NVIDIA has tested some dense INT8 and FP16 pre-trained models from NGC and […]

Ochin Raspberry Pi CM4 carrier board is made for drones and robots

Ochin Raspberry Pi 4 carrier board for robots drones

There are plenty of carrier boards for Raspberry Pi CM4, but the Ochin looks a bit different, as it is specifically designed for drones and robots, and the compact carrier board exposes most interfaces through low-profile GHS connectors instead of standard ports or headers. About the size of the Raspberry Pi CM4 itself, the board also comes with a USB Type-C port to flash the eMMC flash, two MIPI CSI connectors and four USB 2.0 GHS connectors to add cameras to your robotics projects, and supports LiPo batteries. Ochin specifications: Supported modules – Raspberry Pi CM4 with Broadcom BCM2711 quad-core Cortex-A72 processor, up to 8GB RAM, up to 32GB eMMC flash (the CM4 Lite is not supported since there’s no microSD card on the board), 4Kp60 H.265 decode, 1080p30 H.264 encode, and optional WiFI 5 and Bluetooth 5.0 USB – 1x USB 2.0 Type-C port Camera I/F – 1x 4-lane […]

Sipeed MetaSense RGB ToF 3D depth cameras are made for MCUs & ROS Robots (Crowfunding)

Sipeed MetaSense RGB ToF 3D Depth Cameras

We’ve just written about the Arducam ToF camera to add depth sensing to Raspberry Pi, but there are now more choices, as Sipeed has just introduced its MetaSense ToF (Time-of-Flight) camera family for microcontrollers and robots running ROS with two models offering different sets of features and capabilities. The MetaSense A075V USB camera offers 320×240 depth resolution plus an extra RGB sensor, an IMU unit, and a CPU with built-in NPU that makes it ideal for ROS 1/2 robots, while the lower-end MetaSense A010 ToF camera offers up to 100×100 resolution, integrates a 1.14-inch LCD display to visualize depth data in real-time and can be connected to a microcontroller host, or even a Raspberry Pi, through UART or USB. MetaSense A075V specifications: SoC – Unnamed quad-core Arm Cortex-A7 processor @ 1.5 GHz with 0.4 TOPS NPU System Memory – 128 MB RAM Storage – 128MB flash Cameras 800×600 @ 30 […]

Radxa CM5 – A Rockchip RK3588S module (somewhat) compatible with Raspberry Pi CM4

Radxa CM5

Radxa has been working on the ROCK 5 Compute Module (aka Radxa CM5) system-on-module compatible with Raspberry Pi CM4, but based on the more powerful Rockchip RK3588S octa-core Cortex-A76/A55 SoC. Just like the Raspberry Pi Compute Module 4, it comes in a 55 x 40mm form factor, but instead of just two high-density 100-pin board-to-board connectors, the module includes three to cater for the additional I/Os from the Rockchip processor, just like they did for the Radxa CM3 equipped with a Rockchip RK3566 processor. Radxa CM5 specifications: SoC – Rockchip RK3588S octa-core processor with 4x Cortex‑A76  cores @ up to 2.4GHz, 4x Cortex‑A55 core @ 1.8GHz Arm Mali-G610 MP4 “Odin” GPU Video decoder – 8Kp60 H.265, VP9, AVS2, 8Kp30 H.264 AVC/MVC, 4Kp60 AV1, 1080p60 MPEG-2/-1, VC-1, VP8 Video encoder – 8Kp30 H.265/H.264 video encoder 6 TOPS NPU System Memory – 4GB, 8GB, or 16GB LPDDR4x‑4224 SDRAM Storage – Optional 8GB, […]

Intel Loihi 2 high-efficiency neuromorphic chip works with the Lava open-source framework

Intel Loihi 2

Neuromorphic AI accelerator chips relying on spiking neural networks (SNN), which we’ve seen from companies such as Innatera or Brainchip, will be used more and more in the future as they provide much higher efficiency compared to traditional deep neural networks (DNN) solutions. Intel is also working on SNN, and recently announced the Loihi 2 second-generation neuromorphic research chip with up to 1 million neurons (the human brain has 86 billion of those on average) that delivers up to 175x lower energy to learn a new object instance with similar or better speed and accuracy compared to conventional methods running on a central processing unit (CPU). The new Loihi 2 neuromorphic chip offers the following improvement over the first generation Loihi: Up to 10x faster processing capability (2x for simple neuron state, 5x for synaptic operations, 10x for spike generation) Up to 60x more inter-chip bandwidth achieved through a combination […]

AAEON launches UP Xtreme i11 & UP Squared 6000 robotic development kits

UP Xtreme i11 robotics development kit

AAEON’s UP Bridge the Gap community has partnered with Intel to release two robotic development kits based on either UP Xtreme i11 or UP Squared 6000 single board computers in order to simplify robotics evaluation and development. Both robotic development kits come with a four-wheeled robot prototype that can move omnidirectionally, sense and map its environment, avoid obstacles, and detect people and objects. Since those capabilities are already implemented, users can save time and money working on further customization for specific use cases. Key features and specifications: SBC UP Squared 6000 SBC – Recommended for power efficiency and extended battery power SoC – Intel Atom x6425RE Elkhart Lake processor clocked up to 1.90 GHz with Intel UHD Graphics System Memory – 8GB LPDDR4 Storage – 64GB eMMC flash, SATA port UP Xtreme i11 SBC – Recommended for higher performance SoC Intel Core i7-1185GRE clocked at up to 4.40 GHz and […]

SyncBot educational mobile robot supports NVIDIA Xavier NX or Intel Tiger Lake controller

SyncBot educational mobile robot RBX-I2000 controller

Syncbotic Syncbot is a four-wheel autonomous mobile robot (AMR) platform for research and education that can be fitted with an NVIDIA Xavier NX or an Intel Apollo Lake/Tiger Lake-based controller running Ubuntu 20.04 operating system with ROS 2 framework, and comes with an motion control MCU board with an EtherCat master and running Zephyr OS. The robot comes with four 400W TECO servo motors, can handle up to 80kg payloads for sensors and a robotic arm, features 12V and 24V power output for sensors, four USB 3.0 ports, and can also be equipped with an eight-camera kit with Intel RealSense and ToF cameras. Syncbot AMR specifications: Robot Controller Platform (one or the other) SyncBotic A100 evaluation ki (Apollo Lake E3940) SyncBotic SBC-T800 series (Intel Tiger Lake UP3) SyncBotic SBC W series (Intel Tiger Lake UP3, waterproof version) SyncBotic NSync-200 series (NVIDIA NX) Dimensions – 200 x 190 mm STM32-based Motion […]