robotics News - Page 12 of 30 - CNX Software

October 13, 2022 by Jean-Luc Aufranc (CNXSoft) - No Comments

Vecow launches NVIDIA Jetson AGX Orin-based EAC-5000 Series Edge AI computing system

Vecow EAC-5000 Jetson AGX Orin embedded computer

Vecow EAC-5000 is a rugged AI computing system powered by NVIDIA’s Jetson AGX Orin 32GB or 64GB system-on-module designed for advanced edge AI applications such as in-vehicle computing, robotic control, machine vision, intelligent video analytics, and mobile robots. The embedded computer delivers up to 275 TOPS of AI performance thanks to the NVIDIA module, supports up to eight GMSL2 cameras, various wireless connectivity options with 6 antennas, 9V to 50V wide range DC power input, and operates in the -20°C to 70°C temperature range. Vecow EAC-5000 specifications: System-on-Module EAC-5000-R32 – NVIDIA Jetson AGX Orin 32GB with CPU – 8-core Arm Cortex-A78AE v8.2 64-bit processor @ 2.2 GHz with 2MB L2 + 4MB L3 cache GPU / AI accelerators NVIDIA Ampere architecture with 1792 NVIDIA CUDA cores and 56 Tensor Cores @ 1 GHz DL Accelerator – 2x NVDLA v2.0 @ up to 1.4 GHz Vision Accelerator – PVA v2.0 (Programmable […]

October 7, 2022January 23, 2023 by Jean-Luc Aufranc (CNXSoft) - No Comments

UP Element i12 Edge embedded computer is equipped with Intel’s NUC 12 Compute Element

AAEON’s UP Bridge the Gap has announced the UP Element i12 Edge fanless embedded computer based on the Intel NUC 12 Compute Element and designed for the autonomous mobile robot (AMR) and industrial automation markets. The computer is fitted with NUC 12 Compute Element equipped with 12th generation Alder Lake hybrid processor from Celeron 7350 to Intel Core i7-1255U hybrid processor, supports up to 32GB LPDDR5 memory, NVMe support, offers three Ethernet ports including one 2.5GbE, several USB ports, two RS232/422/485 interfaces, a DIO header, and more. UP Element i12 Edge specifications: System-on-module – Intel NUC 12 Compute Element with: Alder Lake SoC (one or the other) Intel Core i7-1255U 10-core processor with 2x Performance cores @ up to 4.7 GHz, 8x Efficiency cores @ up to 3.5 GHz, Intel Iris Xe Graphics; PBP: 15W Intel Core i5-1235U 10-core processor with 2x Performance cores @ up to 4.4 GHz, 8x Efficiency […]

September 23, 2022November 30, 2022 by Jean-Luc Aufranc (CNXSoft) - 3 Comments

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

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 […]

September 21, 2022 by Jean-Luc Aufranc (CNXSoft) - 9 Comments

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

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 […]

September 19, 2022September 22, 2022 by Jean-Luc Aufranc (CNXSoft) - 2 Comments

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 […]

September 11, 2022September 12, 2022 by Jean-Luc Aufranc (CNXSoft) - 3 Comments

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

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 […]

September 8, 2022September 12, 2022 by Jean-Luc Aufranc (CNXSoft) - 17 Comments

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

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, […]

September 3, 2022September 3, 2022 by Jean-Luc Aufranc (CNXSoft) - No Comments

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

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 […]