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【Nvidia】Jetson AGX Orin/ Jetson Orin nano 硬件测试调试内容(On going)
# 1. 行动项 * [x] 笔记本刷机（ubuntu20.04 -》 ubuntu 22.04，确认sdkmanager 应用，docker均可用）（`见2.0`） * [x] 刷写镜像 * [ ] ~~DRIVE（需要专门Drive 硬件，）~~ * [x] Jetpack jetson专用6.0刷写完成，CUDA AI等模块均完成刷写，container runtime安装get.docker.com需要翻墙 * [ ] ~~其他镜像列表见Annexe1，都需要专用硬件~~ * [ ] 硬件调试 * [x] can 2CH CAN FD HAT [Description](https://www.waveshare.net/wiki/2-CH_CAN_FD_HAT)(`见2.1`) * [x] sense HAT(C) [Description HAT(C)](https://www.waveshare.com/wiki/Sense_HAT_(C)) , [Schematic](https://www.waveshare.net/w/upload/a/af/Sense_HAT_%28C%29_Schematic.pdf)（`见2.2`） * [ ] 摄像头 `进行中` （`见2.3`） * agx orin 32g [userguide](https://developer.nvidia.com/embedded/learn/jetson-agx-orin-devkit-user-guide/index.html), [hardware layout](https://developer.nvidia.com/embedded/learn/jetson-agx-orin-devkit-user-guide/developer_kit_layout.html) * [ ] Orin上测试Bevformer，Sparse4D执行效率 * [ ] 镜像添加prompt-RT * [ ] Orin上测试机器人仿真机测试,omniverse+issacsim+orin执行 * [ ] ~~摸一下Orin的Drive都有哪些功能~~ * [ ] 摸一下Orin硬实时能力 * [ ] OTA Tools # 2 过程重点记录 ## 2.0 笔记本刷机 * use directly ubuntu22.04 to solve the problems below. * wifi driver https://forum.manjaro.org/t/realtek-rtl8111-8168-8411-cant-see-wifi-connections-after-manjaro-installation/126376/18 * ethernet driver: 8111/8168/8411 https://www.realtek.com/Download/List?cate_id=584 * pc 电脑刷机后网络无法使用 - ipv6禁用可恢复 ``` sudo nano /etc/sysctl.conf add: net.ipv6.conf.all.disable_ipv6 = 1 net.ipv6.conf.default.disable_ipv6 = 1 net.ipv6.conf.lo.disable_ipv6 = 1 sudo sysctl -p sudo systemctl restart networking ``` ## 2.1 Two-Channel Isolated CAN FD Expansion HAT * `1. loopback test` * CAN(`ok`, in the picture 1st,3rd are good, 2nd is remove the short of Pin29 and Pin31 of can0): https://docs.nvidia.com/jetson/archives/r35.4.1/DeveloperGuide/text/HR/ControllerAreaNetworkCan.html?#loopback-test  * SPI(`ok, in 3.1`): https://forums.developer.nvidia.com/t/jetson-orin-spi-interface-with-mcp2518-spi-module/272853 * `2. links` * [https://forums.developer.nvidia.com/t/jetson-orin-spi-interface-with-mcp2518-spi-module/272853](https://forums.developer.nvidia.com/t/jetson-orin-spi-interface-with-mcp2518-spi-module/272853) * [https://wiki.seeedstudio.com/2-Channel-CAN-BUS-FD-Shield-for-Raspberry-Pi/#using-can-bus-shiled-with-jetson-nano](https://wiki.seeedstudio.com/2-Channel-CAN-BUS-FD-Shield-for-Raspberry-Pi/#using-can-bus-shiled-with-jetson-nano) * `3. steps` * 3.1. enable spi to check if the HAT workers ``` sudo /opt/nvidia/jetson-io/jetson-io.py # And then follow the CLI to enable spi header, save the configuration and exit. # The data could be received from CAN. ```  * 3.2. Use as a CAN Interface. `ifconfig -a` could find the CAN Interface * 3.2.1 (Optional if do the compilation on Host PC)Configure Environment on host machine ``` #Install Required Packages sudo apt update sudo apt install build-essential gcc make git libncurses5-dev bc flex bison #Install Additional Tools for Cross-Compilation sudo apt install gcc-aarch64-linux-gnu #Install Device Tree Compiler (DTC) sudo apt install device-tree-compiler ``` * 3.2.2 Download jeston linux source, 36.3 is what we used version https://developer.nvidia.com/embedded/downloads#?search=linux&tx=$product,jetson_agx_orin  https://developer.nvidia.com/embedded/jetson-linux-archive  https://developer.nvidia.com/embedded/jetson-linux-r363 * 3.2.3 Modified the code to adapt new linux kernel and build the driver * I checked the pin40 of the Jetson Nano. The pin40 of SPI is exectly the same so https://wiki.seeedstudio.com/2-Channel-CAN-BUS-FD-Shield-for-Raspberry-Pi/#using-can-bus-shiled-with-jetson-nano this project could be used. Follow this link to compile it on the devkit, you may meet the error below.  * Function `can_rx_offload_get_echo_skb` and `can_put_echo_skb` are because the `rx-offload.h` and `rx-offload.c` file update in the linux kernal. Replace them and modify the code. (`I will give all the modification in the Annexe`) * Function `get_canfd_dlc` and `get_can_dlc` could be solved by define a new macro  * The other errors follow the suggestions in color green. It is because the modification in `can-utils/include/linux/can/lib.h` * For the errors in `spi-tegra114.c`, just replace the file with the newest version in kernal file.  * The compilation could be fine.  * 3.2.4 Modify the device tree Replay by this link: https://forums.developer.nvidia.com/t/jetson-agx-orin-spi-interface-with-2-ch-can-fd-hat-mcp2518fd-spi-module/302264 Solution: https://elinux.org/Jetson/L4T/peripheral/#MCP2515_Verification the file for JetPack 6.0 is in link 36.2. ==ps: Please check carefully the model that you use. For example I used P3730(with P3701 as orin and P3737 as carrier board, so what I used as dtb is something like *p3737-0000+p3701-0000.dtb*)==  * 4. Result: * Jetpack 5.1.3 - 5.1.x  1. driver build with no error  2. the device tree file dtb sames changed  3. Driver Added  4. Interface appeared  * Jetpack 6.3 - 6.x: follow the steps in `steps 3` which is exactly the modification done for Jetpack6x * 5. ==Attention==: For Jetson AGX which have only one spi-IO from the jetson-io list. It needs to use one spi channel to control 2CAN mode. ## 2.2 Sense HAT（C）调试 * 硬件连接，指示灯亮，供电正确    * 数据调试 * Orin I2C5 对应7，修改各个传感器python测试脚本对应的I2C通道  * IMU正常  * LPS22HB压力温度传感器正常  * SGM58031正常  ## 2.3 相机调试 * Availble hardware   * 接插件/摄像头 * Orin上（J509） Samtec QSH-060-01-H-D-A * 对应接插件型号 Samtec QTH-060-01-H-D-A 官网48个起买，价格如下。淘宝上买了2个，`确认，无法直接连接FPC，需要买与Jetson AGX Orin能够适配的载板。`  * [完整连接方案sony-imx327-ultra-low-light-camera](https://www.e-consystems.com/nvidia-cameras/jetson-agx-xavier-cameras/sony-imx327-ultra-low-light-camera.asp) * [IMX219](https://www.digikey.com/en/products/detail/seeed-technology-co.,-ltd/114992263/12396924?utm_adgroup=&utm_source=google&utm_medium=cpc&utm_campaign=PMax%20Shopping_Product_Low%20ROAS%20Categories&utm_term=&utm_content=&utm_id=go_cmp-20243063506_adg-_ad-__dev-c_ext-_prd-12396924_sig-Cj0KCQjwh7K1BhCZARIsAKOrVqEpIVCOvcSxv1rX5EXwRPPhG4f3wZsL6ohing94GYLM2lEHFhO1dvwaAh3dEALw_wcB&gad_source=1&gclid=Cj0KCQjwh7K1BhCZARIsAKOrVqEpIVCOvcSxv1rX5EXwRPPhG4f3wZsL6ohing94GYLM2lEHFhO1dvwaAh3dEALw_wcB) * 目前手上的的摄像头参数为如图所示，串行芯片max96717F手册link，MAX96716A/B/F, a quad-port MAX96712 or MAX96722 等几款解串都可以使用。https://www.analog.com/media/en/technical-documentation/data-sheets/max96717f.pdf，   * https://www.sensing-world.com/en/pd.jsp?id=20&fromMid=1656&recommendFromPid=0 * Useful link * [connection](https://forums.developer.nvidia.com/t/how-can-i-connect-this-camera/140966) * [Hardware Design Guide](https://developer.download.nvidia.cn/assets/embedded/secure/jetson/xavier/docs/Jetson_AGX_Xavier_and_Orin_Series_Camera_Design_Guide_DG-09364-001_v1.5.pdf?myHpjLkYdPSrdnl-2A5gxWP3LyA362Vz-CJaygWRP5uAwu6V8d3-4jJ0nqLQeKrHsElwfnCAoihXique0RyYYp4OFO034MqrGynRxOuRSpR2xdchw978Y7kFmdp-JwU1Gu7l8oHJuHjBK9elkSyrCeWzqNJsaPmFEuGg-E1s0Ux4KmaenxziRqCOev7lzRXY0cG9L1kDkx-KzYPKA54nyG9hOT3RHPv3cfNQLqzKfzmk6vwL&t=eyJscyI6ImdzZW8iLCJsc2QiOiJodHRwczovL3d3dy5nb29nbGUuY29tLyJ9) * [https://github.com/NVIDIA-AI-IOT/argus_camera](https://github.com/NVIDIA-AI-IOT/argus_camera) * [https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_argus_camera](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_argus_camera) * Try: * Try on Jetson Nano. (The maximun Jetpack version is JetPack 4.6.5, I checked the kernal code it exactly have a lot of difference between 4.6.5(Linux 32.7.5) 5.1.2(Linux 35.4.1))  * Try on Jetson TX2 NX. It is the same as Jetson Nano. It is a 4GB Mem version that nano develop kit could be used as this link saild. https://forums.developer.nvidia.com/t/jetson-tx2-nx-on-jetson-nano-dev-board-b01/226011 * Try on Jetson Orin nano which could use the jetpack 5.1.2. ### 2.3.1 Steps to Adapt camera and gsml board on Rpi * 1. Use camera with max9295a and make it work on orin nx(Jetpack 5.1.2) with is exactly the demo given by saller. * 2. Tried to modify the demo code at the register part to make max9296a adapt max96717F, to allow camera with max96717F serialization chip. * 3. Put the second step on Raspberrypi 4. The Core part is below: * 3.1. Check how to modify the device tree on raspberry pi4 * 3.2. Check how to adapt the camera driver on raspberry pi4 * 3.3. Try to catch the stream, normally use the `v4l2 framework` ## 2.4 AI能力测试 ## 2.5 实时内核 * https://docs.nvidia.com/jetson/archives/r35.1/DeveloperGuide/text/SD/Kernel/KernelCustomization.html ## 2.6 机器人能力测试Omniverse+IssacSim+AGX Orin ## 2.7 OTA https://docs.nvidia.com/jetson/archives/r36.3/DeveloperGuide/SD/SoftwarePackagesAndTheUpdateMechanism.html#updating-jetson-linux-with-image-based-over-the-air-update # 3 Annexe ## 3.1 Nvidia SDKManager  ``` 1. NVIDIA Jetson NVIDIA Jetson 是 NVIDIA 开发的一系列用于人工智能和计算机视觉的嵌入式计算平台。Jetson 平台包含多个型号的开发板和模块，支持 AI、机器学习、深度学习、机器人、无人机和物联网设备的开发。它们通常用于需要高性能计算能力但受限于功耗和空间的应用场景。 2. Holoscan NVIDIA Holoscan 是一个用于医疗成像和边缘 AI 的实时数据处理平台。Holoscan 平台能够处理来自多个传感器和设备的大量数据流，使得医疗专业人员能够进行实时分析和决策。它特别适用于需要高性能数据处理的医疗应用，如超声波成像、内窥镜检查和手术导航。 3. Rivermax NVIDIA Rivermax 是一个高效的网络传输 SDK，专为需要高带宽、低延迟的应用而设计。它主要用于专业音视频广播行业，支持通过 IP 网络进行高质量的媒体传输。Rivermax 可以优化数据流在 NVIDIA 网卡上的传输性能，提供无缝的媒体流解决方案。 4. DeepStream NVIDIA DeepStream 是一个流式处理分析 SDK，专为视频分析应用而设计。DeepStream 提供了从摄像头、传感器或文件中摄取视频流并执行实时分析的工具。它常用于智能城市、零售分析、安全监控和工业检测等领域，支持使用 GPU 进行高效的视频处理和人工智能推理。 5. ARC-OTA NVIDIA ARC-OTA (Automated, Reliable, Connected - Over-the-Air) 是一个用于远程更新和管理嵌入式设备的软件解决方案。ARC-OTA 允许开发者通过无线网络向 Jetson 或其他嵌入式设备推送软件更新和补丁，确保设备始终运行最新的软件版本。它提供了一种安全、可靠且高效的方式来维护设备的软件生态。 6. Ethernet Switch NVIDIA Ethernet Switch 指的是 NVIDIA 提供的以太网交换机产品，通常用于数据中心和企业网络中。这些交换机提供高吞吐量、低延迟和先进的网络功能，以支持现代应用程序和虚拟化环境。NVIDIA 的以太网交换机结合了 Mellanox 技术，适合于需要高性能网络基础设施的企业。 7. RAPIDS NVIDIA RAPIDS 是一个基于 GPU 的数据科学和机器学习库集。它支持加速数据处理和分析，利用 CUDA 并行计算框架提升机器学习模型的训练和推理速度。RAPIDS 提供类似于 pandas、scikit-learn 等 Python 库的 API，适合需要处理大规模数据集并进行快速分析的应用。 8. DRIVE NVIDIA DRIVE 是 NVIDIA 的自动驾驶平台，提供了软硬件工具用于开发自动驾驶汽车。DRIVE 平台集成了 AI 计算、传感器处理、仿真和测试工具，支持 L2+ 至 L5 级别的自动驾驶开发。NVIDIA DRIVE 适用于车载信息娱乐系统、自动驾驶控制和数据处理等多个领域。 9. DOCA NVIDIA DOCA (Data Center Infrastructure on a Chip Architecture) 是一个用于加速数据中心应用程序的编程框架。DOCA 提供了一套 API 和库，允许开发者在 NVIDIA DPU（数据处理单元）上构建高效的网络、安全和存储解决方案。它适用于需要增强性能和安全性的现代数据中心基础设施。 这些 SDK 和平台涵盖了从嵌入式 AI 到自动驾驶再到数据中心的广泛应用领域，展示了 NVIDIA 在硬件和软件结合方面的强大能力。 ``` ## Annexe 3.2 mcp22518fd modification for build ``` c // mcp25xxfd.h #define get_can_dlc(i) (min_t(u8, (i), CAN_MAX_DLC)) #define get_canfd_dlc(i) (min_t(u8, (i), CANFD_MAX_DLC)) ``` ``` c // mcp25xxfd-core.c static netdev_tx_t mcp25xxfd_start_xmit(struct sk_buff *skb, struct net_device *ndev) { struct mcp25xxfd_priv *priv = netdev_priv(ndev); struct mcp25xxfd_tx_ring *tx_ring = priv->tx; struct mcp25xxfd_tx_obj *tx_obj; const canid_t can_id = ((struct canfd_frame *)skb->data)->can_id; u8 tx_head; int err; // Declare frame_len and canfd_frame at the beginning unsigned int frame_len; struct canfd_frame *cf; if (can_dropped_invalid_skb(ndev, skb)) return NETDEV_TX_OK; mcp25xxfd_log(priv, can_id); if (tx_ring->head - tx_ring->tail >= tx_ring->obj_num) { netdev_dbg(priv->ndev, "Stopping tx-queue (tx_head=0x%08x, tx_tail=0x%08x, len=%d).\n", tx_ring->head, tx_ring->tail, tx_ring->head - tx_ring->tail); mcp25xxfd_log_busy(priv, can_id); netif_stop_queue(ndev); return NETDEV_TX_BUSY; } tx_obj = mcp25xxfd_get_tx_obj_next(tx_ring); mcp25xxfd_tx_obj_from_skb(priv, tx_obj, skb, tx_ring->head); /* Stop queue if we occupy the complete TX FIFO */ tx_head = mcp25xxfd_get_tx_head(tx_ring); tx_ring->head++; if (tx_ring->head - tx_ring->tail >= tx_ring->obj_num) { mcp25xxfd_log_stop(priv, can_id); netif_stop_queue(ndev); } // Initialize canfd_frame pointer cf = (struct canfd_frame *)skb->data; // Determine the frame length if (cf->can_id & CAN_RTR_FLAG) { // Remote Transmission Request frames have no data frame_len = 0; } else if (cf->can_id & CAN_EFF_FLAG) { // Extended Frame Format (EFF) with CAN FD support frame_len = (cf->len > CAN_MAX_DLEN) ? CAN_MAX_DLEN : cf->len; } else { // Standard Frame Format (SFF) frame_len = (cf->len > CAN_MAX_DLEN) ? CAN_MAX_DLEN : cf->len; } // Call can_put_echo_skb with the additional frame_len parameter can_put_echo_skb(skb, ndev, tx_head, frame_len); err = mcp25xxfd_tx_obj_write(priv, tx_obj); if (err) goto out_err; return NETDEV_TX_OK; out_err: netdev_err(priv->ndev, "ERROR in %s: %d\n", __func__, err); mcp25xxfd_dump(priv); mcp25xxfd_log_dump(priv); return NETDEV_TX_OK; } ``` ``` c // mcp25xxfd-core.c static int mcp25xxfd_handle_tefif_one(struct mcp25xxfd_priv *priv, const struct mcp25xxfd_hw_tef_obj *hw_tef_obj) { struct mcp25xxfd_tx_ring *tx_ring = priv->tx; struct net_device_stats *stats = &priv->ndev->stats; u32 seq, seq_masked, tef_tail_masked; int err; unsigned int frame_len = 0; // Declare variable to store frame length seq = FIELD_GET(MCP25XXFD_OBJ_FLAGS_SEQ_MCP2518FD_MASK, hw_tef_obj->flags); /* Use the MCP2517FD mask on the MCP2518FD, too. We only * compare 7 bits, this should be enough to detect * net-yet-completed, i.e. old TEF objects. */ seq_masked = seq & FIELD_GET(MCP25XXFD_OBJ_FLAGS_SEQ_MCP2517FD_MASK, -1UL); tef_tail_masked = priv->tef.tail & FIELD_GET(MCP25XXFD_OBJ_FLAGS_SEQ_MCP2517FD_MASK, -1UL); if (seq_masked != tef_tail_masked) return mcp25xxfd_handle_tefif_recover(priv, seq); mcp25xxfd_log(priv, hw_tef_obj->id); // Call can_rx_offload_get_echo_skb with the additional frame_len_ptr argument stats->tx_bytes += can_rx_offload_get_echo_skb(&priv->offload, mcp25xxfd_get_tef_tail(priv), hw_tef_obj->ts, &frame_len); // Pass the address of frame_len stats->tx_packets++; /* finally increment the TEF pointer */ err = regmap_update_bits(priv->map_reg, MCP25XXFD_REG_TEFCON, GENMASK(15, 8), MCP25XXFD_REG_TEFCON_UINC); if (err) return err; priv->tef.tail++; tx_ring->tail++; return mcp25xxfd_check_tef_tail(priv); } ``` ## Annexe 3.3 Build Nvidia Customization Kernal * Build the kernal https://docs.nvidia.com/jetson/archives/r34.1/DeveloperGuide/text/SD/Kernel/KernelCustomization.html `good practice`: https://forums.developer.nvidia.com/t/kernel-make-execution-creates-hardware-folder-on-my-desktop/262213/27 * Modifying the Kernel Configuration https://docs.nvidia.com/jetson/archives/r34.1/DeveloperGuide/text/AT/JetsonLinuxDevelopmentTools/DebuggingTheKernel.html?highlight=defconfig ## Annexe 3.4 If sdkmanager for docker cannot connect the target device link: https://forums.developer.nvidia.com/t/docker-sdk-manger-error/246313/7 ## Annexe 3.5 If you meet some errors below when flashing the device, it coule be the problem of `Power`, check whether the power of your adaptor is enough (An Interasting error) * skip generating mem_bct because sdram_config is not defined * failed to read rcm_state link: https://forums.developer.nvidia.com/t/docker-sdk-manger-error/246313 ## Annexe 3.6 Toolchain of Nvidia `check clearly ther version correspondant to each version between Jetpack/Linux/Toolchain/sdkmanager`, the errors could happen if the version are not match. * error meeting for flash on SD card: https://forums.developer.nvidia.com/t/jetson-orin-nano-devkit-disk-encryption-could-not-stat-device-dev-mmcblk0-no-such-file-or-directory/268976 * check the storage device that you use, NVMe(M.2 storage), SD Card or EMMC ## Annexe 3.7 About CSI Type https://www.arducam.com/raspberry-pi-camera-pinout/ ## Annexe 3.8 RaspberryPi Utils * Build kernal & dts: https://www.raspberrypi.com/documentation/computers/linux_kernel.html#download-kernel-source * Download image: https://www.raspberrypi.com/software/operating-systems/ * camera config: https://www.raspberrypi.com/documentation/computers/camera_software.html#mode * camera software: https://www.raspberrypi.com/documentation/computers/camera_software.html#tuning-files * use unxz command to transform xz file to img file and than flash the image to sdcard https://www.raspberrypi.com/documentation/computers/getting-started.html * if use image flasher always meet errors, tried to use `dd` command to flash the image to sdcard directly  ## Annexe 3.9 Develop camera sensor driver https://developer.ridgerun.com/wiki/index.php/Camera_Sensor_Basics
dingfeng
2024年8月19日 11:23
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