'embeded' 카테고리의 글 목록 (2 Page)

'embeded'에 해당되는 글 1618건

2026.07.24 nvidia jetson agx thor 아니고 nvidia thor?
2026.07.21 jetson nano + llama.cpp vulkan
2026.07.20 jetson nano jtop
2026.07.20 jetson nano 4GB / qwen3.5 0.8B
2026.07.20 jetson nano 과 llama.cpp (cuda 가속, 빌드 포기)
2026.07.20 jetson nano devloper kit SD card image(A02)
2026.07.15 RCM(Remote Center of Motion)
2026.07.08 로봇 제어 Move
2026.07.03 elephant robototics mycobot ros2
2026.07.02 mycobot ros1/ros2

nvidia jetson agx thor 아니고 nvidia thor?

회사에 굴러 다니고 있어서 전기 먹여 봄

일단 blackwell 기반이니까 50 시리즈와 동급이란거구만

[링크 : https://www.nvidia.com/ko-kr/autonomous-machines/embedded-systems/jetson-thor/]

와우 금액 미쳤...

판매가 (VAT 별도/포함)
8,750,000원 (9,625,000원)

[링크 : https://www.devicemart.co.kr/goods/view?no=15796725&srsltid=AfmBOooug8Uqa3UGdtlryrdtVMt2Y_GPgt9JIldPH-1rxrBXUMnJPjMN]

음.. 소비전력 안보이고, 메모리 사용량 미지원인가?

메모리 128GB

그나저나 내부적으로 말이 오락가락 했나 dmidecode 에서는 AGX Thor 라고 나온다.

nvidia@localhost:~$ uname -a
Linux localhost.localdomain 6.8.12-tegra #1 SMP PREEMPT Tue Dec 30 15:40:41 PST 2025 aarch64 aarch64 aarch64 GNU/Linux

nvidia@localhost:~$ free -h
               total        used        free      shared  buff/cache   available
Mem:           122Gi       4.4Gi       117Gi        25Mi       2.2Gi       118Gi
Swap:          2.0Gi          0B       2.0Gi

nvidia@localhost:~$ cat /proc/meminfo
MemTotal:       128790212 kB
MemFree:        122909432 kB
MemAvailable:   124174080 kB
Buffers:           75332 kB
Cached:          2157868 kB
SwapCached:            0 kB
Active:           488036 kB
Inactive:        2835216 kB
Active(anon):       7456 kB
Inactive(anon):  1136416 kB
Active(file):     480580 kB
Inactive(file):  1698800 kB
Unevictable:       27380 kB
Mlocked:              16 kB
SwapTotal:       2097148 kB
SwapFree:        2097148 kB
Dirty:            214136 kB
Writeback:             0 kB
AnonPages:       1111168 kB
Mapped:           662168 kB
Shmem:             26448 kB
KReclaimable:     115036 kB
Slab:             331556 kB
SReclaimable:     115036 kB
SUnreclaim:       216520 kB
KernelStack:       16736 kB
PageTables:        27972 kB
SecPageTables:         0 kB
NFS_Unstable:          0 kB
Bounce:                0 kB
WritebackTmp:          0 kB
CommitLimit:    66492252 kB
Committed_AS:    7415448 kB
VmallocTotal:   133141626880 kB
VmallocUsed:      111644 kB
VmallocChunk:          0 kB
Percpu:             9688 kB
HardwareCorrupted:     0 kB
AnonHugePages:    161792 kB
ShmemHugePages:        0 kB
ShmemPmdMapped:        0 kB
FileHugePages:         0 kB
FilePmdMapped:         0 kB
CmaTotal:         524288 kB
CmaFree:          484248 kB
HugePages_Total:       0
HugePages_Free:        0
HugePages_Rsvd:        0
HugePages_Surp:        0
Hugepagesize:       2048 kB
Hugetlb:               0 kB

nvidia@localhost:~$ cat /proc/cpuinfo
processor : 0
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 1
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 2
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 3
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 4
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 5
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 6
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 7
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 8
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 9
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 10
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 11
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 12
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

processor : 13
BogoMIPS : 2000.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd83
CPU revision : 0

$ sudo dmidecode
# dmidecode 3.5
Getting SMBIOS data from sysfs.
SMBIOS 3.6.0 present.
# SMBIOS implementations newer than version 3.5.0 are not
# fully supported by this version of dmidecode.
Table at 0x1FBB130000.

Handle 0x0000, DMI type 0, 26 bytes
BIOS Information
Vendor: EDK II
Version: 38.4.0-gcid-43443517
Release Date: 12/30/2025
ROM Size: 0 MB
Characteristics:
PCI is supported
PNP is supported
BIOS is upgradeable
BIOS shadowing is allowed
Boot from CD is supported
Selectable boot is supported
ACPI is supported
Targeted content distribution is supported
UEFI is supported
BIOS Revision: 0.0
Firmware Revision: 0.0

Handle 0x0001, DMI type 1, 27 bytes
System Information
Manufacturer: NVIDIA
Product Name: NVIDIA Jetson AGX Thor Developer Kit
Version: Not Specified
Serial Number: 1423525040774
UUID: 5ecc6819-51f8-517e-b26f-0f0163b2c9ab
Wake-up Type: Power Switch
SKU Number: Unknown
Family: Unknown

Handle 0x0002, DMI type 3, 25 bytes
Chassis Information
Manufacturer: NVIDIA
Type: Other
Lock: Not Present
Version: Unknown
Serial Number: Unknown
Asset Tag: 699-13834-0008-400 H.1
Boot-up State: Unknown
Power Supply State: Unknown
Thermal State: Unknown
Security Status: Unknown
OEM Information: 0x00000000
Height: 1 U
Number Of Power Cords: 1
Contained Elements: 0
SKU Number: Unknown

Handle 0x0003, DMI type 2, 17 bytes
Base Board Information
Manufacturer: NVIDIA
Product Name: Jetson
Version: Not Specified
Serial Number: 1423525040774
Asset Tag: 699-13834-0008-400 H.1
Features:
Board is a hosting board
Board is replaceable
Location In Chassis: Unknown
Chassis Handle: 0x0002
Type: Motherboard
Contained Object Handles: 0

Handle 0x0004, DMI type 13, 22 bytes
BIOS Language Information
Language Description Format: Long
Installable Languages: 1
en|US|iso8859-1
Currently Installed Language: en|US|iso8859-1

Handle 0x0005, DMI type 32, 11 bytes
System Boot Information
Status: No errors detected

Handle 0x0006, DMI type 7, 27 bytes
Cache Information
Socket Designation: L1 Instruction Cache
Configuration: Enabled, Not Socketed, Level 1
Operational Mode: Unknown
Location: Unknown
Installed Size: 896 kB
Maximum Size: 896 kB
Supported SRAM Types:
Unknown
Installed SRAM Type: Unknown
Speed: Unknown
Error Correction Type: Unknown
System Type: Instruction
Associativity: 4-way Set-associative

Handle 0x0007, DMI type 7, 27 bytes
Cache Information
Socket Designation: L1 Data Cache
Configuration: Enabled, Not Socketed, Level 1
Operational Mode: Unknown
Location: Unknown
Installed Size: 896 kB
Maximum Size: 896 kB
Supported SRAM Types:
Unknown
Installed SRAM Type: Unknown
Speed: Unknown
Error Correction Type: Unknown
System Type: Data
Associativity: 4-way Set-associative

Handle 0x0008, DMI type 7, 27 bytes
Cache Information
Socket Designation: L2 Cache
Configuration: Enabled, Not Socketed, Level 2
Operational Mode: Unknown
Location: Unknown
Installed Size: 14 MB
Maximum Size: 14 MB
Supported SRAM Types:
Unknown
Installed SRAM Type: Unknown
Speed: Unknown
Error Correction Type: Unknown
System Type: Unified
Associativity: 8-way Set-associative

Handle 0x0009, DMI type 4, 50 bytes
Processor Information
Socket Designation: Not Specified
Type: Central Processor
Family: ARMv8
Manufacturer: NVIDIA
ID: 64 02 6B 03 01 04 00 00
Signature: JEP-106 Bank 0x03 Manufacturer 0x6b, SoC ID 0x0264, SoC Revision 0x00000401
Version: Thor
Voltage: Unknown
External Clock: 1000 MHz
Max Speed: 2601 MHz
Current Speed: 2601 MHz
Status: Populated, Enabled
Upgrade: Unknown
L1 Cache Handle: 0x0007
L2 Cache Handle: 0x0008
L3 Cache Handle: Not Provided
Serial Number: Not Specified
Asset Tag: Not Specified
Part Number: Not Specified
Core Count: 14
Core Enabled: 14
Thread Count: 14
Characteristics:
64-bit capable
Multi-Core
Execute Protection
Enhanced Virtualization
Arm64 SoC ID

Handle 0x000A, DMI type 16, 23 bytes
Physical Memory Array
Location: System Board Or Motherboard
Use: System Memory
Error Correction Type: Single-bit ECC
Maximum Capacity: 128 GB
Error Information Handle: Not Provided
Number Of Devices: 1

Handle 0x000B, DMI type 19, 31 bytes
Memory Array Mapped Address
Starting Address: 0x00080000000
Ending Address: 0x0207FFFFFFF
Range Size: 128 GB
Physical Array Handle: 0x000A
Partition Width: 1

Handle 0xFEFF, DMI type 127, 4 bytes
End Of Table

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'embeded > jetson' 카테고리의 다른 글

nvidia jetson agx thor dev kit 설치 - USB boot (0)	2026.07.24
nvidia MIG(multiple instance GPU) - thor (0)	2026.07.24
jetson nano + llama.cpp vulkan (0)	2026.07.21
jetson nano jtop (0)	2026.07.20
jetson nano 4GB / qwen3.5 0.8B (0)	2026.07.20

Posted by 구차니

jetson nano + llama.cpp vulkan

jetson nano 4GB 에서 시도함.

cuda는 버전에 낮아서 안되고(llama.cpp가 cuda 12/13 지원. 얘는 10.x)

docker에서는 nvidia vulkan이 없어서 가속을 할 수 없어서 native vulkan빌드 부터 개고생해서 올렸는데

노력대비(2일 풀로 가동..) 효율이 좋다고 할 순 없겠지만,

그래도 나름 괜찮은 성능과 vulkan 가속이라 cpu 점유율 낮출 수 있다는게 그나마 위안인가..

그런데 이러면 발열은 어떨려나? cpu쪽이 더 낮을까.. gpu 쓰는게 낮을까?

옆에 스크롤이 점만해졌음 ㅋㅋㅋ 중반쯤에 포기하자는거 끝까지 멱살잡고 캐리해서 진행해버림

ㅋㅋ 이러니 ai가 반란을 일으키고 터미네이터가 되는건가 ㅋㅋ

2GB 메모리라면 최소한 스왑 6GB 이상은 해야 할 것으로 보인다.

sudo fallocate -l 4G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile

일단 gcc9를 설치하면 손쉽게 행복해짐

sudo apt-get install -y software-properties-common
sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install -y gcc-9 g++-9

빌드를 위한 헤더들 및 버전 맞추기

dpkg -l | grep nvidia-l4t
sudo apt-get update
sudo apt-get install --only-upgrade -y \
  nvidia-l4t-core \
  nvidia-l4t-bootloader \
  nvidia-l4t-configs \
  nvidia-l4t-gputools \
  nvidia-l4t-initrd \
  nvidia-l4t-jetson-io \
  nvidia-l4t-oem-config \
  nvidia-l4t-tools
sudo ldconfig
vulkaninfo --summary   # 여기서 GPU(NVIDIA Tegra X1 (nvgpu))가 정상 인식되는지 확인

sudo apt-get install -y libvulkan-dev libvulkan1
sudo apt-get install -y nvidia-l4t-libvulkan

glslang 설치

cd ~
git clone https://github.com/KhronosGroup/glslang.git
cd glslang
python3 update_glslang_sources.py
mkdir build && cd build
export LDFLAGS="-lstdc++fs"
/opt/cmake/bin/cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local \
-DCMAKE_C_COMPILER=gcc-9 -DCMAKE_CXX_COMPILER=g++-9
make -j$(nproc)
sudo make install
which glslangValidator

shaderc 설치

cd ~
git clone https://github.com/google/shaderc.git
cd shaderc
python3 utils/git-sync-deps
mkdir build && cd build
export LDFLAGS="-lstdc++fs"
/opt/cmake/bin/cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local \
  -DCMAKE_C_COMPILER=gcc-9 -DCMAKE_CXX_COMPILER=g++-9 \
  -DSHADERC_SKIP_TESTS=ON -DSHADERC_SKIP_EXAMPLES=ON \
  -DSPIRV_SKIP_EXECUTABLES=ON
make glslc_exe -j$(nproc)
sudo cp glslc/glslc /usr/local/bin/
which glslc
glslc --version

vulkan 관련 헤더 SPIRV 설치

cd ~
git clone https://github.com/KhronosGroup/SPIRV-Headers.git
cd SPIRV-Headers
mkdir build && cd build
/opt/cmake/bin/cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local
sudo make install

cmake version 3.31.8 설치

/opt/cmake/bin/cmake .. -DGGML_VULKAN=ON -DCMAKE_C_COMPILER=gcc-9 -DCMAKE_CXX_COMPILER=g++-9 -DCMAKE_C_FLAGS="-Wno-error"

아래는 빌드시 문제가 되서 수정한 부분

$ git status
On branch master
Your branch is up to date with 'origin/master'.

Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git checkout -- <file>..." to discard changes in working directory)

modified:   ../../ggml/src/ggml-cpu/ggml-cpu-impl.h
modified:   ../../tools/ui/CMakeLists.txt

no changes added to commit (use "git add" and/or "git commit -a")
jetson@jetson-desktop:~/src/llama.cpp/build/bin$ git diff
diff --git a/ggml/src/ggml-cpu/ggml-cpu-impl.h b/ggml/src/ggml-cpu/ggml-cpu-impl.h
index 5d1ca5ffc..f39d56694 100644
--- a/ggml/src/ggml-cpu/ggml-cpu-impl.h
+++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h
@@ -296,12 +296,28 @@ inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) {

#define ggml_vld1q_s16_x2 vld1q_s16_x2
#define ggml_vld1q_u8_x2  vld1q_u8_x2
-#define ggml_vld1q_u8_x4  vld1q_u8_x4
#define ggml_vld1q_s8_x2  vld1q_s8_x2
-#define ggml_vld1q_s8_x4  vld1q_s8_x4
#define ggml_vqtbl1q_s8   vqtbl1q_s8
#define ggml_vqtbl1q_u8   vqtbl1q_u8

+inline static uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) {
+    uint8x16x4_t res;
+    res.val[0] = vld1q_u8(ptr + 0);
+    res.val[1] = vld1q_u8(ptr + 16);
+    res.val[2] = vld1q_u8(ptr + 32);
+    res.val[3] = vld1q_u8(ptr + 48);
+    return res;
+}
+
+inline static int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
+    int8x16x4_t res;
+    res.val[0] = vld1q_s8(ptr + 0);
+    res.val[1] = vld1q_s8(ptr + 16);
+    res.val[2] = vld1q_s8(ptr + 32);
+    res.val[3] = vld1q_s8(ptr + 48);
+    return res;
+}
+
#endif // !defined(__aarch64__)

#if !defined(__ARM_FEATURE_DOTPROD)
diff --git a/tools/ui/CMakeLists.txt b/tools/ui/CMakeLists.txt
index 74bca417e..488fdc0be 100644
--- a/tools/ui/CMakeLists.txt
+++ b/tools/ui/CMakeLists.txt
@@ -105,3 +105,5 @@ endif()
target_include_directories(${TARGET} PUBLIC
     ${CMAKE_CURRENT_BINARY_DIR}
)
+
+target_link_libraries(llama-ui-embed PRIVATE stdc++fs)

git log. 일단 현시점 최신 버전인 b10068, 2026년 7월 18일자 소스

commit 571d0d540df04f25298d0e159e520d9fc62ed121 (HEAD -> master, tag: b10068, origin/master, origin/HEAD)
Author: Ruixiang Wang <wangruixiang07@outlook.com>
Date: Sat Jul 18 15:02:18 2026 +0200

클로드 만세!!! string 머시기 없다고 파이썬으로 해치워 버림

cd ~/src/llama.cpp
python3 << 'PYEOF'
path = "ggml/src/ggml-cpu/ggml-cpu-impl.h"
with open(path, "r") as f:
    content = f.read()

old = """#define ggml_vld1q_s16_x2 vld1q_s16_x2
#define ggml_vld1q_u8_x2  vld1q_u8_x2
#define ggml_vld1q_u8_x4  vld1q_u8_x4
#define ggml_vld1q_s8_x2  vld1q_s8_x2
#define ggml_vld1q_s8_x4  vld1q_s8_x4
#define ggml_vqtbl1q_s8   vqtbl1q_s8
#define ggml_vqtbl1q_u8   vqtbl1q_u8"""

new = """#define ggml_vld1q_s16_x2 vld1q_s16_x2
#define ggml_vld1q_u8_x2  vld1q_u8_x2
#define ggml_vld1q_s8_x2  vld1q_s8_x2
#define ggml_vqtbl1q_s8   vqtbl1q_s8
#define ggml_vqtbl1q_u8   vqtbl1q_u8

inline static uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) {
    uint8x16x4_t res;
    res.val[0] = vld1q_u8(ptr + 0);
    res.val[1] = vld1q_u8(ptr + 16);
    res.val[2] = vld1q_u8(ptr + 32);
    res.val[3] = vld1q_u8(ptr + 48);
    return res;
}

inline static int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
    int8x16x4_t res;
    res.val[0] = vld1q_s8(ptr + 0);
    res.val[1] = vld1q_s8(ptr + 16);
    res.val[2] = vld1q_s8(ptr + 32);
    res.val[3] = vld1q_s8(ptr + 48);
    return res;
}"""

assert old in content, "패턴을 못 찾음 - 파일 확인 필요"
content = content.replace(old, new)
with open(path, "w") as f:
    f.write(content)
print("패치 완료")
PYEOF

llama.cpp vulkan 활성화 해서 빌드.

-j4 하고 싶은데 두번 정도 메모리 엄청 먹으면서 하나 빌드하는게 있어서 늦더라도 -j1 추천

cd ~/src/llama.cpp
mkdir -p build && cd build
rm -rf *
export LDFLAGS="-lstdc++fs"
/opt/cmake/bin/cmake .. -DGGML_VULKAN=ON \
-DCMAKE_C_COMPILER=gcc-9 -DCMAKE_CXX_COMPILER=g++-9 \
-DCMAKE_C_FLAGS="-Wno-error"

make llama-cli llama-server -j1

그렇게 빌드된 녀석을 qwen3.5-0.8B-Q4 돌리면!

$ ./llama-cli -m ~/llm/Qwen3.5-0.8B-Q4_0.gguf --chat-template-kwargs "{\"enable_thinking\": false}" -c 4096 -ngl 99 -fa on

Loading model...

▄▄ ▄▄
██ ██
██ ██  ▀▀█▄ ███▄███▄  ▀▀█▄    ▄████ ████▄ ████▄
██ ██ ▄█▀██ ██ ██ ██ ▄█▀██    ██    ██ ██ ██ ██
██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
                                    ██    ██
                                    ▀▀    ▀▀

build      : b10068-571d0d540
model      : /home/jetson/llm/Qwen3.5-0.8B-Q4_0.gguf
ftype      : Q4_0
modalities : text

available commands:
  /exit or Ctrl+C     stop or exit
  /regen              regenerate the last response
  /clear              clear the chat history
  /read <file>        add a text file
  /glob <pattern>     add text files using globbing pattern

> 안녕?
안녕하세요! 어시, 어떻게 도와드릴 수 있을까요?

[ Prompt: 21.4 t/s | Generation: 9.1 t/s ]

> 안녕?
안녕하세요! 어시, 어떻게 도와드릴 수 있을까요?

[ Prompt: 23.0 t/s | Generation: 9.1 t/s ]

> 너에 대해 소개
안녕하세요! 저는 인공지능(AI) 기술의 능력을 활용하여 정보를 제공하거나 논리적 사고를 도와줄 수 있는 **공급 서비스 봇 (Assistant)**입니다.

나는 다음과 같은 역할을 수행할 수 있습니다:
*   **정보 제공**: 지식 기반의 정보를 검색하고 요약 또는 정리해 줄 수 있습니다.
*   **논리적 분석**: 문장 구조나 논리 논리를 분석하여 설명을 돕습니다.
*   **문제 해결**: 질문에 답하거나 상황을 분석할 수 있습니다.
*   **대화 및 학습**: 대화와 학습을 통해 사용자 경험을 개선해 줍니다.

이러한 기능을 통해 **AI**를 대신할 수 있는 인간과 유사한 능력으로 **사용자**에게 도움을 줄 수 있습니다.

어떤 내용을 원하시는가요? (예: 답변, 설명, 대화, 학습 자료 등)

[ Prompt: 21.8 t/s | Generation: 8.5 t/s ]

> 코드 작성 python hello world 출력
네, 안녕하세요! 저는 인공지능(AI) 기술의 능력을 활용하여 정보를 제공하거나 논리적 사고를 도와드릴 수 있는 서비스 봇입니다.

제대안은 **Python**을 사용하여 `print` 함수로 `hello world`를 출력하는 코드를 작성해 드립니다.

```python
print("hello world")
```

이 코드를 실행하면 다음 문장이 출력됩니다:
`hello world`

혹시 다른 언어 (예: JavaScript, Java 등) 의 `hello world` 예제를 작성하고 싶으시다면 말씀해 주세요!

[ Prompt: 37.1 t/s | Generation: 8.4 t/s ]

jtop 에서 GPU 잘 올라간다. ㅋㅋㅋ

메모리는 1.2G / 3.9G. 잘하면 2G 모델에서도 돌아가겠는데?

요건 사용하지 않을때 GPU 정보. 76.8MHz 까지 떨어지다니 ㄷㄷ

혹시나 몰라서 동일 조건에서 gpu 사용하지 않도록 ngl 0 으로 하고 테스트

$ ./llama-cli -m ~/llm/Qwen3.5-0.8B-Q4_0.gguf --chat-template-kwargs "{\"enable_thinking\": false}" -c 4096 -ngl 0 -fa on

Loading model...

▄▄ ▄▄
██ ██
██ ██  ▀▀█▄ ███▄███▄  ▀▀█▄    ▄████ ████▄ ████▄
██ ██ ▄█▀██ ██ ██ ██ ▄█▀██    ██    ██ ██ ██ ██
██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
                                    ██    ██
                                    ▀▀    ▀▀

build      : b10068-571d0d540
model      : /home/jetson/llm/Qwen3.5-0.8B-Q4_0.gguf
ftype      : Q4_0
modalities : text

available commands:
  /exit or Ctrl+C     stop or exit
  /regen              regenerate the last response
  /clear              clear the chat history
  /read <file>        add a text file
  /glob <pattern>     add text files using globbing pattern

> 안녕?
안녕하세요! 네, 알겠습니다. 저는 인공지능으로 대화하고 정보를 제공해 드릴 수 있는 도구입니다. 무엇을 도와드릴까요? 😊

[ Prompt: 5.9 t/s | Generation: 3.3 t/s ]

> 너에 대해 소개해줘
안녕하세요! 네, 알겠습니다. 저는 인공지능으로 대화하고 정보를 제공해 드릴 수 있는 도구입니다. 무엇을 도와드릴까요? 😊

[ Prompt: 9.2 t/s | Generation: 3.3 t/s ]

> 파이썬으로 현재 디렉토리 목록 보는 프로그램 작서앻줘ㅏ
프라이썬으로 현재 디렉토리 목록을 보고 싶으신가요? 😊

이제 바로 `dir` 명령어에 대한 설명부터 실행 방법까지 알려드릴게요.

### 📂 디렉토리 목록을 표시하는 명령어

프라이썬에서 파일이나 디렉토리를 표시하려면 먼저 `dir` 명령어를 사용합니다.

```bash
cd <디렉토리명>
```

예를 들어, `C:\Users\박\Documents`를 표시하려면:
```bash
cd C:\Users\박\Documents
```
**단순히 `dir`을 입력하면 현재 디렉토리를 표시할 수 있습니다.**

---

### 🛠️ 실행 방법

#### 1. **프라이썬을 실행하기**
프라이썬은 웹 브라우저로 작동하는 프로그램으로, 브라우저에서 직접 실행해 볼 수 있습니다.

**단순 브라우저에서 실행하기:**
*   **Chrome/Edge/ChromeOS:** `https://www.python.org/` 에서 `dir` 명령어 추가하기 (F12 또는 `F11`를 누ار)
*   **Mac OS:** `Finder` → `Extensions` → `File Explorer` → `Python` → `dir`

**프라이썬을 설치하기:**
1.  `https://www.python.org/` 에서 `Install Python` 클릭
2.  설치 후 `~` 경로에 추가되면, 브라우저에서 `file://` 주소창을 입력하고 입력한 URL 을 보시면 됩니다.

---

### 💻 기본 실행 예시

```python
# 위 설명에서 예시처럼, 현재 디렉토리를 확인
dir
```

혹시 특정 파일이나 디렉토리를 찾으셨다면 `dir` 대신 `find` 명령어도 사용할 수 있습니다.
```bash
find <디렉토리명> -name "파일명"
```

어떤 작업을 하되든 프라이썬이 도와드릴게요! 😊

[ Prompt: 10.0 t/s | Generation: 3.1 t/s ]

어.. 그래도 2.7배. 약간 과장하면 3배 상승이네?

저작자표시 (새창열림)

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jetson nano jtop

jetson nano 에는 nvidia-smi가 없어서 비슷한거 찾는데

gpt님께서 jtop을 점지해주심

sudo apt install python3-pip
sudo -H pip3 install -U jetson-stats
sudo systemctl restart jtop.service
jtop

cuda 예제 빌드해서 갈구니까

GPU에 올라가긴 한다

+

저작자표시 (새창열림)

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jetson nano 4GB / qwen3.5 0.8B

콘솔로 하니 이상하게 종료시 엔터가 미친듯이 나와서 웹으로 시도.

vulkan 인데도 vulkan 가속을 못 받는지 cpu 100%로 돈다.

최대 성능으로 가동

$ sudo nvpmodel -q
NVPM WARN: fan mode is not set!
NV Power Mode: MAXN
0

docker 로 서비스 실행

$ sudo docker run -p 8080:8080 -v /home/jetson/llm:/models ghcr.io/ggml-org/llama.cpp:server-vulkan --chat-template-kwargs "{\"enable_thinking\": false}"  -m /models/Qwen3.5-0.8B-Q4_0.gguf -c 4096 --port 8080 --host 0.0.0.0
warn: LLAMA_ARG_HOST environment variable is set, but will be overwritten by command line argument --host
0.00.136.037 W Setting 'enable_thinking' via --chat-template-kwargs is deprecated. Use --reasoning on / --reasoning off instead.
0.00.136.924 I cmn  common_param: common_params_print_info: verbosity = 3 (adjust with the `-lv N` CLI arg)
0.00.138.536 W srv  llama_server: -----------------
0.00.138.551 W srv  llama_server: CORS is set to allow all origins ('*') and no API key is set
0.00.138.552 W srv  llama_server: this can be a security risk (cross-origin attacks)
0.00.138.553 W srv  llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655
0.00.138.554 W srv  llama_server: -----------------
0.00.139.997 I srv    load_model: loading model '/models/Qwen3.5-0.8B-Q4_0.gguf'
0.04.582.967 I srv    load_model: initializing, n_slots = 4, n_ctx_slot = 4096, kv_unified = 'true'
0.04.681.224 I srv  llama_server: model loaded
0.04.681.254 I srv  llama_server: listening on http://0.0.0.0:8080
0.19.911.095 I slot get_availabl: id  3 | task -1 | selected slot by LRU, t_last = -1
0.19.911.286 I slot launch_slot_: id  3 | task 0 | processing task, is_child = 0
0.22.612.244 I slot print_timing: id  3 | task 0 | prompt eval time =    1093.85 ms /    13 tokens (   84.14 ms per token,    11.88 tokens per second)
0.22.612.263 I slot print_timing: id  3 | task 0 |        eval time =    1606.96 ms /    12 tokens (  133.91 ms per token,     7.47 tokens per second)
0.22.612.266 I slot print_timing: id  3 | task 0 |       total time =    2700.81 ms /    25 tokens
0.22.612.276 I slot print_timing: id  3 | task 0 |    graphs reused =         11
0.22.612.345 I slot      release: id  3 | task 0 | stop processing: n_tokens = 24, truncated = 0
0.32.329.433 I slot get_availabl: id  3 | task -1 | selected slot by LCP similarity, sim_best = 0.243 (> 0.100 thold), f_keep = 0.375
0.32.406.612 I slot launch_slot_: id  3 | task 14 | processing task, is_child = 0
0.37.382.135 I slot print_timing: id  3 | task 14 | prompt eval time =    2254.95 ms /    28 tokens (   80.53 ms per token,    12.42 tokens per second)
0.37.382.154 I slot print_timing: id  3 | task 14 |        eval time =    2720.41 ms /    18 tokens (  151.13 ms per token,     6.62 tokens per second)
0.37.382.157 I slot print_timing: id  3 | task 14 |       total time =    4975.37 ms /    46 tokens
0.37.382.160 I slot print_timing: id  3 | task 14 |    graphs reused =         27
0.37.382.205 I slot      release: id  3 | task 14 | stop processing: n_tokens = 54, truncated = 0
0.52.606.713 I slot get_availabl: id  3 | task -1 | selected slot by LCP similarity, sim_best = 0.471 (> 0.100 thold), f_keep = 0.611
0.52.606.847 I slot launch_slot_: id  3 | task 35 | processing task, is_child = 0
1.10.385.311 I slot print_timing: id  3 | task 35 | n_decoded =    100, tg =   6.70 t/s, tg_3s =   6.70 t/s
1.11.031.932 I slot print_timing: id  3 | task 35 | prompt eval time =    2847.89 ms /    37 tokens (   76.97 ms per token,    12.99 tokens per second)
1.11.031.951 I slot print_timing: id  3 | task 35 |        eval time =   15577.07 ms /   104 tokens (  149.78 ms per token,     6.68 tokens per second)
1.11.031.954 I slot print_timing: id  3 | task 35 |       total time =   18424.96 ms /   141 tokens
1.11.031.960 I slot print_timing: id  3 | task 35 |    graphs reused =        129
1.11.032.011 I slot      release: id  3 | task 35 | stop processing: n_tokens = 173, truncated = 0
3.50.029.790 I slot get_availabl: id  3 | task -1 | selected slot by LCP similarity, sim_best = 0.333 (> 0.100 thold), f_keep = 0.382
3.50.175.603 I slot launch_slot_: id  3 | task 142 | processing task, is_child = 0
3.57.905.059 I slot print_timing: id  3 | task 142 | prompt processing, n_tokens =    105, progress = 0.86, t =   7.73 s / 13.58 tokens per second
3.59.618.519 I slot print_timing: id  3 | task 142 | prompt processing, n_tokens =    128, progress = 0.98, t =   9.44 s / 13.56 tokens per second
4.10.877.834 I slot print_timing: id  3 | task 142 | prompt eval time =    9819.15 ms /   132 tokens (   74.39 ms per token,    13.44 tokens per second)
4.10.877.855 I slot print_timing: id  3 | task 142 |        eval time =   10882.92 ms /    72 tokens (  151.15 ms per token,     6.62 tokens per second)
4.10.877.858 I slot print_timing: id  3 | task 142 |       total time =   20702.07 ms /   204 tokens
4.10.877.862 I slot print_timing: id  3 | task 142 |    graphs reused =        198
4.10.877.944 I slot      release: id  3 | task 142 | stop processing: n_tokens = 269, truncated = 0

cpu 에서 10 token/sec 미만으로 나온다.

msec	token	token/sec
2700.81 (안녕)	25	9.25648231456489
4975.37 (안녕)	46	9.24554354751506
18424.96 (너에 대해 소개)	141	7.65266247525096
20702.07 (golang으로 hello world)	204	9.85408705506261

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jetson nano 과 llama.cpp (cuda 가속, 빌드 포기)

by claude

Jetson Nano 사양
Compute Capability: 5.3

Jetson Nano는 Maxwell 아키텍처 기반 GPU(128 CUDA 코어)를 사용하며, compute capability는 5.3입니다.

CUDA 버전

Jetson Nano는 JetPack 버전에 따라 CUDA 버전이 결정됩니다.
Nano에서 지원되는 마지막(최신) JetPack은 JetPack 4.6.x 계열이며, 이때 CUDA 10.2가 탑재됩니다.
참고로 Jetson Nano는 32비트 시절부터 나온 구형 라인업(2019년 출시)이라 JetPack 5.x(CUDA 11.x 이상)는 지원하지 않습니다. JetPack 5 이상은 Xavier, Orin 계열부터 지원됩니다

netson nano / maxwell 기반 128 cuda core

GPU NVIDIA Maxwell architecture with 128 NVIDIA CUDA® cores
CPU Quad-core ARM Cortex-A57 MPCore processor
Memory 4 GB 64-bit LPDDR4, 1600MHz 25.6 GB/s

[링크 : https://developer.nvidia.com/embedded/jetson-nano]

맥스웰 compute capability - 5.x

all compute-capability 3.x (Kepler) devices but are not supported on compute-capability 5.x (Maxwell)

[링크 : https://docs.nvidia.com/cuda/maxwell-compatibility-guide/]

cuda 10.2

$ cat /etc/nv_tegra_release
# R32 (release), REVISION: 7.1, GCID: 29818004, BOARD: t210ref, EABI: aarch64, DATE: Sat Feb 19 17:05:08 UTC 2022

$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Sun_Feb_28_22:34:44_PST_2021
Cuda compilation tools, release 10.2, V10.2.300
Build cuda_10.2_r440.TC440_70.29663091_0

b10068 인데 cuda 12 와 cuda 13에 대해서 지원한다.

소스에서 빌드한다고 해서 될 게 아닌듯..

Windows:

Windows x64 (CPU)
Windows arm64 (CPU)
Windows arm64 (OpenCL Adreno)
Windows x64 (CUDA 12) - CUDA 12.4 DLLs
Windows x64 (CUDA 13) - CUDA 13.3 DLLs
Windows x64 (Vulkan)
Windows x64 (OpenVINO)
Windows x64 (SYCL)
Windows x64 (HIP)

[링크 : https://github.com/ggml-org/llama.cpp/releases]

+

이건 컴파일러 버전이 낮아서라고.. 에라이

[ 5%] Building CXX object tools/ui/CMakeFiles/llama-ui-embed.dir/embed.cpp.o
/home/jetson/src/llama.cpp/tools/ui/embed.cpp:17:10: fatal error: filesystem: No such file or directory
#include <filesystem>
^~~~~~~~~~~~
compilation terminated.

filesystem은 c+17에 도입되었고 8.x 부터 지원.

[링크 : https://jtrimind.github.io/troubleshooting/filesystem/]

$ g++ --version
g++ (Ubuntu/Linaro 7.5.0-3ubuntu1~18.04) 7.5.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

$ sudo apt install gcc-8 g++-8

$ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 800 --slave /usr/bin/g++ g++ /usr/bin/g++-8
update-alternatives: using /usr/bin/gcc-8 to provide /usr/bin/gcc (gcc) in auto mode

$ g++ --version
g++ (Ubuntu/Linaro 8.4.0-1ubuntu1~18.04) 8.4.0
Copyright (C) 2018 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

cmake 깔고 이상한짓 해도 안되서 포기. vulkan도 잘 안되는 것 같고 머지..?

sudo apt-get install libssl-dev libssl1.1
/opt/cmake/bin/cmake -B build -DLLAMA_BUILD_SERVER=ON -DLLAMA_BUILD_TOOLS=OFF -DLLAMA_BUILD_UI=OFF
/opt/cmake/bin/cmake --build build -j4

sharerc 빌드하고 하려니 이 난리. 에라이

/home/jetson/src/shaderc/third_party/abseil_cpp/absl/base/policy_checks.h:59:2: error: #error "This package requires GCC 10 or higher."
#error "This package requires GCC 10 or higher."

저작자표시 (새창열림)

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jetson nano devloper kit SD card image(A02)

jetpack 이 4.5.1 vs 4.6.1 이라서

많은건 안바라고(!) 용량만 좀 넉넉하면 좋겠네

[링크 : https://developer.nvidia.com/embedded/downloads]

이건 이전에 구워놨던 jetracer 용으로 만들어진 이미지

[링크 : https://github.com/NVIDIA-AI-IOT/jetracer/blob/master/docs/software_setup.md]

저작자표시 (새창열림)

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Posted by 구차니

RCM(Remote Center of Motion)

다빈치 수술 로봇 같이 특정 점을 반드시 지나도록 할 때 그 지나는 점을 RCM 이라고 한다.

TCP 랑은 다르다는데.. 이해가 잘 안가는 중 ㅠㅠ

[링크 : https://pinkwink.kr/756]

저작자표시 (새창열림)

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Posted by 구차니

로봇 제어 Move

MoveJ - 먼가 이상하게(?) 휘어져서 갈때. joint 위주

MoveL - 그냥 직선으로만 움직일때 TCP를 선형으로(관절은 더 난리핌)

MoveP - 속도 중심(process?)

MoveCircle - viapoint를 찍어서 시작 - via - 끝 점을 통과하는 arc를 지나도록 계산.

[링크 : https://www.universal-robots.com/manuals/ko/HTML/SW5_25_1/Content/prod-usr-man/complianceUR3e/SW_sections/first_program/move.htm]

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mycobot280 pi / TCP(tool center point) (0)	2026.06.30

Posted by 구차니

elephant robototics mycobot ros2

일단은 micro HDMI 연결해서 시도.

다행히 x11 forwarding으로 되네. 모니터 안달아도 되겠다

ros2shell
ros2 run turtlesim turtlesim_node
ros2 run turtlesim turtle_teleop_key

하라는 대로 해보면 아래처럼 나온다.

ssh로 해서 귀찮아서 백그라운드로 돌리고 쑈하는 중

G(gola), B(?) 누르면 아래처럼 메시지가 나온다.

(ros2 galactic py3)er@er:~$ ros2 run turtlesim turtlesim_node
[INFO] [1783047470.975415679] [turtlesim]: Starting turtlesim with node name /turtlesim
[INFO] [1783047470.985410507] [turtlesim]: Spawning turtle [turtle1] at x=[5.544445], y=[5.544445], theta=[0.000000]
^Z
[1]+ Stopped ros2 run turtlesim turtlesim_node
(ros2 galactic py3)er@er:~$ bg
[1]+ ros2 run turtlesim turtlesim_node &
(ros2 galactic py3)er@er:~$ ros2 run turtlesim turtle_teleop_key
Reading from keyboard
---------------------------
Use arrow keys to move the turtle.
Use G|B|V|C|D|E|R|T keys to rotate to absolute orientations. 'F' to cancel a rotation.
'Q' to quit.
[INFO] [1783047541.329042626] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047541.872528190] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047541.888733657] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047541.904818475] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047541.919834899] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047541.952188870] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047541.984665508] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047542.016009140] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047542.048402795] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047542.080809062] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047542.112165583] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047542.144652053] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047542.176082352] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047542.192223411] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047542.224715381] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047542.256142236] [turtlesim]: Rotation goal completed successfully
[WARN] [1783047543.335142919] [turtlesim]: Rotation goal received before a previous goal finished. Aborting previous goal
[INFO] [1783047543.806747242] [turtlesim]: Rotation goal completed successfully
[WARN] [1783047545.833930732] [turtlesim]: Rotation goal received before a previous goal finished. Aborting previous goal
[INFO] [1783047545.834608550] [turtlesim]: Rotation goal completed successfully
[INFO] [1783047547.348717894] [turtlesim]: Rotation goal completed successfully

B 눌러서 살짝 돌아간 거북이

실행하면 먼가 나오는데

(ros2 galactic py3)er@er:~$ rqt_graph

hide 쪽 다 끄니 먼가 좀 나온다.

아니면 위에 topic을 적어주면 먼가 나온다.

[링크 : https://m.blog.naver.com/zeta0807/221775210962]

-v 옵션 줘서 topic list를 하면 위의 그래프와 동일한 내용이 나온다.

(ros2 galactic py3)er@er:~$ ros2 topic -h
usage: ros2 topic [-h] [--include-hidden-topics] Call `ros2 topic <command> -h` for more detailed usage. ...

Various topic related sub-commands

optional arguments:
  -h, --help            show this help message and exit
  --include-hidden-topics
                        Consider hidden topics as well

Commands:
  bw     Display bandwidth used by topic
  delay  Display delay of topic from timestamp in header
  echo   Output messages from a topic
  find   Output a list of available topics of a given type
  hz     Print the average publishing rate to screen
  info   Print information about a topic
  list   Output a list of available topics
  pub    Publish a message to a topic
  type   Print a topic's type

  Call `ros2 topic <command> -h` for more detailed usage.

(ros2 galactic py3)er@er:~$ ros2 topic list
/parameter_events
/rosout
/turtle1/cmd_vel
/turtle1/color_sensor
/turtle1/pose

(ros2 galactic py3)er@er:~$ ros2 topic list -v
Published topics:
* /parameter_events [rcl_interfaces/msg/ParameterEvent] 2 publishers
* /rosout [rcl_interfaces/msg/Log] 2 publishers
* /turtle1/color_sensor [turtlesim/msg/Color] 1 publisher
* /turtle1/pose [turtlesim/msg/Pose] 1 publisher

Subscribed topics:
* /parameter_events [rcl_interfaces/msg/ParameterEvent] 2 subscribers
* /turtle1/cmd_vel [geometry_msgs/msg/Twist] 1 subscriber

[링크 : https://docs.elephantrobotics.com/docs/gitbook-en/12-ApplicationBaseROS/12.2-ROS2/12.2.2-基础教程.html]

하라는 대로 하니 되긴한다. 좋네~

(ros2 galactic py3)er@er:~/colcon_ws$ colcon build --symlink-install
Starting >>> mycobot_description
Starting >>> mycobot_interfaces
Starting >>> mypalletizer_interfaces
Starting >>> mecharm_interfaces
Finished <<< mycobot_description [9.82s]
[Processing: mecharm_interfaces, mycobot_interfaces, mypalletizer_interfaces]
[Processing: mecharm_interfaces, mycobot_interfaces, mypalletizer_interfaces]
Finished <<< mecharm_interfaces [1min 10s]
[Processing: mycobot_interfaces, mypalletizer_interfaces]
[Processing: mycobot_interfaces, mypalletizer_interfaces]
Finished <<< mycobot_interfaces [2min 26s]
Starting >>> mycobot_communication
Starting >>> mecharm_communication
Finished <<< mypalletizer_interfaces [2min 28s]
Starting >>> mypalletizer_communication
Finished <<< mycobot_communication [10.3s]
Starting >>> mybuddy
Starting >>> mycobot_280
Finished <<< mecharm_communication [10.4s]
Starting >>> mecharm
Finished <<< mypalletizer_communication [10.6s]
Starting >>> mecharm_pi
Finished <<< mecharm [13.1s]
Starting >>> mycobot_280jn
Finished <<< mybuddy [13.4s]
Starting >>> mycobot_280pi
Finished <<< mycobot_280 [13.5s]
Starting >>> mycobot_320
Finished <<< mecharm_pi [11.9s]
Starting >>> mycobot_320pi
Finished <<< mycobot_280jn [12.5s]
Starting >>> mypalletizer_260
Finished <<< mycobot_320 [12.7s]
Finished <<< mycobot_280pi [14.6s]
Starting >>> mypalletizer_260_pi
Starting >>> ultraarm
Finished <<< mycobot_320pi [11.3s]
Finished <<< mypalletizer_260 [10.2s]
Finished <<< mypalletizer_260_pi [10.3s]
Finished <<< ultraarm [10.3s]

Summary: 18 packages finished [3min 17s]
(ros2 galactic py3)er@er:~/colcon_ws$ source install/setup.bash
(ros2 galactic py3)er@er:~/colcon_ws$ ros2 launch mycobot_280pi slider_control.launch.py

[INFO] [launch]: All log files can be found below /home/er/.ros/log/2026-07-03-11-17-17-203959-er-9929
[INFO] [launch]: Default logging verbosity is set to INFO
[INFO] [robot_state_publisher-1]: process started with pid [9959]
[INFO] [joint_state_publisher_gui-2]: process started with pid [9961]
[INFO] [rviz2-3]: process started with pid [9963]
[INFO] [slider_control-4]: process started with pid [9965]
[robot_state_publisher-1] Link joint2 had 1 children
[robot_state_publisher-1] Link joint3 had 1 children
[robot_state_publisher-1] Link joint4 had 1 children
[robot_state_publisher-1] Link joint5 had 1 children
[robot_state_publisher-1] Link joint6 had 1 children
[robot_state_publisher-1] Link joint6_flange had 0 children
[robot_state_publisher-1] [INFO] [1783048639.394243790] [robot_state_publisher]: got segment joint1
[robot_state_publisher-1] [INFO] [1783048639.394549934] [robot_state_publisher]: got segment joint2
[robot_state_publisher-1] [INFO] [1783048639.394636748] [robot_state_publisher]: got segment joint3
[robot_state_publisher-1] [INFO] [1783048639.394680932] [robot_state_publisher]: got segment joint4
[robot_state_publisher-1] [INFO] [1783048639.394718784] [robot_state_publisher]: got segment joint5
[robot_state_publisher-1] [INFO] [1783048639.394756061] [robot_state_publisher]: got segment joint6
[robot_state_publisher-1] [INFO] [1783048639.394793246] [robot_state_publisher]: got segment joint6_flange
[joint_state_publisher_gui-2] [INFO] [1783048643.531952578] [joint_state_publisher]: Waiting for robot_description to be published on the robot_description topic...
[joint_state_publisher_gui-2] [INFO] [1783048643.610075662] [joint_state_publisher]: Centering
[joint_state_publisher_gui-2] [INFO] [1783048644.027434405] [joint_state_publisher]: Centering
[rviz2-3] [INFO] [1783048644.403594556] [rviz2]: Stereo is NOT SUPPORTED
[rviz2-3] [INFO] [1783048644.404085494] [rviz2]: OpenGl version: 3.1 (GLSL 1.4)
[rviz2-3] [INFO] [1783048647.151156049] [rviz2]: Stereo is NOT SUPPORTED
[slider_control-4] array('d', [0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
[slider_control-4] array('d', [-0.0004613180000001549, -0.0004613180000001549, -0.0004613180000001549, -0.0004613180000001549, -0.0004613180000001549, -0.0004613180000001549])

실제 장비가 움직이는진 모르겠지만 뜨긴한다.

$ ros2 launch mycobot_280 test.launch.py
[INFO] [launch]: All log files can be found below /home/er/.ros/log/2026-07-03-11-23-38-154196-er-10335
[INFO] [launch]: Default logging verbosity is set to INFO
[INFO] [robot_state_publisher-1]: process started with pid [10367]
[INFO] [joint_state_publisher_gui-2]: process started with pid [10369]
[INFO] [rviz2-3]: process started with pid [10371]
[robot_state_publisher-1] Link joint2 had 1 children
[robot_state_publisher-1] Link joint3 had 1 children
[robot_state_publisher-1] Link joint4 had 1 children
[robot_state_publisher-1] Link joint5 had 1 children
[robot_state_publisher-1] Link joint6 had 1 children
[robot_state_publisher-1] Link joint6_flange had 0 children
[robot_state_publisher-1] Link env had 0 children
[robot_state_publisher-1] [INFO] [1783049020.099496084] [robot_state_publisher]: got segment env
[robot_state_publisher-1] [INFO] [1783049020.099850118] [robot_state_publisher]: got segment joint1
[robot_state_publisher-1] [INFO] [1783049020.099978210] [robot_state_publisher]: got segment joint2
[robot_state_publisher-1] [INFO] [1783049020.100036728] [robot_state_publisher]: got segment joint3
[robot_state_publisher-1] [INFO] [1783049020.100077783] [robot_state_publisher]: got segment joint4
[robot_state_publisher-1] [INFO] [1783049020.100116357] [robot_state_publisher]: got segment joint5
[robot_state_publisher-1] [INFO] [1783049020.100152116] [robot_state_publisher]: got segment joint6
[robot_state_publisher-1] [INFO] [1783049020.100192041] [robot_state_publisher]: got segment joint6_flange
[rviz2-3] [INFO] [1783049023.998647585] [rviz2]: Stereo is NOT SUPPORTED
[rviz2-3] [INFO] [1783049023.999256988] [rviz2]: OpenGl version: 3.1 (GLSL 1.4)
[joint_state_publisher_gui-2] [INFO] [1783049024.105868138] [joint_state_publisher]: Waiting for robot_description to be published on the robot_description topic...
[joint_state_publisher_gui-2] [INFO] [1783049024.137961673] [joint_state_publisher]: Centering
[joint_state_publisher_gui-2] [INFO] [1783049024.490631764] [joint_state_publisher]: Centering
[rviz2-3] [INFO] [1783049026.693983324] [rviz2]: Stereo is NOT SUPPORTED

[링크 : https://docs.elephantrobotics.com/docs/gitbook-en/12-ApplicationBaseROS/12.2-ROS2/12.2.4-rviz介绍及使用/myCobot-280.html]

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mycobot280 pi / TCP(tool center point) (0)	2026.06.30

Posted by 구차니

mycobot ros1/ros2

설치는 나중에 해보고 일단 사용법이나 찾아보는 중

매번 해보려고 해도 잘 안되서 답답하네

장치에서 깔려있는걸로 바로 시도해볼수 있나보다.

# The default serial port name of mycobot 280-PI version is "/dev/ttyAMA0", and the baud rate is 1000000.
ros2 launch mycobot_280pi slider_control.launch.py

[링크 : https://docs.elephantrobotics.com/docs/gitbook-en/12-ApplicationBaseROS/12.2-ROS2/12.2.4-rviz介绍及使用/myCobot-280.html]

# The default serial port name of mycobot 280-PI version is "/dev/ttyAMA0", and the baud rate is 1000000.
ros2 launch mycobot_280pi teleop_keyboard.launch.py

Mycobot Teleop Keyboard Controller
---------------------------
Movimg options(control coordinations [x,y,z,rx,ry,rz]):
              w(x+)

    a(y-)     s(x-)     d(y+)

    z(z-) x(z+)

u(rx+)   i(ry+)   o(rz+)
j(rx-)   k(ry-)   l(rz-)

Gripper control:
    g - open
    h - close

Other:
    1 - Go to init pose
    2 - Go to home pose
    3 - Resave home pose
    q - Quit

currently:    speed: 10    change percent: 2

[링크 : https://docs.elephantrobotics.com/docs/gitbook-en/12-ApplicationBaseROS/12.2-ROS2/12.2.4-rviz介绍及使用/myCobot-280.html]

[링크 : https://docs.elephantrobotics.com/docs/gitbook-en/12-ApplicationBaseROS/12.2-ROS2/12.2.2-基础教程.html]

[링크 : https://docs.elephantrobotics.com/docs/gitbook-en/12-ApplicationBaseROS/12.2-ROS2/12.2.3-ROS2介绍.html]

저작자표시 (새창열림)

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mycobot 280 pi / python (0)	2026.06.29

Posted by 구차니

구차니의 잡동사니 모음

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nvidia jetson agx thor 아니고 nvidia thor?

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