[Backup] Using the DSP on Gumstix with Yocto from SleepyRobot

本文介绍如何使用Yocto构建系统为Gumstix开发板配置数字信号处理器(DSP),包括安装必要软件、配置环境变量、解决构建过程中遇到的问题,并通过测试验证DSP是否正常工作。

Using the DSP on Gumstix with Yocto

Lots of discussion on the mailing list lately on how to get the DSP working using the new yocto build system. This works for me…

Start with the standard instructions and make sure you can build gumstix-console-image.

I’m using 64-bit (AMD64) Ubuntu 12.04 LTS, so I needed to also install ia32-libs for the TI installer. You may not need this, depending on your build host.

sudo apt-get install ia32-libs

Now, add the meta-ti layer:

cd ~/yocto/poky
git clone git://git.yoctoproject.org/meta-ti
cd meta-ti
git checkout denzil

Then edit ~/yocto/build/conf/bblayers.conf to add the meta-ti layer so that it looks something like this:

BBLAYERS ?= " \
  /home/chris/yocto/poky/meta \
  /home/chris/yocto/poky/meta-yocto \
  /home/chris/yocto/poky/meta-openembedded/meta-gnome \
  /home/chris/yocto/poky/meta-openembedded/meta-oe \
  /home/chris/yocto/poky/meta-openembedded/meta-xfce \
  /home/chris/yocto/poky/meta-gumstix \
  /home/chris/yocto/poky/meta-ti \
  "

Edit ~/yocto/build/conf/local.conf and add the following line to ignore some of the meta-ti recipes:

BBMASK ?= ".*/meta-ti/recipes-(misc|bsp/formfactor)/"

Fix up the toolchain_path env variable by editing ~/yocto/poky/meta-gumstix/conf/machine/overo.inc and adding the line below. This fixes the problem finding the compiler when building dsplink.

TOOLCHAIN_PATH ?= "${STAGING_DIR_NATIVE}${prefix_native}/bin/${TUNE_PKGARCH}${HOST_VENDOR}-${HOST_OS}"
TOOLCHAIN_SYSPATH ?= "${TOOLCHAIN_PATH}/${TARGET_SYS}"

Then edit ~/yocto/poky/meta-gumstix/conf/machine/include/omap3.inc and just before the tune-cortexa8.inc line, add the line below. This fixes the problem building the DMAI recipe.

require conf/machine/include/soc-family.inc

Now, download ti_cgt_c6000_7.2.7_setup_linux_x86.bin from TI and place in your ~/yocto/build/downloads directory. Touch ti_cgt_c6000_7.2.7_setup_linux_x86.bin.done in the same directory.

Ok! You should now be able to “bitbake gstreamer-ti”. It will take a bit of time.

If this works, you can build an image that has gstreamer-ti along with other gstreamer-plugins. Here is one such image recipe:gumstix-dsp-image.bb

Once the image is built, create your sd card. When booting for the first time, stop the u-boot countdown and change the env vars to leave a memory hole for cmemk.

setenv mem 'mem=96M@0x80000000 mem=384M@0x88000000'
setenv mpurate 800
setenv mmcargs 'setenv bootargs console=${console} mpurate=${mpurate} vram=${vram} omapfb.mode=dvi:${dvimode} omapfb.debug=y omapfb.vram=${fbvram} omapdss.def_disp=${defaultdisplay} ${mem} root=${mmcroot} rootfstype=${mmcrootfstype}'
saveenv
reset

Go ahead and boot into linux, then edit /lib/systemd/system/gstti-init.service and remove the line which starts “ConditionKernelCommandLine=”. This condition causes the modules to only get loaded if the memory hole is very specific.

Now reboot one more time.

Once booted, check your modules and make sure cmemk loaded for you:

root@overo:~# lsmod                                                             
Module                  Size  Used by                                           
mt9v032                 7237  4294967295                                        
omap3_isp             131955  0                                                 
v4l2_common             8933  2 mt9v032,omap3_isp                               
videodev               99128  3 mt9v032,omap3_isp,v4l2_common                   
media                  12714  3 mt9v032,omap3_isp,videodev                      
ads7846                10488  0                                                 
sdmak                   4076  0                                                 
lpm_omap3530            6797  0                                                 
dsplinkk              131408  1 lpm_omap3530                                    
cmemk                  22005  0                                                 
rfcomm                 56119  0                                                 
hidp                   16251  0                                                 
bluetooth             258745  4 rfcomm,hidp                                     
rfkill                 17524  1 bluetooth                                       
ipv6                  249350  16      

Test out the dsp with a simple pipeline:

gst-launch videotestsrc num-buffers=5 ! TIVidenc1 codecName=mpeg4enc engineName=codecServer ! fakesink

Good luck.

内容概要:本文围绕六自由度机械臂的人工神经网络(ANN)设计展开,重点研究了正向与逆向运动学求解、正向动力学控制以及基于拉格朗日-欧拉法推导逆向动力学方程,并通过Matlab代码实现相关算法。文章结合理论推导与仿真实践,利用人工神经网络对复杂的非线性关系进行建模与逼近,提升机械臂运动控制的精度与效率。同时涵盖了路径规划中的RRT算法与B样条优化方法,形成从运动学到动力学再到轨迹优化的完整技术链条。; 适合人群:具备一定机器人学、自动控制理论基础,熟悉Matlab编程,从事智能控制、机器人控制、运动学六自由度机械臂ANN人工神经网络设计:正向逆向运动学求解、正向动力学控制、拉格朗日-欧拉法推导逆向动力学方程(Matlab代码实现)建模等相关方向的研究生、科研人员及工程技术人员。; 使用场景及目标:①掌握机械臂正/逆运动学的数学建模与ANN求解方法;②理解拉格朗日-欧拉法在动力学建模中的应用;③实现基于神经网络的动力学补偿与高精度轨迹跟踪控制;④结合RRT与B样条完成平滑路径规划与优化。; 阅读建议:建议读者结合Matlab代码动手实践,先从运动学建模入手,逐步深入动力学分析与神经网络训练,注重理论推导与仿真实验的结合,以充分理解机械臂控制系统的设计流程与优化策略。
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符  | 博主筛选后可见
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值