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Blog Poop Buster - A Vision Thing Idea
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  • Author Author: weiwei2
  • Date Created: 24 Sep 2019 1:30 AM Date Created
  • Views 3993 views
  • Likes 10 likes
  • Comments 21 comments
  • visionthingch
  • beaglebone ai projects
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Poop Buster - A Vision Thing Idea

weiwei2
weiwei2
24 Sep 2019
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Poop Buster

Poop Buster is a robotic poop cleaner for my children pet guinea pigs (there are 2 of them)

image

although they roam the garden during day time , they go back to cage with bedding at night

 

Problem

- guinea pigs poop needs to be manually clean by hand. We selectively pick the poop out and discard them at my garden plants and tree as fertilizer, leaving the bedding material intact

image

Note: guinea pigs are fully vegetarian and their poop actually is kinda not very smelly, not too hard to clean, can be used to fertilize my garden, but it still needs to be cleaned

 

well, we can in fact change the entire bedding and not manually picking up the poop but that will be kinda wasteful for the bedding material

eventually, the bedding material needs changing but by using poop buster to clean it we can prolong the lifespan of the bedding material and making it clean longer

image

end of lifespan of the guinea pig bedding (besides poop, there is also urine, so typically when it is too long duration, eventually we throw it at the garden as fertilizer)

 

 

The solution

The solution is to use a X-Y plotter to selective pick up the poop (the mechanism, either it is a suction, a gripper or others, needs to manually built).

i put the plotter (with only one stepper motor attached) on the guinea pigs cage to illustrate the idea. Coincidentally, the size is very suitable

image

The role of the BeagleBone AI is to perform poop reconigtion with is powerful DSP and EVE cores (look in the reference section for its spec). with its dual cortex-M4 cores it should be able to also be used to control the stepper motor pair on my plotter and the 'poop gripper' mechanism

a quick check on element14 community blog, like this one by Jan Cumps, shows that it is possible to do stepper motor with beaglebone black.

 

with micro-HDMI output i can also connect HDMI monitor to the beaglebone AI to observe its AI poop recognition in action

 

The Implementation Process

technically speaking the poop is extremely different from the bedding material so a classical image processing method may be sufficient for the task. However, AI method like machine learning or deep learning is general purpose image recognition method that we can train the device to seek and destroy the target. A bonus step that can be achieved with AI is that the engine can automatically derive the stage of health of the pets by looking at the composition of the poop (although i won't try to do this step for this contest)

 

References

[1] About BeagleBoneRegistered AI --> copied from https://beagleboard.org/ai

Built on the proven BeagleBoard.orgRegistered open source Linux approach, BeagleBoneRegistered AI fills the gap between small SBCs and more powerful industrial computers. Based on the Texas Instruments AM5729, developers have access to the powerful SoC with the ease of BeagleBoneRegistered Black header and mechanical compatibility. BeagleBoneRegistered AI makes it easy to explore how artificial intelligence (AI) can be used in everyday life via the TI C66x digital-signal-processor (DSP) cores and embedded-vision-engine (EVE) cores supported through an optimized TIDL machine learning OpenCL API with pre-installed tools. Focused on everyday automation in industrial, commercial and home applications.

Processor: Texas Instruments Sitara AM5729

  • Dual ArmRegistered CortexRegistered-A15 microprocessor subsystem
  • 2 C66x floating-point VLIW DSPs
  • 2.5MB of on-chip L3 RAM
  • 2x dual ArmRegistered CortexRegistered-M4 co-processors
  • 4x Embedded Vision Engines (EVEs)
  • 2x dual-core Programmable Real-Time Unit and Industrial Communication SubSystem (PRU-ICSS)
  • 2D-graphics accelerator (BB2D) subsystem
  • Dual-core PowerVRRegistered SGX544Tm 3D GPU
  • IVA-HD subsystem (4K @ 15fps encode and decode support for H.264, 1080p60 for others)
  • more ...

Board Feature Highlights

  • BeagleBone Black mechanical and header compatibility
  • 1GB RAM and 16GB on-board eMMC flash with high-speed interface
  • USB type-C for power and superspeed dual-role controller; and USB type-A host
  • Gigabit Ethernet, 2.4/5GHz WiFi, and Bluetooth
  • microHDMI
  • Zero-download out-of-box software experience with Debian GNU/Linux

 

[2] https://guineadad.com/blogs/news/guinea-pig-poop-chart-learn-what-your-piggies-poop-is-trying-to-tell-you

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Top Comments

  • jw0752
    jw0752 over 5 years ago +3
    I look forward to seeing how you solve some of the technical problems. It should be a great challenge. Thanks for posting. John
  • gecoz
    gecoz over 5 years ago in reply to weiwei2 +3
    Sure, you can certainly do it using AI, especially if you are new to AI and one of your goal for this project is actually to learn more about AI and machine learning.The point I was trying to make is that…
  • phoenixcomm
    phoenixcomm over 5 years ago +2
    Ok, but 2 questions 1. You have picked up a piece of crap with the gripper. Now, what do you do with your prize? 2. How are you going to locate the crap? or are you going to scan it like a tv one dot at…
Parents
  • gecoz
    gecoz over 5 years ago

    Interesting project, but I think applying AI for such task is a bit of an overkill. Supposing you are going to use a camera to locate the droppings, the difference between the guinea pig droppings and the wood shavings is easily identifiable by just looking at the pixels' colour, so it will be far easier (and faster) just to implement your own algorithm rather than using AI. Looking forward to see how this project will evolve.

     

    Fabio

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  • gecoz
    gecoz over 5 years ago

    Interesting project, but I think applying AI for such task is a bit of an overkill. Supposing you are going to use a camera to locate the droppings, the difference between the guinea pig droppings and the wood shavings is easily identifiable by just looking at the pixels' colour, so it will be far easier (and faster) just to implement your own algorithm rather than using AI. Looking forward to see how this project will evolve.

     

    Fabio

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  • weiwei2
    weiwei2 over 5 years ago in reply to gecoz

    ya, that's why i do write this in the implementation process that however i will try out both classical and AI method image image

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  • gecoz
    gecoz over 5 years ago in reply to weiwei2

    Sure, you can certainly do it using AI, especially if you are new to AI and one of your goal for this project is actually to learn more about AI and machine learning.The point I was trying to make is that machine learning is brilliant when used to solve problems that cannot be otherwise solved using traditional algorithms. This doesn't mean that it cannot be used also for simple applications, but it is a bit like using a sledgehammer to put a nail in a wall image.

     

    Fabio

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  • weiwei2
    weiwei2 over 5 years ago in reply to gecoz

    i cannot be more agree with you image

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