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<aside> 🦾 Recycling facilities are experiencing a significant loss of recyclable materials since up to 30% of items in recycling bins are non-recyclable. Canada's plastic recycling rate is at 9%, while the other portion is either burnt or disposed of in landfills . The objective of Project Three is to address the issues mentioned above by developing a system that can identify, classify, and verify the recyclability of containers. This system will use an automated method to ensure that containers are appropriately placed in the designated recycling bins. The answer to this problem consisted of two separate elements. The first component was managed by the modelling sub-team, which focused on the development of a mechanism capable of securely holding the hopper in a horizontal position and then adjusting its dispensing angle via the use of an actuator. The computer sub-team was responsible for managing the second component, which included the development of a Python software. This program used the QBot and QArm tools inside the Quanser simulation environment to recognize, classify, and position the containers.
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Transfer Function in The Stimulation
<aside> 👥 Personal Contributions :
Team’s Work :
<aside> 📝 Throughout project three, we have been tasked with creating a program that can identify, load, transfer, deposit, and return home a Q-Bot. I started first by deciding which sensors I should use for bin identification. First, I picked an ultrasonic sensor, as that was one of the sensors I thought would be useful in the process. Upon initiating the first function dispense, I encountered an issue where three bottles dropped simultaneously. After further consideration, I decided to make the function drop eight bottles, but rotate each time it dispenses and loads. This left an issue of excess bottles on the table after each transfer, so I had to fix my function to dispense, then load and continue, and loading is done or its requirements are not met. Moving on to transfer, I decided to change my choice of sensor to a colour sensor instead, as it was easier to deal with. I was having difficulty deciding where the Q-Bot should stop. I fixed that by slowing down the speed of the Q-Bot and changing angles when stopping. Overall, the return-home function was the one with the smoothest process. Unfortunately, all the functions and work processes would have been smoother if better subteam collaboration was done. Overall, this experience has given me a better understanding of Python, the Quanser environment, and how to work under pressure without much help from a partner.
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<aside> <img src="https://prod-files-secure.s3.us-west-2.amazonaws.com/e6262b4e-9c8a-4e10-83f8-a4a50e622356/2fb6b980-845d-40a0-97f0-5089fee0a19d/Screenshot_2024-04-10_205429.png" alt="https://prod-files-secure.s3.us-west-2.amazonaws.com/e6262b4e-9c8a-4e10-83f8-a4a50e622356/2fb6b980-845d-40a0-97f0-5089fee0a19d/Screenshot_2024-04-10_205429.png" width="40px" /> Python
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<aside> <img src="https://prod-files-secure.s3.us-west-2.amazonaws.com/e6262b4e-9c8a-4e10-83f8-a4a50e622356/887c3a86-ea4f-4eeb-81dc-77d6d5b2a13b/Screenshot_2024-04-10_205304.png" alt="https://prod-files-secure.s3.us-west-2.amazonaws.com/e6262b4e-9c8a-4e10-83f8-a4a50e622356/887c3a86-ea4f-4eeb-81dc-77d6d5b2a13b/Screenshot_2024-04-10_205304.png" width="40px" /> Microsoft Office
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<aside> 🖥️ Quanser
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