The most significant risks which could jeopardize the success of the project are in regards to completing the build of the robot base in time and overall integration testing. To manage these risks, we delegated a week to sourcing materials and ordered parts as soon as we could. For integration, we still hope to finish our individual components within 1-2 weeks. For contingency plans, since we do have 3 slack weeks built in, that should still provide enough buffer time in case we do not finish build within the next week, as we already anticipated integration testing to take 2-3 weeks.
In regards to changes made to the existing design of the system, we have decided to replace our current camera (e-CAM50_CUNX) with another camera (Intel RealSense Depth Camera D435) that will allow us to include depth perception for navigation. We wanted to add this component as while we researched into different, more complicated, path planning algorithms, we realized we needed to keep track of some notion of depth. There were two ways to go about doing this: using something like LiDAR, or simply getting an RGBD camera. We went with the second option to reduce our level of complexity. We believe this camera will be helpful in mapping locations of trash objects relative to the robot, which may be helpful in determining which areas have yet to have been cleaned in the event of obstacles. In terms of costs, the camera is significantly more expensive than our old camera, but we believe it will be a worthwhile investment. Furthermore, since our team has not yet integrated the camera with our system, and the connection is just via USB, we don’t believe there is much build cost associated with acquiring the new camera.
Global Factors
When considering our use case requirements and target audience/ project stakeholders, we researched the issue, street trash, in great detail. We considered how street trash is seen/dealt with in different countries. The primary area in which global factors play a role, in terms of the design of the solution, is the types of trash that is most prominent in street litter, the type of roads/ terrain that trash tends to accumulate on, and its environment/ surroundings. We noticed that plastic bottles are one of the most prominent types of trash seen in street garbage in different areas. The types of trash our solution is being trained to collect include plastic water bottles, soda cans, and crumpled paper. This can be expanded upon after completion of capstone to further meet the need for other countries by training the model on other datasets and including other types of trash.
In terms of the terrain, while researching, we noticed that most areas with significant street garbage were lined with paved roads. Hence, our current solution uses Meccanum wheels, which work well on paved roads. However, to accommodate different areas and environments, the solution is flexible for changes. The wheels of the robot can be changed to allow for access on different terrains. In terms of the external environment, different areas/ locations can have different levels of foot traffic and crowd control. Our robot is designed to avoid obstacles larger than 1ft in height; however, it does not have a more complex algorithm that distinguishes between types of obstacles (people/cars). Hence, this would be an additional global factor to consider meeting after capstone; but it was mentioned here due to its relevancy to the problem.
Overall, the primary global factors our solution has been designed to accommodate include the specific type of trash seen in different areas and the type of terrain/ environment the robot must work in.
Cultural Factors
In consideration of cultural factors, our garbage collection robot would aid in reducing litter. Littering is against the law in developed countries, and littering can also be seen as morally dubious. Since our garbage collection robot collects trash off of the streets, it also helps reduce the amount of effective litter. Furthermore, different cultures have different attitudes to levels of personal hygiene and cleanliness. However, most agree that cleanliness is important, particularly after the spread of disease due to improper handling of waste. As our garbage collecting robot aims to improve overall sanitization, it also aids in upholding cleanliness.
Additionally, our garbage collecting robot is not tailored to any specific culture, but rather to improve society collectively. Hence, we do not consider cultural factors to be a main focus for our robot, but rather we see our robot aiming to fulfill a goal for the greater good.
Environmental Factors
Our garbage cleaning robot addresses the urgent need for satisfying environmental factors that benefit the community as a whole. Most importantly, our robot will provide a cleaner and more hygienic environment by reducing the significant amount of garbage on the streets. Waste on the streets is not only visually unappealing but also a safety hazard, causing diseases such as asthma and cancer. The cleaning feature of the robot alleviates such public health issues while also preventing garbage from spreading out to other areas and causing the same problems.
Additionally, our robot is designed to ensure public safety. We believe protecting the environment includes avoiding the destruction of ecosystems as well as harming humans and animals. Instead of using a vacuum cleaning mechanism or wheels, our robot uses a roller mechanism that keeps living creatures from accidentally getting inside the robot. Also, our robot employs object detection on the Jetson Nano Orin to avoid collisions with obstacles, assuring the safety of pedestrians. Moreover, we use sturdy aluminum extrusions and acrylic boards to prevent any leakage of harmful chemicals.
Finally, we will implement smart routing into our robot. This helps to reduce energy consumption caused by extra costly trips using an inefficient path-planning algorithm. In doing so, we can conserve battery energy and, subsequently, reduce the carbon footprint.
A was written by Hirani, B was written by Ritu, and C was written by Ella.
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