PRE2024 3 Group14

WALL-E

Group members

Name	Student ID	Current Study Programme
Chantal Smits	1689339	Electrical Engineering
Julien Rodriguez	1829599	Computer Science and Engineering
Mihail Abramov	1813978	Applied Mathematics & Computer Science and Engineering
Anbiya Popal	1838849	Electrical Engineering
Danila Bogdanovs	1782746	Electrical Engineering

Introduction

The struggles of waste collection are a big issue nowadays. Waste is found in the streets, in the rivers and oceans, and plastics can even be found in some of our food now, due to a lack of recycling. Most people have struggled with wether some piece of litter goes into plastic, paper or waste bin, or alternatively, they are out somewhere and cannot find any bins at all. At festivals and other big events like it, a lot of waste is often left on the big fields. Clearing this trash is a task that needs to be done, but is very labour intensive. Therefore we think that this should, and could be automized. We want to create a set of robots, one flying above the field to locate the trash, and then a few for each different type of waste (platic, paper, glass, etc.). We think that these robots can be used for these fields most efficiently, since after the festivals, there is no need for them to interact with humans. People can be very unpredictable and we would rather focus on the functionallity of the robot and to make it as good as possible for this scenario. For this project we want to plan out the design of the robot, both it's hardware and software, but we do not think it is reasonable to create a prototype yet, both because there is not enough time or budget, and then we do not have to rush the design stages to make sure all of the parts get to us in time.

Approach

Phase 1 - research

Define the topic of the project and the approach taken. Perform literature research on the topic.

Phase 2 - requirements

Collect user requirements through interviews. Specify the requirements.

Phase 3 - implementation

Write the algorithm and make design of the robot. Build a prototype and perform simulations.

Phase 4 - finalization

Test the prototype and finalize both code and design. Give a presentation.

Planning

Based on the chosen approach the planning of the project was created. The four phases of the project were split into more specific tasks and the timing for each task is specified. The planning is summarized in the following Gantt chart below:

The research should be performed in the first two week. The requirements identification will be performed up to week 3. Having the results from research and requirements phases, the implementation phase will be performed. Implementation will span from week 4 to week 6. The finalization phase concludes the project in weeks 6 and 7 after the implementation is completed.

Milestones

End of...

Week 1 - having a plan for the project

Week 2 - finishing the research

Week 3 - finishing analysis and specifying user requirements

Week 5 - completing algorithm and design for the prototype

Week 6 - making a simulation and building a prototype

Week 7 - tested the prototype and presented the project

Literature Review

Autonomous trash collecting robots are designed to identify, collect, and dispose of waste materials efficiently. Many of these robots use computer vision, AI-based object detection, and autonomous navigation to function effectively.

Sivasankar et al. (2017) describe an early-stage autonomous trash robot capable of collecting garbage using a predefined route. Nagayo et al. (2019) further enhance this concept by incorporating a wireless charging system, ensuring that the robot remains operational for extended periods in a campus environment.

A more advanced model by Kulshreshtha et al. (2021) integrates YOLOv4-tiny, a deep-learning-based object detection model, enabling higher accuracy in trash detection. Othman et al. (2020) contribute by designing an automated trash collector that uses sensors to improve object detection and classification.

Furthermore, Nayak et al. (2009) introduce the "TailGator", a semi-autonomous trash-collecting system designed for outdoor environments. These studies show how new waste management systems are powered by artificial intelligence in some way. Recent studies also researched multi-robot systems, where drones and ground robots collaborate to identify and collect litter. Milburn et al. (2023) present the Tandem Rover and Aerial Scrap Harvester (TRASH) system, which integrates a ground robot and a drone to collect waste more efficiently. Together with the aerial mapping waste detection system presented by Akbari et al., these two systems represent a state-of-the-art, multi-robot waste management approach. Integrating the two would result in a fully autonomous, intelligent waste collection system that detects waste using drones, maps and classifies waste with computer vision and GPS data and deploys a ground robot to collect waste, optimizing routes based on the drone’s findings.

Trash robots contribute significantly to environmental sustainability. MacLeod et al. (2021) highlight the global plastic pollution crisis, emphasizing the need for employing robots in waste collection. He et al. (2022) discuss microplastic pollution, noting how robotic trash collectors could aid in reducing the spread of plastic waste.

In urban settings, robotic solutions have been proposed to address litter issues caused by nightlife and tourism. Becherucci & Pon (2014) compare waste accumulation in nightlife-dense areas, while Fallahranjbar et al. (2018) propose urban planning strategies combined with robotic waste collection. Burlakovs et al. (2020) propose the use of IoT-based smart waste bins which help optimize waste collection schedules using AI-based analytics. These studies give sufficient grounds on which one could say that deploying robotic trash collection systems in highly polluted urban spaces has social and environmental benefits.

However, these technological advancements are having several challenges that complicate the adoption of trash robots. Firstly, most trash robots rely on battery power, limiting the time for which one such robot could operate. A solution could be deploying multiple robots that could work in “shifts”, but this sparks another challenge: high costs and scalability issues. Sunil & Shanavas (2023) discuss how autonomous office waste collection robots face financial and technical barriers in deployment, meaning that financial struggles are to be expected when deploying such systems on city scale. Lastly ethical concerns set in, particularly around labor displacement and public safety (Jamil et al., 2023). Additionally, regulatory frameworks must be developed to standardize the deployment of trash robots in urban settings.

To summarize, current technological advancements allow for trash detection, autonomous navigation and multi-robot collaboration. This has the potential of reducing plastic pollution and optimizing urban and tourist area waste management. Future research needs to be done on more energy-efficient designs, lowering costs of the robotic solutions and policy and regulation frameworks.

User and stakeholder analysis

The target users for our product would be:

• Event organizers who seek efficient and cost-effective cleaning solutions for outdoor events.
• Cleanup crews, who need support for manual trash collection. They could learn how to operate the robot, or work alongside it to improve efficiency.

Stakeholders:

• City waste management departments can be consulted on integration feasibility and potentially subsidize the product.
• Environmental agencies who focus on waste reduction can explore environmental benefits of the robot.
• General public, even though the robot is most efficient in an environment without humans, it must be able to detect their presence and act as safely as possible.

All of these users and stakeholders can be interviewed to provide important information on:

• Willingness to integrate automated solutions
• User perception
• Potential improvements of robot's design
• Safety concerns
• Potential cost savings
• Impact on environment
• Impact on cleaner's workload
• and so on...

Planning

Week	Student	Work Done	Total Time
1	Julien	Brainstorm meeting + planning (2h)/ Wrote User and stakeholder study (1h)	3h
	Mihail
	Chantal	Introduction lecture (1h) brainstorm meeting + planning (2h) problem statement & objectives (1h)	4h
	Danila	Introduction lecture (1h) brainstorm meeting + planning (2h) literature review (4h)	7h
	Anbiya	brainstorm meeting + planning (2h) literature review (4h)	6h
2	Julien	group meeting (2h)
	Mihail	group meeting (2h)
	Chantal	group meeting (2h)
	Danila	group meeting (2h)	2h
	Anbiya	research into cleaning companies and email (1h) group meeting (2h)	3h
3	Julien
	Mihail
	Chantal
	Danila	group meeting (5h) interviews (1h)	6h
	Anbiya
4	Julien
	Mihail
	Chantal
	Danila
	Anbiya
5	Julien
	Mihail
	Chantal
	Danila
	Anbiya
6	Julien
	Mihail
	Chantal
	Danila
	Anbiya
7	Julien
	Mihail
	Chantal
	Danila
	Anbiya