PRE2024 3 Group14

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

A lot the streets of big cities are full of litter. This is something that is noticed and bothers a lot of people. The litter is often washed away by rain and eventually ends up in our oceans. Therefore we are designing a robot that roams the Eindhoven citycenter streets and collects this litter. Currently this is done by people once a day which is not only labour intensive, but also means that the litter can be on the streets for up to 24 hours, which means that the trash is more likely to end up in the sewers, and eventually the oceans.

For this project we want to plan out the design of the robot, both it's hardware and software. We are focussing this project on the people that will use it and therefore we are basing our requirements on both our own experiences in the eindhoven center and on a few interviews with other people.

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 primary users of our product include city waste management departments, cleaning staff, urban residents, local businesses and tourists. City waste management teams wish to lower operational costs and improve street cleanliness but have to deal with high manual labor expenses and workforce limitations. The robot addresses these issues by offering round-the-clock cleaning. Municipal cleaning staff reap benefit from reduced physical efforts as the robot can perform repetitive tasks and leave more complex tasks for the staff. In addition, regular waste collection will improve the quality of life of the city’s residents who desire cleaner streets for hygienic and aesthetic reasons, local businesses who depend on tidy surroundings to attract their clientele and tourists who want to enhance their overall experience and who can build a good reputation for the city.

The stakeholders, parties indirectly affected by the robot’s implementation include city officials/ urban planners and environmental agencies. City officials and urban planners must take into account public perception, cleanliness standards, and budgeting, with the potential benefit of improved waste management and city image. Environmental agencies vouch for pollution reduction and sustainable waste disposal, which the robot could support through eco-friendly features, like being fully electric. Additionally, waste management companies must be able to integrate the robot into their existing collection systems, improving operations and facilitating collaboration.

Planning

Week	Student	Work Done	Total Time
1	Julien	Brainstorm meeting + planning (2h)/ Wrote User and stakeholder study (1h)	3h
	Mihail	brainstorm meeting + planning (2h), described approach and planning of project (3h)	5h
	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), taking pictures of stratum (1h)	3h
	Mihail	group meeting (2h), refined interview questions (1h)	3h
	Chantal	group meeting (2h), write interview questions (1h)	3h
	Danila	group meeting (2h)	2h
	Anbiya	research into cleaning companies and email (1h) group meeting (2h)	3h
3	Julien	Group meeting (5h), Conducted interviews (3h)
	Mihail	group meetings (4h), Conduct interviews (3h), Redesigning the idea (3h)	10h
	Chantal	group meeting (4h) Conduct interviews (3h), extract user opinions from interviews (2h)	9h
	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