Project Overview

Breathing intelligence into industrial processes with a pinch of creativity and a dash of code.

  • Project Title: Mobile Bulk Goods Analysis Station
  • Duration: Winter 2019
  • Role: Developer and AI Specialist
  • Technologies Used: Revolution Pi, Python, Java, Bosch XDK, XDK Workbench, Eclipse, Figma

Problem Statement and Objectives

  • Problem Description: The classification of bulk goods at Zeppelin Systems lacked efficiency due to stationary and cumbersome analysis methods.
  • Project Objectives: To create a minimum viable product (MVP) for a portable and easy-to-use bulk goods analysis station, complemented by a companion app to optimize the operator's workflow.
  • Target Audience/Market: The immediate target audience was the engineering and operational staff at Zeppelin Systems. However, the solution has potential applicability across various industries dealing with bulk goods classification.

Challenges and Solutions

  • Key Challenges: Designing a compact analysis station that was both robust and precise, along with ensuring ease of use for workers not familiar with intricate technology.
  • Solutions Developed: We engineered a mobile station using the Revolution Pi and attached sensors, while leveraging Python and Java for the software backend and app development. Bosch XDK sensors were chosen for their reliability and XDK Workbench, Eclipse, along with Figma helped us in streamlining the development process.
  • Impact of Solutions: The portable nature of the analysis station, alongside the intuitive app, allowed for quicker and more flexible classification of bulk goods.

Development Process

  • Lifecycle Overview: The project followed a rapid prototyping approach typical of hackathons, with an emphasize on iterative development and MVP creation.
  • Phases of Development: Brainstorming and ideation, MVP development, coding the backend and app interface, and user experience refinement.
  • Collaboration: Collaboration was key, with five engineers co-creating and synthesizing diverse expertise to build a functional prototype within the hackathon's constraints.

Achievements and Outcomes

  • Milestones: Successful development of an operational MVP within the hackathon timeframe and presenting a live demonstration of our solution.
  • Final Outcomes: An innovative and feasible approach to bulk goods classification, showcasing potential for further development and industrial application.
  • Personal Learning: Gained invaluable experience in fast-paced, team-oriented development and reinforced my belief in the power of AI to revolutionize conventional industry processes.

Visuals and Demonstrations

  • Screenshots/Diagrams: [Include visuals of the MVP and the app interface. #todo]
  • Live Demos/Repositories: [Provide links to demos or repositories if publicly available. #todo]

Conclusion

  • Project Impact: The hackathon project not only provided a viable solution to Zeppelin Systems but also served as an affirmation of my developed skills in AI and robotics.
  • Career Reflection: This challenge was a microcosm of my broader ambitions, poignantly demonstrating that my academic pursuits in AI and robotics have real-world applications that can simplify and enhance industrial operations.

[Please note, additional information such as specific visuals, repository links, or detailed feedback from Zeppelin Systems would be needed to complete this section. #todo]