team photo

Figure 1
project photo

Figure 2
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Team 06

Team Members

Faculty Advisor

Marc Lopez
Ryan Le
Ziqing Ai

Wajid Chishty

Sponsor

Belcan

sponsored by
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Mobile Modular Assistant (MoMoA)

The multidisciplinary team will choose an off-the-shelf (COTS) drone that fits the needs of the systems engineering design challenge. The objective of this project is to design a drone (example shown in Figure 1) with a common module interface so that it can be easily repurposed for different objectives. The scope of this project defines two module applications with the potential of a third module application to be defined by the student team. The two module applications are: 1) Garden Fruit/Vegetable Picker – the drone should be able to identify and pick different fruits. 2) Dog Walker – the drone should be able follow a dog walking at night with a flashlight. Figure 1. Example Drone with Camera (Team will Choose another Drone to Fit Requirement Needs.) The team will meet early in the Fall semester of 2024 as a large group with the sponsor to be sure all communication and design requirements of the subgroups are well understood. The current scope plan is shown in Figure 2 and is subject to be modified after the first meeting. The UMich students will be responsible for the drone selection and the design/build of the common module user interface. A simple prototype of the interface will be sent to UConn for testing so that the modular components can be tested and designed in parallel. The UConn students will design the module components under the configuration requirements so that they can be interfaced with the UMich drone. In particular, the UConn ME team will be responsible for designing the end effectors, identify the servo motors, and design/build the module rig so that it is compatible with the interface connections. The UConn ECE team will be responsible for circuit design, microcontroller programming, device instrumentation (servo motors, sensors, etc.). The UConn CSE team will be in charge of research and programming of algorithms for object recognition and tracking. Interface and integration of the work and functionalities developed by different teams will

Our team collaborated with Computer Science & Engineering 35,Electrical and Computer Engineering 7 on this project.