The document discusses the design of collaborative robots for spaceship monitoring, focusing on damage inspection during NASA's hackathon. It introduces an artificial bee colony algorithm for efficient damage assessment and highlights the robot's hardware design inspired by natural instincts. The project aims to enhance astronaut safety, reduce extravehicular activity time, and improve damage categorization through AI.

1. Issue 1-1 Design by Nature
Team 13 : Tabby Rangers 2018 NASA Hackathon
Collaborative Spaceship
Monitoring Robots

2. 呂明臻 楊喬宇 葛承恩 紀曦昀 林佑晟
Ming Joey James CC Steven
National Taiwan University
National Taiwan University
of Science and Technology
Graduate Institute of
Electrical Engineering
Department of
Industrial Design
National Taiwan University
Graduate Institute of
Networking and Multimedia
National Taiwan University
of Science and Technology
Department of Electronic &
Computer Engineering
Program in Digital Content
& Technology
National Chengchi
University

3. • Limited extra-vehicular activity (EVA) time
• Limit of optical instrument
• Limited by light source and angle
Current Situation
Challenge：Design by Nature
@ 2015 JAXANASA
In Space, damage inspection only can be
done by astronauts

4. CSMS - Damage Inspection System
Design Highlight
Analyze Damage Status
Semi-Automatically
• Artificial bee colony algorithm
• Structured light scanning
Hardware Structure

5. Flight Algorithm＝Full Scan + Partial Scan
Full Scan Partial Scan＝Dynamic Programming
1. Predefine block of spacecraft
2. Dynamic dispatch Beebots to each block
＝Human-Robot Collaboration
1. Be aware of potential damage, but don’t
know tis detail
2. Switch to small range scanning

6. Artificial Bee Colony Algorithm
Originated from bee swarm finding flowers by communication with each other.
The algorithm can help CSMS to find the most damaged area of spacecraft.

7. Artificial Bee Colony Algorithm
Originated from bee swarm finding flowers by communication with each other.
The algorithm can help CSMS to find the most damaged area of spacecraft.

8. CSMS will project IR light pattern and use its distortion to determine the shape of
the surface.
Structured light scanning
Our Prototype by Kinect

9. Azure Machine Learning Training
Structured
Light
Scanning
Depth ImageSurface Damage
Training AI
with photos
first
categoriz
ation
of
damage
CSMS will categorize the damage type first to help decision making for astronauts.

10. Collaborative Spaceship Monitoring Robots
Movement Control：Gas thruster
Attitude Control：CMS
(Control Moment Gyroscope)
• Inspired by cat：self rotation structure for
Conservation of angular momentum
Design Highlight Hardware Structure

11. Our Prototype by Lego Mindstorm EV3
Control Moment Gyroscope
Inspired by the natural instinct of cat
preventing from injured of falling from high
ground.

12. • Safety：Reduce EVA time of astronauts
• Efficiency：Collaboration between
human and Robot
• Precision：Detailed damage inspection
• AI：Damage categorization assistant
Contribution

14. Github Page