Problem
Fetching, transporting, and managing equipment uses valuable employee time. Manual equipment management is labor intensive, error prone, and limited by employee availability.
Our solution
Our Approach to Design
Design
There were several sketches and models until we had 2D and 3D representations of the final prototype. Next, the computer-aided design and model was created for all the components of the device. Then, all the components were 3D printed and we checked tolerances and adjustment to then reprint them again if necessary. Finally, we post-processed the parts, sanding and painting.
Primary research
We interviewed a class instructor, a lab manager, and a student to understand how the current inventory management system works in the University of Washington laboratories. Then we created surveys for students and lab staff, to understand the manual process to manage the laboratory equipment and items. Also, we sent surveys to potential users, including users of labs, warehouse employees, and libraries administrators to obtain information related to potential features and concerns about interacting with robots.
User Testing
First, we did 1:1 User Evaluation to test the Hardware/Software and the check-in/check-out process. Participants performed a series of tasks under instruction by one of our team members. Notes and video recording were taken, six participants are involved in each round of the 1:1 evaluation. Then, we ran a Fly On The Wall session to observe the Human-Robot Interaction. We took notes of people’s behaviors when the mobile robot was navigating through the environment, without and with sound alerts. The robot received a series of navigation goals sent by the operator and it was up to the navigation stack to do the routing and planning. We observed users for 20 mins in the GIX laboratory.
Functional Testing
First, we defined metrics for each part of the system. For the navigation, we sent a navigation goal to the Fetch and then we measured the success rate, time, distance from the nav goal, and the number of collisions. Next, for the Fetch and Kinova grasping, we also measured the pick and place time and success rate.
Secondary Research
We started with Technology and Business Research to find existing solutions for the problem and also review the hardware limitations and challenges. We also did a competitive analysis to determine which could be the competitive advantage of the system we were proposing.
Implementation
All the components of the system, the robotic arm, mobile robot, and the web app are connected to the decision engine. The decision engine manages distributed system and plans the action pipeline it could manage multiple robots and asynchronous message routing. The web app has integrated the Checkin and Checkout item functionality for items added to the database. Kinova robot arm sorts and kits the items available in the stock area. The Kinova arm uses OpenCV and the detection of ArUco markers to sort the items. Fetch mobile robot transports kitted items between the user and stock area. Fetch use the Intel RealSense T265 tracking camera for navigation and ArUco markers for nav recalibration, also it includes sound alerts for user interaction.
My takeaways
Developing a system that integrates a Webapp, a robotic arm, and a mobile robot helped me to understand how complex robotic systems work and are integrated. There are a lot of challenges related to the hardware, for instance, the size of the items that the arm could grasp or how accurate the navigation could be in an environment full of people. Finally, I learned about Human-Robot interaction, users are used to human interactions and we needed to add sound and visuals to try to imitate the human interaction.
