Problem
Have a low-cost 3D mapping system combining a 3D sensor with a mobile robot.
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
First, we started looking for previous implementations of the mobile robot with ROS and depth cameras. Then we start looking for methods to reconstruct 3D images using the depth sensor and a mobile robot.
Implementation
Ubuntu 16.04 was installed in the board and then Robotic Operating System where the interface was developed. The R200 camera implements a long-range 3D image system and stereo vision. The camera can provide color, depth and infrared video transmissions, in addition, it provides texture information, for this the overlay is used in a depth image to create a cloud of color points and superimpose it in a 3D model for reconstruction. The ROS meta-operating system will be used to control both the sensor and the robot and visualize the maps created with the RVIZ graphical interface that includes ROS. The system use the SLAM technique (Simultaneous Localization and Mapping) which consists of: the extraction of a reference point, data association, estimation of the state, updating the state and updating of the reference point. The camera will act as a publisher, it will publish the environment information, the actuators of the robot will act as subscribers, and finally the robot will publish its location.
