Boosting user confidence and engagement by improving a notification system design
UX Design
Design Iteration
Design Research
User Engagement
Usability Testing
Prototype
Multi-Robot Control
Autonomous Driving
Data Management
Overview
Role
UX Designer
UI Designer
UX Researcher
Duration
1 month
Team
2 UX/UI Designers
1 CTO/Program Lead
6 Software Engineers
3 Machine Learning Engineers
5 Hardware Engineers
3 Test Engineers
Introduction
OWSI is a multi-robot control system driven by  Swarm Intelligenceand and Machine Learning. The system assists users in controlling different types of robots collaborating and completing missions as planned. Users can plan, monitor, and analyze robot activities and performances.
Project Goals
Designing a trust and safety operation system by providing reliable fraud detection and accurate operational processes for a multi-robot control system.
Outcome Highlight
  1. Upgraded notification system. Redesigned UI components for multi-robot control system and built up a dynamic UI Library for diverse customer needs.
  2. Enhanced the notification effectivity.
  3. Leveled up the design system for multiple stakeholders and objects.
Notification System and Components Final Design
"How could we design a trust and safety operation system for users?"
Challenges
Challenge 1
Users may overlook urgent messages when managing multiple tasks if the notification designs are too similar.
How users could recognize the objective of the message?
How users could know the data transferring between different objectives?
Challenge 2
Users felt a loss of control when the data transfer between different robots was hidden.
How users could know whether the message required an action?
How users could avoid missing any urgent messages?
Defined problems and insights by hosting a workshop
To understand the workflows and stakeholders' insights, I organized a collaborative workshop that involved engineers and program managers. I designed a brainstorming matrix to frame each mission stages and understand the users' actions, touchpoints, thoughts, frustrations, and opportunities.
1. Learned about the user flows and operation procedures.
In the workshop, I clarified the robot collaboration and procedures, then simplified the complex workflows using visual storytelling methods to ensure team alignment.
2. Summarized and analyzed the POVs of stakeholders
I summarized and categorized the insights and thoughts of each stakeholder.
Operator
  • If something is wrong, where to find debug procedure/ which to follow?
  • If there are many alerts, how do I know what to look at first?
  • Add notes for my reaction to one certain notification.
Geologist
  • I want to know the key KPIs and the rock’s status.
  • I want to know the robot’s next activity.
  • I might need to check the rockface photos in one place.
Tech Support
  • I want to read all of the notification at one place.
  • I want to know what the operators reactions for the error detection or notification.
Manager
  • I don’t want to read all notification.
  • I only want to read the big changes and urgent information.
Visitor
  • I will be curious about the robots’ performance and activities.
  • I hope I won’t mess up the task if I touch something accidentally.
3. Set up the goals of the notifications
Learned from the stakeholders' POVs, not only providing a message, the users want to recognize the object quickly, diagnose the problem, and know how to recover the issue.

Recognize

Diagnose

Recover

Challenge 1
Users may overlook urgent messages when managing multiple tasks if the notification designs are too similar.
To keep the consistency of all of the products in OffWorld, we shared the same template with other systems. All of the notifications will be presented in the same place with a similar design.
1. Categorized the notification inventory.
I clarified the categories of the notification messages and the audiences to ensure the messages were delivered to the right stakeholders.
Categories of notification messages
  • KPIs Update
  • Surveyor created lidar maps.
  • Surveyor gathered 124 rock photos.
  • Excavator finished the conduction.
  • Robot Activity
  • Surveyor starts scanning plan.
  • Surveyor starts photo shooting plan.
  • Surveyor transferred the rock photos to the excavator.
  • Next Step
    (Call-To-Action)
  • Should allow the Surveyor to transfer the photos to the Excavator?
  • Should allow the Excavator to load rock photos?
  • To start a plan, select a robot on the map or squad display.
  • Sure you want to delete?
  • Fraud Detection
    (Call-To-Action)
  • The loading didn't go through. Please do the following actions.
  • Surveyor battery is running low, should allow the Surveyor to run back home?
  • Detected abnormal operation of the Excavator, do you want to pause it?
2. Identified the target audience for the notifications.
3. Clarify the urgency levels.
4. Redesigned the notificaton UI design based on urgency.
Increased operator response and clarity of urgency
To increase operator response and clarity of urgency, I moved the warning message window to the center of the interface to call attention and action to debug the error. This avoids a big mistake or damage that could terminate the mission.
Before
After
Clarify the urgency by adding warning icons.
Added a yellow alert icon to help users recognize the urgent level quickly.
Before
After
Lowered the KPIs summary notification level
Lowered the KPIs summary notification level to optimize the user experience and reduce the instruction from the notification,  by moving the window to the right side and increased the readability by emphasizing key metrics.
Before
After
Challenge 2
Users felt a loss of control when the data transfer between different robots was hidden.
There was a gap between the robots' collaboration and the users. Initially, there was no feature requirement for the data transfer notification. However, after a few design iterations, I realized the human-computer communication was missing. After I added this feature, I received the most positive feedback during the usability testing from the participants.
1. Received the feedback from the Machine Learning Team.
Tested out the transfer work flows with the machine learning team and we gained feedback about the experience.
2. Reframed design goals to improve the UI design.
Tested out the transfer work flows with the machine learning team and we gained feedback about the experience.
Enhancing the UX writing.
Simplifying the display of photos.
Improving the Call-For-Action buttons to be scannable and quick-decision-making.
Adding a zoom-in feature that allows users to confirm the photos’ details before transferring.
3. Help users make quick decisions and move forward by allowing them to preview photos before transferring.
Before
After
4. Strengthen users’ trust and improve human-robot communication by visualizing data transfer processing between robots.
Before
After
5. Final design prototype.
Summary
Increased notification efficiency and satisfaction
Reduced errors and improved human-robot communication through more efficient notification systems.

3.94

Satisfaction out of 5

Delivered MVP in 3 months
Led the end-to-end process from brainstorming to usability testing, iterations, and final design hand-off.

3mon

Delivered the MVP

Crafted Innovative Multi-Robot Control System
Simplified complex robot interactions with a design that aligns with business goals.

Multi

Robot control

Other ProjectOptimizing human-robot collaboration and communication in a multi-robot system    ↗