The most significant risk for our team right now would be the integration of the Robot with the WebApp. We are thinking of using WebSockets because of its low-latency nature with full-duplex communication which would allow better real-time communication between the robot and the WebApp. However, a key challenge is that none of us have prior experience with using WebSockets in this specific context, creating uncertainty around implementation and potential delays. To manage this risk, we plan on scheduling dedicated time for learning WebSocket integration and seeking advice from mentors or who have used WebSockets. As for our contingency plans, we plan to possibly switch to a standard HTTP-based communication using REST APIs over WiFi, (though this might introduce higher latency), or using a physical Ethernet connection to reduce the risk of network disruptions, (though this would reduce flexibility in robot placement and mobility).
Another possible challenge is integrating the DCI display with the Raspberrypi. Ensuring a reasonable frame per second value along with a smooth facial transition, such as blinking and smiling, to ensure human-like interaction with the bot. To implement this, we will use certain python graphics libraries like Pygame for simple 2D rendering, or Kivy for a more advanced interface. To maximize the lifespan of the screen, we will be using screensaver and sleep mode during idle moments or periodically change the content displayed on the screen by making slight changes. This can be done during timer countdowns and is generally not a concern if the user is not using the bot.
We also got together as a group to update our system specification diagram, which we included in our design proposal.
We decided to allocate specific time to the WebSockets integration of the robot next week.
Part A was written by Mahlet Mesfin
Our StudyBuddyBot is a study companion meant to motivate students to study, track their study habits and provide relaxation when they take breaks in between their study sessions. This allows students to have a good experience while being productive inducing psychological satisfaction. The bot can guide students to follow optimal study schedules(such as the Pomodoro technique), which ensures a well balanced approach to work and rest. This will help prevent overworking, overall leading to a better mental and emotional health. In addition, we will be incorporating reminders for the user to take breaks in reasonable intervals reducing fatigue and eye strain.
The game feature of this StudyBuddyBot allows for a short but fun experience during these study sessions, timed well so that they don’t cause prolonged distraction. This will also help in fostering the sense of companionship and reducing the feeling of isolation for those who can’t focus well in the presence of other individuals. This can boost the well-being of someone through emotional support.
Part B was written by Shannon Yang
The StudyBuddyBot will improve productivity and the well-being of students in academic environments. It will serve as a structured study companion that can help students. In situations where students have limited access to in-person interaction due to cultural factors, the robot is able to simulate a studying environment with a friend. The features for interaction that the robot has can also help to bridge gaps in the social and emotional support systems that students may lack from their surroundings. Some of the robot’s features could also be used to cater to specific cultural or social preferences (for example, setting prayer time reminders for those who observe religious practices). By incorporating both study assistance and social engagement, the robot aligns with the growing trend of technology being used to support mental health and productivity, acknowledging the cultural and social importance of companionship in learning and promoting greater work-life balance.
Part C was written by Jeffrey Jehng
With the StudyBuddyBot, we want to use cost-effective components to balance affordability with quality. By implementing a modular design, we can have a scalable distribution in the future and ensure durability of the final product.
An example of our final product use-case could be in a school setting, where administration/students may have a limited budget for these educational tools. By designing the StudyBuddyBot with affordable components and integration with a companion web-app to decrease the need for high-performance hardware, we can focus on developing key functionalities such as robot interaction and features to motivate student studying. The emphasis on affordable components under our $600 budget can make our design a cost-effective solution to assist schools in integrating advanced technology into the classroom.