PRE2019 4 Group1

Students: Bryan Buster, Edyta Koper, Sietze Gelderloos, Matthijs Logeman, Bart Wesselink.

Problem statement and objectives

With about one-third of the world’s population living under some form of quarantine due to the COVID-19 outbreak , scientists sound the alarm on the negative psychological effects of the current situation [2]. Studies on the impact of massive self-isolation in the past, such as in Canada and China in 2003 during the SARS outbreak or in west African countries caused by Ebola in 2014, have shown that the psychological side effects of quarantine can be observed several months or even years after an epidemic is contained [3]. Among others, prolong self-isolation may lead to higher risk of depression, anxiety, poor concentration and lowered motivation level [2]. The negative effects on well-being can be mitigated by introducing measurements which help in the process of accommodation to a new situation during the quarantine. Such measurements should aim at reducing the boredom (1), improving the communication within a social network (2) and keeping people informed (3) [2]. With the following project, we propose an in-house assistant that addresses these three objectives. Although the assistant is designed with a view of self-isolation caused by disease outbreak, it is also applicable for people who spend a considerable part of their time at home.

[1] Buchholz, K., & Richter, F. (2020, April 3). Infographic: What Share of the World Population Is Already on COVID-19 Lockdown? Retrieved from https://www.statista.com/chart/21240/enforced-covid-19-lockdowns-by-people-affected-per-country/

[2] Brooks, S. K., Webster, R. K., Smith, L. E., Woodland, L., Wessely, S., Greenberg, N., & Rubin, G. J. (2020). The Psychological Impact of Quarantine and How to Reduce It: Rapid Review of the Evidence. SSRN Electronic Journal. doi: 10.2139/ssrn.3532534

[3] Liu, X., Kakade, M., Fuller, C. J., Fan, B., Fang, Y., Kong, J., … Wu, P. (2012). Depression after exposure to stressful events: lessons learned from the severe acute respiratory syndrome epidemic. Comprehensive Psychiatry, 53(1), 15–23. doi: 10.1016/j.comppsych.2011.02.003

Approach

In order for us to tackle the problem as described in the problem statement, we will start with careful research on topics that require our attention, like how and where we can support mental and physical health of people. From this research, we can create a solution consisting of different disciplines and techniques.

From there, we will start building an assistant that has the following features:

• Speech:
  • Text to speech (TTS): being able to interact with an assistant via speech is a key part of the assistant, to tackle the loneliness problem. Using hardware microphones and pre-existing software, we can create a ‘living’ assistant.
  • Speech to text (STT): being able to talk back to the assistant sparks up the conversation, and makes the assistant more human-like.
• Engaging in conversation: the robot has functionalities to hear people, and talk back, but maintaining context and having a real conversation is very important.
• Movement: the robot has functionalities that enable it to move around a surface, and autonomously move around obstacles to reach some target.
• Exercising: to counter the lack of physical activities, the robot has functionalities that can prompt or motivate the user to be physically active, and to track the user’s activity.
• General information: the robot prompts the user to hear the latest news twice every day, once in the morning and once in the evening. Users can always request a new update through a voice command.
• Graphical User Interface: when the user is unable to talk to the assistant, there will be an option to interact with a graphical user interface, displayed on a touch screen. InterFACE.
• Vision: skeleton tracking, facial detection. Use this data to count jumping jacks, look at someone when talking to. (Closely linked with navigation)
• Navigation: the robot will be able to move around the room and approach or follow the user.

Using different feature sets allows our team members to (partly) work individually, speeding up the development process in the difficult time that we are in right now. The parts should be tested together. Not only in the end, but also when building. This can be done by scheduling meetings with the person responsible for building the physical robot. The team members can share code via a source like GitHub, which then can be uploaded onto the robot, allowing members to test their part.

When all parts work together, a video will be created, highlighting all bot functionalities.

Deliverables

By the end of the project, we will deliver the following items:

• Project Description (report via wiki)
• Project Video demonstrating functionalities
• Physical assistant
• Source code

Milestones

Week	Deliverable
1 (April 20th till April 26th)	Problem statement, think of concept
2 (April 27th till May 3rd)	Further research, list of required components
3 (May 4th till May 10th)	Drive and power delivery finished, performed all research and acquire components/software for construction
4 (May 11th till May 17th)	Finish physical construction
5 (May 18th till May 24th)	-
6 (May 25th till April 31th)	Finish software
7 (June 1st till June 7th)	Finish integration and test with prospective user
8 (June 8th till June 14th)	Create presentation, finish Wiki
9 (June 15th till June 21th)	Presentation, on Wednesday

State of the Art

Indoor localisation and navigation

Indoor localisation has become a highly researched topic over the past years. A robust and compute-efficient solution is important for several fields of robotics. Whether the application is an automated guided vehicle (AGV) in a distribution warehouse or a vacuum robot, the problem is essentially the same. Let us take these two examples and find out how state-of-the-art examples solve this problem.

Logistics AGVs

AGVs for use in warehouses are typically designed solely for use inside a warehouse. Time and money can be invested to provide the AGV with an accurate map of its surroundings before deployment. The warehouse is also custom-built, so fiducial markers are less intrusive than they would be when placed in a home environment. These fiducial markers allow Amazon’s warehouse AGVs to locate themselves in space very accurately. Knowing their location, Amazon’s AGVs find their way around warehouses as follows. They send a route request to a centralised system, which takes into account other AGVs’ paths in order to generate an efficient path, and commands the AGV exactly which path to take. The AGV then traverses the grid marked out with floor-mounted fiducial markers. The AGVs don’t blindly follow this path, however, as they keep on the lookout for any unexpected objects on their path. The system described in Amazon’s relevant patents also includes elegant solutions to detect and resolve possible collisions, and even the notion of dedicated larger cells in the ground grid, used by AGVs to be able to make a turn at elevated speeds.

Robotic vacuum robots

A closer-to-home example could be found when looking at Irobot’s implementation of VSLAM (Visual Simultaneous Localisation And Mapping) in their robotic vacuum cleaners. SLAM is a family of algorithms intended for vehicles, robots and (3d) scanning equipment to both localise themselves in space and to augment the existing map of their surroundings with new sensor-derived information. The patent involving an implementation of VSLAM by Irobot describes a SLAM variant which uses two cameras to collect information about its surroundings. It uses this information to build an accurate map of its environment, in order to cleverly plan a path to efficiently clean all floor surfaces it can reach. The patent also includes mechanisms to detect smudges or scratches on the lenses of the cameras, and notify the owner of this fact. Other solutions for gathering environmental information for robotic vacuum cleaners use a planar LiDAR sensor to gather information about boundaries of the floor surface.

SLAM algorithms

TODO