About four month ago I met with guys from Innopolis University. They were Master students and where interested in master thesis. In my opinion AIRA is a pretty good research field for masters in computer science and robotics. We started our common work in September and now I’d like to share our results with community.

Duckietown robotics education project is international initiative by ETH Zürich (Switzerland), University of Montréal (Canada), National Chiao Tung University (Taiwan), Toyota Technological Institute at Chicago (the USA). The project started in 2016 and now is used in about 20 other universities, including Innopolis University. Software of Duckietown is opensource, hardware platform consists of the “Duckiebots”, simple autonomous robots, and the “Duckietowns”, the infrastructure where the Duckiebots navigate.

Duckietown in ROS visualization system

Since mobile robots are widely used and can represent real case — transportation, it was decided to consider taxi service prototype.

Common robot liability approach by AIRA consists of three stages:

Task formalization Results formalization as a log of work Result validation based on task

Let’s consider Alisa a customer; she makes smart contract (liability) with Bob, who is taxi service provider (robot). Alisa sends transaction with a task (objective) like a destination point. Bob moves a taxi and sends transactions with internal log of operations. Finally, Carol, who is the protocol validator compares the task and the result and does decision: if Bob liability is fulfilled or not.

In my opinion main result of Ruslan and Konstantin work is not only implementation the concept of fully autonomous taxi service provider on Duckietown test bed. But the first answer for the question: how should Carol compare the task and the result log to do correct decision. Guys are considering on taxi service described by agent-based system, it’s similar to some kind of multi agent systems.

As you can see on the picture above, robot log of work is compiled to properties by specially developed tool. Model checker gets validation model and set of properties to check correctness of robot work. Here PRISM probabilistic model checker is used.