Tomáš Pětříček

Kristýna Petrlíková, Tomas Petricek

Vega is a popular declarative language for creating interactive data visualizations. It supports reactive data transformations using its streaming dataflow architecture. Despite its widespread adoption, the exact semantics of Vega is subtle and poorly documented. This leads to incorrect or confusing visualizations and difficult-to-understand error messages. This paper makes two contributions. First, we define a graph-based operational semantics, providing a precise model of the streaming dataflow architecture of Vega. Second, we present a type system for the core data transformation language of Vega, which can prevent a range of common errors. We show that our type system is sound with respect to the semantics. While the dataflow architecture of Vega closely resembles well-studied models such as functional reactive programming and adaptive computation, there are important differences. The novelty of our work lies in making these precise and providing static analysis for such a reactive data visualization language. The result is a checker for Vega that can catch common real-world errors.

Tomáš Rouček, Martin Pecka, Petr Čížek et al.

We present a field report of CTU-CRAS-NORLAB team from the Subterranean Challenge (SubT) organised by the Defense Advanced Research Projects Agency (DARPA). The contest seeks to advance technologies that would improve the safety and efficiency of search-and-rescue operations in GPS-denied environments. During the contest rounds, teams of mobile robots have to find specific objects while operating in environments with limited radio communication, e.g. mining tunnels, underground stations or natural caverns. We present a heterogeneous exploration robotic system of the CTU-CRAS-NORLAB team, which achieved the third rank at the SubT Tunnel and Urban Circuit rounds and surpassed the performance of all other non-DARPA-funded teams. The field report describes the team's hardware, sensors, algorithms and strategies, and discusses the lessons learned by participating at the DARPA SubT contest.