Autonomous mobile robots continuously perceive the world, plan or replan to achieve objectives, and execute the selected actions. Videos of autonomous robots are often naturally used to aid in replaying and demonstrating robot performance. However, plain videos contain no information about the ongoing internals of the robots. In this work, we contribute an approach to automate the overlay of visual annotations on videos of robots' execution to capture information underlying their reasoning. We concretely focus our presentation on the complex robot soccer domain, where the high speed of the robots' execution results from action planning for collaboration and response to the adversary.

CMDragons 2015 is the champion of the RoboCup Small Size League of autonomous robot soccer. The team won all of its six games, scoring a total of 48 goals and conceding 0. This unprecedented dominant performance is the result of various features, but we particularly credit our novel offense multi-robot coordination. This paper thus presents our Selectively Reactive Coordination (SRC) algorithm, consisting of two layers: A coordinated opponent-agnostic layer enables the team to create its own plans, setting the pace of the game in offense. An individual opponent-reactive action selection layer enables the robots to maintain reactivity to different opponents. We demonstrate the effectiveness of our coordination through results from RoboCup 2015, and through controlled experiments using a physics-based simulator and an automated referee.

The CMDragons Small Size League (SSL) team won all of its 6 games at RoboCup 2015, scoring a total of 48 goals and conceding 0. This paper presents the core coordination algorithms in offense and defense that enabled such successful performance. We first describe the coordinated plays layer that distributes the team’s robots into offensive and defensive subteams. We then describe the offense and defense coordination algorithms to control these subteams. Effective coordination enables our robots to attain a remarkable level of team-oriented gameplay, persistent offense, and reliability during regular gameplay, shifting our strategy away from stopped ball plays. We support these statements and the effectiveness of our algorithms with statistics from our performance at RoboCup 2015. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.