For autonomous multi-robot teams, the individual team members are tasked with completing their assigned tasks as defined by a team plan provided by a centralized team planner. However in complex dynamic domains, the team plans are generated by the team planner with assumptions due to the complexity of modeling the domain. Failures in execution are therefore inevitable for the team members, and as such, replanning will occur for the team. In this paper, we introduce a rationale-driven team plan representation that provides rationales on why actions were chosen by the team planner. During a failure, the individual team members autonomously use our described intra-robot replanning algorithm to select all applicable replan policies for a given rationale. We then describe a method to learn the predicted cost of each replan policy, given a state of the environment, in order for the individual robots to select the lowest costing replan policy to improve team performance.

Individual team members are the building blocks of successful multi-robot teams in dynamic competitive domains. The current approach to designing a team is to divide the planning into a hierarchy by separating team coordination and task assignment - global planning - from task planning and execution - local planning. The global planner must make assumptions based on simplified models of dynamics and/or opponents, and as such certain conditions are assumed true when globally planning but are not always true at local execution time. In this paper, we describe several algorithms for intra-robot replanning that allow the individual robots to enable the conditions of their tasks. We then demonstrate improvements in task completion when the robots are capable of replanning their task(s) and their teammates' task(s) in a simplified robot soccer domain. We further show preliminary results on learning when to replan.

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.