Towards interactive coordination of heterogeneous robotic teams – Introduction of a reoptimization framework

Abstract

The coordination of heterogeneous robotic teams demands suitable planning algorithms based on an appropriate model of the problem instance. While there exists a great variety of automated planning algorithms, the modeling of a problem instance often requires expertise in problem recognition and faculty of abstraction—a task which can be done best by humans. In order to exploit the synergy potential inherent in human-machine cooperative planning, we propose a new reoptimization framework based on a genetic algorithm (GA) for heterogeneous multi-robot task allocation problems including cooperative tasks and precedence constraints. The main idea of the reoptimization framework is to reuse insights from previous solutions of similar problem instances. In particular, a modified problem instance, resulting for example from adding or deleting individual tasks, is solved based on the solution of the unmodified problem instance. To this end, we introduce suitable heuristics for the adaption of the initial solution based on the considered problem modification. The simulative investigation of the proposed approach shows great positive effects compared to the application of a standardized GA that does not make use of the solution of the unmodified problem instance.