In heterogeneous multi-robot teams, robustness and flexibility are increased by the diversity of the robots, each contributing different capabilities. Yet platform-independence is desirable when planning actions for the various robots. We propose a platform-independent model of robot capabilities which we use as a planning domain. We extend existing planning techniques to support two requirements: generating new objects during planning; and, required concurrency of actions due to data flow which can be cyclic. The first requires online action instantiation, the second a small extension of the Planning Domain Definition Language (PDDL): allowing predicates in continuous effects. We evaluate the planner on benchmark domains and present results on an example object transportation task in simulation.

The increasing variety of robotic systems create the need for flexible architectures enabling easy integration of new robot configurations into existing multi-robot systems. This requires methods for general reasoning about what different robots are capable of doing. Teamwork is a very important factor in complex, dynamic domains. In heterogeneous teams, robustness and flexibility are increased by the diversity of the robots, each contributing different capabilities. Consequently it is reasonable to explicitly take the robots' capabilities into account when determining which robot is best suited for a task. This work develops a framework that formalizes robots' capabilities, relating to hard- and software configurations and providing a means to estimate a robot's suitability for a task. A learning algorithm for robot capabilities is included.