In recent years, there has been a growing interest in multiple robots performing a single task through different types of collaboration. There are two software challenges when deploying collaborative robots: how to specify a cooperative mission and how to program each robot to accomplish its mission. In this paper, we propose a novel software development framework to support distributed robot systems, swarm robots, and their hybrid. We extend the service-oriented and model-based (SeMo) framework [1] to improve the robustness, scalability, and flexibility of robot collaboration. To enable a casual user to specify various types of cooperative missions easily, the high-level mission scripting language is extended with new features such as team hierarchy, group service, one-to-many communication. The script program is refined to the robot codes through two intermediate steps, strategy description and task graph generation, in the proposed framework. The viability of the proposed framework is evidenced by two preliminary experiments using real robots and a robot simulator.

In the near future, it will be common that a variety of robots are cooperating to perform a mission in various fields. A key technical challenge to realize this vision is software challenge on how to specify the mission at the user level and how to program each robot separately. In this paper, we propose a novel software development framework that separates mission specification and robot behavior programming. For mission specification, a novel scripting language is proposed with the expression capability of dynamic mode change and multitasking. For robot behavior programming, an extended dataflow model is used for task-level behavior specification that does not depend on the robot hardware platform. In the proposed framework, the actual robot software is automatically generated from the model. The viability of the proposed framework is demonstrated with two real-life experiments.

Even though dataflow models are good at exploiting task-level parallelism of an application, it is difficult to exploit the parallelism of loop structures since they are not explicitly specified in existent dataflow models. To overcome this drawback, we propose a novel extension to the SDF model, called SDF/L graph, specifying the loop structures explicitly in a hierarchical fashion. With a given SDF/L graph specification and the mapping and scheduling information, an application can be automatically parallelized on a multicore system. The enhanced expression capability by the proposed extension is verified with two applications, k-means clustering and deep neural network application.