Kyonghwan Yoon

In addition to the various requirements that a multi-robot framework should meet, swarm robotics applications also demand robustness, flexibility, and scalability. While several frameworks have been developed for multi-robot operation, they mostly fall short of adequately supporting some of these essential requirements. In this work, we introduce a novel multi-robot programming framework called HiSARM (High-level Specification, Automatic code generation, and Retargetable deployment for Multi-robot systems), designed to assist both mission planners and robot software developers. HiSARM employs a high-level language to enable mission planners to specify collaborative tasks among multiple robots intuitively. From these scripts written in a high-level language, executable robot code is automatically generated. Mission planners can easily customize the executable robot code according to their specific needs within HiSARM. Additionally, HiSARM provides multiple verification environments by introducing a formal intermediate representation and enabling retargetable deployment of the binary file across various domains, including simulation and real robots. For robot software developers, HiSARM eases the development process for robot software developers by categorizing software components and auto-generating swarm-related functions. We tested HiSARM with multiple scenarios in simulation and real robot environments, demonstrating that it effectively supports all the necessary features for multi-robot applications, including swarm operations.

Programming for swarm robots is challenging due to platform diversity and the gap between individual and swarm behaviors. To tackle this challenge, we propose a component-based software synthesis method from a high-level specification. To support heterogeneous robots and maximize code reuse, we adopt a component-based approach that classifies software components into three categories: robot, algorithm, and consensus. We generate a task graph model for an individual robot from a high-level specification and use a software synthesizer to generate the target code from the task graph model. Through a proof-of-concept implementation with a group searching application, the viability of the proposed technique is demonstrated.

Software designed to run on memory-limited embedded platforms must not violate peak memory requirements. This article proposes a memory usage estimation technique in a model-based software development methodology to meet these goals for code that is synthesized from a dataflow-model-based specification. —Sudeep Pasricha, Colorado State University, USA