Scalable HIL Simulator for Multi-Agent Systems Interacting in Physical Environments

Important application domains of multi-agent systems require agent interactions in physical environments (e.g. mobile robotics, intelligent transportation systems, etc.). Sensing and sensory data interpretation play ever increasing role because sensing becomes the primary way of collecting information about the environment. In the most challenging cases the environment is unstructured, which results in demanding data interpretation and control algorithms. Similarly, the control and decision-making algorithms inevitably become more complex in order to be able to cope with the unstructured and dynamic environment. Consequently a sophisticated evaluation/test environment is required, which provides full control of the circumstances, reproducibility and flexible mix of real and virtual components. The paper presents the runtime architecture of a simulation environment (MARS), which assures scalable real-time performance and a modeling framework, which supports incorporating high fidelity sensor models. The tool is capable of simulating accurate agent interactions in physical environments and creating mixed virtual-real worlds for testing multi-agent systems