Shuhei Emoto

Systems Engineering for Industrial Cyber–Physical Systems Using Aspects

One of the biggest challenges in cyber-physical system (CPS) design is their intrinsic complexity, heterogeneity, and multidisciplinary nature. Emerging distributed CPSs integrate a wide range of heterogeneous aspects such as physical dynamics, control, machine learning, and error handling. Furthermore, system components are often distributed over multiple physical locations, hardware platforms, and communication networks. While model-based design (MBD) has tremendously improved the design process, CPS design remains a difficult task. Models are meant to improve understanding of a system, yet this quality is often lost when models become too complicated. In this paper, we show how to use aspect-oriented (AO) modeling techniques in MBD as a systematic way to segregate domains of expertise and cross-cutting concerns within the model. We demonstrate these concepts on actor-oriented models of an industrial robotic swarm application and illustrate the use of AO modeling techniques to manage the complexity. We also show how to use AO modeling for design-space exploration.

Information seeking and model predictive control of a cooperative multi-robot system

In this paper, we propose a cooperative multi-robot control system, operating in an unfamiliar or unstructured environment. We focus on a robust model predictive control (robust-MPC) framework that enables robotic agents to operate in uncertain environments, and study the effect of observation uncertainties that arise from sensor noise on cooperative control performance. The proposed system relies on cooperative observation based on an information-seeking theory, in which the system not only can compensate uncertainty, but also takes actions to mitigate it. We carry out a case study that demonstrates a multi-robot collision avoidance scenario in an unknown environment. Simulation results show that the combination of robust-MPC methods and cooperative observation enables the cooperative multi-robot system to move efficiently and reach the goal faster than an uncooperative scenario.

Cooperative multi-robot information acquisition based on distributed robust model predictive control

In this paper, we propose a distributed multi-robot control system working in dynamic and uncertain environments. Robust model predictive control (robust MPC) enables robots to deal with uncertainties. However, the performance of the robust MPC is dependent on the amount of uncertainty that derives from noisy measurements, communication disturbance, etc. The proposed system includes multiple observation robots that gather information cooperatively as well as a main robot controlled by robust MPC. Therefore, the system works for not only treating the uncertainty but also decreasing it. A simulation result of a collision avoidance shows that the information acquisition by the observation robots enables the main robot to move efficiently and arrive at the goal faster than a case without the observation robots. We also focus on a problem that a large number of observation robots will increase the frequency of inter-robot collision avoidances, and thus negatively affect to the performance of the main robot. Simulation results under various conditions on a disturbance level and a measurement range of sensors clarifies an adequate number of observation robots as well as the design guideline about sensors and networks.

Information Seeking of a Robot Swarm based on Robust Model Predictive Control

In this paper, we propose a cooperative multi-robot control system, operating in dynamic and uncertain environments. We focus on a robust model predictive control (robust MPC) framework that enables robotic agents to operate in uncertain environments. The proposed system includes multiple observation robots that gather information cooperatively as well as a main robot controlled by the robust MPC. Therefore, the system works for not only treating the uncertainty but also decreasing it so that the performance of the robust MPC can be improved. We carry out a Monte Carlo simulation of a multi-robot collision avoidance scenario and analyze a required time for the main robot to arrive at the goal. Simulation results show that the information acquisition by the observation robots enables the main robot to move efficiently and arrive at the goal faster than a case without the observation robots.