Model-driven design space exploration for multi-robot systems in simulation

Abstract

Multi-robot systems are increasingly deployed to provide services and accomplish missions whose complexity or cost is too high for a single robot to achieve on its own. Although multi-robot systems offer increased reliability via redundancy and enable the execution of more challenging missions, engineering these systems is very complex. This complexity affects not only the architecture modelling of the robotic team but also the modelling and analysis of the collaborative intelligence enabling the team to complete its mission. Existing approaches for the development of multi-robot applications do not provide a systematic mechanism for capturing these aspects and assessing the robustness of multi-robot systems. We address this gap by introducing ATLAS, a novel model-driven approach supporting the systematic design space exploration and robustness analysis of multi-robot systems in simulation. The ATLAS domain-specific language enables modelling the architecture of the robotic team and its mission and facilitates the specification of the team’s intelligence. We evaluate ATLAS and demonstrate its effectiveness in three simulated case studies: a healthcare Turtlebot-based mission and two unmanned underwater vehicle missions developed using the Gazebo/ROS and MOOS-IvP robotic platforms, respectively.

Towards a Research Agenda for Understanding and Managing Uncertainty in Self-Adaptive Systems

“…There are multiple ways in which ML systems can fail either through adversarial/malicious behavior or due to faults [64]. This will require systematic methods for assessing and assuring their quality to gain confidence in their correctness [55], to quantify the uncertainty introduced by their ML components [20], and to identify deficiencies [65].…”