It is no secret that robotic systems are expanding into many human roles or are augmenting human roles. The Robot Operating System is an open-source standard for the robotic industry that enables locomotion, manipulation, navigation, and recognition tasks by integrating sensors, motors, and controllers into reusable modules over a distributed messaging architecture. As reliance on robotic systems increases, these systems become high value targets, for example, in autonomous vehicles where human life is at risk. As Robot Operating System has become a de facto standard for many robotic systems, the security of Robot Operating System becomes an important consideration for deployed systems. The original Robot Operating System implementations were not designed to mitigate the security risks associated with hostile actors. Robot Operating System 2, the next generation of the Robot Operating System, addresses this shortcoming, leveraging Data Distributed Services for its messaging architecture and Data Distributed Services security extension for its data protection in motion. This article provides a systematic review of Robot Operating System 2 and identifies potential risks for this new robotic system paradigm. A Robot Operating System 2 robotic system is viewed as a series of layers from the hardware that include sensors, motors, and controllers to the software layers, which include the operating system, security services, protocols, messaging, and the cognitive layer for observation, learning, and action. Since Robot Operating System 2 and security are new considerations for robotics systems as they move into mainstream, many questions emerge. For example, can some portions be secure and other portions be non-secure? Does everything need to be secure? What are the trade-offs between, security, performance, latency and throughput? What about real-time robotic systems? This article provides an overview of the Robot Operating System 2 paradigm and represents a first step toward answering these questions.

This paper surveys the area of "Trust Metrics" related to security for autonomous robotic systems. As the robotics industry undergoes a transformation from programmed, task oriented, systems to Artificial Intelligence-enabled learning, these autonomous systems become vulnerable to several security risks, making a security assessment of these systems of critical importance. Therefore, our focus is on a holistic approach for assessing system trust which requires incorporating system, hardware, software, cognitive robustness, and supplier level trust metrics into a unified model of trust. We set out to determine if there were already trust metrics that defined such a holistic system approach. While there are extensive writings related to various aspects of robotic systems such as, risk management, safety, security assurance and so on, each source only covered subsets of an overall system and did not consistently incorporate the relevant costs in their metrics. This paper attempts to put this prior work into perspective, and to show how it might be extended to develop useful system-level trust metrics for evaluating complex robotic (and other) systems.

This paper surveys the area of “Trust Metrics” related to security for autonomous robotic systems. As the robotics industry undergoes a transformation from programmed, task oriented, systems to Artificial Intelligence-enabled learning, these autonomous systems become vulnerable to several security risks, making a security assessment of these systems of critical importance. Therefore, our focus is on a holistic approach for assessing system trust which requires incorporating system, hardware, software, cognitive robustness, and supplier level trust metrics into a unified model of trust. We set out to determine if there were already trust metrics that defined such a holistic system approach. While there are extensive writings related to various aspects of robotic systems such as, risk management, safety, security assurance and so on, each source only covered subsets of an overall system and did not consistently incorporate the relevant costs in their metrics. This paper attempts to put this prior work into perspective, and to show how it might be extended to develop useful systemlevel trust metrics for evaluating complex robotic (and other) systems.