This paper discusses the need for a specialized vulnerability enumeration for robots and robot components and introduces the Robot Vulnerability Database (RVD)

ROS is the most popular framework in robotics research and it also grows in terms of industrial use. This makes ROS a worthwhile target for attackers especially since security is not addressed by the core framework itself. Its open architecture and flexibility are also the reasons why ROS suffers from security issues. For example, in ROS it is possible to isolate single nodes from the rest of the application without the ROS master, the other nodes or even the node itself (i.e., its business code) noticing it. This is true for publishers, subscribers and services alike. This makes attacks very difficult to spot at runtime. Penetration testing is the most common security testing practice. The goal is to test an application for possible security flaws. To better facilitate penetration testing for ROS, we introduce ROSPenTo and Roschaos, tools that make use of the vulnerabilities of ROS and demonstrate how ROS applications can be sabotaged by an attacker. In this tutorial you will learn about the ROS XML-RPC API, which is our main attack point. You will see, how API attacks on ROS work in depth. You will get to know Roschaos and ROSPentTo, two tools, which can be used to manipulate running ROS applications.

Today, camera networks are pervasively used in smart environments such as intelligent homes, industrial automa tion or surveillance. These applications often require cameras to be aware of their spatial neighbors or even to operate on a common ground plane. A major concern in the use of sensor networks in general is their robustness and reliability even in the presence of attackers. This paper addresses the challenge of detecting malicious nodes during the calibration phase of camera networks. Such a resilient calibration enables robust and reliable localization results and the elimination of attackers right after the network deployment. Specifically, we consider the problem of identifying subverted nodes which manipulate calibration data and can not be detected through standard cryptographic methods. The experiments in our network show that our self-calibration algorithm enables location-unknown cameras to successfully detect malicious nodes while autonomously calibrating the network.

With ongoing research in robotics, some specific architectural approaches of robotic systems earn more and more interest by all kinds of industries. Mobile manipulators–robots consisting of a mobile base and a serial manipulator–provide the ability to make robotic manipulation location-independent, which will be an essential feature in future production. Such robot platforms offer a high level of flexibility and efficiency of robot applications. Especially under the aspect of modularity, mobile manipulators would provide even more flexibility by offering the possibility to exchange or extend the robot hardware for specific applications. To achieve this, modularity also has to be considered in software. In this paper, we present a software architecture for modular mobile manipulation applications. It provides mechanism for reconfigurability, easy programming, and an easy approach for adding external hardware components. Being targeted at industrial use, the architecture also considers security and software deployment aspects. These considerations will, in combination with all the other aspects, be presented by means of two modular mobile manipulation platforms and a set of representative scenarios.

Using the effects of quantum mechanics for computing challenges has been an often discussed topic for decades. The frequent successes and early products in this area, which we have seen in recent years, indicate that we are currently entering a new era of computing. This paradigm shift will also impact the work of robotic scientists and the applications of robotics. New possibilities as well as new approaches to known problems will enable the creation of even more powerful and intelligent robots that make use of quantum computing cloud services or co-processors. In this position paper, we discuss potential application areas and also point out open research topics in quantum computing for robotics. We go into detail on the impact of quantum computing in artificial intelligence and machine learning, sensing and perception, kinematics as well as system diagnosis. For each topic we point out where quantum computing could be applied based on results from current research.