Biblio

In recent years, humanoid robots have become quite ubiquitous finding wide applicability in many different fields, spanning from education to entertainment and assistance. They can be considered as more complex cyber-physical systems (CPS) and, as such, they are exposed to the same vulnerabilities. This can be very dangerous for people acting that close with these robots, since attackers by exploiting their vulnerabilities, can not only violate people's privacy, but, more importantly, they can command the robot behavior causing them bodily harm, thus leading to devastating consequences. In this paper, we propose a solution not yet investigated in this field, which relies on the use of secure enclaves, which in our opinion could represent a valuable solution for coping with most of the possible attacks, while suggesting developers to adopt such a precaution during the robot design phase.

In this paper, the design as well as complete implementation of a robot which can be autonomously controlled for surveillance. It can be seamlessly integrated into an existing security system already present. The robot's inherent ability allows it to map the interiors of an unexplored building and steer autonomously using its self-ruling and pilot feature. It uses a 2D LIDAR to map its environment in real-time and HD camera records suspicious activity. It also features an in-built display with touch based commands and voice recognition that enables people to interact with the robot during any situation.

A Robot Operating System (ROS) plays a significant role in organizing industrial robots for manufacturing. With an increasing number of the robots, the operators integrate a ROS with networked communication to share the data. This cyber-physical nature exposes the ROS to cyber attacks. To this end, this paper proposes a cross-layer approach to achieve secure and resilient control of a ROS. In the physical layer, due to the delay caused by the security mechanism, we design a time-delay controller for the ROS agent. In the cyber layer, we define cyber states and use Markov Decision Process to evaluate the tradeoffs between physical and security performance. Due to the uncertainty of the cyber state, we extend the MDP to a Partially Observed Markov Decision Process (POMDP). We propose a threshold solution based on our theoretical results. Finally, we present numerical examples to evaluate the performance of the secure and resilient mechanism.

Robots are becoming more and more prevalent in many real world scenarios. Housekeeping, medical aid, human assistance are a few common implementations of robots. Military and Security are also major areas where robotics is being researched and implemented. Robots with the purpose of surveillance in war zones and terrorist scenarios need specific functionalities to perform their tasks with precision and efficiency. In this paper, we present a model of Military Surveillance Robot developed using Robot Operating System. The map generation based on Kinect sensor is presented and some test case scenarios are discussed with results.

The field of robotics has matured using artificial intelligence and machine learning such that intelligent robots are being developed in the form of autonomous vehicles. The anticipated widespread use of intelligent robots and their potential to do harm has raised interest in their security. This research evaluates a cyberattack on the machine learning policy of an autonomous vehicle by designing and attacking a robotic vehicle operating in a dynamic environment. The primary contribution of this research is an initial assessment of effective manipulation through an indirect attack on a robotic vehicle using the Q learning algorithm for real-time routing control. Secondly, the research highlights the effectiveness of this attack along with relevant artifact issues.