Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms

open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

Security Challenges for Swarm Robotics

Swarm robotics is a relatively new technology that is being explored for its potential use in a variety of dierent applications and environments. Previous emerging technologies have often overlooked security until later developmen- tal stages, when it has had to be undesirably (and sometimes expensively) retrotted. We identify a number of security challenges for swarm robotics and argue that now is the right time to address these issues and seek solu- tions. We also identify several idiosyncrasies of swarm robotics that present some unique security challenges. In particular, swarms of robots potentially employ dierent types of communication channels; have special concepts of identity; and exhibit adaptive emergent behaviour which could be modied by an intruder. Addressing these issues now will prevent undesirable conse- quences for many applications of this type of technology

InfoSwarms: Drone Swarms and Information Warfare

Drone swarms, which can be used at sea, on land, in the air, and even in space, are fundamentally information-dependent weapons. No study to date has examined drone swarms in the context of information warfare writ large. This article explores the dependence of these swarms on information and the resultant connections with areas of information warfare—electronic, cyber, space, and psychological—drawing on open-source research and qualitative reasoning. Overall, the article offers insights into how this important emerging technology fits into the broader defense ecosystem and outlines practical approaches to strengthening related information warfare capabilities

Spatio-temporal patterns act as computational mechanisms governing emergent behavior in robotic swarms

Our goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their self-coordinating emergent behavior, has proven ineffective, largely due to the swarm's inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micro-macro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm's emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

Introducing Intelligence and Autonomy into Industrial Robots to Address Operations into Dangerous Area

The paper addresses the issue to use new generation robotic systems inside industrial facilities in order to complete operations in dangerous area. The new robotic systems are currently adopting the autonomous approach already in use in military sector; however, in this context the intensity of operations and the necessity to interact with high productivity systems introduce different challenges. Despite the problems, it is evident that this approach could provide very interesting improvements in terms of safety for humans especially in relations to dangerous area. For instance, in confined spaces, Oil & Gas or Hot Metal Industry these new autonomous systems could reduce the number of injures and casualties. In addition, these systems could increase the operation efficiency in this complex frameworks as well as the possibility to carry out inspections systematically; in this sense, this could result in improving the overall reliability, productivity and safety of the whole Industrial Plant. Therefore, it is important to consider that these systems could be used to address also security aspects such as access control, however they could result vulnerable to new threats such as the cyber ones and need to be properly designed in terms of single entities, algorithms, infrastructure and architecture. From this point of view, it is evident that Modeling and Simulation represent the main approach to design properly these new systems. In this paper, the authors present the use of autonomous systems introducing advanced capabilities supported by Artificial Intelligence to deal with complex operations in dangerous industrial frameworks. The proposed examples in oil and gas and hot metal industry confirm the potential of these systems and demonstrate as simulation supports their introduction in terms of engineering, testing, installation, ramp up and training

Cyber Security Assessment of the Robot Operating System 2 for Aerial Networks

Best Student Paper, 2nd PlaceThe article of record as published may be found at https://doi.org/10.1109/SYSCON.2019.88368242019 IEEE International Systems Communications Conference (SYSCON)The Robot Operating System (ROS) is a widely adopted standard robotic middleware. However, its preliminary design is devoid of any network security features. Military grade unmanned systems must be guarded against network threats. ROS 2 is built upon the Data Distribution Service (DDS) standard and is designed to provide solutions to identified ROS 1 security vulnerabilities by incorporating authentication, encryption, and process profile features, which rely on public key infrastructure. The Department of Defense is looking to use ROS 2 for its military-centric robotics platform. This paper seeks to demonstrate that ROS 2 and its DDS security architecture can serve as a functional platform for use in military grade unmanned systems, particularly in unmanned Naval aerial swarms. In this paper, we focus on the viability of ROS 2 to safeguard communications between swarms and a ground control station (GCS). We test ROS 2’s ability to mitigate and withstand certain cyber threats, specifically that of rogue nodes injecting unauthorized data and accessing services that will disable parts of the UAV swarm. We use the Gazebo robotics simulator to target individual UAVs to ascertain the effectiveness of our attack vectors under specific conditions. We demonstrate the effectiveness of ROS 2 in mitigating the chosen attack vectors but observed a measurable operational delay within our simulations.This work was funded and sponsored by the Office of Naval Research via the Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) at NPS

A Novel Hybrid Spotted Hyena-Swarm Optimization (HS-FFO) Framework for Effective Feature Selection in IOT Based Cloud Security Data

Internet of Things (IoT) has gained its major insight in terms of its deployment and applications. Since IoT exhibits more heterogeneous characteristics in transmitting the real time application data, these data are vulnerable to many security threats. To safeguard the data, machine and deep learning based security systems has been proposed. But this system suffers the computational burden that impedes threat detection capability. Hence the feature selection plays an important role in designing the complexity aware IoT systems to defend the security attacks in the system. This paper propose the novel ensemble of spotted hyena with firefly algorithm to choose the best features and minimise the redundant data features that can boost the detection system's computational effectiveness.  Firstly, an effective firefly optimized feature correlation method is developed.  Then, in order to enhance the exploration and search path, operators of fireflies are combined with Spotted Hyena to assist the swarms in leaving the regionally best solutions. The experimentation has been carried out using the different IoT cloud security datasets such as NSL-KDD-99 , UNSW and CIDCC -001 datasets and contrasted with ten cutting-edge feature extraction techniques, like PSO (particle swarm optimization), BAT, Firefly, ACO(Ant Colony Optimization), Improved PSO, CAT, RAT, Spotted Hyena, SHO and  BOC(Bee-Colony Optimization) algorithms. Results demonstrates the proposed hybrid model has achieved the better feature selection mechanism with less convergence  time and aids better for intelligent threat detection system with the high performance of detection