Resilience of multi-robot systems to physical masquerade attacks

The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and security-critical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.Accepted manuscrip

Resiliency-oriented operation of distribution networks under unexpected wildfires using multi-horizon information-gap decision theory

Extreme events may trigger cascading outages of different components in power systems and cause a substantial loss of load. Forest wildfires, as a common type of extreme events, may damage transmission/distribution lines across the forest and disconnect a large number of consumers from the electric network. Hence, this paper presents a robust scheduling model based on the notion of information-gap decision theory (IGDT) to enhance the resilience of a distribution network exposed to wildfires. Since the thermal rating of a transmission/distribution line is a function of its temperature and current, it is assumed that the tie-line connecting the distribution network to the main grid is equipped with a dynamic thermal rating (DTR) system aiming at accurately evaluating the impact of a wildfire on the ampacity of the tie-line. The proposed approach as a multi-horizon IGDT-based optimization problem finds a robust operation plan protected against the uncertainty of wind power, solar power, load, and ampacity of tie-lines under a specific uncertainty budget (UB). Since all uncertain parameters compete to maximize their robust regions under a specific uncertainty budget, the proposed multi-horizon IGDT-based model is solved by the augmented normalized normal constraint (ANNC) method as an effective multi-objective optimization approach. Moreover, a posteriori out-of-sample analysis is used to find (i) the best solution among the set of Pareto optimal solutions obtained from the ANNC method given a specific uncertainty budget, and (ii) the best resiliency level by varying the uncertainty budget and finding the optimal uncertainty budget. The proposed approach is tested on a 33-bus distribution network under different circumstances. The case study under different conditions verifies the effectiveness of the proposed operation planning model to enhance the resilience of a distribution network under a close wildfire. © 2022 The Author(s

Power Optimizations in MTJ-based Neural Networks through Stochastic Computing

Artificial Neural Networks (ANNs) have found widespread applications in tasks such as pattern recognition and image classification. However, hardware implementations of ANNs using conventional binary arithmetic units are computationally expensive, energy-intensive and have large area overheads. Stochastic Computing (SC) is an emerging paradigm which replaces these conventional units with simple logic circuits and is particularly suitable for fault-tolerant applications. Spintronic devices, such as Magnetic Tunnel Junctions (MTJs), are capable of replacing CMOS in memory and logic circuits. In this work, we propose an energy-efficient use of MTJs, which exhibit probabilistic switching behavior, as Stochastic Number Generators (SNGs), which forms the basis of our NN implementation in the SC domain. Further, error resilient target applications of NNs allow us to introduce Approximate Computing, a framework wherein accuracy of computations is traded-off for substantial reductions in power consumption. We propose approximating the synaptic weights in our MTJ-based NN implementation, in ways brought about by properties of our MTJ-SNG, to achieve energy-efficiency. We design an algorithm that can perform such approximations within a given error tolerance in a single-layer NN in an optimal way owing to the convexity of the problem formulation. We then use this algorithm and develop a heuristic approach for approximating multi-layer NNs. To give a perspective of the effectiveness of our approach, a 43% reduction in power consumption was obtained with less than 1% accuracy loss on a standard classification problem, with 26% being brought about by the proposed algorithm.Comment: Accepted in the 2017 IEEE/ACM International Conference on Low Power Electronics and Desig

Fast Damage Recovery in Robotics with the T-Resilience Algorithm

Damage recovery is critical for autonomous robots that need to operate for a long time without assistance. Most current methods are complex and costly because they require anticipating each potential damage in order to have a contingency plan ready. As an alternative, we introduce the T-resilience algorithm, a new algorithm that allows robots to quickly and autonomously discover compensatory behaviors in unanticipated situations. This algorithm equips the robot with a self-model and discovers new behaviors by learning to avoid those that perform differently in the self-model and in reality. Our algorithm thus does not identify the damaged parts but it implicitly searches for efficient behaviors that do not use them. We evaluate the T-Resilience algorithm on a hexapod robot that needs to adapt to leg removal, broken legs and motor failures; we compare it to stochastic local search, policy gradient and the self-modeling algorithm proposed by Bongard et al. The behavior of the robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using only 25 tests on the robot and an overall running time of 20 minutes, T-Resilience consistently leads to substantially better results than the other approaches