In this article, we develop a resilient binary hypothesis testing framework for decision making in adversarial multirobot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a centralized fusion center (FC) even when, first, there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and second, the FC uses one-shot noisy measurements from all robots. We derive two algorithms to achieve this. The first is the two-stage approach (2SA) that estimates the legitimacy of robots based on received trust observations, and provably minimizes the probability of detection error in the worst-case malicious attack. For the 2SA, we assume that the proportion of malicious robots is known but arbitrary. For the case of an unknown proportion of malicious robots, we develop the adversarial generalized likelihood ratio test (A-GLRT) that uses both the reported robot measurements and trust observations to simultaneously estimate the trustworthiness of robots, their reporting strategy, and the correct hypothesis. We exploit particular structures in the problem to show that this approach remains computationally tractable even with unknown problem parameters. We deploy both algorithms in a hardware experiment where a group of robots conducts crowdsensing of traffic conditions subject to a Sybil attack on a mock-up road network. We extract the trust observations for each robot from communication signals, which provide statistical information on the uniqueness of the sender. We show that even when the malicious robots are in the majority, the FC can reduce the probability of detection error to 30.5% and 29% for the 2SA and the A-GLRT algorithms, respectively.

In this article, we develop an algorithm for resilient path planning, where a team of robots must navigate in a resilient formation such that they achieve$F$-resilience, meaning they can coordinate in the presence of up to$F$adversaries. Resilient formations are those having high connectivity often achieved by driving robots close together. Unfortunately, the objective of maintaining resilience can often times conflict with achieving collision and obstacle avoidance. We seek to provide safe navigation while maintaining resilience by employing a local controller that uses control barrier functions (CBFs). CBF-based formulations are amenable to satisfying multiple objectives, but can be prone to deadlock if any of the objectives conflict with each other. Furthermore, it is difficult to know a priori where this may occur in a given environment. To this end, we 1) characterize when the environment will force a tradeoff between safe navigation and resilience, and 2) develop an algorithm that derives a new representation of the environment in which areas where resilience cannot be provably guaranteed are blocked off. This algorithm can be used to plan a path through an environment that always provably admits a resilient formation. If the algorithm cannot find such a path, an alternative CBF is proposed where resilience can be treated as asoft constraint. For this case, a nested form of the CBF is executed and a critical gain is derived that provably prioritizes navigation over resilience while resilience is not attainable. Finally, in addition to simulation results, we run hardware experiments with six GoPiGo differential-drive robots that achieve$F$-resilient consensus while navigating through a cluttered environment, to showcase the applicability of our methods in the presence of adversaries.

We develop a resilient binary hypothesis testing frame-work for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a centralized Fusion Center (FC) even when i) there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and ii) the FC uses one-shot noisy measurements from all robots. We derive two algorithms to achieve this. The first is the Two Stage Approach (2SA) that estimates the legitimacy of robots based on received trust observations, and provably minimizes the probability of detection error in the worst-case malicious attack. Here, the proportion of malicious robots is known but arbitrary. For the case of an unknown proportion of malicious robots, we develop the Adversarial Generalized Likelihood Ratio Test (A-GLRT) that uses both the reported robot measurements and trust observations to estimate the trustworthiness of robots, their reporting strategy, and the correct hypothesis simultaneously. We exploit special problem structure to show that this approach remains computationally tractable despite several unknown problem parameters. We deploy both algorithms in a hardware experiment where a group of robots conducts crowdsensing of traffic conditions on a mock-up road network similar in spirit to Google Maps, subject to a Sybil attack. We extract the trust observations for each robot from actual communication signals which provide statistical information on the uniqueness of the sender. We show that even when the malicious robots are in the majority, the FC can reduce the probability of detection error to 30.5% and 29% for the 2SA and the A-GLRT respectively.

We study how information flows through a multi-robot network in order to better understand how to provide resilience to malicious information. While the notion of global resilience is well studied, one way existing methods provide global resilience is by bringing robots closer together to improve the connectivity of the network. However, large changes in network structure can impede the team from performing other functions such as coverage, where the robots need to spread apart. Our goal is to mitigate the trade-off between resilience and network structure preservation by applying resilience locally in areas of the network where it is needed most. We introduce a metric, Influence, to identify vulnerable regions in the network requiring resilience. We design a control law targeting local resilience to the vulnerable areas by improving the connectivity of robots within these areas so that each robot has at least$2F+1$vertex-disjoint communication paths between itself and the high influence robot in the vulnerable area. We demonstrate the performance of our local resilience controller in simulation and in hardware by applying it to a coverage problem and comparing our results with an existing global resilience strategy. For the specific hardware experiments, we show that our control provides local resilience to vulnerable areas in the network while only requiring 9.90% and 15.14% deviations from the desired team formation compared to the global strategy.

In this article, we characterize the advantage of using a robot’s neighborhood to find and eliminate adversarial robots in the presence of a Sybil attack. We show that by leveraging the opinions of their neighbors on the trustworthiness of transmitted data, robots can detect adversaries with high probability. We characterize the number of communication rounds required to be a function of the communication quality and of the proportion of legitimate to malicious robots. This result enables increased resiliency of many multirobot algorithms. Because our results are finite time and not asymptotic, they are particularly well-suited for problems of a time critical nature. We develop two algorithms,FindSpoofedRobotsthat determines trusted neighbors with high probability, andFindResilientAdjacencyMatrixthat enables distributed computation of graph properties in an adversarial setting. We apply our methods to a flocking problem where a team of robots must track a moving target in the presence of adversarial robots. We show that by using our algorithms, the team of robots are able to maintain tracking ability of the dynamic target.