Localization is one of the most important tasks for mobile robot operations. Without such capability, a robot may wander toward unsafe states and never complete a desired task. Such capability is even more important in multi-robot system (MRS) operations in which their motion is coordinated based on consensus schemes that leverage information from surrounding neighbors. Thus, in the event of compromised or malfunctioning on-board positioning sensing (e.g., due to cyber attacks or faults) on individual robots, the entire robotic system may be hijacked toward undesired states. In this work, we target this problem by proposing a decentralized framework where: i) robots with loss of localization capabilities detect the anomalous behavior then generate a notification signal within information exchanges to alert neighboring robots, and ii) neighboring robots leverage their mobility to aid in recovery allowing compromised robots to re-localize. Our framework is validated in simulations and lab experiments on proximity-based formations of homogeneous unmanned multi-robot swarms.

Cyber-attacks, failures, and implementation errors inside the controller of an autonomous system can affect its correct behavior leading to unsafe states and degraded performance. In this paper, we focus on such problems specifically on cyber-attacks that manipulate controller parameters like the gains in a feedback controller or that triggers different behaviors or block inputs based on specific values of the state and tracking error. If such attacks are undetected, they can lead to the partial or complete loss of system's control authority, resulting in a hijacking and leading the autonomous system towards unforeseen states. To deal with this problem, we propose a runtime monitoring and recovery scheme in which: 1) we leverage the residual between the expected and the received measurements to detect inconsistencies in the generated inputs and 2) provide a recovery method for counteracting the malicious effects to allow for resilient operations by manipulating the reference signal and state vector provided to the system to avoid the affected regions in the state and error space. We validate our approach with Matlab simulations and experiments on unmanned ground vehicles resiliently performing operations in the presence of malicious attacks to on-board controllers.

Cooperative multirobot systems coordinate their motion by exchanging information through consensus schemes to achieve a common goal. In the event of stealthy cyber attacks, compromised measurements and communication broadcasts can hijack a portion or the entire system toward undesired states. However, in order for these attacks to be effective, they have to exhibit nonrandom characteristics that contradict the expected multirobot system behavior. To deal with these hidden attacks, we propose a runtime monitoring framework that considers the signedresidual, defined as the difference between the expected and the received information to identify and isolate unexpected nonrandom behavior within the multirobot system. Specifically, the technique that we propose—namedCumulative Signdetector—monitors and compares changes in signed values of residual with their expected occurrences to detect inconsistencies and trigger alarms when an attack is discovered. Our results are validated theoretically by providing detection bounds and are demonstrated with simulations and experiments on swarms of unmanned ground vehicles under different attacks in comparison with state-of-the-art residual-based detection schemes.

A resilient multi-vehicle system cooperatively performs tasks by exchanging information, detecting, and removing cyber attacks that have the intent of hijacking or diminishing performance of the entire system. In this paper, we propose a framework to: i) detect and isolate misbehaving vehicles in the network, and ii) securely encrypt information among the network to alert and attract nearby vehicles toward points of interest in the environment without explicitly broadcasting safety-critical information. To accomplish these goals, we lever-age a decentralized virtual spring-damper mesh physics model for formation control on each vehicle. To discover inconsistent behavior of any vehicle in the network, we consider an approach that monitors for changes in sign behavior of an inter-vehicle residual that does not match with an expectation. Similarly, to disguise important information and trigger vehicles to switch to different behaviors, we leverage side-channel information on the state of the vehicles and characterize a hidden spring-damper signature model detectable by neighbor vehicles. Our framework is demonstrated in simulation and experiments on formations of unmanned ground vehicles (UGVs) in the presence of malicious man-in-the-middle communication attacks.