In this paper, we solve a joint cooperative localization and path planning problem for a group of Autonomous Aerial Vehicles (AAVs) in GPS-denied areas using nonlinear model predictive control (NMPC). A moving horizon estimator (MHE) is used to estimate the vehicle states with the help of relative bearing information to known landmarks and other vehicles. The goal of the NMPC is to devise optimal paths for each vehicle between a given source and destination while maintaining desired localization accuracy. Estimating localization covariance in the NMPC is computationally intensive; hence, we develop an approximate analytical closed-form expression based on the relationship between covariance and path lengths to landmarks. Using this expression while computing NMPC commands reduces the computational complexity significantly. We present numerical simulations to validate the proposed approach for different numbers of vehicles and landmark configurations. We also compare the results with EKF and RRT*based methods to show the superiority of the proposed closed-form approach.

We show that a stream of automated vehicles traveling along the highway can be destabilized to catastrophic effect through modification of the control laws of individual vehicles. Specifically, one active attacker who introduces errors, in addition to one or many passive attackers who amplify the error, may, by the modification of a single parameter, induce oscillatory traffic jams that cause delay, driver discomfort, excess energy expenditure, and increased risk of accidents that could result in serious injury or death. We determine the conditions under which an attacker(s) is able to violate the primary design criterion of automated vehicle streams, known as string stability, to guarantee system instability. Furthermore, we prove that once the stream has been destabilized it will continually deviate from the desired state, even in the absence of additional input to the system-i.e. the jammed condition will self-perpetuate. Through a comparison with a behavioral human driver model, this work demonstrates that automated vehicle systems are more vulnerable to disruption than their non-automated counterparts. The postulated attack is demonstrated on a scaled system and identification of attackers is discussed.

In this paper, we show that a single, maliciously controlled vehicle can destabilize a vehicular platoon, to catastrophic effect, through local modifications to the prevailing control law. Specifically, by combining changes to the gains of the associated law with the appropriate vehicle movements, the attacker can cause the platoon to oscillate at a resonant frequency, causing accidents that could result in serious injury or death. We determine the range of gains, and their corresponding frequencies, that allow an attacker to violate the string stability and stability criteria at different positions in the platoon. Furthermore, we prove that the attack can be successful at any position in the platoon and at frequencies that can be realized by the other vehicles in the platoon. Our work implies that neither the string stability nor stability conditions, when used singly, ensure proper platoon operation, and that neither can be used to ensure the other. Finally, we show that an attacker is theoretically capable of gaining control over the individual position and velocity (states) of other vehicles in the platoon; two attacks are demonstrated for this vulnerability.

In this paper, we present a cooperative target-centric formation control strategy that maintains the dynamic graph connectivity for a system of unmanned aerial vehicles. The connectivity of a graph plays a critical role in dynamic networks of multiple agents since it represents a level of information sharing capability of a system. The connectivity of a network of unmanned systems changes as the state dependent graph evolves over time, revealing the risk of the system being uncontrollable during null connectivity. We show that the existing formation controller fails to make the formation when the network connectivity is lost. We propose a novel formation control strategy which keeps the multi-agent graph connected throughout the system dynamic process. The convergence of the formation controller is shown with the help of the Lyapunov theory. Simulation results validate the effectiveness of the proposed control law.

In this paper, we investigate the nonlinear observability properties of bearing-only cooperative localization. We establish a link between observability and a graph that represents measurements and communication between the robots. It is shown that graph theoretic properties like the connectivity and the existence of a path between two nodes can be used to explain the observability of the system. We obtain the maximum rank of the observability matrix without global information and derive conditions under which the maximum rank can be achieved. Furthermore, we show that for complete observability, all of the nodes in the graph must have a path to at least two different landmarks of known location.