Dynamic Networks Of Hybrid Automata Research Articles

UAVs Dynamic Mission Management in Adversarial Environments

We address a dynamic configuration strategy for teams of Unmanned Air Vehicles (UAVs). A team is a collection of UAVs which may evolve through different organizations, called configurations. The team configuration may change with time to adapt to environmental changes, uncertainty, and adversarial actions. Uncertainty comes from the stochastic nature of the environment and from incomplete knowledge of adversary behaviors. To each configuration, there corresponds a set of different properties for the UAVs in the team. The design for the configuration control problem involves a distributed hierarchical control architecture where the properties of the system can be formally analyzed. We do this in the framework of dynamic networks of hybrid automata. We present results from simulation to demonstrate different scenarios for adversarial response.

MULTI-UAV PLATFORM FOR INTEGRATION IN MIXED-INITIATIVE COORDINATED MISSIONS

The Apollo system for unmanned air vehicle (UAV) control is described. This is done in the context of the AsasF project from Porto University. Apollo is the UAV controller component of the AsasF control hierarchy. Apollo provides a uniform vehicle interface which is targeted at multi-vehicle operations in a mixed initiative environment allowing the intervention of experienced human operators. Apollo decouples the details of UAV control from the organization of external controllers in the AsasF control hierarchy. Apollo has a layered structure modelled in the framework of dynamic networks of hybrid automata. There is a mission supervisor, a vehicle supervisor and manoeuvre controllers, one per type of manoeuvre.

The SHIFT programming language for dynamic networks of hybrid automata

SHIFT is a programming language for describing and simulating dynamic networks of hybrid automata. Such systems consist of components which can be created, interconnected, and destroyed as the system evolves. Components exhibit hybrid behavior, e.g. continuous-time phases separated by instantaneous discrete-event transitions. Components may evolve independently, or they may interact through selected state variables and events. The interaction network itself may evolve. The SHIFT model and language were motivated by our need for tools that support dynamically reconfigurable hybrid systems. Our primary application is the specification and analysis of different designs for automatic control of vehicles and highway systems. From our previous experience in modeling, analysis, and implementation, we adopted the hybrid systems approach for modeling the system components. Since spatial relationships between vehicles change as they move, our application is characterized by a dynamically changing network of interactions between system components. SHIFT has also since been used in coordinated autonomous submarines, air traffic control systems, and material handling systems. We examine other work related to the SHIFT approach. In we describe the main features of the SHIFT language-states, inputs, outputs, differential equations, and algebraic definitions, discrete states, and state transitions. We give a simplified version of the SHIFT model. We discuss the models of a type, a component, and the world and give the formal semantics of the model.