Embedded systems are a key enabling technology for the recent vast increase in functionality of a huge list of critical infrastructures. Hybrid automata can be used to model system-level behaviors for the large category of systems that exhibit strong couplings between discrete and continuous dynamics. Many software tools have been developed for hybrid automata to enable model-based design of embedded systems and these software tools are constructed by using their own modeling languages. Model-based design frameworks, such as Model-Integrated Computing (MIC), Model Driven Architecture (MDA), and Model Driven Design (MDD), have been advocated to raise the level of abstraction in software tool design by placing stronger emphasis on the use of software models in the software tool design process. In particular, MIC places strong emphasis on the use of Domain Specific Modeling Languages(DSMLs) and model transformations in design flows. Practical and effective development of formal specifications for DSML semantics within model-based tools can be challenging, but could positively impact adoption and reuse of these tools. The semantic anchoring methodology was developed to address this challenge by formally tying DSMLs to a "semantic unit", which is a formal specification that captures the operational semantics of a specific model of computation. Leveraging our prior work with semantic units, we develop a semantic unit for hybrid automata. In this paper, we explicitly specify the operational semantics of hybrid automata, and develop the corresponding semantic unit and model transformation rules. We demonstrate the effectiveness of the infrastructure in a practical case study involving the hybrid automata DSMLs, HyVisual and ReachLab.

Unmanned aerial vehicles (UAVs) represent an important class of networked robotic applications that must be both highly dependable and autonomous. This paper addresses sensor selection and placement problems for distributed failure diagnosis in such networks where multiple vehicles must agree on the fault status of another UAV. An integer linear programming (ILP) approach is proposed to solve these problems. The ILP models of interest are developed and solved using two different solvers. Experimental results indicate that the proposed models are tractable for medium-sized topologies

Embedded software and systems are at the intersection of electrical engineering, computer engineering, and computer science, with, increasing importance, in mechanical engineering. Despite the clear need for knowledge of systems modeling and analysis (covered in electrical and other engineering disciplines) and analysis of computational processes (covered in computer science), few academic programs have integrated the two disciplines into a cohesive program of study. This paper describes the efforts conducted at Vanderbilt University to establish a curriculum that addresses the needs of embedded software and systems. Given the compartmentalized nature of traditional engineering schools, where each discipline has an independent program of study, we have had to devise innovative ways to bring together the two disciplines. The paper also describes our current efforts in using learning technology to construct, manage, and deliver sophisticated computer-aided learning modules that can supplement the traditional course structure in the individual disciplines through out-of-class and in-class use.

Given a group of autonomous vehicles, an initial configuration, a final configuration, a set of inter- and intra-vehicle constraints, and a time for reconfiguration, the Formation Reconfiguration Planning problem is focused on determining a nominal input trajectory for each vehicle such that the group can start from the initial configuration and reach its final configuration at the specified time while satisfying the set of inter-and intra-vehicle constraints. In this paper, we are interested in solving the Formation Reconfiguration Planning problem for a specific class of systems and a particular form of input signals so that the problem can be reformulated as an optimization problem which can be solved more efficiently, especially for a large group of vehicles.

Automatic control systems typically incorporate legacy code and components that were originally designed to operate independently. Furthermore, they operate under stringent safety and timing constraints. Current design strategies deal with these requirements and characteristics with ad hoc approaches. In particular, when designing control laws, implementation constraints are often ignored or cursorily estimated. Indeed, costly redesigns are needed after a prototype of the control system is built because of missed timing constraints and subtle transient errors. In this paper, we use the concepts of platform-based design to develop a methodology for the design of automatic control systems that builds in modularity and correct-by-construction procedures. We illustrate our strategy by describing the (successful) application of the methodology to the design of a time-based control system for a helicopter-based uninhabited aerial vehicle.

We are developing a system for autonomous navigation of unmanned aerial vehicles (UAVs) based on computer vision. A UAV is equipped with on-board cameras and each UAV is provided with noisy estimates of its own state, coming from GPS/INS. The mission of the UAV is low altitude navigation from an initial position to a final position in a partially known 3-D environment while avoiding obstacles and minimizing path length. We use a hierarchical approach to path planning. We distinguish between a global offline computation, based on a coarse known model of the environment and a local online computation, based on the information coming from the vision system. A UAV builds and updates a virtual 3-D model of the surrounding environment by processing image sequences and fusing them with sensor data. Based on such a model the UAV will plan a path from its current position to the terminal point. It will then follow such path, getting more data from the on-board cameras, and refining map and local path in real time.