Modern societies depend on complex and critical infrastructures for energy, transportation, sustenance, medical care, emergency response, communications security. As computers, automation, and information technology (IT) have advanced, these technologies have been exploited to enhance the efficiency of operating the processes that make up these infrastructures. Where commonalities exist between different infrastructures or elements of a common infrastructure, such as in the electric power grid, the potential for even greater efficiencies has fueled the desire to integrate intelligent sensing and control architectures and methods over a large region. The results of this integration are the highly interconnected and interdependent control systems of today. However, as we have observed over recent decades, natural disasters, and terrorist attacks can produce failures in complex systems such as utility and transportation infrastructure the consequences of which resulting enormous losses to economy and security. To minimize the impact of these crippling events, society requires control system architectures and methods that maximize the resilience of the complex systems upon which society depends. By “resilience” we mean the capacity of a control system to maintain state awareness and to proactively maintain a safe level of operational normalcy in response to anomalies, including threats of a malicious and unexpected nature[1]. Threats are those elements that counter normalcy and destabilize control system networks, including human error and malicious human attacks, complex latencies and interdependencies.

In this paper we consider complex mechanical systems that operate within harsh, uncertain environments where mission success depends critically upon the system's ability to monitor and maintain its own internal health. In particular, we focus on the problem of reconfiguring a system's internal resource distribution subsystems in response to one or more damage events. We present a graph-based approach that guarantees the resulting states adhere to a desirable resource segregation constraint. We also provide results using this approach in conjunction with optimization techniques to reconfigure a representative hardware test platform.

A new self-reconfigurable robot is presented. The robot is a hybrid chain/lattice design with several novel features. An active mechanical docking mechanism provides inter-module connection, along with optical and electrical interface. The docking mechanisms function additionally as driven wheels. Internal slip rings provide unlimited rotary motion to the wheels, allowing the modules to move independently by driving on flat surfaces, or in assemblies negotiating more complex terrain. Modules in the system are mechanically homogeneous, with three identical docking mechanisms within a module. Each mechanical dock is driven by a high torque actuator to enable movement of large segments within a multi-module structure, as well as low-speed driving. Preliminary experimental results demonstrate locomotion, mechanical docking, and lifting of a single module.

Some auxiliary systems of next generation naval ships will utilize distributed automatic control. Such distributed control systems will use interconnected sensors, actuators, controllers and networking components to diagnose and reconfigure the auxiliary systems. Testing these systems will be difficult with traditional methods of fault analysis due to the interconnected and automatic nature of these subsystems. We have designed a suite of genetic algorithms to find interesting and hidden damage scenarios in a testbed of a naval subsystem. Given this knowledge, we use a genetic algorithm to improve upon the design of this subsystem.

Effects-based operations have been demonstrated on hardware to realize practical and autonomously performed behaviors utilizing a variety of vehicle platforms including ground, water surface, and aerial vehicles. The heterogeneous vehicle swarms enact a variety of cooperative behaviors autonomously, using reactive, effects-based algorithms and broadcast-only, decentralized communications. Control is achieved through the use of stigmergic potential fields, a form of cooperative motor schema behavior based upon mathematical functions that are associated with entities in the operational environment of the vehicle. The video demonstration shows footage from actual hardware tests that have demonstrated empirically how reactive swarming behaviors using stigmergic potential fields can offer robustly sufficient behavior with improved total system survivability and total operational effectiveness, particularly under dynamic environmental and operational conditions.