A collaborative robotic team may need to allocate multiple tasks within an unknown and highly dynamic environment. Highly dynamic environments entail constantly changing states of its entities, objects, and situational characteristics. Tasks in dynamic environments can have unknown information, changing requirements, and can result in unforeseen goal states. (e.g., searching an unfamiliar building can result in an unknown number of rooms, or open areas to be further searched). Essentially, the task requirements can change, the asset/robot ability to perform the task can change, and the environment can change such that an allocation of tasks may need to be re-allocated. The allocation process must be flexible enough and ad-hoc in nature to compensate for such dynamics. This report presents the results of multiple investigations into various market-based, ad-hoc methods as a means to flexibly allocate tasks across a mobile robotic team in unknown and highly dynamic environments. These ad-hoc methods can be controlled from a centralized point or implemented in a decentralized mode. The decentralized control is of greater interest since it reduces processing bottlenecks, eliminates single points of failures, and can encourage network dataflow that is more natural to the ad-hoc nature of the mobile team of robotic vehicles. This is especially true when the number of tasks and robots are scaled up (i.e., swarm robotics). The approach utilizes weighted formulas that represent a robot’s ability to engage each of the identified tasks, and a task’s allocation is based on comparing the results of these weighted formulae. The allocation process is improved via optimizing the formulas’ weights based on deep-learning methods.
Proc. SPIE. 10651, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2018
KEYWORDS: Complex systems, Computing systems, Telecommunications, System identification, Systems engineering, Data communications, Systems modeling, Information security, Computer security, Process engineering
Mission objectives for cyber systems operating in a tactical or deployed operational environment must address not only security but also resilience. A cyber resilient architecture is engineered for completing mission objectives in the “face of persistent, stealthy, and sophisticated attacks of cyber resources (MITRE, 2011)”. Ever evolving adversaries drive the need for system architectures to protect cyber resources but enable operations during an attack to achieve mission objectives. Similar to cyber security, resilience must be engineered into all layers of system architecture at inception, baking protections for security and redundancies for resilience through all layers of the system architecture.
We study the problem of dispersing a group of small robots in an unknown environment. The objective is to
cover the environment as much as possible while staying within communications range. We assume there is no
central control, the environment is unknown and with complex obstacles, the robots operate without any central
control, and have only limited communications with other robots and limited sensing capabilities. We present
algorithms and validate them experimentally in the Player/Stage simulation environment.
To be useful in the real world, robots need to be able to move safely in unstructured environments and achieve their given tasks despite unexpected environmental changes or failures of some of their sensors. The variability of the world makes it impractical to develop very detailed plans of actions before execution since the world might change before execution begins and thus invalidate the plan. We propose to generate the very detailed plan of actions needed to control a robot at execution time. This allows to obtain up-to-date information about the environment through sensors and to use it in deciding how to achieve the task. Our theory is based on the premise that proper application of knowledge in the integration and utilization of sensors and actuators increases the robustness of execution. In our approach we produce the detailed plan of primitive actions and execute it by using an object-oriented approach, in which primitive components contain domain specific knowledge and knowledge about the available sensors and actuators. These primitives perform signal and control processing as well as serve as an interface to high-level planning processes. This paper addresses the issue of what knowledge needs to be available about sensors, actuators and processes in order to be able to integrate their usage, and control them during execution. The proposed methods of execution works for any sensor/actuator existing on the robot when given such knowledge.
Conventional fieldable signal processing systems utilize custom hardware manufactured
and configured specifically for a single signal processing application. Developing new
systems or reconfiguring existing systems involves great expense and time expenditure.
We at Alliant Techsystems have developed a signal processing system based on
commercially available hardware which is completely software programmable and yet small
and fast enough to be used in fieldable multisensor signal processing applications. This
paper will discuss Alliant's reconfigurable signal processing system.
Imaging Systems have traditionally required large development cycles to transition from non-real-time implementations on general purpose computers to final real-time system prototypes using custom hardware. This paper presents a flexible realtime prototyping approach for the Conceptual Definition, Demonstration and Validation phases of development for imaging system applications such as forward observe, perimeterdefense, or "mobile barrier." A target acquisition and tracking system that has utilized this approach will be discussed and tracking system that has utilized this approach will be discussed and used to compare hardware, software, resources and schedule factors to other imaging system development programs. The testbed is shown to maintain a high degree of algorithm flexibility allowing field test experiences to be rapidly incorporated into the system. The entire system is programmable using high order languages to minimize software costs and enhance maintainability. This system was developed and integrated into a mobile lab for field testing. During real-time testing the system was upgraded and modified to provide high detection performance with low false alarm rates. This approach has led to a more complete understanding of the problem being addressed and has positioned this system closer to its final product form.