Ontological Problem-Solving Framework for Dynamically Configuring Sensor Systems and Algorithms

The deployment of ubiquitous sensor systems and algorithms has led to many challenges, such as matching sensor systems to compatible algorithms which are capable of satisfying a task. Compounding the challenges is the lack of the requisite knowledge models needed to discover sensors and algorithms and to subsequently integrate their capabilities to satisfy a specific task. A novel ontological problem-solving framework has been designed to match sensors to compatible algorithms to form synthesized systems, which are capable of satisfying a task and then assigning the synthesized systems to high-level missions. The approach designed for the ontological problem-solving framework has been instantiated in the context of a persistence surveillance prototype environment, which includes profiling sensor systems and algorithms to demonstrate proof-of-concept principles. Even though the problem-solving approach was instantiated with profiling sensor systems and algorithms, the ontological framework may be useful with other heterogeneous sensing-system environments

Ontological Problem-Solving Framework for Assigning Sensor Systems and Algorithms to High-Level Missions

The lack of knowledge models to represent sensor systems, algorithms, and missions makes opportunistically discovering a synthesis of systems and algorithms that can satisfy high-level mission specifications impractical. A novel ontological problem-solving framework has been designed that leverages knowledge models describing sensors, algorithms, and high-level missions to facilitate automated inference of assigning systems to subtasks that may satisfy a given mission specification. To demonstrate the efficacy of the ontological problem-solving architecture, a family of persistence surveillance sensor systems and algorithms has been instantiated in a prototype environment to demonstrate the assignment of systems to subtasks of high-level missions

A Human-Centered Approach for Designing Decision Support Systems

The choice to include the human in the decision process affects four key areas of system design: problem representation, system analysis and design, solution technique selection, and interface requirements specification. I introduce a design methodology that captures the necessary choices associated with each of these areas. In particular I show how this methodology is applied to the design of an actual decision Support system for satellite operations scheduling. Supporting the user\u27s ability to monitor the actions of the system and to guide the decision process of the system are two key considerations in the successful design of a decision support system. Both of these points rely on the correct specification of human-computer interaction points. Traditional, computer-centered system design approaches do not do this well, if at all, and are insufficient for the design of decision support systems. These approaches typically leave the definition of human-computer interaction points till after the component and system level designs are complete. This is too late however since the component and system level design decisions can impose inflexible constraints on the choice of the human-computer interaction points. This often leads to the design of human-computer interaction points that are only good enough. These approaches result in ill-conceived problem representations and poor user-system interaction points because the system lacks the underlying architecture to support these constructs efficiently. Decision support systems require a new, human-centered design approach rather than the traditional computer-centered approaches

Context classification for service robots

This dissertation presents a solution for environment sensing using sensor fusion techniques and a context/environment classification of the surroundings in a service robot, so it could change his behavior according to the different rea-soning outputs. As an example, if a robot knows he is outdoors, in a field environment, there can be a sandy ground, in which it should slow down. Contrariwise in indoor environments, that situation is statistically unlikely to happen (sandy ground). This simple assumption denotes the importance of context-aware in automated guided vehicles

Self-management Framework for Mobile Autonomous Systems

The advent of mobile and ubiquitous systems has enabled the development of autonomous systems such as wireless-sensors for environmental data collection and teams of collaborating Unmanned Autonomous Vehicles (UAVs) used in missions unsuitable for humans. However, with these range of new application domains comes a new challenge – enabling self-management in mobile autonomous systems. The primary challenge in using autonomous systems for real-life missions is shifting the burden of management from humans to these systems themselves without loss of the ability to adapt to failures, changes in context, and changing user requirements. Autonomous systems have to be able to manage themselves individually as well as to form self-managing teams that are able to recover or adapt to failures, protect themselves from attacks and optimise performance. This thesis proposes a novel distributed policy-based framework that enables autonomous systems to perform self management individually and as a team. The framework allows missions to be specified in terms of roles in an adaptable and reusable way, enables dynamic and secure team formation with a utility-based approach for optimal role assignment, caters for communication link maintenance among team members and recovery from failure. Adaptive management is achieved by employing an architecture that uses policy-based techniques to allow dynamic modification of the management strategy relating to resources, role behaviour, team and communications management, without reloading the basic software within the system. Evaluation of the framework shows that it is scalable with respect to the number of roles, and consequently the number of autonomous systems participating in the mission. It is also shown to be optimal with respect to role assignments, and robust to intermittent communication link disconnections and permanent team-member failures. The prototype implementation was tested on mobile robots as a proof-ofconcept demonstration

Knowledge-Driven Agile Sensor-Mission Assignment

In this paper, we show how knowledge representation and reasoning techniques can support sensor-mission assignment, proceeding from a high-level specification of information requirements, to the allocation of assets such as sensors and platforms. In our previous work, we showed how assets can be matched to mission tasks by formalising the military missions and means framework in terms of an ontology, and using this ontology to drive a matchmaking process derived from the area of semantic Web services. The work reported here extends the earlier approach in two important ways: (1) by providing a richer and more realistic way for a user to specify their information requirements, and (2) by using the results of the semantic matchmaking process to define the search space for efficient asset allocation algorithms. We accomplish (1) by means of a rule-based representation of the NIIRS approach to relating sensed data to the tasks that data may support. We illustrate (2)by showing how the output of our matching process can drive a well-known efficient combinatorial auction algorithm (CASS). Finally, we summarise the status of our illustration-of-concept application, SAM (Sensor Assignment to Missions), and discuss various roles such an application can play in supporting sensormission assignment

Proceedings of the 2004 ONR Decision-Support Workshop Series: Interoperability

In August of 1998 the Collaborative Agent Design Research Center (CADRC) of the California Polytechnic State University in San Luis Obispo (Cal Poly), approached Dr. Phillip Abraham of the Office of Naval Research (ONR) with the proposal for an annual workshop focusing on emerging concepts in decision-support systems for military applications. The proposal was considered timely by the ONR Logistics Program Office for at least two reasons. First, rapid advances in information systems technology over the past decade had produced distributed collaborative computer-assistance capabilities with profound potential for providing meaningful support to military decision makers. Indeed, some systems based on these new capabilities such as the Integrated Marine Multi-Agent Command and Control System (IMMACCS) and the Integrated Computerized Deployment System (ICODES) had already reached the field-testing and final product stages, respectively. Second, over the past two decades the US Navy and Marine Corps had been increasingly challenged by missions demanding the rapid deployment of forces into hostile or devastate dterritories with minimum or non-existent indigenous support capabilities. Under these conditions Marine Corps forces had to rely mostly, if not entirely, on sea-based support and sustainment operations. Particularly today, operational strategies such as Operational Maneuver From The Sea (OMFTS) and Sea To Objective Maneuver (STOM) are very much in need of intelligent, near real-time and adaptive decision-support tools to assist military commanders and their staff under conditions of rapid change and overwhelming data loads. In the light of these developments the Logistics Program Office of ONR considered it timely to provide an annual forum for the interchange of ideas, needs and concepts that would address the decision-support requirements and opportunities in combined Navy and Marine Corps sea-based warfare and humanitarian relief operations. The first ONR Workshop was held April 20-22, 1999 at the Embassy Suites Hotel in San Luis Obispo, California. It focused on advances in technology with particular emphasis on an emerging family of powerful computer-based tools, and concluded that the most able members of this family of tools appear to be computer-based agents that are capable of communicating within a virtual environment of the real world. From 2001 onward the venue of the Workshop moved from the West Coast to Washington, and in 2003 the sponsorship was taken over by ONR’s Littoral Combat/Power Projection (FNC) Program Office (Program Manager: Mr. Barry Blumenthal). Themes and keynote speakers of past Workshops have included: 1999: ‘Collaborative Decision Making Tools’ Vadm Jerry Tuttle (USN Ret.); LtGen Paul Van Riper (USMC Ret.);Radm Leland Kollmorgen (USN Ret.); and, Dr. Gary Klein (KleinAssociates) 2000: ‘The Human-Computer Partnership in Decision-Support’ Dr. Ronald DeMarco (Associate Technical Director, ONR); Radm CharlesMunns; Col Robert Schmidle; and, Col Ray Cole (USMC Ret.) 2001: ‘Continuing the Revolution in Military Affairs’ Mr. Andrew Marshall (Director, Office of Net Assessment, OSD); and,Radm Jay M. Cohen (Chief of Naval Research, ONR) 2002: ‘Transformation ... ’ Vadm Jerry Tuttle (USN Ret.); and, Steve Cooper (CIO, Office ofHomeland Security) 2003: ‘Developing the New Infostructure’ Richard P. Lee (Assistant Deputy Under Secretary, OSD); and, MichaelO’Neil (Boeing) 2004: ‘Interoperability’ MajGen Bradley M. Lott (USMC), Deputy Commanding General, Marine Corps Combat Development Command; Donald Diggs, Director, C2 Policy, OASD (NII

Multi-robot mission optimisation : an online approach for optimised, long range inspection and sampling missions

Mission execution optimisation is an essential aspect for the real world deployment of robotic systems. Execution optimisation can affect the outcome of a mission by allowing longer missions to be executed or by minimising the execution time of a mission. This work proposes methods for optimising inspection and sensing missions undertaken by a team of robots operating under communication and budget constraints. Regarding the inspection missions, it proposes the use of an information sharing architecture that is tolerant of communication errors combined with multirobot task allocation approaches that are inspired by the optimisation literature. Regarding the optimisation of sensing missions under budget constraints novel heuristic approaches are proposed that allow optimisation to be performed online. These methods are then combined to allow the online optimisation of long-range sensing missions performed by a team of robots communicating through a noisy channel and having budget constraints. All the proposed approaches have been evaluated using simulations and real-world robots. The gathered results are discussed in detail and show the benefits and the constraints of the proposed approaches, along with suggestions for further future directions