Safe Distributed Coordination of Heterogeneous Robots through Dynamic Simple Temporal Networks

Research on autonomous intelligent systems has focused on how robots can robustly carry out missions in uncertain and harsh environments with very little or no human intervention. Robotic execution languages such as RAPs, ESL, and TDL improve robustness by managing functionally redundant procedures for achieving goals. The model-based programming approach extends this by guaranteeing correctness of execution through pre-planning of non-deterministic timed threads of activities. Executing model-based programs effectively on distributed autonomous platforms requires distributing this pre-planning process. This thesis presents a distributed planner for modelbased programs whose planning and execution is distributed among agents with widely varying levels of processor power and memory resources. We make two key contributions. First, we reformulate a model-based program, which describes cooperative activities, into a hierarchical dynamic simple temporal network. This enables efficient distributed coordination of robots and supports deployment on heterogeneous robots. Second, we introduce a distributed temporal planner, called DTP, which solves hierarchical dynamic simple temporal networks with the assistance of the distributed Bellman-Ford shortest path algorithm. The implementation of DTP has been demonstrated successfully on a wide range of randomly generated examples and on a pursuer-evader challenge problem in simulation

Addressing Complexity and Intelligence in Systems Dependability Evaluation

Engineering and computing systems are increasingly complex, intelligent, and open adaptive. When it comes to the dependability evaluation of such systems, there are certain challenges posed by the characteristics of “complexity” and “intelligence”. The first aspect of complexity is the dependability modelling of large systems with many interconnected components and dynamic behaviours such as Priority, Sequencing and Repairs. To address this, the thesis proposes a novel hierarchical solution to dynamic fault tree analysis using Semi-Markov Processes. A second aspect of complexity is the environmental conditions that may impact dependability and their modelling. For instance, weather and logistics can influence maintenance actions and hence dependability of an offshore wind farm. The thesis proposes a semi-Markov-based maintenance model called “Butterfly Maintenance Model (BMM)” to model this complexity and accommodate it in dependability evaluation. A third aspect of complexity is the open nature of system of systems like swarms of drones which makes complete design-time dependability analysis infeasible. To address this aspect, the thesis proposes a dynamic dependability evaluation method using Fault Trees and Markov-Models at runtime.The challenge of “intelligence” arises because Machine Learning (ML) components do not exhibit programmed behaviour; their behaviour is learned from data. However, in traditional dependability analysis, systems are assumed to be programmed or designed. When a system has learned from data, then a distributional shift of operational data from training data may cause ML to behave incorrectly, e.g., misclassify objects. To address this, a new approach called SafeML is developed that uses statistical distance measures for monitoring the performance of ML against such distributional shifts. The thesis develops the proposed models, and evaluates them on case studies, highlighting improvements to the state-of-the-art, limitations and future work

Advancing automation and robotics technology for the Space Station Freedom and for the US economy

Described here is the progress made by Levels 1, 2, and 3 of the Space Station Freedom in developing and applying advanced automation and robotics technology. Emphasis was placed on the Space Station Freedom program responses to specific recommendations made in the Advanced Technology Advisory Committee (ATAC) Progress Report 13, and issues of A&R implementation into the payload operations integration Center at Marshall Space Flight Center. Assessments are presented for these and other areas as they apply to the advancement of automation and robotics technology for Space Station Freedom

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments

Cooperative Monitoring to Diagnose Multiagent Plans

Diagnosing the execution of a Multiagent Plan (MAP) means identifying and explaining action failures (i.e., actions that did not reach their expected effects). Current approaches to MAP diagnosis are substantially centralized, and assume that action failures are inde-pendent of each other. In this paper, the diagnosis of MAPs, executed in a dynamic and partially observable environment, is addressed in a fully distributed and asynchronous way; in addition, action failures are no longer assumed as independent of each other. The paper presents a novel methodology, named Cooperative Weak-Committed Moni-toring (CWCM), enabling agents to cooperate while monitoring their own actions. Coop-eration helps the agents to cope with very scarcely observable environments: what an agent cannot observe directly can be acquired from other agents. CWCM exploits nondetermin-istic action models to carry out two main tasks: detecting action failures and building trajectory-sets (i.e., structures representing the knowledge an agent has about the environ-ment in the recent past). Relying on trajectory-sets, each agent is able to explain its own action failures in terms of exogenous events that have occurred during the execution of the actions themselves. To cope with dependent failures, CWCM is coupled with a diagnostic engine that distinguishes between primary and secondary action failures. An experimental analysis demonstrates that the CWCM methodology, together with the proposed diagnostic inferences, are effective in identifying and explaining action failures even in scenarios where the system observability is significantly reduced. 1

Real-Time Supervision for Human Robot Teams in Complex Task Domains

Ongoing research on multi-robot teams is focused on methods and systems to be utilized in dynamic and dangerous environments such as search and rescue missions, often with a human operator in the loop to supervise the system and make critical decisions. To increase the size of the team controlled by an operator, and to reduce the operator\u27s mental workload, the robots will have to be more autonomous and reliable so that tasks can be issued at a higher level. Typical in these domains, such high-level tasks are often composed of smaller tasks with dependencies and constraints. Assigning suitable robot platforms to execute these tasks is a combinatorial optimization problem. Operations Research and AI techniques can handle large numbers of robot allocations in real time, however most of these algorithms are opaque to humans; they provide no explanation or insight about how the solution is produced. Recent studies suggest that interaction between the human operator and robot team requires human-centric approaches for collaborative planning and task allocation, since black-box solutions are often too complex to examine under stressful conditions and are often discarded by experts. The main contribution of this thesis is a methodology to help operators make decisions about complex task allocation in real time for high stress missions. First a novel, human-centric graphical model, TAG, is described to analyze and predict the complexity of task assignment and scheduling problem instances, taking into account the spatial distribution of resources and tasks. Then, the TAG model is extended for dynamic environments to the MAP model. Two user studies were conducted, first in static and then in dynamic environments, in order to identify and empirically verify the key factors, derived from the graphical model, which affect the decision making of human supervisors during task assignment for a team of robots. In these user studies, participants used software tools developed for this work. One of these software tools allows for two different levels of autonomy for the interaction scheme: manual control and collaborative control, with an option to invoke an automated assignment tool. Findings relating to the impact of decision support functionality on the mental workload and the performance of the supervisor are presented. Finally, steering of the common algorithms utilized by decision support tools, using the strategies employed by user study participants, related to the TAG and MAP model parameters, are discussed

Fault-tolerant software: dependability/performance trade-offs, concurrency and system support

PhD ThesisAs the use of computer systems becomes more and more widespread in applications that demand high levels of dependability, these applications themselves are growing in complexity in a rapid rate, especially in the areas that require concurrent and distributed computing. Such complex systems are very prone to faults and errors. No matter how rigorously fault avoidance and fault removal techniques are applied, software design faults often remain in systems when they are delivered to the customers. In fact, residual software faults are becoming the significant underlying cause of system failures and the lack of dependability. There is tremendous need for systematic techniques for building dependable software, including the fault tolerance techniques that ensure software-based systems to operate dependably even when potential faults are present. However, although there has been a large amount of research in the area of fault-tolerant software, existing techniques are not yet sufficiently mature as a practical engineering discipline for realistic applications. In particular, they are often inadequate when applied to highly concurrent and distributed software. This thesis develops new techniques for building fault-tolerant software, addresses the problem of achieving high levels of dependability in concurrent and distributed object systems, and studies system-level support for implementing dependable software. Two schemes are developed - the t/(n-l)-VP approach is aimed at increasing software reliability and controlling additional complexity, while the SCOP approach presents an adaptive way of dynamically adjusting software reliability and efficiency aspects. As a more general framework for constructing dependable concurrent and distributed software, the Coordinated Atomic (CA) Action scheme is examined thoroughly. Key properties of CA actions are formalized, conceptual model and mechanisms for handling application level exceptions are devised, and object-based diversity techniques are introduced to cope with potential software faults. These three schemes are evaluated analytically and validated by controlled experiments. System-level support is also addressed with a multi-level system architecture. An architectural pattern for implementing fault-tolerant objects is documented in detail to capture existing solutions and our previous experience. An industrial safety-critical application, the Fault-Tolerant Production Cell, is used as a case study to examine most of the concepts and techniques developed in this research.ESPRIT

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Supporting adaptiveness of cyber-physical processes through action-based formalisms

Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system