Homeostatic action selection for simultaneous multi-tasking

Mobile robots are rapidly developing and gaining in competence, but the potential of available hardware still far outstrips our ability to harness. Domain-specific applications are most successful due to customised programming tailored to a narrow area of application. Resulting systems lack extensibility and autonomy, leading to increased cost of development. This thesis investigates the possibility of designing and implementing a general framework capable of simultaneously coordinating multiple tasks that can be added or removed in a plug and play manner. A homeostatic mechanism is proposed for resolving the contentions inevitably arising between tasks competing for the use of the same robot actuators. In order to evaluate the developed system, demonstrator tasks are constructed to reach a goal location, prevent collision, follow a contour around obstacles and balance a ball within a spherical bowl atop the robot. Experiments show preliminary success with the homeostatic coordination mechanism but a restriction to local search causes issues that preclude conclusive evaluation. Future work identifies avenues for further research and suggests switching to a planner with the sufficient foresight to continue evaluation."This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/K503162/1]." -- Acknowledgement

An early-stage decision-support framework for the implementation of intelligent automation

The constant pressure on manufacturing companies to improve productivity, reduce the lead time and progress in quality requires new technological developments and adoption.The rapid development of smart technology and robotics and autonomous systems (RAS) technology has a profound impact on manufacturing automation and might determine winners and losers of the next generation’s manufacturing competition. Simultaneously, recent smart technology developments in the areas enable an automation response to new production paradigms such as mass customisation and product-lifecycle considerations in the context of Industry 4.0. New paradigms, like mass customisation, increased both the complexity of the tasks and the risk due to smart technology integration. From a manufacturing automation perspective, intelligent automation has been identified as a possible response to arising demands. The presented research aims to support the industrial uptake of intelligent automation into manufacturing businesses by quantifying risks at the early design stage and business case development. An early-stage decision-support framework for the implementation of intelligent automation in manufacturing businesses is presented in this thesis.The framework is informed by an extensive literature review, updated and verified with surveys and workshops to add to the knowledge base due to the rapid development of the associated technologies. A paradigm shift from cost to a risk-modelling perspective is proposed to provide a more flexible and generic approach applicable throughout the current technology landscape. The proposed probabilistic decision-support framework consists of three parts:• A clustering algorithm to identify the manufacturing functions in manual processes from task analysis to mitigate early-stage design uncertainties• A Bayesian Belief Network (BBN) informed by an expert elicitation via the DELPHI method, where the identified functions become the unit of analysis.• A Markov-Chain Monte-Carlo method modelling the effects of uncertainties on the critical success factors to address issues of factor interdependencies after expert elicitation.Based on the overall decision framework a toolbox was developed in Microsoft Excel. Five different case studies are used to test and validate the framework. Evaluation of the results derived from the toolbox from the industrial feedback suggests a positive validation for commercial use. The main contributions to knowledge in the presented thesis arise from the following four points:• Early-stage decision-support framework for business case evaluation of intelligent automation.• Translating manual tasks to automation function via a novel clustering approach• Application of a Markov-Chain Monte-Carlo Method to simulate correlation between decision criteria• Causal relationship among Critical Success Factors has been established from business and technical perspectives.The implications on practise might be promising. The feedback arising from the created tool was promising from the industry, and a practical realisation of the decision-support tool seems to be desired from an industrial point of view.With respect to further work, the decision-support tool might have established a ground to analyse a human task automatically for automation purposes. The established clustering mechanisms and the related attributes could be connected to sensorial data and analyse a manufacturing task autonomously without the subjective input of task analysis experts. To enable such an autonomous process, however, the psychophysiological understanding must be increased in the future.</div

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

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

Supporting design planning through process model simulation

Modelling and analysis of design processes is non-trivial and predicting the behaviour of such systems is especially challenging. Nonetheless, the effective planning of design projects is critical for many engineering companies. Based on the findings from a thorough literature review and an extensive industrial case study, this thesis identified four themes on which subsequent process analyses focused: modelling and representation; scale and connectivity; rework, and the product-process link. The case study also provided a deeper understanding of the practical challenges of planning in industry. In order to meet these challenges, both the Signposting modelling framework and simulation tool were enhanced to provide increased functionality for process modelling, representation and analysis. Further, novel approaches for process analysis - the use of hypothetical models and confidence profiles that link product and process information - were proposed. A software tool was developed to automatically generate such hypothetical models. Simulation analyses were performed on both hypothetical and real-world models. The results elucidated the effects of structural variations in terms of scale and connectivity on project performance. They also showed how task reordering due to rework can lead to major process delays, even when the time taken to rework failed tasks is extremely short. Further, the simulation analyses demonstrated how confidence profiles could be used to identify rework early in the process and reduce the resulting project schedule impact. The results from the simulation analyses were evaluated against historical data from the case study company and heuristics for design planning were defined. Overall, this research demonstrated how simulation analysis of both real-world and hypothetical models, using the enhanced Signposting tool, can provide useful insights into design process planning.Prof. John Clarkson; Cambridge European Trust; Isaac Newton Fund; Churchill College

Sequential Decision Making under Uncertainty for Sensor Management in Mobile Robotics

Sensor management refers to the control of the degrees of freedom in a sensing system. The objective of sensor management is to improve performance e.g. by obtaining more accurate information or by achieving other operational goals. Sensor management is viewed as a sequential decision making process, where decisions at any time are made conditional on the past decisions and measurement data. At the time of deciding a control action for a sensing system the measurement data that will be obtained are unknown. Thus, informally speaking, a solution to a sensor management problem is a policy that determines which sensing action to undertake given the current information on the state of the process under investigation and contingent on any possible realisation of future measurement data outcomes.This thesis studies sensor management framing the contingent planning problem in the partially observable Markov decision process (POMDP) framework. In particular, applications in mobile robotics are considered. Mobile robots are viewed as controllable sensor platforms.Based on earlier work on POMDP based robot control, and distinguishing between the two cases of either exploiting or gathering information, we define four canonical sensor management problem types in mobile robotics. In each of the problem types, we exploit the structural properties of their inputs to improve efficiency of applicable contingent planning algorithms.In particular, we consider sensor management problems for information gathering where the utility of the possible control policies is quantified by mutual information (MI). We identify the relationship between the POMDP formulation of an environment monitoring problem and another contingent planning problem known as a multi-armed bandit (MAB). In a robotic exploration task, we derive a novel approximation for MI.Through both simulation and real-world experiments in mobile robotics domains, we determine the applicability, advantages, and disadvantages of a POMDP based approach to sensor management in mobile robotics