233 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Active SLAM: A Review On Last Decade
This article presents a comprehensive review of the Active Simultaneous
Localization and Mapping (A-SLAM) research conducted over the past decade. It
explores the formulation, applications, and methodologies employed in A-SLAM,
particularly in trajectory generation and control-action selection, drawing on
concepts from Information Theory (IT) and the Theory of Optimal Experimental
Design (TOED). This review includes both qualitative and quantitative analyses
of various approaches, deployment scenarios, configurations, path-planning
methods, and utility functions within A-SLAM research. Furthermore, this
article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM),
focusing on collaborative aspects within SLAM systems. It includes a thorough
examination of collaborative parameters and approaches, supported by both
qualitative and statistical assessments. This study also identifies limitations
in the existing literature and suggests potential avenues for future research.
This survey serves as a valuable resource for researchers seeking insights into
A-SLAM methods and techniques, offering a current overview of A-SLAM
formulation.Comment: 34 pages, 8 figures, 6 table
Expectations and expertise in artificial intelligence: specialist views and historical perspectives on conceptualisation, promise, and funding
Artificial intelligence’s (AI) distinctiveness as a technoscientific field that imitates the ability to think went through a resurgence of interest post-2010, attracting a flood of scientific and popular expectations as to its utopian or dystopian transformative consequences. This thesis offers observations about the formation and dynamics of expectations based on documentary material from the previous periods of perceived AI hype (1960-1975 and 1980-1990, including in-between periods of perceived dormancy), and 25 interviews with UK-based AI specialists, directly involved with its development, who commented on the issues during the crucial period of uncertainty (2017-2019) and intense negotiation through which AI gained momentum prior to its regulation and relatively stabilised new rounds of long-term investment (2020-2021). This examination applies and contributes to longitudinal studies in the sociology of expectations (SoE) and studies of experience and expertise (SEE) frameworks, proposing a historical sociology of expertise and expectations framework. The research questions, focusing on the interplay between hype mobilisation and governance, are: (1) What is the relationship between AI practical development and the broader expectational environment, in terms of funding and conceptualisation of AI? (2) To what extent does informal and non-developer assessment of expectations influence formal articulations of foresight? (3) What can historical examinations of AI’s conceptual and promissory settings tell about the current rebranding of AI?
The following contributions are made: (1) I extend SEE by paying greater attention to the interplay between technoscientific experts and wider collective arenas of discourse amongst non-specialists and showing how AI’s contemporary research cultures are overwhelmingly influenced by the hype environment but also contribute to it. This further highlights the interaction between competing rationales focusing on exploratory, curiosity-driven scientific research against exploitation-oriented strategies at formal and informal levels. (2) I suggest benefits of examining promissory environments in AI and related technoscientific fields longitudinally, treating contemporary expectations as historical products of sociotechnical trajectories through an authoritative historical reading of AI’s shifting conceptualisation and attached expectations as a response to availability of funding and broader national imaginaries. This comes with the benefit of better perceiving technological hype as migrating from social group to social group instead of fading through reductionist cycles of disillusionment; either by rebranding of technical operations, or by the investigation of a given field by non-technical practitioners. It also sensitises to critically examine broader social expectations as factors for shifts in perception about theoretical/basic science research transforming into applied technological fields. Finally, (3) I offer a model for understanding the significance of interplay between conceptualisations, promising, and motivations across groups within competing dynamics of collective and individual expectations and diverse sources of expertise
Towards full-scale autonomy for multi-vehicle systems planning and acting in extreme environments
Currently, robotic technology offers flexible platforms for addressing many challenging problems that arise in extreme environments. These problems’ nature enhances
the use of heterogeneous multi-vehicle systems which can coordinate and collaborate
to achieve a common set of goals. While such applications have previously been
explored in limited contexts, long-term deployments in such settings often require
an advanced level of autonomy to maintain operability.
The success of planning and acting approaches for multi-robot systems are conditioned by including reasoning regarding temporal, resource and knowledge requirements, and world dynamics. Automated planning provides the tools to enable intelligent behaviours in robotic systems. However, whilst many planning approaches and
plan execution techniques have been proposed, these solutions highlight an inability
to consistently build and execute high-quality plans.
Motivated by these challenges, this thesis presents developments advancing state-of-the-art temporal planning and acting to address multi-robot problems. We propose a set of advanced techniques, methods and tools to build a high-level temporal
planning and execution system that can devise, execute and monitor plans suitable for long-term missions in extreme environments. We introduce a new task
allocation strategy, called HRTA, that optimises the task distribution amongst the
heterogeneous fleet, relaxes the planning problem and boosts the plan search. We
implement the TraCE planner that enforces contingent planning considering propositional temporal and numeric constraints to deal with partial observability about
the initial state. Our developments regarding robust plan execution and mission
adaptability include the HLMA, which efficiently optimises the task allocation and
refines the planning model considering the experience from robots’ previous mission
executions. We introduce the SEA failure solver that, combined with online planning, overcomes unexpected situations during mission execution, deals with joint
goals implementation, and enhances mission operability in long-term deployments.
Finally, we demonstrate the efficiency of our approaches with a series of experiments
using a new set of real-world planning domains.Engineering and Physical Sciences Research Council (EPSRC) grant EP/R026173/
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