Information Theory in Motion Planning and Control
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: closed (30 June 2021)
Special Issue Editors
Interests: robust and optimal control; multi-agent systems; networked control systems; robot perception and decision making
Interests: optimal and nonlinear control; differential games; multi-agent systems; planning and decision making
Special Issue Information
Dear Colleagues,
Recent advances in autonomous systems have made it clear that a key aspect to the successful development of these systems is the harmonious integration of a diverse set of disciplines, including perception, cognition, control, decision making, and planning, among others. Motion planning and control techniques, in particular, have been bolstered in recent years partly because of the advancement of capable computational platforms and the availability of low-cost sensors, combined with the prevalence of statistical (machine) learning techniques and methodologies, which have allowed operation in poorly characterized or previously unknown environments. Both in motion planning and control, a fundamental issue is uncertainty characterization and uncertainty mitigation using feedback. There is a growing realization in the community that information theory can play a larger role in this context, as it can provide the correct framework, along with the right set of tools, to answer important questions such as what is relevant in the problem and what is not, what is the best way to transmit information between the controller and the sensor, what signals to communicate between various agents in a multi-agent network to manage bandwidth and/or mitigate external attacks, etc. Information theory can provide the missing link to close perception–action–communication (PAC) loops in complex autonomous systems. There is a growing body of the literature where information-theoretic concepts play roles in several contexts, including state representations, strategic perception, communication and coordination in multi-agent systems, and the analysis of machine learning algorithms.
This Special Issue calls for emerging applications of information theory broadly in the field of robotics and control. Both application-driven research and cultivating and promoting non-conventional uses of information theory in robotics and control, as well as theory-oriented research papers in these areas are solicited.
Topics relevant to this Special Issue include (but are not limited to):
- Intelligent perception;
- Information theory in reinforcement learning;
- Multi-agent and networked control systems;
- Information-theoretic state representations;
- Statistical mechanics in control and decision making;
- Joint communication, sensing, and control;
- Resource-constrained control, planning, and perception;
- Entropy and feedback systems.
Prof. Dr. Takashi Tanaka
Prof. Dr. Panagiotis Tsiotras
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- information theory
- robotics
- path planning
- motion planning
- autonomy
- perception
- multi-agent systems
- systems and control
- machine learning
- networked control systems
- statistical mechanics
- entropy
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