The Transactions on Control of Network Systems invites submissions for a special issue on Control of Very-large Scale Robotic (VLSR) Networks, scheduled for publication in March 2021.
Submissions Open: November 11, 2019
Submission Deadline: March 15, 2020
Submissions instructions can be found on the TCNS special issue web page
- Silvia Ferrari, Professor, Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY
- Richard Linares, Charles Stark Draper Assistant Professor, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA
- Thomas A. Wettergren, Senior Technologist (ST) for Operational and Information Science, Naval Undersea Warfare Center (NUWC), Newport, RI
- Keith LeGrand, Senior Member of Technical Staff, Sandia National Laboratories, Albuquerque, NM
Scope: Future applications of robotics and autonomous systems will involve increasingly large numbers of collaborative robots, sensors, and unmanned vehicles that are each capable of collecting, processing, and acting upon information with little or no human intervention. By sharing and coordinating information, plans, and decisions, these very-large-scale robotic (VLSR) networks can dramatically improve their performance in various industrial and military applications. Sensing and control of collaborative agents, however, present many technical challenges, including required computations that increase with the number of agents, and the challenge of accounting for information and uncertainties propagating through the network. Probability density function (PDF) based methods and partial differential equation (PDE) models are emerging, promising approaches for deriving decentralized control strategies that scale up to VLSR networks comprised of hundreds of agents. Random finite set (RFS) theory and finite set statistics (FISST) have also emerged as a unifying approach to estimation and tracking via multi-object PDFs that can be used to describe the state of multiple objects utilizing multiple sensor measurements.
TCNS solicits original contributions which propose new scalable theory and algorithms for solving emerging challenges in the control and estimation of complex, large-scale systems, including rigorous contributions on new theoretical analysis and new methods for VLSR control, tracking, and estimation, as well as emerging VLSR applications of partial differential equation (PDE) and probability density function (PDF)-based methods, as well as other novel approaches. This special issue also solicits original contributions that explore the use of random finite set (RFS) and other emerging theories on complex systems for representing the statistical properties of large collections of agents by compact and efficient representations, such as multi-object PDFs. Furthermore, probabilistic methods have been shown to generalize traditional Bayesian filtering and estimation to include flexible detection, sensing, and dynamic behaviors to many agents. However, several unsolved technical issues remain, including but not limited to the scalability of information value functions, the assimilation of heterogeneous data, and how to determine the object dynamics from data.
Special Topics include but not limited to:
- Controllability and observability in PDE models of VLSR networks
- Optimality conditions for PDE and PDF-based control and estimation
- Efficient numerical and analytical solutions to large-scale control problems
- Solutions to large-scale control problems that utilize RFS theory
- New PDE or RFS models and state representations of VLSR systems
- New theory and analysis of VLSR discrete and continuous representations and dynamics
- Probabilistic solutions to swarming systems
- New theoretical results in stochastic control of large-scale systems leveraging RFS theory
- New formulations of estimation and control problem using RFS theory
- New applications of RFS theory to networked systems
Information on the submission process and manuscript format can be found at: https://cemse.kaust.edu.sa/tcns/information-authors
For more information, visit our TCNS Special Issue page.