RSS Workshop: Distributed Control and Estimation for Robotic Vehicle Networks


July 12, 2014 at the Robotics: Science and Systems Conference
University of California Berkeley 

Important Dates

  • April 1, 2014 -- abstract submission period begins (send all submissions to rssdceworkshop2014@gmail.com)
  • May 15 May 19, 2014-- abstract submissions due for poster participation (deadline extended)
  • June 1 June 9, 2014 -- poster acceptance notification
  • June 15 June 20, 2014 -- final revised extended abstracts due
  • July 12, 2014 -- workshop (at RSS in Berkeley, CA, July 12-16, 2014)

Workshop Description

Applications for autonomous multi-vehicle networks have grown significantly in recent years, and have stimulated research on distributed strategies for optimal/robust cooperative autonomy in multi-vehicle systems. Ideally, distributed approaches not only perform as well as centralized methods, but also lead to better scalability, naturally parallelized computation, and resilience to communication loss and hardware failures. In practice, it is usually convenient to assume that distributed control and distributed estimation problems can be treated separately. While state-of-the-art techniques for distributed planning (e.g. graph-based trajectory generation, consensus-/graph-based task allocation) and perception (e.g. multi-robot SLAM/SAM, Bayesian/consensus sensor fusion for cooperative tracking) can be combined with good results, the assumed “separation principle” is heuristic and leaves open many questions: how should off-the-shelf solutions for different parts of the same problem be jointly selected or modified to work best together, and what guarantees (if any) are there for optimal/robust behavior? Alternative integrated approaches have also emerged for multi-vehicle systems (e.g. distributed optimization, model predictive control, reinforcement learning), which formally capture and exploit subtle yet important dynamic linkages between the control and estimation problems. However, these approaches raise their own questions: are the assumptions/approximations required for analytical and computational tractability reasonable for general applications, and how can state-of-the-art planning/perception methods for individual mobile robots be leveraged?

This workshop will bring together control/planning and estimation/perception specialists from the robotics and controls communities who are interested in autonomous multi-vehicle networks to: (i) discuss these and other related research questions; (ii) promote new ideas for unifying distributed control and estimation, while improving awareness of state-of-the-art techniques; and (iii) foster interactions for developing theoretical ideas and practical applications.

In addition to attending invited talks by top experts in the field and interacting with them via Q&A panel discussions, workshop participants will have the opportunity to submit extended abstracts for select presentation at the workshop poster spotlight talks and interactive poster session (following single-blind peer review). Selected poster abstract submissions will also be invited to submit their work to a future special issue of IEEE Control Systems Magazine (to be arranged by the workshop organizers).

Some representative topics of interest include (but are not limited to): 
  • optimal/robust multi-robot planning, task assignment, navigation, guidance, and/or control 
  • optimal/robust multi-robot perception, mapping, learning, scene understanding, and/or object search/tracking 
  • control and estimation under ad hoc/constrained/unknown communication topologies 
  • emerging approaches for unified control and estimation (information theoretic methods, learning-based, etc.) 
  • networked algorithmic solutions that combine joint estimation and control
  • distributed model predictive control
  • decentralized model-based/Bayesian estimation and learning
  • distributed optimization for networked robotic vehicle control and estimation
  • characterization of performance gaps and trade-offs between centralized and distributed algorithms
  • analysis and algorithms for understanding and coping with uncertainties in networked robotic vehicle systems
  • novel distributed control and estimation strategies and applications for networks of aerial, space, ground, or aquatic robots
  • control and estimation of heterogeneous vehicle systems (e.g. mixtures of unmanned ground/air/aquatic robots, marsupial systems, etc.)
  • distributed control and estimation for long-term/life-long autonomy in robotic vehicle networks

Confirmed Speakers and Topics


-Jonathan How, MIT : "Multi-agent Mission Planning in Contested Communication Environments"

-Gaurav Sukhatme, University of Southern California: "Exploring the Ocean with Robots: Is Communication the Challenge?"

-Solmaz Kia, University of California Irvine : "Decentralized Recursive Cooperative Localization for Groups of Mobile Robots "

-Jay Farrell, University of California Riverside: "Distributed Camera Control for Opportunistic Visual Sensing"

-Silvia Ferrari, Duke University: "Distributed Optimal Control for Target Tracking"

-Mark Campbell, Cornell University : "Distributed Bayesian Estimation over Communication Networks"

-Stergios Roumeliotis, University of Minnesota: "Distributed 3D Localization and Mapping Using Mobile Devices" (cancelled)


Expert Q&A Discussion Panel

The workshop expert discussion session will also feature the following industry and academic panelists:

-Vijay Kumar, University of Pennsylvania, GRASP Laboratory and UPS Foundation Professor (cancelled)

-Alberto Speranzon, United Technologies Research Center, Research Scientist 


Organizing Committee


-Nisar Ahmed, University of Colorado Boulder, nisar.ahmed@colorado.edu

-Sonia Martinez, University of California San Diego, soniamd@ucsd.edu

-Jorge Cortes, University of California San Diego, cortes@ucsd.edu


Program Committee

Girish Chowdhary, Oklahoma State University
Tim Chung, Naval Postgraduate School
Christopher Clark, Harvey Mudd College
Eric Frew, University of Colorado Boulder
Ben Grocholsky, Carnegie Mellon University Robotics Institute
M. Ani Hsieh, Drexel University
Solmaz Kia, University of California San Diego
Derek Kingston, Air Force Research Laboratory
Lantao Liu, Carnegie Mellon University Robotics Institute
Eduardo Montijano, Centro Universitario de la Defensa, Zaragoza
Cameron Nowzari, University of Pennsylvania
Fabio Pasqualetti, University of California Riverside
Marco Pavone, Stanford University
Wei Ren, University of California Riverside
Dean Richert, University of California San Diego
Luis Rodrigues, Concordia University
Steve Rounds, Deere & Company
Jonathan Schoenberg, Arzentech, Inc.
Alberto Speranzon, United Technologies Research Center
Michael Zavlanos, Duke University
Milos Zefran, University of Illinois Chicago
Fumin Zhang, Georgia Tech