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Technical Brief

Decentralized Rendezvous of Nonholonomic Robots With Sensing and Connectivity Constraints

[+] Author and Article Information
Zhen Kan

Department of Mechanical and Industrial Engineering,
The University of Iowa,
Iowa City, IA 52242
e-mail: zhen-kan@uiowa.edu

Justin R. Klotz

Department of Mechanical and Aerospace Engineering,
University of Florida,
Gainesville, FL 32611
e-mail: jklotz@ufl.edu

John M. Shea

Department of Electrical and Computer Engineering,
University of Florida,
Gainesville, FL 32611
e-mail: jshea@ece.ufl.edu

Emily A. Doucette

Munitions Directorate,
Air Force Research Laboratory,
Eglin Air Force Base,
Valparaiso, FL 32542
e-mail: emily.doucette@eglin.af.mil

Warren E. Dixon

Department of Mechanical and Aerospace Engineering,
University of Florida,
Gainesville, FL 32611
e-mail: wdixon@ufl.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received November 2, 2015; final manuscript received August 15, 2016; published online November 9, 2016. Assoc. Editor: Jongeun Choi.This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Dyn. Sys., Meas., Control 139(2), 024501 (Nov 09, 2016) (7 pages) Paper No: DS-15-1542; doi: 10.1115/1.4034745 History: Received November 02, 2015; Revised August 15, 2016

A group of wheeled robots with nonholonomic constraints is considered to rendezvous at a common specified setpoint with a desired orientation while maintaining network connectivity and ensuring collision avoidance within the robots. Given communication and sensing constraints for each robot, only a subset of the robots are aware or informed of the global destination, and the remaining robots must move within the network connectivity constraint so that the informed robots (IRs) can guide the group to the goal. The mobile robots are also required to avoid collisions with each other outside a neighborhood of the common rendezvous point. To achieve the rendezvous control objective, decentralized time-varying controllers are developed based on a navigation function framework to steer the robots to perform rendezvous while preserving network connectivity and ensuring collision avoidance. Only local sensing feedback, which includes position feedback from immediate neighbors and absolute orientation measurement, is used to navigate the robots and enables radio silence during navigation. Simulation results demonstrate the performance of the developed approach.

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Figures

Grahic Jump Location
Fig. 1

An example of a dipolar navigation function with a workspace of Rw = 5 and destination located at the origin with a desired orientation θ* = 0. In the dipolar potential field, the destination is modeled as the unique minimum, while the workspace boundary is modeled as the maximum. The surface x = 0 divides the workspace into two parts and forces all the flow lines approaching the origin with the desired orientation, so that the robots can be driven to the origin with θ* = 0 by following the flow lines.

Grahic Jump Location
Fig. 4

Plot of position and orientation error for each mobile robot

Grahic Jump Location
Fig. 3

The evolution of inter-robot distance

Grahic Jump Location
Fig. 2

Plot of robot trajectories with solid line and dotted–dashed line indicating the trajectory of the informed robot (IR) and the follower robot (FR), respectively

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