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Research Papers

Nearest Neighbor-Based Rendezvous for Sparsely Connected Mobile Agents

[+] Author and Article Information
Ahmad A. Masoud

Electrical Engineering Department,
King Fahd University of Petroleum
and Minerals (KFUPM),
P.O. Box 287,
Dhahran 31261, Saudi Arabia
e-mail: masoud@kfupm.edu.sa

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 12, 2015; final manuscript received August 4, 2015; published online September 2, 2015. Assoc. Editor: Jongeun Choi.

J. Dyn. Sys., Meas., Control 137(12), 121002 (Sep 02, 2015) (18 pages) Paper No: DS-15-1106; doi: 10.1115/1.4031248 History: Received March 12, 2015; Revised August 04, 2015

In this paper, a convergent, nearest-neighbor, control protocol is suggested for agents with nontrivial dynamics. The protocol guarantees convergence to a common point in space even if each agent is restricted to communicate with a single nearest neighbor. The neighbor, however, is required to lie outside an arbitrarily small priority zone surrounding the agent. The control protocol consists of two layers interconnected in a provably correct manner. The first layer provides the guidance signal to a rendezvous point assuming that the agents have first-order dynamics. The other layer converts in a decentralized manner the guidance signal to a control signal that suits realistic agents, such as unmanned ground vehicles (UGVs), unmanned aerial vehicles (UAVs), and holonomic agents with second-order dynamics.

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Figures

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Fig. 3

Distances relative to agent i

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Fig. 2

Priority buffer arrangement

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Fig. 1

Nearest-neighbor consensus protocol does not guarantee convergence

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Fig. 6

NADF-based control protocol

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Fig. 4

If an agent enters βi, it remains in βi

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Fig. 5

Joint sharing of an agent guarantees connectedness

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Fig. 7

A carlike mobile robot

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Fig. 11

Modified protocol guarantees convergence, L = 1

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Fig. 12

Time to converge versus log(ε)

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Fig. 8

Converting guidance into control for a UAV

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Fig. 10

The time derivative of H will converge to Ω

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Fig. 13

dxn versus time, ε = 1

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Fig. 14

dxn versus time, ε = 3

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Fig. 31

Five single-integrator agents using the suggested protocol

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Fig. 32

Five double integrator agents using the suggested control protocol

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Fig. 33

Control signals for the fifth agent in Fig. 32

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Fig. 34

Agents in Fig. 32 with 80% actuator saturation

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Fig. 28

Double integrator agents with external drift

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Fig. 29

Double integrator agents subject to both external drift and 2 s communication delays

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Fig. 30

Cooperative tracking, double integrator agents

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Fig. 19

A directed communication graph

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Fig. 20

Single-integrator agents using the graph in Fig. 19

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Fig. 21

Double integrator agents using the communication graph in Fig. 19

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Fig. 22

Double integrator agents using the communication graph in Fig. 19, NADF used

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Fig. 23

x and y components of the control signal for agent 1

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Fig. 24

Double integrator agents using the communication graph in Fig. 19, Kd = 0 and b = 2

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Fig. 25

Double integrator agents with actuator saturation

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Fig. 26

Double integrator agents with 2 s communication delay

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Fig. 27

Maximum interagent distance versus time for different communication delays, double integrator agents

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Fig. 35

Control signals for the fifth agent in Fig. 34

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Fig. 36

Trajectories for carlike agents, K1 = 0.5 and K2 = 4

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Fig. 37

Orientation, agent 5

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Fig. 38

Control signals agent 5

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Fig. 15

x and y trajectories of agent 1, suggested protocol

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Fig. 16

Random behavior, ε = 0.25

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Fig. 17

Deterministic behavior, ε = 2

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Fig. 18

Complex behavior, ε = 1

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Fig. 39

Trajectories for carlike agents, K1 = 0.5 and K2 = 10

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Fig. 40

Two jets synchronizing their orientations

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Fig. 41

Banking angle, jet 2

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Fig. 42

Tangential force, jet 2

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Fig. 43

Normal lift force, jet

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