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On Maintaining Connectivity of a Colony of Autonomous Explorer Mobile Robots as Palma Olate 1 and Cristian Duran-Faundez 2 Jonathan Mat Departamento de Ingenier a El ectrica y Electr onica Universidad del B o-B o,

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SLIDE 1

On Maintaining Connectivity of a Colony of Autonomous Explorer Mobile Robots

Jonathan Mat´ ıas Palma Olate1 and Cristian Duran-Faundez2

Departamento de Ingenier´ ıa El´ ectrica y Electr´

  • nica

Universidad del B´ ıo-B´ ıo, Concepci´

  • n, Chile.

October 21, 2014

JMP & CDF (UBB) October 21, 2014 1 / 37

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SLIDE 2

Indice

1 Introduction to Problem of Maintaining Connectivity.

Methods of solution

2 A multi-robot exploring application

Tethering Optimal Point Orientation Method

3 Algorithm

Evaluation Method. Simulation.

4 Conclusion

Conclusion and Comments Acknowledgment

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SLIDE 3

Introduction to Problem of Maintaining Connectivity.

Robotics.

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SLIDE 4

Introduction to Problem of Maintaining Connectivity. Methods of solution

Methods to maintain a communication link of explorer robot.

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SLIDE 5

Introduction to Problem of Maintaining Connectivity. Methods of solution

Methods to maintain a communication link of explorer robot.

A) Direct Communication.

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SLIDE 6

Introduction to Problem of Maintaining Connectivity. Methods of solution

Methods to maintain a communication link of explorer robot.

A) Direct Communication.

  • Limited coverage Transmit.
  • Unique link, not robust.
  • High power transmission
  • ver long distances.

JMP & CDF (UBB) October 21, 2014 6 / 37

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SLIDE 7

Introduction to Problem of Maintaining Connectivity. Methods of solution

Methods to maintain a communication link of explorer robot.

B)Communication with Static Router.

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SLIDE 8

Introduction to Problem of Maintaining Connectivity. Methods of solution

Methodsto maintain a communication link of explorer robot.

B)Communication with Static Router.

  • Previous infrastructure.
  • High cost of

implementation and maintenance.

  • Robustness to the failure of

a unit.

  • A subset of the total units

the network participateing actively in the link communication.

JMP & CDF (UBB) October 21, 2014 8 / 37

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SLIDE 9

Introduction to Problem of Maintaining Connectivity. Methods of solution

Methods to maintain a communication link of explorer robot (Proposal).

C)Communication using Mobile Router.

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SLIDE 10

Introduction to Problem of Maintaining Connectivity. Methods of solution

Methods to maintain a communication link of explorer robot (Proposal).

C)Communication using Mobile Router.

  • Dynamic deployment.
  • Minimize energy and cost.
  • Design flexibility.
  • High level of technical

complexity in

  • implementation. today.

JMP & CDF (UBB) October 21, 2014 10 / 37

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SLIDE 11

Introduction to Problem of Maintaining Connectivity. Methods of solution

Methods to maintain a communication link of explorer robot (Proposal).

C)Communication using Mobile Router.

  • Dynamic deployment.
  • Minimize energy and cost.
  • Design flexibility.
  • High level of technical

complexity in

  • implementation. today.

JMP & CDF (UBB) October 21, 2014 11 / 37

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SLIDE 12

A multi-robot exploring application Tethering

Tethering

Tethering is the robot task of following a mobile agent (human, robot, etc.), with all the different required capabilities to it, in order to provide network connectivity (Zickler and Veloso 2010). The reference we propose includes the following kind of nodes:

  • A set Base Station : Gateways

(GW ).

  • A set Explorer Robot : Targets

(TG).

  • A set Router Robots :

Gangway of data (GG).

JMP & CDF (UBB) October 21, 2014 12 / 37

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SLIDE 13

A multi-robot exploring application Tethering

Network Topology Options

Link type to use? Model network of interest simple-link. Figure Network Topology link green.

Figure: Network Topology.

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A multi-robot exploring application Tethering

This scenario entails a set of sub-problems, including:

  • Definition of a link quality metric.
  • Drive router robots to optimal positions to forward data packets.
  • Minimizing the amount of robot routers maintaining global

performance.

  • Addressing robustness. Which involves methods to deal with

communication errors and incidentals such as robot failures.

  • Sharing tasks. To provide ways to make robots share some tasks,

e.g., to make a router robot take other router’s job allowing the second one to go to the base station for recharge or maintenance.

JMP & CDF (UBB) October 21, 2014 14 / 37

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SLIDE 15

A multi-robot exploring application Optimal Point

Definition of Link Quality Metric.

Metric quality standard of wireless communication.

  • Packet Loss Rate PLR.
  • link quality indicator LQI.
  • Received Signal Strength

Indicator RSSI.

0.5 1 1.5 2 2.5 3 1 2 3 4 5 6 −100 −90 −80 −70 −60 −50 −40 −30 −20 Metros (m) Metros (m) RSSI (dB)

GW R S S I ( d B ) Metros (M)

Figure: Example RSSI vs Distance

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SLIDE 16

A multi-robot exploring application Optimal Point

Optimal point (PO) to ideal model.

PO Geometric: midpoint between the line formed superior and inferior units.

Figure: Network: two GW and on GG

PO based on the RSSI: coordinate where the RSSI is equal and also the sum of them is maximum

0.5 1 1.5 2 0.5 1 1.5 2 2.5 3 −80 −70 −60 −50 −40 −30 −20 −10 Metros (m) Metros (m) RSSI (dB)

GW GW RSSI (dB) Metros (M)

Figure: RSSI to GW the network.

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SLIDE 17

A multi-robot exploring application Orientation Method

Orientation Method. Indicator MIn Dif .

Let us define VS and VI as the communication links between a router robot and the neighboring node closest to the explorer and the base station. Obteniandiendo las relaciones. Difa = |RSSIVS − RSSIVI| Difd = RSSIVS−RSSIVI

Difa

Difa is relevant because PO difference of RSSI to PO it zero. Status indicators can be defined. Decrease Difa, which is calculated as: MInDif =

  • 1

if Difa(k) − Difa(k − 1) < 0

  • therwise

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SLIDE 18

A multi-robot exploring application Orientation Method

Orientation Method. Indicators Max and Min link Vx.

The VX it VL farthest link and VC closer link. Decreases of RSSI values for a communication link VX, can be calculated as: MinVC = 1 if RSSIVX(k) − RSSIVX(k − 1) < 0

  • therwise

(1) Increase of RSSI values for a communication link VX, which is defined as: MaxVL = 1 if RSSIVX(k) − RSSIVX(k − 1) > 0

  • therwise

(2)

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SLIDE 19

A multi-robot exploring application Orientation Method

  • Actions. Discrete displacements.

The Figure presents the eight possible actions [A1, A2, .....A8]. It also implements a ninth action A9 that is return.

Figure: Actions.

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SLIDE 20

A multi-robot exploring application Orientation Method

States based on the indicators MinDif , MinVC and MaxVL.

Are defined the state S to function. The Indicators are binaries i ∈ [1, 2.....23], having eight possible states Si = ϕ(MinDif , MinVC, MaxVL) State compact SS: Are defined the state compact SS1 and SS2. . SS1 = ϕ(MinDif , MinVC, MaxVL); MinDif = MinVC = MaxVL = 1 SS2 =otherwise

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SLIDE 21

Algorithm

Algorithm Proposal

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SLIDE 22

Algorithm

Objectives.

  • Design an algorithm that achieves converge to units in the vicinity of the
  • ptimum point (PO), maximizing both of RSSI values link.
  • Design an algorithm that orientation method depends only on current and

past RSSI values, does not consider additional information of the environment.

  • Evaluate the performance of the algorithm in a simulation problem

Tethering.

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Algorithm

Algorithm, Heuristics based to Q-learning.

The action selection is performed by using a Q-learning-based vector Q(SS × Ai). We adopted as learning coefficient α = 0.5, and we assign a reward +1. For the proposed model there are four transitions (SSk−1 → SSk), i.e. transitions of passing from an state SSk−1 in previous iteration k − 1 to the current state SSk. These transitions are: (SS1 → SS2), (SS2 → SS2), (SS1 → SS1), and (SS2 → SS2). Require: RSSI captures Require: Calculation of goals and categorizations for states SS(k) and SS(k − 1).

1: if SSk−1 = SS1 and SSk = SS2

then

2:

return A9

3: end if 4: if SSk−1 = SS2 and SSk = SS2

then

5:

return random Ai; i ∈ [1, 8]

6: end if 7: Ac = MaxAcInQ(Q, SS) 8: Q(SS, Ac) ← (1 − α)Q(SS, Ac) +

α[r(SS, Ac) + λ maxA∈As′ Q(s

′, A ′)]

9: return Ac

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SLIDE 24

Algorithm Evaluation Method.

Evaluation performance the algorithm.

Evaluation of the proposed algorithm was by the mean and standard deviation of a set of simulations to k = 500 iterations. The Network used it the figure.

Model to Maintaining Connectivity the Robot Explore.

The graphs are the mean and standard deviation for 1000 simulations.

JMP & CDF (UBB) October 21, 2014 24 / 37

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Algorithm Evaluation Method.

Description of the Simulation, parameters an values.

parameters : values k : 500. Vpi : 0, 0 0, −0.15 0, −0.3 0, −0.45 0, 0.1 . ∆Paso : 0.1(m). Model RSSI : log normal shadowing. M × P : 20. State : SS1 y SS2. . Actions : A1 A2 A3, A3, A5 A6 A7, A8 and A9. Reward : SS1 = 1 y SS2 = −1. Learning Rates : γ = 0.5 y α = 0.5. TG mov : zig zag

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Algorithm Simulation.

Simulation

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Algorithm Simulation.

Positions of units, center of mass for k = 5 iterations.

1 2 3 4 5 6 7 8 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 Meters (M) Meters (M) GG1 GG2 GG3 GW TG JMP & CDF (UBB) October 21, 2014 27 / 37

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Algorithm Simulation.

Positions of units, center of mass for k = 100 iterations.

1 2 3 4 5 6 7 8 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 Meters (M) Meters (M) GG1 GG2 GG3 GW TG JMP & CDF (UBB) October 21, 2014 28 / 37

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Algorithm Simulation.

Positions of units, center of mass for k = 200 iterations.

1 2 3 4 5 6 7 8 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 Meters (M) Meters (M) GG1 GG2 GG3 GW TG JMP & CDF (UBB) October 21, 2014 29 / 37

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SLIDE 30

Algorithm Simulation.

Positions of units, center of mass for k = 300 iterations.

1 2 3 4 5 6 7 8 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 Meters (M) Meters (M) GG1 GG2 GG3 GW TG JMP & CDF (UBB) October 21, 2014 30 / 37

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Algorithm Simulation.

Positions of units, center of mass for k = 400 iterations.

1 2 3 4 5 6 7 8 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 Meters (M) Meters (M) GG1 GG2 GG3 GW TG JMP & CDF (UBB) October 21, 2014 31 / 37

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Algorithm Simulation.

Positions of units, center of mass for k = 500 iterations.

1 2 3 4 5 6 7 8 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 Meters (M) Meters (M) GG1 GG2 GG3 GW TG JMP & CDF (UBB) October 21, 2014 32 / 37

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Algorithm Simulation.

Mean the Difa and Standard Deviation.

200 400 600 20 40 60 Iteracion (k) RSSI (db) Difa GG1

((a)) Mean Difa GG1.

200 400 600 20 40 60 80 Iteracion (k) RSSI (db) Difa GG2

((b)) Mean Difa GG2.

200 400 600 20 40 60 80 Iteracion (k) RSSI (db) Difa GG3

((c)) Mean Difa GG3.

200 400 600 10 20 30 40 Iteracion (k) RSSI (db) DE Difa GG1

((d)) S deviation Difa GG1.

200 400 600 10 20 30 40 50 Iteracion (k) RSSI (db) DE Difa GG2

((e)) S deviation Difa GG2.

200 400 600 10 20 30 40 50 Iteracion (k) RSSI (db) DE Difa GG3

((f)) S deviation Difa GG3.

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Algorithm Simulation.

Mean the error of RSSI and Standard Deviation .

100 200 300 400 500 600 10 20 30 40 50 60 Iteration (k) RSSI (db) Error RSSI VI Error RSSI VS

((g)) Error RSSI, GG1.

100 200 300 400 500 600 10 20 30 40 50 60 70 Iteration (k) RSSI (db) Error RSSI VI Error RSSI VS

((h)) Error RSSI, GG2.

100 200 300 400 500 600 10 20 30 40 50 60 Iteration (k) RSSI (db) Error RSSI VI Error RSSI VS

((i)) Error RSSI, GG3.

100 200 300 400 500 600 5 10 15 20 25 30 Iteration (k) RSSI (db) DE Error RSSI VI DE Error RSSI VS

((j)) S deviation error

GG1.

100 200 300 400 500 600 5 10 15 20 25 30 Iteration (k) RSSI (db) DE Error RSSI VI DE Error RSSI VS

((k)) S deviation error

GG2.

100 200 300 400 500 600 5 10 15 20 25 30 Iteration (k) RSSI (db) DE Error RSSI VI DE Error RSSI VS

((l)) S deviation error

GG3.

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Conclusion Conclusion and Comments

Conclusion and Comments.

This paper describes a kind of application for problem and sub-problems communication whit explorer robots and a base station using a colony of autonomous router robots. The model to single link the algorithm based to RSSI for maintaining communication it feasible. This heuristic is used to select the next action to perform by a robotic router, combining simple decisions with a Q-learning-based decision process. Simulation results over 1000 repetitions of a simulation scheme shows good performance of the algorithm to make converge router robots to near-optimal positions, considering the simulated models restrictions

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Conclusion Conclusion and Comments

Acknowledgment. This work was supported by the Research Department of the University of Bio-Bio (Project DIUBB 121910 2/R).

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Conclusion Conclusion and Comments

End.

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