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Physicomimetics forSwarm Formations andObstacle Avoidance

Suranga Hettiarachchi Ph.D.

Computer Science and Multimedia

Eastern Oregon University

Funded by Joint Ground Robotics Enterprise - DOD

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Focus of the Talk

Improved performance in swarm obstacleavoidance: Scales to far higher numbers of robots and

obstacles than the norm Hardware Implementation

Implemented obstacle avoidance algorithm onreal robots

ObstacleAvoidance

HardwareImplementation

Simulated RobotSwarms

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Outline

Robot Swarms

Physicomimetics Framework

Swarm Learning

Obstacle Avoidance with Physical Robots

Conclusion and Future Work

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Robot Swarms

Robot swarms can act as distributedcomputers, solving problems that asingle robot cannot

For many tasks, having a swarmmaintain cohesiveness while avoidingobstacles and performing the task is ofvital importance

Example :Chemical Plume

Source Tracingpicture: Maxelbots at UW-DRL

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Swarm Advantages

Swarms of robots are effective:

They can perform tasks that one expensiverobot cannot.

Example: UAVs for surveillance Swarms are robust:

Even if some robots fail, the swarm can stillachieve the task.

Robots can be reused:

Functionally specific agents can be used tosolve different problems

picture: global aircraft

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Outline

Robot Swarms

Physicomimetics Framework

Swarm Learning

Obstacle Avoidance with Physical Robots

Conclusion and Future Work

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Physicomimetics for Robot Control

Biomimetics: Gain inspiration frombiological systems and ethology.

Physicomimetics: Gain inspiration from

physical systems. Good for formations.

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Why we mimic physics?

Aggregate behaviors seen in classical physics ispotentially reproducible with collections ofmobile agent.

Incorporate our understanding of classicalphysics to derive collective behavior of robots.

We are not restricted to copying physicsprecisely, so modifications are possible.

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PhysicomimeticsFramework

Robots have limited sensor range,

and friction for stabilization

FF

FFF

Virtual forces F on a robot A by other

robots ai and the environment cause a

d displacement in its behavior.

d

a1a

2

a3

a4

A

Environment

Robots are controlled

via virtual forces

from nearby robots,

goals, and obstacles.

F = ma control law.

Seven robots form a hexagon

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Two Classes of Force Laws

p

ji

r

mGmF

7

6

13

12224

r

c

r

dF

The left Newtonian force law, is good for creatingswarms in rigid formations. The right Lennard-Jones force law (LJ) more easily models fluid

behavior, which is potentially better for maintaining

cohesion while avoiding obstacles.

The classic law Novel use of LJ force law for robot control

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What do these forcelaws look like?

Change in Force MagnitudeWith Varying Distance forRobot Robot Interactions

Fmax = 1.0

Fmax = 4.0

Desired Robot Separation Distance = 50

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Outline

Robot Swarms

Physicomimetics Framework

Swarm Learning

Obstacle Avoidance with Physical Robots

Conclusion and Future Work

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Swarm Learning

Typically, the interactions between the swarmmembers are learned via simulation.

Swarm Simulation

Initial RulesFinal Rules

that achieve thedesired behavior

Evolutionary Algorithm(EA)

FitnessRules

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Swarm Simulation Environment

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Learning Approach

An Evolutionary Algorithm (EA) is used to evolvethe rules for the robots in the swarm.

A global observer assigns fitness to the rules

based on the collective behavior of the swarm inthe simulation.

Each member of the swarm uses the same rules.The swarm is a homogeneous distributed

system. For physicomimetics, the rules are vectors

of force law parameters.

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Force Law Parameters

Parameters of the Newtonian force lawG-gravitational constant of robot-robot interactionsP- power of the force law for robot-robot interactionsFmax- maximum force of robot-robot interactions

Similar 3-tuples for obstacle/goal-robot interactions.

Parameters of the LJ force law- strength of the robot-robot interactionsc- non-negative attractive robot-robot parameter

d- non-negative repulsive robot-robot parameterFmax- maximum force of robot-robot interactions

Similar 4-tuples for obstacle/goal-robot interactions.

Gr-r Pr-r Fmaxr-r Gr-o Pr-o Fmaxr-o Gr-g Pr-g Fmaxr-g

r-r cr-r dr-r Fmaxr-r r-o cr-o dr-o Fmaxr-o

r-g cr-g dr-g Fmaxr-g

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Measuring Fitness

Connectivity (Cohesion) : maximum numberof robots connected via a communication path.

Reachability (Survivability) : percentage of

robots that reach the goal. Time to Goal : time taken by at least 80% of

the robots to reach the goal.

goalconnectivity4R

reachability

High fitness corresponds to high connectivity,high reachability, and low time to goal.

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Connectivity of Robots

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ForceLaw

Robots Obstacles

20 40 60 80 100

Newt20 1160 1260 1290 1530 1920

100 - - - - -

LJ

20 470 480 490 510 520

100 640 650 670 680 690

Time for 80% of the Robotsto Reach the Goal

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Summary of Results

We compared the performance of the bestNewtonian force law found by the EA to the best LJforce law.

The Newtonian force law produces more rigidstructures making it difficult to navigate throughobstacles. This causes poor performance, despitehigh connectivity.

LJ is superior, because the swarm acts as aviscous fluid. Connectivity is maintained while

allowing the robots to reach the goal in a timelymanner.

The LJ force law demonstrates scalability in thenumber of robots and obstacles.

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Outline

Robot Swarms

Physicomimetics Framework

Swarm Learning

Obstacle Avoidance with Physical Robots

Conclusion and Future Work

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Obstacle Avoidancewith Robots

Use three Maxelbot robots

Use 2D trilateration localization

algorithm (Not a part of this talk)

Design and develop obstacle avoidance module(OAM)

Implement physicomimetics on a real outdoorrobot

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Hardware Architectureof Maxelbot

MiniDRAGON formotor control,

executesPhysicomimetics

MiniDRAGON fortrilateration,

provides robotcoordinates

OAMAtoD conversion

RF and acoustic sensors

IR sensors

I2C

I2C

I2C

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Formation ControlMethodology

Measure the quality of AP-lite withoutrepulsions from obstacles

All experiments are conducted outdoor

Three Maxelbots: One leader and two followers Results averaged over five runs

Leader remotely controlled (NO AP-lite)

Robots DO NOT have obstacle avoidance

capability Focus is on the formation control, not the

obstacle avoidance

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Why AP-lite?

Capable of maintaining formations of robots

Designed as a leader-follower algorithm

Allows robots to move quickly, due to minimalcommunication

Can use theory to set parameters

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Triangular Formation

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Linear Formation

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Physicomimetics forObstacle Avoidance

Constant virtual attractive goal force in frontof the leader

Virtual repulsive forces from four sensors

mounted on the front of the leader, if obstaclesdetected

The resultant force creates a change in velocitydue to F = ma

Power supply to motors are changed based onthe forces acting on the leader.

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Obstacle AvoidanceMethodology

Measure the performance of physicomimeticswith repulsion from obstacles

All experiments are conducted outdoor

Three Maxelbots: One leader and two followers

Graphs show the correlation between rawsensor readings and motor power

Leader uses the physicomimetics algorithmwith the obstacle avoidance module

Focus is on the obstacle avoidance by theleader, not the formation control

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Maxelbot Turning Left - Obstacle on the Right

-100

0

100

200

300

400

500

600

700

800

1 1001 2001 3001 4001 5001 6001 7001 8001 9001

Time

SensorReadinga

ndMotorPower

Right-most Sensor Reading

Power to Left Motor

If there is an obstacle on the right, power to left motor is reduced

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If there is an obstacle in front, power to both motors is reduced

Maxelbot Stopping Behavior - Both Middle Sensors Detect an Obstacle

0

100

200

300

400

500

600

1 1001 2001 3001 4001 5001 6001 7001 8001 9001

Time

S

ensorReadinga

andMotorPow

er

Ave. of the Two Middle Sensors

Ave. of the Motor Power

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Further Analysis of SensorReading and Motor Power

Scatter plots show how much one variable is affectedby the other

Provide a broader picture of change in motor powerwhen the robot sensors detects obstacles

Shows the correlation of motor power with distance

to an obstacle in inches (the robots ignore obstaclesgreater than 30 away)

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Maxelbot Turning Left - Obstacle on the Right

-20

-10

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80 90 100

Distance to Obstacle (inches)

PowertoLeftMotor

Lag in starting due to AP inertia.Helps counteract noisy sensors.

Lag in stopping due to AP inertia.Helps counteract noisy sensors.

Right sensorsees obstacle

Right middle sensorsees obstacle

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Maxelbot Stopping Behavior - Both Middle Sensors Detect an

Obstacle

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80 90 100

Distance to Obstacle (inches)

AverageofLeftand

RightMotorPower

Power will be reduced if theoutermost sensors see anobstacle when the inner

sensors do not.

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Outline

Robot Swarms

Physicomimetics Framework

Swarm Learning

Obstacle Avoidance with Physical Robots

Conclusion and Future Work

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Future Work

Provide obstacle avoidance capability to all therobots in the formation Develop robots with greater data exchangecapability

Adapt the physicomimetics framework toincorporate performance feedback for specifictasks and situational awareness Extend the physicomimetics framework forsensing and performing tasks in a marine

environment (with Harbor Branch) Introduce robot/human roles and interactions todistributed evolution architecture

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Thank You

Questions?

Movie of 3 Maxelbots,Leader has OAM