Multi-Robot Systems

CSCI 7000-006Monday, August 31, 2009

Nikolaus Correll

So far

• Introduction to robotics and multi-robot systems

• Similar algorithms and properties for robot teams, robot swarms and smart materials

Today

• Reactive algorithms• Environmental templates• Collaboration in reactive swarms

Reactive Algorithms

• Directly couple perception to action

• Extremely simple hardware (analog electronics will do)

• Robustness out of simplicity

• Potential for miniaturization

• First instance: Grey Walter’s tortoises

© The i-Swarm project

Concept: Braitenberg Vehicles

• Couple perception to action

• Sensor input coupled to actuator output

• Inspired by brain architecture – left/right hemisphere– Neural network

• Course question: how do the vehicles behave with respect to a light source?

Light Sensor

Motors

More complex behaviors

• Braitenberg– More sensors (e.g. camera)– More connections (e.g.

brain)• Synthesis by genetic

algorithms– Modify random connections– Unfit individuals fall of the

table• Hierarchical Decompositon

Subsumption Architecture (Brooks)

• Decompose behavior into modules– Collision avoidance, light

following, etc.• Arrange modules in layers

representing goals• Upper layers subsume lower

layers• Difficult to design with

increasing complexity

Avoid Obstacles

Wander around

Explore world

Brooks, R. (1986). "A robust layered control system for a mobile robot". Robotics and Automation, IEEE Journal of 2 (1): 14–23.

Alternative view: Artificial Potential Fields

• Aka virtual physics, motor schemes

• Goals are represented by virtual forces (attraction/repulsion)

• Forces are calculated from sensor input

• Addition yields vector field that the robots follow

• Obvious problem: local minima and cycles

© Craig Reynolds

Further Reading

• Valention Braitenberg“Experiments in synthetic psychology”, 1986

• Rodney Brooks“Elephants don’t play chess”, 1990

• Ronald Arkin“Behavior-based Robotics”, 1998

Example: Jet Turbine Inspection• Goal: surround every

blade in a turbine with a robotic sensor

• Robots need to be small, only local communication

• Alice (ASL, EPFL), sugar cube, 368bytes of RAM

Robotic Platform• Alice miniature robot [Caprari2005]• PIC microcontroller (368 bytes

RAM, 8Kb FLASH)• Length of 22mm• Maximal speed of 4cm/s, stepper

motors• 4 IR modules serve as very crude

proximity sensors (3cm) and local communication devices

• Energetic autonomy 5h-10h

Baseline: Randomized Coverage without Localization

Search Inspect Translate

Search Inspect Translatealong blade

Avoid Obstacle

Wall | Robot Obstacle clear

Blade pt

1-pt

Tt expired

Robot Capabilities

• Sensing: infrared distance sensors• Computation: FSM, wall following• Actuation: differential wheels• Communication: none

Analysis (Intuition)

• Collaboration: implicit• Completeness: probabilistic, asymptotic• Probability to leave blade at round or sharp tip

affects robot distribution

Experimental Results

20, 25, 30 robots

Spatial distribution for pt=0

• Leaving the blades at a tip generates drift in the environment

• “Enviromental Template”

• Probability to inspect some of the blades higher

Exploiting environmental templates: example from Biology

• Probability to pick up or drop certain objects is a function of local temperature

• Temperature gradient controls location of objects 3.00 a.m. 3.00 p.m.

T

Location of Eggs, Larvae, and Pupae in the nest of the ant Acantholepis Custodiens,© Guy Theraulaz

Randomized Coverage with Collaboration

Search Inspect MobileMarker

Avoid Obstacle

Wall | Robot Obstacle clear

Blade pt

1-pt | Marker

Tt expired

Translate Inspect Inspect

Robot Capabilities

• Sensing: infrared distance sensors• Computation: FSM, wall following• Actuation: differential wheels• Communication: single bit (blade busy or not)

Improvement of CollaborationReal

Macroscopic Model

Example 2: Stick-Pulling

• Goal: pull sticks out of the ground

• Two robots need to collaborate

A. Martinoli, K. Easton, and W. Agassounon. Modeling Swarm Robotic Systems: A Case Study in Collaborative Distributed Manipulation. Int. Journal of Robotics Research, 23(4):415-436, 2004.

Robotic Platform

• 16 MHz Motorola CPU• Incremental wheel encoders• 6 frontal infra-red sensors• Position feedback in arm (communication!)

Robot Capabilities

• Sensing: infrared distance sensors, detect stick• Computation: FSM, wall following• Actuation: differential wheels• Communication: explicit, physical via stick

• Course question: what happens if time-out is too high?

Analysis (Intuition)

• Time-out during wait key for performance• Less robots than sticks– Time-out too low: collaboration unlikely– Time-out too high: robot depletion

• More robots than sticks– The longer the time-out, the better

• Optimal value for gripping time when less robots than sticks?

Experimental Results

A. Martinoli, K. Easton, and W. Agassounon. Modeling Swarm Robotic Systems: A Case Study in Collaborative Distributed Manipulation. Int. Journal of Robotics Research, 23(4):415-436, 2004.

Example 3: Aggregation

• Goal: aggregate objects into structures

• Inspired by nest-building of termites

• Algorithm– Search for seeds– Pick-up seed– Drop close to other seeds– Only seeds at end of

cluster are identified as such -> Line formation

Martinoli, A., Ijspeert, A.J. and Mondada, F. (1999) Understanding collective aggregation mechanisms: from probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems, 29(1) pp. 51-63.

Aggregation

Martinoli, A., Ijspeert, A.J. and Mondada, F. (1999) Understanding collective aggregation mechanisms: from probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems, 29(1) pp. 51-63.

Results

Martinoli, A., Ijspeert, A.J. and Mondada, F. (1999) Understanding collective aggregation mechanisms: from probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems, 29(1) pp. 51-63.

Summary

• Reactive control: tight coupling between perception and actuation

• Behavior is function of controller and environment

• Collaboration in reactive swarms– Implicit– Explicit: via the environment and local

communication

Next Sessions

• Wednesday: More on reactive algorithms– threshold-based algorithms– message propagation

• Friday: First lab