The SWARM-BOTS project

M. Dorigo, E. Tuci, R. Groß, V. Trianni, T.H. Labella, S. Nouyan, J.-L. Deneubourg, G. Baldassarre, S. Nolfi, F. Mondada, D. Floreano, L.M. Gambardella

www.swarm-bots.org

the SWARM-BOTS project:
General Information

IST-2000-31010 (FET)

Started on October 1st, 2001

Lasts 42 months

Budget: approx 2 millions EUR

Web site: www.swarm-bots.org

the SWARM-BOTS project:
General Purposes

The SWARM-BOT project aims to study a novel swarm robotics system

It is directly inspired by the collective behavior of social insects and other animal societies

It focuses on self-organization and self-assembling of autonomous agents

Its main scientific challenge lays in the development of a novel hardware and of innovative control solutions

the SWARM-BOTS project:
s-bots and swarm-bots

The elementary hardware unit is an autonomous robot called s-bot

S-bots have limited individual capabilities, but they can connect to each other, forming a swarm-bot

Exploiting the cooperation of its components, a swarm-bot can cope with problems that individual s-bots cannot solve

the SWARM-BOTS project:
Layout of the Presentation

Description of the hardware

Brief overview of the simulation tool

Control activities

Project scenario

Obtained results

Hardware:
Innovative Features (1)

Differential drive motion provided by both tracks and wheels (Treels ©)

Turret rotating with respect to the chassis

Two connection facilities

Rigid gripper

Flexible gripper

Colored LEDs (light ring)

Hardware:
Innovative Features (2)

Many sensory systems

Proximity sensors

Ambient light sensors

Ground sensors

Light barrier in the grippers

Omni-directional camera

Sound signaling system

Traction and torque sensors

Humidity and temperature

3-axis accelerometer

Simulation Features

We developed a 3D Dynamics Simulator (based on the Vortex™ SDK)

Detailed modeling of mechanical parts

Precise simulation of rigid body dynamics and collision

Modular implementation

Multi-robot handling facilities

Simulated sensor have been characterized and compared using the real robot

Simulation:
Different levels of detail

detailed	medium	simple	fast

Control Activities

A target scenario has informed the research activities of the SWARM-BOT project

Controllers have been designed for a variety of individual and collective behaviors

Aggregation

Coordinated motion

Cooperative transport (prey retrieval)

Path formation and exploration

Adaptive task allocation

Navigation on rough terrain

Functional self-assembling

Control Activities:
The Project Scenario

Find object and aggregate around it

Pull object and search for goal

Change shape and move in a coordinate way avoiding obstacles

Control Activities:
Aggregation

Aggregation is one of the basic abilities required by a swarm-bot

S-bots are positioned in an arena bigger than their perceptual range

S-bots use their sound signaling system to aggregate

Artificial evolution of neural networks

Obtained simple and scalable behaviors (tried up to 40 s-bots)

Porting done on the detailed simulation model

Control Activities:
Coordinated Motion

Coordinated motion is another basic ability for a swarm-bot

Connected s-bot has to negotiate a common direction of motion

Coordinated motion strategies exploits the traction sensor

Artificial evolution of neural networks

Obtained simple and scalable behaviors (tried up to 40 s-bots)

Porting done on the detailed simulation model

Control Activities:
Cooperative Transport

Inspiration from ants behavior

The goal is designing cooperative transport strategies for heavy loads

S-bots may require to connect in a swarm-bot due to

The number of s-bots

The size and shape of the prey

Behaviors evolved using artificial evolution

Normal and blind robots

Efficiency in the transport

Control Activities:
Path Formation and Exploration

Path formation is performed using s-bots as colored beacons that can be followed by others s-bots (explorers)

Exploration by means of chain formation

starts from a home location (nest)

extends in multiple directions, eventually connecting to the goal

Two main strategies designed

Static chain formation

Moving chain formation

Control Activities:
Adaptive Task Allocation

The aim is designing an adaptive task allocation mechanism that

Allocates a sufficient number of s-bots to a given task

Does not reduce the efficiency of the colony

Task allocation has been studied in the context of collective prey retrieval

The agents adjust the probability to leave the nest basing on previous success/failures

This self-organizing process results in the choice of an optimal number of active agents

Control Activities:
All-Terrain Navigation

All-terrain navigation is studied as the result of the cooperation of s-bots connected in a swarm-bot formation

A first step is given by hole avoidance

S-bots have to move coordinately avoiding to fall into holes or out of the arena borders

Evolved strategies generalize to

Different size/shape of the swarm-bot

Use of flexible links

Obstacle avoidance

Control Activities:
Functional Self-Assembling

Functional self-assembling is the self-organized creation of a physically connected structure which is functional to the accomplishment of a given task

Studied in a simplified scenario, still rather complex

S-bots show a wide range of individual and collective behaviors

All behaviors are integrated in a single, evolved neural network

Conclusions

The SWARM-BOT project is bringing many innovations

Advanced hardware for self-assembling robots

Interesting results in the control activities

Future Works

Implementation of the designed control strategy with the real s-bots

Integration of different basic behavior for the solution of the project scenario

Study of non-reactive behaviors (integration over time, give up mechanisms, collective choices)

Thank you

www.swarm-bots.org

EPFL, CH (D. Floreano)

CNR, I (S. Nolfi & D.Parisi)

ULB, B (M. Dorigo & J.-L. Deneubourg)

Control

IDSIA, CH (L. M. Gambardella)

Simulation

Hardware

SWARM-BOTS
project partners

info day 29/9/97 MEL-ARI NANO

DG III

1997-09-26T18:18:58

Vito Trianni