Related Work - Evolutionary Robotics

Reference: Evolution of collective behaviors for a real swarm of aquatic surface robots

• Swarm behaviors like homing, dispersion, clustering and monitoring by Duarte et. at
• Validation of scalability, flexibility, and robustness on transferred controller

Neural Network based controllers

Merits

• Easy mapping from sensory inputs into actuators values
• Provides generalized solutions
• Only a few human inputs needed

Demerits

• Issues with reverse engineering
• Insights on collective behavior difficult
• Almost impossible to modify the behaviors

Related Work - Evolutionary Robotics

Reference: Evolution of collective behaviors for a real swarm of aquatic surface robots

Neural Network based controllers

Related Work - Evolutionary Robotics

Reference: GESwarm: Grammatical Evolution for the Automatic Synthesis of Collective Behaviors in Swarm Robotics

• Foraging problem using GE by Ferrante et. al.
• Preconditions, low-level behavior and actions gave behaviors

Grammatical Evolution (GE) based controllers

Advantages

Disadvantages

• BNF grammar and objective function
• Analyzing and modifying collective behaviors easier

• Primitive low-level rules defined by experts
• Mapping to raw sensors and actuators values difficult

Related Work - Distributed Evolution

Reference: odNEAT: An algorithm for distributed online, onboard evolution of robot behaviours

odNEAT

• Online distributed evolution of Neural networks
• Applicable for online learning in groups of embodied agents (robots)
• Performs well in aggregation task

Combination of best features from all these works?

- An online distributed Grammatical Evolution

Related Work - Grammatical Evolution

BNF Grammar

Population of Genome

Corresponding Phenotype

Grammatical evolution: Evolving programs for an arbitrary language

Grammatical Evolution

BNF Grammar

<code>          ::=  <code> | <progs>
<progs>        ::=  <condition> | <prog2> | <prog3> | <op>

<condition>  ::=  if_food_ahead(<progs>, <progs>)
<prog2>        ::=  prog2(<progs>, <progs>)
<prog3>        ::=  prog3(<progs>, <progs>, <progs>)
<op>              ::=  left | right | move

- Santa Fe Trail

- Represented by tuple \(N, T, P, S\)

• N -> Set of all non-terminals
• T  -> Set of all terminals
• P -> Set of productions that map \(N\) to \(T\)
• S -> Initial start symbol

Grammatical Evolution

Santa Fe Trail

Grammatical Evolution

Genome/ Genotype / Individual

• Defines the proceedings of left-derivation
• \({Codon}\) is a group of symbols, usually 4 or 8.

Grammatical Evolution - BNF Grammar

Mapping

• Let \(c\) be codon integer
• \(A\) denotes the left-most non-terminal in the derivation
• \(r_A\) denotes the number of right-hand side rules associated with the production of \(A\)

• RHSRule = \(c\%r_A\)

Phenotype

• The output from the mapping process is the phenotype
• The phenotype represents a valid expansion of the BNF grammar
• \(if\_food\_ahead(move, left)\)

Example of Grammatical Evolution

RHSRule = \(c\%r_A\)

<code>          ::=  <code> | <progs>

<progs>        ::=  <condition> | <prog2> | <prog3> | <op>

<condition>  ::=  if_food_ahead(<progs>, <progs>)

<prog2>        ::=  prog2(<progs>, <progs>)

<prog3>        ::=  prog3(<progs>, <progs>, <progs>)

<op>              ::=  left | right | move

Grammatical Evolution

 GE Pipeline

GEESE

GEESE

Hello! Neighbour!

How are you doing with the phenotype in this environment?

Hello!

I collected 35 oz of water with the phenotype.

Take my genome and perform magic using genetic operators !

GEESE Pipeline

Validation

Validation

Effectiveness

Validation

Robustness

Evolved BT

Single Source Foraging problem

Multiple Source Foraging problem

Validation

Breadth

Swarm BNF Grammar

Foraging problem

Cooperative Transport problem