Innovative Parallel Task ParadigmSwarming with Stigmergy

Antonio Luca Alfeo

Maurizio Palmieri

Contextualized Swarming Definition

Biology

“distributed problem-solving devices inspired by collective behavior of

social insect colonies and other animal societies”

[Bonabeau, Dorigo, Theraulaz - Swarm Intelligence: From Natural to Artificial Systems]

Unpiloted Air Vehicles (UAV)

“collection of autonomous individuals relying on local sensing and reactive

behaviors interacting such that a global behavior

emerges from the interactions”

[Clough - Emergent Behavior (Swarming): Tool Kit for Building UAV Autonomy]

Military

“the systematic pulsing of force and/or fire by dispersed, internetted units,

so as to strike the adversary from all directions simultaneously”

[Arquilla and Ronfeldt. Swarming and the Future of Conflict]

UAV’s problem UAV’s employment reduces the threat to human operators.

Manpower requirement doesn’t decrease due to the need of flight crew.

High number of UAV leads to coordination issues caused by a Centralized

Command and Control(C ) approach.

Decisions are made by the central commander and then propagated

through a hierarchy of subordinates.

Intelligence is concentrated only in the central commander.

Problem Specification

This problem has four main characteristics, which we summarize

mnemonically as D :

Diverse: handles diverse functions, platforms, sources, command.

Distributed: system components are apart and interact with each

other to achieve a common goal.

Decentralized: the global pattern of behaviour is the result of an

aggregation of mechanisms acting upon local components.

Dynamic: the environment changes over the time.

The Solution? Swarming! Swarming techniques are inspired by insect pheromones.

Support diverse functions and information sources.

Provide distributed and decentralized systems that scale well.

Reduce vulnerability to attack or system overload.

Adapt well to dynamic changes in the environment.

Offer a powerful mechanism for coordinating UAV’s.

Approach 1: Intelligent Agents Try to endow the individual agent with intelligence.

The resulting systems can be described as a finite-state machine.

Interactions among entities are one class of events that can trigger a

transition from one state to another.

Intelligent Agents Benefits

Swarming will result if the local interactions leads to self-organization.

The states of each vehicle map directly onto features of the domain in a

way that human users can easily understand.

The interactions of agents are understandable.

Address a variety of tasks and missions by properly choosing states and

transitions.

Intelligent Agents Drawbacks Performance can deteriorate if the problem isn’t mapped neatly onto the

environment.

Defining the set of states and transitions is a non-trivial knowledge

engineering task.

The architecture encourages commands between entities rather than

negotiation, leading to rigid task assignment (like C ).

As the number of platforms increases, coordination becomes increasingly

difficult, leading to scaling problems with large populations.

Approach 2: Stigmergy Multi-agent coordination mechanism that rely on informations exchanged

between agents and their shared dynamical environment.

Intelligence resides not in a single distinguished agent (like C ) nor in each

individual agent (the intelligent agent model), but in the interactions

among the individually non-intelligent agents.

Examples from natural systems show that stigmergic systems can generate

robust, complex, intelligent behavior.

Stigmergy Features

Simplicity: The logic for individual agents is much simpler than for an

individually intelligent agent.

Scalable: Stigmergic mechanisms scale well to large numbers of entities.

Robustness: the system’s performance is robust against the loss of a few

individuals.

Environmental Integration: the environmental dynamics are directly

integrated into the system’s control, enhancing system performance.

Military Applications

Maintaining communications networks.

Recognizing patterns in sensor arrays.

Coordinating multi-phase missions.

Carrying supplies over the battlefield.

Surveillance and patrol.

Target acquisition.

Target tracking.

Convergent attack.

Carrying Supplies Advantages Uses drones instead of humans.

Topology kwnoledge is not required.

Harder for enemy to get them all down.

Robustness against single failure.

No hierarchy among UAV’s.

Few communications with BS.

No direct communication among drones.

Reduced environmental interference.

Target acquiring simulation

Pheromones Communication in stigmergy paradigm takes place by depositing

little information (PHEROMONEs) on a media support.

Pheromones are characterized by :

◦ Shape (e.g. pyramid, gaussian).

◦ Deposition location.

◦ Temporal transformation function ( e.g. diffusion, evaporation).

◦ Semantic interpretation at high level.

Agents It is an entity that interacts with the media, the environment and other

agents.

Possibile interactions are:

◦ Deposit given type pheromones in its location (must be aware of it).

◦ Ask for pheromones in its position on the media (amount and type).

◦ React to the media support and the perceived surroundings

(e.g. where to go, what pheromones to deposit).

Media Support It is the virtual media checked by the agents for pheromones.

It has to:

◦ Deposit pheromones received from agents.

◦ Add new pheromones to those already existing.

◦ Transmit pheromones deposited near the requesting agent location.

◦ Apply time transformation function related to each pheromone type.

Our Case in Practice

Traffic Advantages Base station, owning the support media, decouples the communication

between agents and performs some processing, reducing the traffic.

The exchanged informations refer to the deposits in a small media area.

The agents do not need to know the entire environment

neither to communicate with all other agents.

Robustness Adavantages Being agents non-intelligent, their behavior can be described by few

simple lines of code, which reduces the probability of error.

Losses are tollerated (maybe degrading performances) since the task is

performed in an autonomous and distributed way.

Efficiency Advantages The interaction of many trivial behaviors gives rise to an emerging

high-level solution.

The observer (whether agent or human) interpreting the semantics of

pheromones builds an auxiliary knowledge useful in subsequent

executions.

In our case the mapping of the area and the calculation of the best route.

Sizing and Parametrization 1/2

Different choices to improve performances of the proposed system:

1) Number of agents: at least 8 to achieve fault resistance.

2) Media-Agents update rate is drone-speed dependant: many drones

fly up to 15 m/s thus the update rate has to be at least 30 hz to be reliable.

3) Analysis Range per drone: the larger the better in term of analysis but

the worst in term of traffic. The most used trade off is to set the range

as large as pheromone’s shape.

Sizing and Parametrization 2/2

4) Shape and dimension of pheromones over the media:

gaussian is the mostly used(due to its statistical properties) large enough to

enable proximity interaction, equivalent to the double of drone’s diameter.

5) Trasformation function parameter: evaporation behavior leads to

history of interaction, diffusion leads to propagation of knowledge over the

media; correctly tuning them grants high level interpretation of system

dynamics.

For example if the position of a target has to be shared its pheromones have

to spread over the media: so they diffuse fast and evaporate slow.

Other contexts may require different combinations.

Performance

Other ApplicationsAnalysis of sensor data to improve performances in team sport in

which coordination is a key feature.

Defining Project Space LimitsThis paradigm has applications in many problems of different fields meeting

these requirements:

Many different sources of data.

Needed high number of agents to achieve loss tolerance and good

performance.

Topological Representability (2D or 3D mapping).

Tollerant to initial delay, due to randomness of the initial solution.

Granted access to support media.

Expanding Project SpaceWARNING! What happens if connection with BS goes down?

We propose an hybrid communication approach between intelligent

agents and stigmergy to handle this situation.

Case 1: BS goes down for less than 5 s

◦ Every agent saves a local portion of the virtual environment that can

be referenced in case of no communcation with the BS.

Case 2: BS goes down for 5 to 15 s

◦ Every agent asks its neighborhood for informations about its local

fragment of virtual environment.

◦ The agents log their deposit and send it to the BS when connection is

available.

Case 3: BS goes down for more than 15 s

◦ Every agent uses its local built map to reach the starting point.

References

Performance of Digital Pheromones for Swarming Vehicle by Sauter

Making Swarming Happen by H. Van Dyke Parunak

Digital Pheromones for Autonomous Coordination of Swarming UAV’s by H.Van Dyke Parunak

Cooperative Control for Multiple Autonomous Vehicles Using Descriptor Functions by Marta Niccolini , Lorenzo Pollini and Mario Innocenti

Collective Robotic Intelligence by C. Ronald Kubeand Hong Zhang

Video of simulation https://www.youtube.com/watch?v=RLIA1EKfSys

THANKS FOR

YOUR ATTENTION!!