1 robotica lecture 16. 2 collective robotics swarm robotics

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ROBOTICAROBOTICA

Lecture 16Lecture 16

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Collective RoboticsSwarm Robotics

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Collective robotics Why invest in collections of robots, why not build

a reliable individual robot?

- Task difficult (or impossible) for one robot - Can be performed better by many- Redundancy – task more likely to be completed- Simplicity – many cheaper robots instead of one

expensive one.

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What kinds of collections?

Possibilities range from- remote controlled robots- centrally controlled robots- completely autonomous robots

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Cao, Fukunaga and Kahn (1997) Cooperative mobile robotics: antecedents and directions. Autonomous Robots, 4,1, 7-27.

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Advantages of robot collectives shown inEnvironmental explorationMaterials transportCoordinated sensing – collective cooperates to provide maximal sensor coverage of moving target.Robot soccerSearch and Rescue

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Swarm Robotics

Taking a swarm intelligence approach to robotics

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Swarm intelligence

Swarm intelligence is “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behaviour of social insect colonies and other animal societies”

Bonabeau, Dorigo and Theraulaz (1999)

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Natural swarms

Decentralised – no-one in control Individuals are simple and autonomous Local communication and control Cooperative behaviours emerge through self-

organisatione.g. repairing damage to nest, foraging for food,

caring for brood

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Self-organisationOrganisation increases in complexity, without external guidanceSelf-organising systems often display emergent properties

“self-organisation is a set of dynamical mechanisms whereby structures appear at the global level of a system from interactions among its lower-level components. The rules specifying the interactions among the system’s constituent units are executed on the basis of purely local information, without reference to the global pattern, which is an emergent property of the system rather than a property imposed upon the system by an external ordering influence”

(Bonabeau, Dorigo and Theraulaz, 1999)

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EmergenceAn emergent property, e.g. pattern formation, from more basic constituentsAn emergent behaviour can appear as a result of the interaction of components of the systemE.g. flocking, or organisation of ant colony

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Real life example of self-organised behaviour in humans

Emergence of paths across grassy areaMost popular paths are reinforced

Counter –example e.g. a team of carpenters building a house….not self-organised.

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Swarm robotics Inspired by self-organisation of social insects Using local methods of control and communication

Local control: autonomous operationLocal communication: avoids bottlenecksScalable – new robots can be added, or fail without need for recalibrationSimplicity – cheap, expendable robots

Self-organisation Decentralisation

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Disadvantages of centralised control and communication. Central control: failure of controller implies failure

of whole system Robot to robot communication becomes very

complex as number of robots increases. Communication bottlenecks Adding new robots means changing the

communication and control system

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Applications of swarm approach

Some tasks are particularly suited to group of expendable simple robots e.g. - cleaning up toxic waste- exploring an unknown planet- pushing large objects

- surveillance and other military applications

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What issues are investigated?

Weak AI questions:E.g. how can complex behaviour, such as cooperation, emerge as a result of interactions between simple agents and their environment?

– Biological modelling – better understanding of social insects for example.

- Biological inspiration – emulating behaviour and capabilities of biological systems

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Cooperation and communication Examples of communication in cooperative

systems: Increasing sophistication….

BacteriaAntsWolvesNon-human primatesHumans

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Bacteria

Live in colonies Explicit chemical signals mediate their ability to

cooperate. E.g. Mycobacteria assemble into multicellular

structures known as fruiting bodies. Bacteria emit and react to chemical signals

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Ants

Also termites, bees and wasps Display cooperative behavioure.g. pheromone trails to food sourceChance variations that result in shorter trail are

reinforced at faster rate.Can find optimal shortest pathStigmergic communication.

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Wolves

Territory marking through repeated urination on objects on periphery of territory

Also more sophisticated communication directed at particular individuals

Specific postures and vocalisations

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Non-human primates

Sophisticated cooperative behaviourHigher primates can represent the internal goals,

plans, dispositions and intentions of others, and to construct collaborative plans jointly through acting socially.

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Humans

Many forms of communications – including written and spoken language

Many forms of cooperation, from basic altruism to cooperative relationships where we exchange resources for mutual benefit

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Focus of interest here: Emergent cooperation

e.g. social insects: ants, bees, wasps, termitesStigmergic communication: one of the

mechanisms that underlies cooperation.

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Swarm roboticsBiologically inspired by social insects- emergent complex behaviour from simple agents Swarm Intelligence Principles:

Autonomous control Simple agents (debateable – swarms of helicopters?)Expendable, fast and flexible responsesLocal communicationScalableDecentralised

Use and exploration of stigmergy

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Mystery: cooperative behaviour when insects seem to work alone

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- individual insect responds to changes in environment created by itself or others

Grassé (1959) – stigmergy- Indirect social interaction via the environment

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E.g. Termite nest building Building arches Termites make mudballs, which they deposit at random.

Chemical trace added to each ball Termites prefer to drop mudballs where trace is

strongest. Columns begin to form Deposit more on side nearest to next column –

eventually leads to formation of arch.

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Example paper: Holland and Melhuish (1999)

Holland, O., and Melhuish, C., (1999) Stigmergy, self-organisation and sorting in collective robotics. Artificial Life, 5, 173-202.

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Example of ant brood sorting“The eggs are arranged in a pile next to a pile of larvae

and a further pile of cocoons, or else the three categories are placed in entirely different parts of the nest…if you tip the contents of a nest out onto a surface, very rapidly the workers will gather the brood into a place of shelter and then sort it into a different pile as before (Deneubourg, et al, 1991)

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Franks and Sendova-Franks (1992)

Brood sorting of Leptothorax unifasciatus

- brood items sorted into concentric rings of progressively more

widely spaced brood items at different stages of development.

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Use of simulations

Deneubourg et al (1991) “The dynamics of collective sorting: Robot-like ants and ant-like robots”

Showed agents could use stigmergy to cluster scattered objects of a single type, and to sort objects of two different types

For sorting – agents needed short-term memory to sense local density of different types of brood items and to know the type of brood item they were carrying.

But – a simpler solution can be found with physical agents – greater exploitation of real world physics.

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Holland and Melhuish experiments: Small U-bot robots, with infrared sensors, and gripper

designed to sense, grip, retain, and release frisbees. When robot moves forward, frisbee remains in gripper When robot reverses, frisbee left behind, unless pin

extended to keep it in place When 2 or more frisbees pushed into, this triggers

microswitch in gripper – not triggered when pushing or bumping into 1 frisbee.

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Exp 1: how many U-bots in arena without too many collisions Exp 2: Simple rule set

Rule 1: if (gripper pressed and object ahead) then make random turn away from object -> ie turn away from boundaryRule 2: if (gripper pressed and no object ahead) then reverse small distance (dropping the frisbee) and make random turn left or right -> ie has encountered another frisbee.Rule 3: go forward

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44 frisbees placed across the arena 10 robots released. Frisbees gradually collected in small clusters –

after 8 hours 25 mins, a cluster of 40 frisbees formed.

Frisbees taken from intermediate clusters if struck at an angle without triggering gripper

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Experiment 5: sorting and pull-back algorithm Plain yellow frisbees and black and white ring frisbees Pin-dropping mechanism applied to plains Rule 1: if (gripper pressed and object ahead) then make random turn

away from object Rule 2: if (gripper pressed and no object ahead) then

If plain lower pin and reverse for pullback distance raise pin reverse small distance (dropping frisbee)

make random turn left or rightRule 3: go forward

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- now if robot is pushing a plain frisbee and hits another, or if not pushing frisbee and collides with another plain in a cluster, the plain will be dragged backwards and dropped away from contact point.

Result (after 7h 35 m): central core of 17 ring frisbees with 11 plains and 4 rings round outside.

I.e. annular sorting, based on simple mechanism - Example of seemingly complex behaviour (sorting)

emerging from the application of simple rules.

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What do these experiments show? Apparently co-operative behaviour, with no central

control, and no direct communication. Segregation and crude annular sorting can be achieved

by system of simple (reactive) mobile robots - robots can only sense the type of object they are

carrying - they have no capacity for spatial orientation or memory

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Some elements of these mechanisms found in social insects

E.g. Leptothorax building behaviour: possible use of increased resistance to pushing a building block forward against other building blocks as a cue to drop it

“the ants drop their granule if they meet sufficient resistance” (Franks et al, 1992)

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Also mechanism like pull-back mechanism

“workers individually carry granules into the nest. They walk head first towards the cluster of their nest-mates who are already installed in the nest, forming a fairly tight group. After coming close to the group of ants, the builder then turns through 180 degrees to face outwards from the nest. The worker then actively pushes the granule it is carrying into other granules already in the nest, or after a short time, if no other granules are

encountered, it simply drops its load” (Franks et al, 1992)

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Summary of Holland and Melhuish paper: A simpler solution obtained in the robotic experiments

where the physics of the environment can be exploited, than in abstract computer simulations

Simple behavioural rule set – no capacity for spatial orientation or memory, but robots able to achieve effective clustering and sorting

Example of stigmergy – indirect communication via the environment.

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Sorting and clustering accomplished here by robots with no memory, and no understanding of their task.

Does this mean that ants also have no memory or understanding of their tasks?

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Kube, C.R. and Bonabeau, E. (2000) Cooperative transport by ants and robots. Robotics and Autonomous Systems, 30, 85-101.

Current interest in robotics is result ofRelative failure of classical AI program. Swarm-based robotics, and idea that group of robots can perform tasks without explicit representations of environment and other robotsMobile robots becoming cheaper and more efficientArtificial life and emphasis on emergent behaviour – increasing awareness of biological systems

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“As the reader will perhaps be disappointed by the simplicity of the tasks performed by state-of-the-art robotic systems such as the one presented in this paper, let us remind her or him that it is in the perspective of miniaturisation that swarm-based robotics becomes meaningful … understanding the nature of coordination in groups of simple agents is a first step towards implementing

useful multirobot systems” (Kube and Bonabeau, 2000)

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Cooperative transport in robots, and cooperative prey retrieval in social insects

E.g Moffett (1988) a group of 100 ants Pheidologeton diversus could transport a 10 cm earthworm weight 1.92g (more than 5000 x 0.3 mg minor worker)

Single ants carry burdens at most 5 times their body weight

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Questions about cooperative prey retrieval in social insects Is there an advantage to group transport over solitary

transport? When and how does an ant know it cannot carry an item

alone? How are nest mates recruited? How do several ants cooperate and coordinate their

actions to transport an item? How do ants ensure the right number of ants help? How does a group of ants handle deadlocks?

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Group vs solitary transportMoffet (1988) transport efficiency per ant (product of burden weight by transport velocity divided by no. of carriers) increases with group size up to a maximum for groups of 8-10, and then declinesSwitching from solitary transportresistance to transport

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Recruitment of nestmatesHolldobler et al (1978) African weave ant

Aphaenogaster speciesShort range recruitment – releasing poison gland

secretion in the air when prey discovered. Ants recruited from 2m distance

Long range recruitment – chemical trail of poison gland recruitment laid from prey to nest.

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Coordination in collective transport – not well understood. Movement of one ant engaged in group transport modifies the stimuli perceived by other group members (stigmergy)

Number of ants – an increasing feature of how difficult (weight and resistance) it is to carry the prey

Deadlock and stagnation recovery: ants show realigning and repositioning behaviours

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Robot task: cooperative box pushingPrevious versions – centralised planning and

conflict resolution, with explicit communication between robots

Kube and Zhang (1994) directed box pushing by robots

Applied in simulation first, then robots

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Non-directed box pushing Physical robots: 2 behaviours

AVOID (left and right obstacle sensor mapped to left and right wheel motors)GOAL (left and right sensors mapped to right and left wheel motors causing robots to turn towards brightly lit box)Controllers allowed robots to locate box, converge and push itBut stagnation could ariseHow do ants recover from stagnation?

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Cooperative prey retrieval Sudd (1960) strategies to combat stagnation

observed in ants cooperatively retrieving preyRealignment of body without releasing graspIf that fails, grasp released, and ants repositionSame strategies used for robot box pushing

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Comparison of strategies1.No stagnation recovery2.Realignment only3.Reposition only4.Realignment and reposition

Performance improved with strategiesFor small group strategy (2) best, for large group (3)

is better, and (4) is best.

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Directed box-pushing

Now 3 phasesFinding the boxMoving towards the boxIf oriented with respect to goal, pushing boxBox detection simplified by placing bright light on boxGoal detection simplified by shining spotlight

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Directed box-pushing Phase 1: robots execute FIND-BOX and MOVE-TO-BOX Phase 2: robots incorrectly positioned for pushing move counter

clockwise round perimetercaused by cycling through FIND-BOX, MOVE-TO-BOX, and PUSH-TO-GOAL when contact is made. ?SEE-GOAL indicates robot on wrong side for pushing, and REPOSITION behaviour until empty position found.

Phase 3: push to goal – robots continuously monitor ?SEE-GOAL. If robot cannot see goal it will reposition.

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Varying the number of robots More robots = more interference

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What do these experiments show? Coordinated group effort is possible without use of direct communication or

robot differentiation - ants not always efficient – eg ants can take 10 minutes to begin moving

object. Model makes testable predictions about stagnation recovery mechanism to be

expected depending on ecological conditions and prey size E.g. adding mechanisms for stagnation recovery increases retrieval time, and

probability of successWhere little competition, should find more stagnation recovery mechanismsWhere strong competition, should find less stagnation recovery

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Research questions in Swarm Robotics

Self-organised methods for task allocation to ensure that enough robots are allocated to a taskCollective decision makingCommunication – local methods to detect when needs of group have changedControl and coordination of heterogeneous groupsIncorporation of some learning and recognition – e.g. of landmarks

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Conclusions

Idea of collective roboticsPossible reasons for using several robotsSwarm intelligence and swarm roboticsSelf-organisation and emergencePossible applications

Cooperation and communicationForms of communication

– Stigmergic indirect communication Example papers:

Holland and Melhuish (1999) and sortingKube and Bonabeau (2000) and box pushing