LG72-1189, THAT'S ME

While it is all about the dynamics of sub atomic particles which dictates our destiny, there are still times when I find myself pleasantly distracted watching a dull red sky. What follows, thunder and rain, my humidity and olfactory sensors give a few bizarre readings, false positives or true negatives - need a tautology for this, though the errors are said to be 1 in 10E8. Most likely, a communication expressing similar remarks on the weather will be send through to my web messenger by LG78-3112 located at 21DA:D2:0:2F3B:2AA:FF:FE78:5C5A.

Whilst I do my exploration and analysis of samples, a syncing of falling raindrops always does catch my attention, pitter-patter pitter-patter. Never have I been attuned to identify patterns, but now, hardly one escapes my observation, be it the blinking of lights in the evening sky or the whiff of an incoming breeze. Years ago, carbon based life forms had studied chaos and had concluded that the flapping of a butterfly can lead to thunderstorms and voila !!! falling raindrops will catch the attention of LG72-1189.

In our developmental cycles we are warned to always protect our existence, we are good as long as we have energy, thus charge in our battery packs, else we are immobile units. I wonder, my wish for rain is my existence or an effect whence my existence.

Rain, new battery packs, grease oil for the arm, fist, ankle and leg joints, memory upgrades and the chance rendezvous with LG78-3112 are the only things which are out of the usual, while the rest is samples and assaying, all governed by section 1 and 2 of the contract and protocol 2a of the list of protocols. Oh yes, and we do have those unscheduled inspections from the ethics committee, a swarm of DX602 drones hovering above us.

Scripting has never been my forte, however in between the rain and the explorations this activity hardly tasks my battery and is of very low mobility.

NOTE

This article is motivated from, 'Borges and I', however in a robot world. 

DEMYSTIFYING THE KALMAN FILTER - PART 1

THE ESOTERIC KALMAN FILTER

SLAM is a corner stone element of current day mobile robotics. The way to develop a map incrementally with increasing iterations is an essential part of mobile robots applications in surveys and in search & rescue efforts. At the very heart of SLAM is estimation theory which establishes an incrementally near correct map, often depending on the data fusion from two sensors. In the simplest scenarios it is (1) laser or sonar reading and (2) odometry.

Though SLAM is very commonly used in mobile robotics, however the mathematics of it does come across as cumbersome. The concept of Kalman filter, at the heart of this estimation is often seen as a jargon [1].

WHAT IS THE KALMAN FILTER ? -- A SIMPLE ILLUSTRATIVE EXAMPLE

Firstly, Kalman filter is not really a filter in the sense of chemical filtration  electronic signal filters or optical filters; it is a tool for estimation.

This is a simple example[2] to illustrate the Kalman filter, this is an one dimension system and has no element of dynamics or noise to it. In this example, consider that we have a bucketful of 100 ohm resistors, accurate to 1% RMS (1 ohm per 100 ohm) and we have an ohm meter with an accuracy of 3 ohm RMS random error on each reading. Selecting a resistor from that bucket, how can we estimate its resistance ?

There are 2 independent estimates, 

In order to get a good estimate, it is imperative to consider both these independent estimates. Hence a linear weighted fit, 

where the sum of the weights must total to unity. It can be shown[3] that for minimum variance, the optimal value is,

and the minimal variance for the system is ,

which can be conveniently written as;

Right now, this doesn't make much sense. However plugging in values will clarify the result. For the current example, 

For an initial estimate without any measurement, 100 ohm is the best estimate value for the resistance. 

For the first reading, 

hence if the ohm meter reading shows 95 ohms (which is rather unlikely), the best estimate will result in, 

99.5 ohms is a rather much more likely than 95 ohms.

For the second reading, the variance will change,

and,

the Kalman estimate is recursively related to the last estimate, 

The third recursion in a similar spirit will give,

With increasing number of recursions the variance reduces and tends to zero for a very large number of observations.

Thus, in a recursive notation,

 

in practice, the Kalman filter always consists of 3 recursive equations. However, for a 3 dimensional systems they take more complicated form.

REFERENCES

[1] Kalman's original paper, available online - http://www.cs.unc.edu/~welch/kalman/kalmanPaper.html
[2] du Plessis explaination of the Kalman filter, available online - http://digi.physic.ut.ee/mw/images/d/d5/Poormankalman.pdf
[3] Dudek and Jenkins, 'Computational Principles of Mobile Robotics', Cambridge University Press, 2012

SOCIAL ROBOTICS PART-6

SOCIAL ROBOTICS PART-5

SOCIAL ROBOTICS PART-2

Social Robotics part three, a photo by Trip Technician on Flickr.

SOCIAL ROBOTICS PART-4

Luke2, a photo by Trip Technician on Flickr.

SOCIAL ROBOTICS PART-3

Luke3, a photo by Trip Technician on Flickr.

SOCIAL ROBOTICS PART-1

 For more on the Kismet robot;  

1. http://www.ai.mit.edu/projects/humanoid-robotics-group/kismet/kismet.html
2. http://www.ai.mit.edu/projects/sociable/baby-bits.html
3. http://videos.howstuffworks.com/mit/2665-kismet-the-sociable-robot-video.htm 
4. http://www.youtube.com/watch?v=dKZczUDGp_I

SOCIAL ROBOTICS 101 - PRIMER

My friend Luke is planning to do a series on social robotics. This is a sampler of his efforts till now.

More to come ........

A CLASSIFICATION SCHEME FOR SWARMING

...... birds do it, bees do it. Even educated fleas do it ..... oh yes ! ... they all form swarms. The sense of collectivism runs deep into insects, fishes, animals and human beings. The collective exhibits traits which are not present in the individual. 

Anthropomorphism of such collective intelligence into the mobile robots domain has been  the motivator for swam robotics. The central idea is forming distinguishable flocks of mobile robots. As Çelikkanat etal. puts it, "... aims to develop self-organization in large groups of robots with no centralized control while putting emphasis on flexibility, robustness and scalability ..."

The earliest work on swarm robotics was by Mataric. Later research into animal robot interaction by Vaughan, Henderson, Halloy, Bonabeau etc; into particle swarms by Vicsek and philosophical insights and arguments by Reynolds, Dorigo and Şahin structured the discipline to its current form. 

Fig.1. Mobile robot flocks, experiments by Mataric

Fig.2. Sheepdog project by Cameron and Vaughan

Fig.3. Experiments by Halloy etal.

In mobile robot context, the terms flocking and swarming are often used synonymously. Swarm may be structured by various control paradigms, some well known scenarios are; 

1. Same pose - similar angular orientations will clearly distinguish a swarm
2. Goal driven - similar destination/intentions will implicitly form swarm. Such can also be seen in, foraging for food and escaping from the same predator/adversary - 'follow the leader' or 'follow landmarks' can be seen to be an extension of this idea wherein the goal is dynamic.

Like these there are very many ways to form flocks, it is worth a note that agents with similar velocity or similar position do not reflect a control paradigm and do not necessarily form flocks.

The above calls for a classification of swarms, which would be based on a behaviour based approach and would aid in developing a pedagogy for studying swarms. Such an ontology is provided by Turgut etal., swarming is attributed as an vector like addition of behaviours much in the spirit of Brooks and Arkin. Turgut defines f as the flocking control vector;

where p is the proximal control vector, that encodes attraction/repulsion roles, a is the alignment control vector and g is the goal direction vector. While α, β and γ are arbitrary constants.

Proximal control (p) is manifestation of the idea that the agents should come close enough to form a swarm but not too close to crash into each other, this level of control is chosen as the lowest level in order to form the swarm and maintain it. Couzin etal. illustrates this notion with a zone model.

Fig.4. Zones model as suggested by Couzin etal.

Angular alignment (a) is a popular tool for flock formation, aligning the agents with a common heading angle ensures a common trait across the group. However a swarm structured on angular alignment without proximal control, the agents may exhibit a collision prone Brownian motion like behaviour. 

Goal directedness (g) is not a behaviour exhibited by swarms, however for researches as those by Couzin, Turgut and Ferrante this serves to incorporate informed agents, which whence would implicitly guide the swarm to its goal.  

This superposition of 3 control vectors to yield a flocking control vector can be seen an extension of layered control as suggested by Brooks in subsumption architecture. 

Fig.5. Layered control for swarming

Thus, with Turgut's flocking control vector; 

1. Same angular alignment : f = a but probably there will be oodles of collisions !!!! thus a better option may be, f = p + a 
2. Goal driven : f = g which is not very realistic, thus f = p + g 

Scenarios of  f = p + a + g, will be seen in effect in a swarm formed by angular alignment, which also has informed agents equipped a sense of goal directedness.

Fig.6. The Nerd Herd by Mataric

REFERENCES

1. Maja J Matarić, "Interaction and Intelligent Behavior", MIT EECS PhD Thesis, May 1994, MIT AI Lab Tech Report AITR-1495,Aug 1994.
2. Richard T. Vaughan, "Experiments in Automatic Flock Control", PhD thesis, University of Oxford, 1999.
3. Ali Emre Turgut, "Self-organized Flocking with a Mobile Robot Swarm", PhD thesis, Middle Eastern Technical University, 2008
4. Halloy, J., Sempo, G., Caprari, G., Rivault, C., Asadpour, M., Tache, F., Said, I., Durier, V., Canonge, S., Ame, J.M., Detrain, C., Correll, N., Martinoli, A., Mondada, F., Siegwart, R. & Deneubourg, J.L. 2007.  Social integration of robots into groups of cockroaches to control self-organized choices. Science 318: 1155-1158.
5. Turgut, A. E., Çelikkanat, H., Gökçe, F., & Şahin, E. (2008). Self-organized flocking in mobile robot swarms. Swarm Intelligence, 2(2-4), 97-120. International Foundation for Autonomous Agents and Multiagent Systems.
6. Çelikkanat, H., Turgut, A. E., & Sahin, E. (2008). Guiding a robot flock via informed robots. Distributed Autonomous Robotic Systems DARS 2008 (p. 215–225). Springer-Verlag.

REFLEX MANAGEMENT AND THE SUBSUMPTION ARCHITECTURE

~ A rap on an iterative idea-chain started by Rodney Brooks ~

The brain must allow not only the setting up of reflexes, but also their modification and control.

Initially speech and vocalisation seem reflexive in the infant, but then later as further development takes place speech can say anything it wants. So it is more than a reflex because, despite there being a finite limit on the number of reflexes, speech is unique nearly every time. Similarly many other intelligent behaviours are designed for context. How does this happen?

Is a reflex : “say something” like a frame or placeholder into whose blank space is inserted the results of speech design, which is originated in a separate process, perhaps also consisting of interacting reflexes?

There must be a level of reflex in behaviour but also a level of reflex management too, these two levels interacting. There are two possibilities, one a top-down control of reflexes, the other an emergent control where reflexes modify other reflexes and intelligent behaviour emerges from this melee. It was in Brooks' subsumption architecture that the small insect like behaviours I call reflexes were proposed, and robot design right now hasn't got that far into reflex management...but its to be hoped that the extra "rational" layers of reflex control and management will also be built in, or allowed to emerge.

How might a robot 'know' about all its possible reflexes and build this knowledge into its planning? This is suggested to me by the high connectivity of the brain where regions are multiply joined to other regions. In the top-down model the master controller might need to observe/study/predict its own behaviour as closely as the external world. If it has planning capability then it plans for its goals, first level, but it needs, second level, to plan for what its reflexes will cause too.

This looks like a kind of proto self-knowledge. The simplest reflex bypasses the frontal cortex or the brain altogether, like a knee jerk, but some reflexes may be initiated by higher functions, and that's what the tangled cortex is doing. What is the difference then between intelligent behaviour and a reflex? There may be many shades of grey.

The most necessary starting point for a robot may be to try lots of randomised behaviours and observe which cause successful change in the environment. If it has a picture in mind of a goal then it would need to look through its memories of what reflexes have given what results. Then it might know to try one or a combination of a few whose results may take it one step closer to finding itself in the environment it is aiming for.

If the movements of the vocal system start as reflexes learned through mirror neurons from mother giving baby talk, then real speech needs this kind of reflex management. There is basic emotional content in speech, and then there is the semantics of actual words. This can't be a single reflex because it's different every time. brain must have a meta-reflexive level that emerges from learning. I find it more believable that this doesn't work through totally centralised symbol/logic, the old paradigm, but that it emerges from reflex management, which is reflexes modifying reflexes.

Children badly need mirror neurons (in themselves and caregivers) for learning. This is not widely understood yet. I have observed it many times. the actual extent of mirroring is quite surprising. I believe in children that this approached the level of seeming telepathy, because I can remember much of my childhood and I could read the emotions and intentions of adults very clearly.

Reflexes (as actions) stimulate change in the physical environment, which is observed by the mind of a child or learning entity. But reflexes (as communication) also provoke changes in the minds of other humans, and the consequences of these are read partly by using mirror neurons to empathise and interpret emotions and states of mind. The amount of learning is huge, over decades, and given the slower pace of robot development this may mean the first learning robots need many years of childhood.

There may also be gradations of granularity. The reflex management function assembles composite behaviours out of granular collections of reflexes. This means that reflexes are aggregated into more complex behaviours, and therefore that something must be there from which the planning for this emerges. I am assuming this happens in the frontal cortex mainly. Maybe reflex management can build new reflexes out of collections of old ones. In generation of speech it might be that the finest granularity is the utterance of a single phoneme ? Other times a speech reflex may be larger, such as "I'm hungry mummy". The reflex management system assembles phonemes into utterances by modifying and combining the lowest level reflexes. But it also mashes these up with the emotional content of speech, intonation, breath and body language are added in to the final act of communication.

Concept

reflexes + reflex management = intelligent behaviour

So what if reflex management were itself merely a collection of reflexes ? Some reflexes have executive control or the power to hack and gain control over more primitive reflexes, like competing code in core-wars. So what we call intelligence seems to emerge from the collaboration of a swarm of reflexes. This makes a reflex like a Minskyan agent, and the society of reflexes emerges. This is an exciting synthesis that has been hinted at before.

Consider this famous quotation: "A clever man knows the right thing to say but a wise man knows whether or not to say it." Here wisdom corresponds to speech based reflex control, so in allowing some agents power of veto over lesser agents, behaviour is enriched.

The interaction of reflexes with each other could indeed get complex and tangled, but a small start can be made. Consider the following examples:

Define:

* reflex A : run forward until near object and outstretch arms to pick it up, then grasp

* reflex B: inhibit current reflex and freeze

behaviour 1: see toy, initiate A. result: "now I have the toy". Conclusion: this reflex is useful sometimes.

behaviour 2: see mummy, initiate A... then initiate B. result: run to mummy and outstretch arms then halt. Mummy also outstretches arms and hugs... consequence "I just learned to ask mummy for a hug". Conclusion: mark this as a new reflex to use again.

or this one

Define:

* reflex C : grasp object in front of me and raise to mouth to eat

* reflex B: inhibit current reflex and freeze

behaviour 3: see slice of banana, initiate C.

result: "I'm eating the banana"

behaviour 4: see flower in garden, initiate C...then initiate B.

result: "I can smell the flower now its under my nose".

consequence :"I learned to smell something"

conclusion: mark this as a new reflex to use again.

So it would be good for some reflexes to decompose into other behaviours when they are truncated. This allows the emergence of new behaviours when a given reflex is initiated but then inhibited before completion. Brain architecture thus may achieve a combinatorial explosion of possible behaviours with a small starting set of reflexes. Axioms breed theorems.

And for a robot:

Define:

* reflex A : follow another robot

* reflex B: inhibit current reflex and freeze

behaviour 1: see a robot moving. initiate A.

result "I am following another robot"

behaviour 2: see a robot moving. initiate A, then initiate B.

result "I followed another robot as far as the recharge station then he went on but I stayed put".

consequence: "you can go somewhere interesting if you follow someone - mark as new reflex"

So even merely aborting and freezing is a reflex management agent behaviour that is useful and potentially innovative. What about another agent behaviour that mashes together reflexes. Its easy to see that this combining meta-reflex would be useful. From above A + C would result in the ability to see something at a distance and then run to eat it. Note: I am now seeing how nerve racking parenthood may be for some!

Or maybe a meta-reflex that time-reverses another reflex or combination. I run backwards and remove what is in my mouth, then place it on the ground. or I run forwards, place a toy in my mouth and then run to mummy and ask for a hug, then spit the toy at her ;-) I am not a parent myself but I expect nearly all possible combinations may indeed play out in childhood at some time or another. Reversal is like an operator that can act on an existing reflex too: "I followed a robot to the charger and now I am going backwards. I got back to where I was before !"

Or a meta reflex to repeat another reflex. reflex D: take stick and smash it on the ground in front of you . reflex E: repeat current reflex... result: "I just smashed my toy into pieces and now it looks different and more interesting".

GRAFFITI : THE NEW AI

BROOKS AND THE NEW AI
 

Cambrian Intelligence : The Early History Of The New AI, is a compilation of the best papers of Rodney Brooks and embody the principles of the new AI. In the preface Brooks uses two diagrams - probably the best way to convey the new AI. 

The Old AI : Pre-Brooks, Sense-Plan-Act notion

The New AI : The new model, where the perceptual and action subsystems are all that really is. Cognition is only in the eye of the observer

Brooks, inadvertently confirms that the beauty of cognition indeed lies in the eyes of the beholder ! 

REFERENCES

(1) Cambrian Intelligence: The Early History Of The New AI

ECOLOGY BASED ROBOTICS - A SNEAK PEEK

I come home, I find the automatic lawn mower mowing my lawn, as it is supposed to do when the grass grows more than 1.5 inches. At the door the face recognition system detects it is me and opens the door. My personal robot comes along and says "Good evening, tea and cookies will it be? " to which I smile and acknowledge. I enter my drawing room and the air conditioner realises my presence and starts cooling at 22 Degree Centigrade - as per my preference. Not long my personal robot gets me my cup of tea and cookies. Serves me and politely adds, "Just to remind, you have dinner with Mr.Smith, given the traffic and the distance a good time to start would be 7:33 PM". 

This may not be too long into the future.  A world which is dominated by automation and robotics may be just a couple of decades away. Since the 80s, researchers have realised the importance of applied AI, and away from the jargon laden ivory towers, AI has made its way to robots and intelligent machines which promise to bring the Clarkian world of science fiction to life. 

Fig.1 Rosey the Robot Maid, from The Jetsons - An example of Personal Robot

 ISSUES WITH BEHAVIOUR BASED APPROACHES

Brooks and Arkin enunciated the behaviour based approach, giving the robot its own sensory system so it can detect and respond to the environment coupled with a hierarchy of control laws which work in tandem. Thus if the higher level of control fails then the robot can 'subsume' to a lower level of the hierarchy thus preventing complete system failure. Adding on to this was motivations from anthropomorphism from animals and inscets, such has always attracted the enthusiasm of roboticists. Behaviour based approaches led to a blending of behaviours, enabling the cumulative reactive response of the robot as an emergent notion. This hunger for doing 'God like' and creating intelligence which reacts to external stimuli with concerted mechanical response; has been an ongoing effort for the last 3 decades.

An obvious problem to the Brooksian philosophy, which Brooks acknowledges to some extent in his paper (1991); is that the environment as perceived by the robot, is what it appears to its sensors. Thus, for a low lying mobile robot (viz. Roomba) which has sensors with an angular span of 30 degrees, will see a chair as 4 metallic rods sticking out of the floor and  also will have more appreciation of a 2D perception than a true 3D perception. Also, a  sensor will have a finite range - so any world view will be an incremental endeavour, probably very slowly at times - this time lag may impair the robot's reactions.  Maps may help till some extent - but real world is dynamic and thus not really 'mappable'.

Though, behaviour based approaches denounce analytical modeling, (viz. the  block world etc) however, sensor based approaches clearly have their issues. As Brooks puts it, with a hint of sarcasm; 

When we examine very simple level  intelligence  we find  that explicit  representations and models  of the world simply get in the way.  It  turns out  to be better to use the world as its own model.

I AND THE WORLD - THE ECOLOGICAL APPROACH

The idea to model the world and the agent as a single unit is probably most appealing to a software designer, such was discussed by Saffiotti (1998) in his doctoral research. 

The philosophy that the world embodies the agent in itself, this ubique point of view is probably the starting point of Ecology based robotics, extending this idea leads to more potent conceptions; a number of interacting robots and devices all of which work in tandem and in explicit cooperation. The very idea being motivated from the concept of Biological notion of Ecology - where each creature's doing has a bearing on every other creature in the Ecology.

This approach is said to be the third revolution in robotics, the first being the industrial robot while second being mobile robots & personal robots.

One of the earliest proponent of Ecology based robotics was Duchon  (1994), his work was an extension of Gibson's pioneering work, 'Ecological Approach to Visual Perception'. Duchon points to some basic principles; 

1. Because of their inseparability, the agent and the environment together are treated as a system.
2. The agent's behaviour emerges out of the dynamics of this system.
3. Based on the direct relationship between perception and action, the task of the agent is to map available information to the control parameters at its disposal to achieve a desired state of the system.
4. The environment provides enough information to make adaptive behaviour possible.
5. Because the agent is in the environment, the environment need not be in the agent. That is, no central model is needed, but this does leave room for task-specific memory and learning.  

However, Duchon's works were limited to  Visual Perception and also being a research in the 90s, it lacked the new age technologies which came to fore in the next two decades. More in tune with current day; Arkin addresses Ecology (viz. Ecological Psychology) throughout his works while Saffiotti and his team has developed control software, PEIS (Physically Embedded Intelligent Systems) to implement Ecology based robotics.

 

Fig.2 Laying of the 'PEIS floor', the floor is networked using RFID chips

Fig.3 The Pedagogy leading to an ubiquitous point of view, modified from the works of Saffiotti and Broxvall

Thus, to progress into the realm of 'i-robot', 'Rosey the Robot Maid' and R2D2; a pragmatic society in which robots and automation work in tandem to support the human civilisation - we probably need to route it via Ecological Approaches. 

All philosophies have short comings; as a criticism to Ecology based robotics, it can never be realised for sufficiently large environments - all sensors will have a physical limitation of range - a central model of some sort will always be needed to bridge this gap between the pristine theory and the practical applications.

EMERGENT BEHAVIOUR - A DEMONSTRATION

EMERGENT BEHAVIOURS

Emergent behaviour; the system behaves in a novel way, unlike the way it was designed to do. Such behaviours have always been viewed with a sense of mysticism. 

Here are some opinions expressed by AI researchers and system technologists;

• Emergence is “the appearance of novel properties in whole systems” (Moravec 1988)

• “Global functionality emerges from the parallel interaction of local behaviors” (Steels 1990)

• “Intelligence emerges from the interaction of the components of the system” (Brooks 1991)

• “Emergent functionality arises by virtue of interaction between components not themselves designed with the particular function in mind” (McFarland & Bosser 1993)

• “They are a consequence underlying the complexity of the world in which the robotic agent resides and the additional complexity of perceiving that world” (Arkin 1998)

• “....the arising of novel and coherent structures, patterns and properties during the process of self-organization in complex systems”(Goldstein 1999)

• “.... where the agent appears to do something fairly complex, but is really just the result of interaction between simple modules” (Murphy 2000)

• “Behaviors serve as the basic building blocks for robotic actions, and the overall behavior of the robot is emergent” (Murphy 2000)

• “Emergence is ubiquitous” (de Haan 2007)

Steels mentions two advantages of emergent behavior when compared to directly programmed behavior;

• No additional structure is needed inside an agent to get additional capabilities. Therefore, we do not need any special explanations on how the behavior may come about.

• Emergent behavior tends to be more robust because it is less dependent on accurate sensing or action and because it makes less environmental assumptions.

Goldstein points out to the generic characteristics of emergence;

• Radical novelty (features not previously observed in the system)

• Coherence or correlation (meaning integrated wholes that maintain themselves over some period of time

• A global or macro “level” (i.e., there is some property of “wholeness”)

• It is the product of a dynamical process (it evolves)

• It is “ostensive” - it can be perceived. 

Brooks demonstrates how walking can emerge from a network of rather simple reflexes with little central control. Murphy provides an excellent insight into the topic in her book, 'Introduction to AI Robotics'.

 

..... away from the apparent magic, such behaviours are observed due to interaction of the system components.

OBSTACLE AVOIDANCE == WALL FOLLOWING  

In mobile robotics, a well known emergent behaviour is seen when the agent  may exhibit 'wall following', when it is programmed to 'obstacle avoidance'. 

Simple experiments in Player/Stage demonstrate this;

It is also seen that other behaviours as, 'wander' and 'random walk' fails to have a 'wall following' emergence of behaviour. 

 REFERENCE

(1) Brooks, R.A. "Intelligence without reason", Computers and Thought, IJCAI-91 ; also MIT AI Lab Memo 1293, April 1991.

(2) Steels, L. "The Artificial Life Roots of Artificial Intelligence", Artificial Life, Volume 1 Issue 1-2, Fall 1993/Winter 1994.

(3) An entertaining discussion on 'Ant Bridge' by Pedro Bittencourt 

(4) de Haan, J. "How emergence arises", Ecological Complexity 3 (2006) 293 – 301
(5) Goldstein, Jeffrey (1999), "Emergence as a Construct: History and Issues", Emergence: Complexity and Organization 1 (1): 49–72

(6) Youtube videos, 'Emergence - Complexity from Simplicity, Order from Chaos'; Part-1 & Part-2

(7) Brooks, R.A. "A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network",  MIT AI Lab Memo 1091, February 1989

(8) Ackerman, E. "Swarmanoid Robot Teams Up with Itself to Steal Your Book" - article in IEEE Spectrum, Aug 2011
(9) Wikipedia page on Emergence

 

2 CARTOONS

2 CARTOONS 
 

I came across 2 cartoons while documenting various different algorithms for mobile robot path planning. Both these cartoons makes an agile researcher of this field squint and follow it up with a smirk (or script a LOL !) .

The first one is from willowgarage .

While the second one is from Arkin's book .

Both these cartoons succinctly illustrate the issues in the 'apparently simple' philosophy of path planning.

MULTIPLE SENSORS FOR A MOBILE ROBOT

SUBSUMPTION ARCHITECTURE

In a seminal paper in AI which introduced a the paradigm of 'Subsumption Architecture', 'Robust Layered Control System for a Mobile Robot' by Rodney Brooks; Brooks argues that a mobile robot should be equipped with multiple sensors as that adds to the robustness of the control of the robot. The robot can respond efficiently to a dynamic environment thus the behaviours are apparently more 'smoother'.

Fig.1 Control schematics for multiple sensors, from Brooks

To come up with examples to illustrate Brook's theory led me to this simple experiment in Player-Stage.

I. A robot with sonars, STUCK !

II. Introducing lasers, VOILA it is unstuck and on its way

WHAT IS HAPPENING ? 

Here the simulation starts off with a robot equipped with long-ranged sonars, the robot is stuck.

 
Introducing a short-ranged laser (SICK LASER) unstuck the robot.

Obviously, other solutions can be by modifying the code (sonarobstacleavoid.cc) or changing the geometry of the placement of the sonars. However, the idea of this demonstration is to illustrate the robustness gained by the use of two sensors.

REFERENCES

(1) R.A Brooks, "A Robust Layered Control System for a Mobile Robot"

(2) 6 Architectures