ai23 lecture4H

50 %
50 %
Information about ai23 lecture4H

Published on January 4, 2008

Author: Julie


Bioinspired Computing Lecture 4:  Bioinspired Computing Lecture 4 Collective Behaviour and Swarm Intelligence Recap…:  Recap… Last Time: command & control vs. self-organisation the sophisticated abilities of insect colonies stigmergy algorithms inspired by insect intelligence Foraging Clustering & Sorting Building Swarming & Flocking This Time: insect-like social robots sorting & clustering cooperative transport Applications sport education entertainment Insect-like Social Robots:  Insect-like Social Robots So far, we have considered software algorithms inspired by the various behaviours of social insects. Researchers are also implementing insect-like robot control systems for a range of tasks. These include single robots inspired by crickets & cockroaches, and multi-robot systems. Before reviewing some of the collective behaviours that have been achieved with multiple robot systems, we will consider the question: Why take a multi-robot approach? single complex robot working alone versus many simple robots working together Multi-Robot Advantages:  Multi-Robot Advantages There are many reasons to consider building multiple simple robots rather than a single complex solution: robustness – a simple machine may be less likely to fail than a complex one redundancy – many robots carrying out the same task ensures that the system can tolerate the failure of a few cost-effectiveness – many simple robots may be built for the price of a single complex machine simplicity – we often may not know how to build a single robot solution cooperation – the task may simply be impossible to carry out by an individual Sorting and Clustering in Reality:  Sorting and Clustering in Reality We have already seen the application of insect-inspired clustering and sorting algorithms in software. Hardware implementations of similar ideas have shown how little intelligence each agent really needs… At UWE Owen Holland, Chris Melhuish and colleagues, have been developing collective robot systems for some time. In experiments with robots the size of footballs, they have shown that individuals with very crude sensors, no memory or sense of spatial location, and no communication, can… segregate two kinds of object sort them into a central cluster of object A surrounded by a ring of object B – annular sorting resembling the brood sorting of ant species Leptothorax unifasciatus. The Basic Sorting Task:  The Basic Sorting Task Several mobile robots equipped with special grippers patrol an arena littered with pucks of two kinds – red and blue. The robots can detect the gripper pushing against anything heavier than a puck, a gripped puck’s colour, and whether an obstacle is directly ahead… Three rules achieve simple clustering: if (gripper pushed & obstacle) then turn at random else if (gripper pushed) then reverse (dropping puck) and turn at random else go forward How Does It Work?:  How Does It Work? Notice that the robots know nothing about the location of a cluster or even the local density of pucks. The robots succeed at the task because… they pick up loose pucks without noticing when they have a puck, they tend to deposit it next to another puck (pushing a puck into another triggers rule 2) when they don’t, they tend to only pick up pucks which are on their own (pucks in groups trigger rule 2) Interestingly, as in some experiments with ants, the robots often clustered the pucks at the edge of the arena. Through artificially varying the probability that rule 1 would fail to fire, the researchers found that they could increase or decrease this edge-seeking tendency. Annular Sorting:  Annular Sorting In an attempt to speed up the clustering, the robots’ rule set was altered slightly to treat blue and red pucks differently: The new robots treat the red pucks as normal, but drag blue pucks back a small distance before letting go of them. These robots did not cluster pucks any faster, but succeeded in roughly arranging the blue pucks in a doughnut-shaped ring around a cluster of red pucks – in a similar fashion to the brood sorting Leptothorax species… if (gripper pushed & obstacle) then turn at random else if (gripper pushed) then if (blue puck) then pull puck backwards endif reverse (dropping puck) and turn at random else go forward Collective Stigmergic Behaviour:  Collective Stigmergic Behaviour These robot experiments show that what appears to be organised collective behaviour can be achieved by very simple robots following very simple rules: the robots made use of properties of their physical environment to help them coordinate their behaviour they exploited the stigmergic effects of each other’s behaviour to achieve their joint task the global-level effects of changes to the rules of individual robots was very hard to predict Through this kind of simple robot research it may be possible to gain an understanding of… natural collective behaviour in insect colonies, etc. the nature of stigmergy itself Natural Collaborative Transport:  Natural Collaborative Transport Social ants often transport large items to their nests in teams, achieving feats that would be beyond a single ant. In contrast, getting robots to work together as a team on a joint project, as we have just seen, is a tricky task. Bonabeau, Dorigo and Theraulaz list some of the interesting questions that this insect behaviour suggests: Do individuals behave differently when they are in a team? How are enough ants recruited when help is needed? How do ants co-operate and co-ordinate their behaviour? How do they handle deadlocks, obstacles, conflicts, etc.? Studying the behaviour of collective behaviour in groups of robots is helping to answer some of these questions... Building Robot Collaboration:  Building Robot Collaboration In addition to helping us understand natural insect behaviour, achieving effective robot collaborative transport techniques would represent a significant feat of engineering. Some engineering considerations: How can interference between team-mates be reduced? What is the optimal team size for a particular task? How should team-mates accommodate each other? How complicated must conflict resolution mechanisms be? How should team-mates communicate with each other? Observation of co-operative transport in ants tends to suggest that an efficient solution could be engineered using a group of robots lacking explicit means of communication, or any centralised control. Ant-Like Robot Box Pushing:  Ant-Like Robot Box Pushing Co-operative box pushing is considered a bench-mark test for teams of multiple robots. Many researchers have taken an approach involving planning and explicit centralized mechanisms for conflict resolution. In contrast, Kube and Zhang have developed an ant-inspired system… The box-pushing task is as follows: A lit box, which is too heavy for a single robot to push, must be moved from some random location in the middle of an arena to the arena’s edge. The sensors on Kube & Zhang’s circular, wheeled robots can detect i) the box, ii) other robots, and iii) nearby obstacles. Each robot is identical and can carry out five different behaviours: GOAL, AVOID, FOLLOW, SLOW, & FIND. A Subsumption Architecture:  A Subsumption Architecture Kube & Zhang use a subsumption architecture: Each behaviour is triggered by a certain stimulus and suppresses those below it in the architecture. The robot wanders until it detects either another robot, which it follows, or the goal, which it approaches. Encountering an obstacle at any time will trigger a simple avoidance behaviour – turning away from the obstacle. This controller enables robots to converge on the box. How do they co-operate in pushing it to the wall? Co-operative Box Pushing:  Co-operative Box Pushing The robots described so far will succeed in locating the box and pushing it to the wall, but they are far from efficient. Like ants, the robots suffer from periods of stagnation in which the box makes no significant progress. Kube and Zhang’s approach to solving this problem is inspired by ant behaviours: realignment and repositioning. If the box has not moved for a while a robot will realign itself by rotating slightly. Realignment alters the forces acting on the box while ensuring that it is still pushed in the same general direction. If realignment proves fruitless, a robot will reposition itself by backing away and re-approaching the box. This allows other robots to act on the box in its absence and changes its influence on the box. Box Pushers in Action…:  Box Pushers in Action… In this movie, six of Kube and Zhang’s robots attempt to push a lit box to the side of the arena. As described, periods of stagnation cause robots to realign, or back off & reposition themselves. Sport - Football:  Sport - Football We have all encountered tasks that demand more sophisticated coordination. A good example is football. RoboCup, a football tournament for teams of robots, has been running for a few years now and acts as an informal way to assess the state-of-the-art in collective robotics. - Many teams are controlled by traditional AI systems: modelling, planning and communicating in order to co-ordinate players on the joint task of scoring goals. Although no insects play anything like football, could a swarm intelligence approach succeed at RoboCup? This decentralised, minimally intelligent approach is clearly lacking in sophisticated reasoning, planning, etc. But could it make up for this through increased speed, robustness, fault tolerance, opportunism, etc? Education – Star Logo  Education – Star Logo The literature on research into swarm systems and collective behaviour in general suggests that designing these systems is very hard. They behave in counter-intuitive ways. Understanding them requires new ideas and lateral thinking. Are swarm systems intrinsically hard to understand, or have we just had too little experience of building and using them? Mitchel Resnick, in his book Turtles, Termites & Traffic Jams, sets out to explore whether exposure to these systems can provide young children with insights into how they behave. His StarLogo is a programming language designed to help users to build systems of distributed, interacting agents. Could exposure to systems like StarLogo help to create a new generation of thinkers who have been “liberated from the confines of the centralised mindset”? Example – Levels of Description:  Example – Levels of Description One theme running through this module is levels of description – at one level a system may comprise many simple interacting individuals, while at a higher level it is a single organised whole, exhibiting complex coordinated behaviour. The high-school students working with Resnick encountered this idea while building a StarLogo traffic jam simulator: While the cars were always moving forward, the jam sometimes seemed to be moving backwards. Some students coped well with this phenomenon – “[The traffic jam is] like a blob, which can move either backward or forward regardless of how the cars are moving” Others (e.g., MIT researchers) had difficulty – “A loop [in a StarLogo program] does the same thing every time. So the whole thing should be repeating itself.” Entertainment - Animation:  Entertainment - Animation One perhaps surprisingly successful application of swarm systems has been in the domain of computer graphics. Rather than animating a flock or herd or squadron or team or crowd by hand, swarm systems are able to generate complex, life-like sequences of behaviour automatically. The behaviour of each member of the group is governed by a control system which determines its movements (and perhaps the noises or gestures that it makes), taking into account the positions and movements of those around it. Systems inspired by insects or simple animals, will be simple to build and fast to simulate – perhaps fast enough to allow the generation of animation sequences in real-time. Behavioural Animation in Action:  Behavioural Animation in Action Perhaps the most famous example of this technique is Craig Reynolds’ Boids – simple artificial birds which flock in a natural, life-like manner despite being controlled by a tiny set of very simple rules… Boids:  Boids Basic flocking model consists of 3 simple steering behaviours which describe how an individual boid manoeuvres based on the positions and velocities its nearby flockmates: Separation: steer to avoid crowding local flockmates Alignment: steer towards the average heading of local flockmates Cohesion: steer to move toward the average position of local flockmates Termite Construction Simulation (D Ladley, Leeds):  Termite Construction Simulation (D Ladley, Leeds) 3 kinds of termite: trail-laying Follow/lay pheromone Nursing Move towards/away queen up to fixed distance Building To place block: Pheromone level must lie within a certain bound and one of: Material above or below (allows vertical stacks) Site shares face with horizontal loc containing material and satisfying (1) (allows stack horiz extension) 1 face of the site must neighbour 3 horizontal locations that each contain material (allows gradual construction of elevated horiz surfaces) Queen: green, ground red, yellow: deposited building material. 1. formation of "pillar-like" structures: simulates reality 2. construction of walls between the pillars Building the queen chamber

Add a comment

Related presentations