Presentation on theme: "Evolving Flocking Simulation and Robotics Dan Sayers iotic.com."— Presentation transcript:
Evolving Flocking Simulation and Robotics Dan Sayers iotic.com
Flocking in Nature Flocking and other group behaviours in animals and humans have natural beauty and have long been a source of fascination Group behaviour in animals gives rise to the phenomena of “swarm cognition” or “group intelligence” in which whole groups respond and act for the collective interest of the group Flocking in animals is usually observed in prey species – studies have demonstrated a link between predation and flocking behaviours
Flocking in Simulation Flocking and other group behaviours have been studied in simulation since the Eighties, using simple rules for individual flock members, from which flocking emerges as a group phenomenon This furthers our understanding of group behaviours in various species (including humans) Also, it given us a method to interact with the beauty of flocking in artistic ways
Simulated Flocking Craig Reynolds – 3 rules: Collision avoidance (separation) Velocity matching (alignment) Flock centering (cohesion) These three (locally applicable) rules are observed to be sufficient for realistic flocking in 2D and 3D simulations Each rule influences a steering force on each agent. These agents were named ‘Boids’ by Reynolds
Evolved Flocking Under Predation In my simulation, flocking is shown to evolve when under the influence of predators. The evolution takes place through a gradual improvement of the connections between sensors and steering forces. The control system for the prey members is very simple – a vector sum of the sensor vectors gives the steering force F = c 1 S 1 + c 2 S 2 + … The amounts (c 1, c 2 etc.) that the different sensors affect the steering force is what is evolved. Prey and predator populations are kept constant.
Flocking “Sensors” Each flock member is capable of responding to fellow members within a defined radius, and within a defined vision angle Prey members also respond to predators within their field of perception
Sensors The prey have sensors for: Average nearby prey position (close) Average nearby prey position (far) Average nearby prey velocity Average nearby obstacle location Average nearby predator location These vectors are added together (multiplied by genetically determined weights) to give the prey’s steering force at any given time
From Simulation to Robotics This simulation contains a number of idealisations: The sensors are ideal in that the information about neighbouring vehicles and obstacles is communicated directly rather than being sensed by real-world, “noisy” sensors The steering vector is also idealised
In the Real World … Robots need to able to survive collisions Real, noisy sensors to deal with Consideration of how to sense nearby boids: Vision Ultrasound Sonar / Radar Wifi / Broadband
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