Decentralised – no-one in control
Individuals are simple and autonomous
Local communication and control
Cooperative behaviours emerge through self-
e.g. repairing damage to nest, foraging for food,
caring for brood
• Collective task completion
• No need for overly complex
• Adaptable to changing environment
Inspired by self-organisation of social insects
Using local methods of control and communication
Local control: autonomous operation
Local communication: avoids bottlenecks
Scalable – new robots can be added, or fail without need for
Simplicity – cheap, expendable robots
Introduction To Gestures
Gestures can originate from any body
Commonly from face/hand.
Gesture recognition-understand human
Help human to interact with machines
without any mechanical devices.
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.
Adding new robots means changing the
communication and control system
Design Of The Proposed System
Goal directed navigation of swarm
– Detection of hand in an image, background objects are
avoided for feature extraction.
– Skin color is the key component.
– Detecting skin and non-skin.
– Detecting image pixels and regions that contains skin-
– Background is controlled.
– Appearance depends on illumination conditions.
– Training phase
– Detection phase
– Collecting a database of skin patches from different
– Choosing a suitable color space
– Learning the parameters of skin classifier
Converting the image into some color space that was
used in training phase.
Classifying each pixel using the skin classifier to either
a skin or non-skin.
RGB color space
Variety of classification techniques
Any pixel which color falls inside the skin color class
boundary is labeled as skin.
Feature-An interesting part of an image.
No exact definition.
Depends on the problem.
Transforming the input data into set of features.
Result is a feature vector.
Features extracted are invariant to image scaling,
rotation and less affected to changes in
SIFT feature extraction.
Applications of swarm approach
Some tasks are particularly suited to group of expendable
e.g. - cleaning up toxic waste
- exploring an unknown planet
- pushing large objects
- surveillance and other military applications
Hand detection and feature extraction removes
noise from the image.
System performance and accuracy will
Swarm robots movement can be controlled
Dumb parts, properly
connected into a swarm,
yield smart results.
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