Subsumption Architecture in Swarm Robotics Cuong Nguyen Viet - - PowerPoint PPT Presentation

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Subsumption Architecture in Swarm Robotics Cuong Nguyen Viet - - PowerPoint PPT Presentation

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Subsumption Architecture in Swarm Robotics Cuong Nguyen Viet 16/11/2015 1 Table of content Motivation Subsumption Architecture Background Architecture decomposition Implementation Swarm robotics Swarm intelligence

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Subsumption Architecture in Swarm Robotics

Cuong Nguyen Viet 16/11/2015

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Table of content

Motivation Subsumption Architecture

  • Background
  • Architecture decomposition
  • Implementation

Swarm robotics

  • Swarm intelligence
  • Subsumption architecture in swarm robotics

Conclusion

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Motivation

Swarm robotics, motivated by collective behaviours

  • f biology swarm, has desirable properties

Effective approach for robot control architecture which emphasize emergence of behaviour from individual interactions

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Subsumption Architecture Background

Developed by Rodney Brooks at MIT in mid 80s Brooks argued that Sense-Plan-Act paradigm in traditional approach is not practical Brooks suggested layered control system in horizontal decomposition

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Bio-inspired Artificial Intelligence: Theories, Methods and

  • Technologies. Chapter 6. Figure 6.4. Figure 6.5.
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Subsumption Architecture Decomposition

Traditional approach:

  • Sense-Plan-Act (SPA) approach

Subsumption architecture:

  • Inherent parallel system

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Bio-inspired Artificial Intelligence: Theories, Methods and

  • Technologies. Chapter 6. Figure 6.7 a)
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Subsumption Architecture Decomposition (cont.)

Layers of behaviour:

  • Each layer is a pre-wired behaviour
  • Higher level build upon lower level for

complex behaviours

  • The layers operate asynchronously

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Bio-inspired Artificial Intelligence: Theories, Methods and

  • Technologies. Chapter 6. Figure 6.7 a)
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Subsumption Architecture Behaviour module

Higher behavioural module subsume the competence of lower behavioural module

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Bio-inspired Artificial Intelligence: Theories, Methods and

  • Technologies. Chapter 6. Figure 6.6
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Subsumption Architecture Features

Key features:

  • No knowledge representation or world

model is used.

  • The behaviours are organized in

bottom up fashion

  • Complex behaviour are fashioned from

combination of simpler ones

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Bio-inspired Artificial Intelligence: Theories, Methods and

  • Technologies. Chapter 6. Figure 6.7 a)
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Subsumption Architecture Implementation

Navigation of a mobile robot

  • An example from Brook (1986)
  • Robot is a wheeled platform with circular

array of sonar sensor

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Subsumption Architecture Implementation (cont.)

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Bio-inspired Artificial Intelligence: Theories, Methods and

  • Technologies. Chapter 6. Figure 6.8.
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Subsumption Architecture Implementation (cont.)

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Bio-inspired Artificial Intelligence: Theories, Methods and

  • Technologies. Chapter 6. Figure 6.9.
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Subsumption Architecture Implementation (cont.)

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Bio-inspired Artificial Intelligence: Theories, Methods and

  • Technologies. Chapter 6. Figure 6.10
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Subsumption Architecture Evaluation

Strength

  • Reactivity
  • Parallelism
  • Incremental design

Weakness

  • Inflexibility at runtime
  • No explicit representation of knowledge

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Studies of large collection of simple agents which can collectively solve problems that are too complex for a single agent Example:

  • Particle Swarm Optimization
  • Ant colony optimization

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Swarm Robotics Swarm intelligence

http://cir.institute/wp- content/uploads/2014/09/birds_vortex_800x450.jpg

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Swarm Robotics Definition

Simple interaction among robots in

  • rder to solve complex problem

Group of 10 to 100 units

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http://singularityhub.com/wp-content/uploads/2009/06/swarm- robots.jpg

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Swarm Robotics Advantages

Potential advantages

  • Robustness
  • Flexibility
  • Scalability

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http://softology.com.au/tutorials/boids/boids04.png

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Swarm Robotics Classes

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http://wyss.harvard.edu/staticfiles/ourwork/br/kilobots-350x233.jpg http://img.scoop. it/c1ZCYbe5UvCb3Y2pbfgHFjl72eJkfbmt4t8yenImKBXEejxNn4ZJNZ 2ss5Ku7Cxt

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Swarm Robotics Control architecture

The process of perceiving environment, reasoning and acting is defined by the robot’s control architecture Behaviour-based control is often used

  • Methodology for adding and fine-tuning

control

  • Distributed and asynchronous robots without

central control

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Swarm Robotics Case study 1

Autonomous robots perform underwater mine countermeasures (UMCM) Two behaviour-based architectures were used for testing and implementation: subsumption and motor schema Behaviour

  • Avoiding mines
  • Avoiding obstacles
  • Aggregation_Seperation

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http://eia.udg.es/~busquets/thesis/thesis_html/img12.png

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Swarm Robotics Case study 1 (cont.)

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[2]. Figure 20. Subsumption architecture of a mine hunting robot [2]. Figure 3. Motor schema architecture for mine hunting

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Swarm Robotics Case study 1 (cont.)

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[2] Figure 21. 3 robots performing UMCM under subsumption architecture

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Swarm Robotics Case study 1 (cont.)

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[2] Figure 18. Robot swarm performing UMCM with motor schema

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Swarm Robotics Case study 1 (cont.)

Subsumption Architecture + Decision structure to pick correct behaviour + Reactive to the environment

  • Inconsistent formation
  • Unpredictability - may suffer

from chaotic instability Motor schema + Individual behaviour modular in nature + Effective in controlling motion of individual robots

  • Lack of decision structure

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Swarm Robotics Case study 1 (cont.)

The motor schema approach is effective for controlling the motion of individual robots with a swarm The subsumption approach shows poor aptitude for swarm

  • control. It lacks coordination except for collision avoidance

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Swarm Robotics Case study 2

Exploration and foraging task is noncooperative - could be performed by one robot Box pushing task

  • Robots cooperate in order to push a box to

set location

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Swarm Robotics Case study 2 (cont.)

Hybrid control architecture

  • Subsumption Architecture
  • Motor schema Architecture

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http://eia.udg.es/~busquets/thesis/thesis_html/img12.png

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Swarm Robotics Case study 2 (cont.)

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[4] Figure 2. Control based hybrid architecture

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Swarm Robotics Case study 2 (cont.)

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The use of low-level communication give more coordination and robustness of interaction The hybrid control architecture is very efficient in cooperative task

[4] Figure 8. Evolution of the number of iteration according to N and Nc

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Conclusion

Subsumption Architecture yields great result - emergence of complex behaviours from simple

  • nes.

Pure subsumption is inadequate in solving certain tasks. Proposed hybrid architecture: cross subsumption, neural networks learning, global knowledge and planning

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Reference

[1] Floreano, D., & Mattiussi, C. (2008). Bio-inspired artificial intelligence: Theories, methods, and technologies. Cambridge, Mass: MIT Press. [2] Tan, Y.C. Synthesis of a controller for swarming robots performing underwater mine countermeasures. U.S.N.A. Trident Scholar project report; no.328, 2004. URI: http://archive.rubicon-foundation.org/3590 [3] Rodney A. Brooks. (1985). A Robust Layered Control System for a Mobile

  • Robot. Technical Report. Massachusetts Institute of Technology, Cambridge,

MA, USA. [4] Adouane, L., Le Fort-Piat, N., "Hybrid behavioral control architecture for the cooperation of minimalist mobile robots," in Robotics and Automation,

  • 2004. Proceedings. ICRA '04. 2004 IEEE International Conference on , vol.4,

no., pp.3735-3740 Vol.4, April 26-May 1, 2004

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