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Disturbed Behaviour in Co-operating Autonomous Robots

Disturbed Behaviour in Co-operating Autonomous Robots. Robert Ghanea-Hercock & David Barnes Salford University, England. Introduction . Autonomous Robots experience behavioural problems, particularly in groups.

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Disturbed Behaviour in Co-operating Autonomous Robots

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  1. Disturbed Behaviour in Co-operating Autonomous Robots Robert Ghanea-Hercock & David Barnes Salford University, England

  2. Introduction • Autonomous Robots experience behavioural problems, particularly in groups. • The problem is to balance the conflicts imposed by a dynamic environment with the need to co-operate with other robots. • Hybrid architectures offer a preliminary framework to build upon.

  3. Problem Domain & Goals • Handling and transporting hazardous materials, i.e. nuclear plant decommissioning. (Work was industrially sponsored by UK Robotics Ltd). • To translate user’s requests into plans and sets of behaviours to control two co-operating fully autonomous mobile robots.

  4. Methodology • A hybrid control system was developed, with a reflective Planning agent linked remotely to the two mobile robots. • Each robot has a reactive behaviour based control system, with a fuzzy rule base controlling the interactions between behaviours.

  5. Fred & Ginger

  6. Adaptivity vs Control? • There appears to be a trade-off between the degree of external control and level of adaptivity a system can express. • Survivability in hostile environments is the critical factor.

  7. Behaviour Synthesis Architecture • B.S.A developed by Barnes at Salford ‘89 • Based on a vector synthesis mechanism, to combine multiple behaviours in parallel. • Each behaviour is a pair of functions: a stimulus-response, and a utility-response function. • The utility-response can be dynamically modified by a meta-control layer, i.e. the fuzzy rule base.

  8. Fuzzy Behaviours • Fuzzy logic can bridge the gap between reactive behaviours and reflective plan sequences. • Firing of each fuzzy rule provides contextual knowledge of the robots interaction with the environment.

  9. Hierarchical behaviour control Adaptive Fuzzy Rule Base Obstacle sensor Behaviour pattern 0 Vector Summation IR sensor Behaviour pattern 1 Beacon sensor Behaviour pattern n

  10. Dynamic Fuzzy Action Surface • Hypothesis: for a goal seeking agent, a state of dynamic imbalance in its control cycle improves its ability to navigate unstructured environments. • The frequency of rule firing therefore has an associated cost function, and a proportional degree of suppression.

  11. Results • The behaviour patterns and fuzzy rules were designed in an off-line simulation, and applied to two B12 mobile robots. • The adaptive fuzzy rule base significantly improved the robots ability to escape from local minima within the laboratory environment.

  12. Results

  13. Results

  14. Conclusions • Adaptive behaviour requires an understanding of the dynamics present in the overall robot-environment-control system. • Dynamic instability can be a positive feature in autonomous agent control strategies. • The frequency of sensory stimuli contains useful context information about the environment, and can be used to modify current behaviour.

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