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Disturbed Behaviour in Co-operating Autonomous
Robots
Robert Ghanea-Hercock & David Barnes
Salford University, England
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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.
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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.
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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.
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Fred & Ginger
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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.
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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.
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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.
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Hierarchical behaviour control
Behaviour pattern n
Behaviour pattern 1
Behaviour pattern 0
Adaptive Fuzzy Rule Base
Vector Summation
Obstacle sensor
IR
sensor
Beacon
sensor
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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.
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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.
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Results
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Results
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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.