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cs685-behaviors - Robotic Behaviors Potential field...

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1 Robotic Behaviors Potential field techniques - trajectory generation - closed feedback-loop control Design of variety of behaviors - motivated by potential field based approach – steering behaviors Closed feedback loop systems - no memory - behaviors no representation of the world - world is implicitely encoded in the potential function Motion planning (later) - Representation of the environment - Different choices - Path planning algorithms Jana Kosecka, GMU Potential Field Methods Idea robot is a particle Environment is represented as a potential field (locally) Advantage – capability to generate on-line collision avoidance Compute force acting on a robot – incremental path planning Example: Robot can translate freely , we can control independently Environment represented by a potential function Force is proportional to the gradient of the potential function
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2 Jana Kosecka, GMU Attractive potential field - Linear function of distance - Quadratic function of distance Combination of two – far away use linear, closer by use parabolic well Jana Kosecka, GMU Repulsive potential field if Minimal distance between the robot and the obstacle else Issues – multiple obstacles – nonconvex obstacles – how to compute distance Can be computed for polygonal and polyhedral obstacles Issues – local minima Heuristics for escaping the local minima Can be used in local and global context Numerical techniques Random walk methods Iterative gradient descent planning Resulting force
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3 How to do the right thing ? How to do the right thing ? Go from A to B in the context of navigation Taking the environment into account Taking the uncertainty into account Using the model of the environment or not ? Perception, Model, Plan, Execute (Motion Controller) © R. Siegwart, I. Nourbakhsh To localize or not? How to navigate between A and B navigation without hitting obstacles detection of goal location Possible by following always the left wall However, how to detect that the goal 5.3 © R. Siegwart, I. Nourbakhsh
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4 Behavior Based Navigation © R. Siegwart, I. Nourbakhsh Model Based Navigation © R. Siegwart, I. Nourbakhsh
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5 - no memory – no look-ahead reacts to the current environmental stimuli/ sensory information - reactive behaviors: Feedback controllers are instances of reactive controllers (mappings between situations and actions mapping between state and control input) Can we achieve bigger functionality if we combine them ? Simplest scenario one situation one action: Motivation – biology, V. Braitenberg’s Vehicles one can design simple continuous feedback strategies or sets of if-then rule state rules. Reactive Architectures Subsumption Architecture Guidelines: • Build the system from bottom up • Components are task achieving behaviors • Components are executed in parallel • Components are organized in layers • Lowest layers handle most basic tasks • Higher levels exploit the lower levels • Each components has its tight connection between Perception and action
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