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ESE 313 February 29, 2011 Adam Komoroski Carol Wong C4: Project Formulation.

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Presentation on theme: "ESE 313 February 29, 2011 Adam Komoroski Carol Wong C4: Project Formulation."— Presentation transcript:

1 ESE 313 February 29, 2011 Adam Komoroski Carol Wong C4: Project Formulation

2 Overview Problem Statement: 1. Desired Behavior: 2. Present Unavailability: 3. Desirability of Bio-inspiration: The Hypothesis 4. The Idea 5. Refutability 6. Necessary Means

3 C.1 : Desired Capability Observed Problem: complete loss of one leg function Observed Result: unpredicted and uncontrollable motions Desired Behavior: fault tolerance; directed, controlled, and purposeful movement. Ability to compensate for the complete loss of function in one leg, restoring motion to a state comparable to full legged functions. Bio-inspiration: Variations in leg load bearing Rat motor cortex injuries Neural plasticity

4 C.2 Present Unavailability "The problem of fault recovery represents a vast, important domain in its own right that is still relatively unexplored in robotics.” [2] - Much research done on fault-tolerable gaits in the specific instance of locked joint failure - Does not apply to Junior’s locomotive system - Ideas can be extrapolated

5 C.3 Desirability of Bio-inspiration Mathematical models available Biological observations that are relevant made Rats with motor cortex injuries: Solid observations made that can be extrapolated to Junior platform Plasticity :Animal analogs have uncanny ability to adapt to injuries and other sustained handicaps Load Bearing Remaining questions: How do we implement an artificial rendering of neural plasticity?

6 C.4 The Idea Current implementation:, :43 Five legged “crawl” All five legs offset from each other Upper left leg (0) loss of function 3,1,5,2,4 Four Buehler clock parameters same across all 5 legs Transition to stable gait Proposed implementation: Focus: purposeful disabling of one corner leg Goal: transition effectively, stabilize resulting crawl gait Optimize velocity of five legged gait Stabilizing transition Mathematical approach: optimize parameters via Nelder -Mead and machine learning algorithms Bio-inspired approach: vary load bearings on select legs

7 C.5 Refutability Evaluation of Performance: Stable gait: readings of IMU, acceleration, center of mass changes Efficient: energy expenditure Directed gait: ability to transverse pre-designated path; observation Controlled gait: velocity controlled and optimized; velocity tracker

8 C.6 Necessary Means Proposition: 1. Bioinspired approach: - Determine parameters that change load bearing on a given leg - Purposefully influence load bearing characteristics on legs - Evaluate performance 2. Mathematical model approach: - Nelder Mead Algorithm: optimization of ‘objective function’ - Objective function: characterizes system parameters and behavior - Machine learning classifiers - Collect data -> observe behavior -> formulate model -> model = objective function


10 Five Legged “Crawl” Gait

11 Nelder Mead Algorithm

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