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Instituto Tecnológico de Aeronáutica São José dos Campos - Brazil

Generation and Testing of Gait Patterns for Walking Machines Using Multi-Objective Optimization and Learning Automata. Jeeves Lopes dos Santos Cairo L. Nascimento Jr. Instituto Tecnológico de Aeronáutica São José dos Campos - Brazil. Motivation.

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Instituto Tecnológico de Aeronáutica São José dos Campos - Brazil

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  1. Generation and Testing of Gait Patterns for Walking Machines Using Multi-Objective Optimization and Learning Automata Jeeves Lopes dos Santos Cairo L. Nascimento Jr. Instituto Tecnológico de Aeronáutica São José dos Campos - Brazil

  2. Motivation • To develop machines withgreatermobilityandintelligencethatcanact as humanhelpers (dirty, dullordangerousjobs), ourmodern era slaves. • To develop a gaitgenerationalgorithm for walking machines withdifferentmorphologieswithouthaving to describemathematicallytheirdynamicmodels. • Theproposedgaitshouldbedefinedconsidering: • themachine’s frontal speed, • thesmoothnessoftherobot CG movements, • the torques applied to eachjoint, • the total energyconsumed for locomotion. 2/10

  3. AdoptedStrategy Coordination Strategy Learning Methodology SimulatedRobot Real Robot • A reinforcement learning algorithm (Learning Automata) is used to search for a gait using the dynamic model built using MATLAB/SIMULINK/SimMechanics. • The proposed solution is evaluated using the real robot. • The performance of the simulated/real robots are compared. 3/10

  4. ProblemFormulation The movement of each joint is described by a function with linearly interpolated NE points and a period T. Complexity reduction: Similar legs use the same functions for each joint but with different time lags. Example: 4 leggedrobot: • 4 similar legs (NP=4); • 3 joints per leg (NA=3); • 4 points in eachfunction (NE=4). From 48 to 16 (4*3+4) variables. 4/10

  5. Criterion for Similarity: Simmetry Distances of each leg to the robot CM: 5/10

  6. Representation and Learning Function Description Start Selection of possible solution Test on the simulated robot Evaluation of the response Lag for leg Adjustment of the probabilitiy matrices Convergence? End Period Possible Periods 6/10

  7. Functionslearned for a tripodrobot 7/10

  8. Experimental Validation • Quadruped robot, • Tripod robot, • Biped robot, • Hybrid robot with 4 legs (2 joints per leg) and unactuated wheels in each foot. 8/10

  9. Conclusions • GOOD: Severalgoodsolutionswerefound for eachofthe 4 cases testedsofar (flat surfaces). • BAD: The designer has to: • set severalparameters for thelearningalgorithm, e.g. min/delta/maxvalues for eachjoint, lagandperiod, • testdifferentfoot-groundfrictionmodels. PossibleLines for Future Work • Test the proposed solution using larger robots with more powerful actuators and in more complex situations, e.g. going up and down inclined surfaces/staircases and running in rough terrain (grass using small/large feet). • Use the comparison between the simulated and real robots to improve the simulation model and search for better solutions (outer optimization loop). 9/10

  10. We thank our sponsors 10/10

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