Control of Biped Robots


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  • 𝑇𝑚𝑔 =𝑙𝜃 − 𝑙𝑔 (𝑙𝜃)𝐹𝑧=𝑚𝑔 ; 𝑙𝜃 = 𝑌𝑚𝑐𝑇𝐹𝑧 =𝑌𝑚𝑐− 𝑙𝑔 (𝑌𝑚𝑐)𝑌𝑍𝑀𝑃=𝑌𝑚𝑐− 𝑙𝑔 (𝑌𝑚𝑐)𝑇/𝑚𝑔 =𝑙𝜃 − 𝑙/𝑔 (𝑙𝜃 ̈)𝐹_𝑧=𝑚𝑔 ; 𝑙𝜃 = 𝑌_𝑚𝑐𝑇/𝐹_𝑧 =𝑌_𝑚𝑐− 𝑙/𝑔 ((𝑌_𝑚𝑐 ) ̈)𝑌_𝑍𝑀𝑃=𝑌_𝑚𝑐− 𝑙/𝑔 ((𝑌_𝑚𝑐 ) ̈)
  • FRL – Fuzzy Reinforcement Learning
  • Control of Biped Robots

    2. 2. INTRODUCTION• Biped is a very interesting area of robotics where thevarious attributes of the mechanism are influenced fromthe human behaviour of walking.• Many aspects of modern life involve the use of intelligentmachines capable of operating under dynamic interactionwith their environment.• Humanoid robots as anthropomorphic walking machineshave been in operation for more than twenty years.Currently, research on humanoid robots and bipedlocomotion is one of the most exciting topics in the field ofrobotics.
    3. 3. CHALLENGES IN BIPEDAL ROBOTS• Bipedal are Hyper DOF system (>20) - Complex Kinematicsand Dynamics.• Complex real-time control architecture.• Complexity limits the trajectory tracking of ease.• Conventional control algorithms for humanoid robots can runinto some problems related to :• Mathematical tractability• Optimisation• Limited extendibility• Limited biological plausibility
    4. 4. BIPED DYNAMICS AND CONTROLBiped DynamicsStatic StabilizationCOG BasedStabilization StrategyDynamicStabilizationInertial Stabilizationstrategy
    5. 5. STATIC WALKING• In static walking, the biped has to move very slowly so that the dynamicscan be ignored.• The biped’s projected center of gravity (PCOG) must be within thesupporting area.Single SupportDouble SupportCOG
    6. 6. ttfidttPtPMinimize dzmpzmp2)()(DYNAMIC WALKING• In dynamic walking, the motion is fast and hence the dynamics cannot benegligible.• In dynamic walking, we should look at the zero moment point (ZMP) ratherthan PCOG.• The stability margin of dynamic walking is much harder to quantify.
    7. 7. ZERO MOMENT POINT• ZMP specifies the point with respect towhich dynamic reaction force at the contactof the foot with the ground does notproduce any moment.• The point where total inertia force equalszero.• ZMP is the indicator of the stability of therobot:• if it is in the foot shadow – stable,• İf not – unstable.
    8. 8. BIPEDCONTROLMETHODOLOGIES Model BasedPassive WalkerInverse PendulumBiped DynamicsBasedDynamic StabilizationWhole Body Cooperative DynamicsModeless Softcomputing BasedConnectionist Theory (ANN)Fuzzy LogicGenetic AlgorithmsHybrid IntelligentControlANFISRF Learning and GA OptimizedTrajectory Panning
    9. 9. 1. MODEL BASED : INVERTED PENDULUM MODELExcept for certain massless leg models, most bipedmodels are nonlinear and do not have analyticalsolutions.Massless leg model is the simplest model. The body of therobot is usually assumed to be point mass and can beviewed to be an inverted pendulum.When the leg inertia and other dynamics like that of theactuator, joint friction, etc. are included, the overalldynamic equations can be very nonlinear and complex.
    10. 10. STABILITY IN IP - MODELLThe equation of motion of a simple inverted pendulummodel can be written as follows :ConsideringRef : Jung-Yup Kim, Ill-Woo Park and Jun-Ho OhHUBO Laboratory, Humanoid Robot ResearchCenter, Department of Mechanical Engineering, KoreaAdvanced Institute of Science and Technology,
    12. 12. 2.BIO-INSPIRED METHODOLOGIES• Biological bipeds has highmobility, adaptability, and stability sowe extract certain algorithms thatare applicable to the conventionalrobots.• Some of those modalities are:1. GAIT2. Passive WalkingRightSupportLeftSupportLeft-to-RightTransitionRight-to-LeftTransitionSwing timecompletedLeft foottouchesdownRight foottouches downSwingtimecompleted
    13. 13. DESIGNING GAITSGenerate intermediate joint angles based onthese constraints:1. Controlling Balance - Whenstanding, when walking2. Controlling Speed - Change step size(swing leg must keep up)3. Controlling Height - Used to control speedand energy efficiency1234Movementof COG4321Active GaitAlways stablePassive GaitSometimes unstable
    14. 14. GAIT CONTROL ALGORITHM• The proposed walking control method divided into several walking stages.1. Lift the left leg to its maximum flexion and height.2. Lower the left leg until it makes complete contact with the ground .3. Lift the right leg to its maximum flexion and height .4. Lower the right leg until it makes complete contact with the ground .5. Bring the robot to a standing pose with both legs landed on the ground
    15. 15. 3. BIPED DYNAMICS – GAIT BASED ZMP CONTROL• The presented control algorithm enables a biped to perform stable walking without using anypre computed trajectories.• The algorithm merges gait trajectory generation and control, and can be used for globalcontrol, for local control along an existing trajectory as well as for online computation of gaittrajectories for stable walking.• Plan the hip and ankle trajectories according to walking constraints and ground constraints.• Derive all joint trajectories by inverse kinematics.• The presented control algorithm was developed for steering an exoskeleton by the forceimposed by the human in it.
    16. 16. BIPEDAL MODELLING• The biped robot is modelled as a chain of seven rigid bodies:[ Both feet, Lower legs, Upper legs ,Trunk ]• The Dynamic Equations are Derived to be:Vector of generalized coordinatesVector of corresponding generalized velocitiesf(q,ω) Influence of both inertial forces and gravityT Generalized forces applied to the systemM(q) The mass distributionTrunkUpperLegLowerLegStance LegSwing LegYZX
    17. 17. Ref : Walking Control Algorithm of Biped HumanoidRobot on Uneven and Inclined FloorJung-Yup Kim, Ill-Woo Park and Jun-Ho Oh
    18. 18. MODELESS SOFT COMPUTING BASED• A humanoid robot aims to select a good value for the swing leg parameters foreach consecutive step so that it achieves stable walking.• A reward function that correctly defines this objective is critical for thereinforcement learning.Supporting FootStable UnstableR = 0 (Reward) R = -1 (Punishment)BIPED LEARNING BY REINFORCEMENT
    19. 19. BIPED LEARNING BY REINFORCEMENT•ExcellentGoodOkVery BadBadSupporting Foot
    20. 20. THE FRL AGENT WITH FUZZY EVALUATIVEFEEDBACKAENASNSAMExternalRL signalFRL Agent(Fuzzy Evaluative Feedback)XStatevariables FOutputActionrˆFFuzzificationFuzzyInference DefuzzificationEvaluation Rule Base• The numerical evaluative feedback is notthe biological plausible.• The fuzzy evaluative feedback is muchcloser to the learning environment in thereal world.• The fuzzy evaluative feedback is based on aform of continuous evaluation.( ) ( ) ( )new oldi i it t tRef : Neuro-Fuzzy Algorithm for a Biped Robotic SystemHataitep Wongsuwarn, and Djitt Laowattan
    21. 21. GENETIC APPROACH• GA minimises the total energy of all actuatorsand the other is the evolutionary programming(EP) layer which optimises the interpolatedconfiguration of biped locomotion robots.• The fitness function at the GA level is connectedto the optimisation of total robot energy inorder to ensure the natural movement of thebiped. The fitness function also contains someconstraints related to the robot motion.• The final result represents an optimisedtrajectory similar to natural human walkingRef:Intelligent Control Algorithms For Humanoid RobotsDusko Kati´c Miomir Vukobratovi´
    22. 22. BIPED BASED ON COGNITIVE PERCEPTION• A practical biped needs to be more like a human - capable of switching between differentknown gaits on familiar terrain and learning new gaits when presented with unknown terrain.• Current Huddles in Biped control and their compensation techniques:Complex Modelling of Dynamics ANFIS Based Inverse Kinematics and ControlStability Control [Inertial , Supportive] GA Optimized stabilization StrategyFriction Compensation Standard Mathematical ModellingSwitchable Gait Fuzzy logic based GaitTerrain Information Visual Perception using PCLEnergy Efficient Learning ANN based Clustering and GA Optimization
    23. 23. TERRAIN PERCEPTION FOR GAITSWITCHINGFRL Agent-yFRL Agent-xBiped StatesFuzzy EvaluativeFeedback Unit(Frontal plane)Fuzzy EvaluativeFeedback Unit(Sagittal plane)Evaluation Rules(Frontal plane)Intuitive Balancing Rules(Frontal plane)Intuitive Balancing Rules(Sagittal plane)Evaluation Rules(Sagittal plane)yrxrzmpyzmpx3D Vision PCL Cognitive
    24. 24. CONCLUSION• To develop Efficient Methodologies, it seems essential to combine force control techniqueswith more advanced algorithms such as adaptive and learning strategies.• When the ground conditions and stability constraint are satisfied, it is desirable to select awalking pattern that requires small torque and velocity of the joint actuators.• Humanoid robots are inevitably restricted to a limited amount of energy supply. It wouldtherefore be advantageous to consider the minimum energy consumption, when cyclicmovements like walking are involved.
    25. 25. REFERENCES• M. Vukobratovi´ c, B. Borovac, D. Šurla, and D. Stoki´ c,Biped Locomotion. Dynamics, Stability, Controland Application.Springer, 1990.• S. Kajita and K. Tani, “Experimental Study of Biped Dynamic Walking,” in 1995 IEEE Int. Conf. onRobotics and Automation,pp. 13• W. T. Miller, “Real-Time Neural Network Control of a Biped Walking Robot,” IEEE Control SystemsMagazine, pp. 41-48, February. 1994.• K. Hashimoto, Y. Sugahara, H. O. Lim, A. Takanishi.: Realization of stable walking on public road withnew biped foot system adaptable to uneven terrain. Paper presented at the IEEE/RAS-EMBSinternational conference on biomedical robotics and biomechatronics, 20-22 Feb. 2006.• J. H. Kim, J. H. Oh.: Walking control of the humanoid platform KHR-1 based on torque feedback control.Paper presented at the IEEE international conference on robotics and automation, New Orieans, LA, 26April - 1 May 2004