Kinematic and Singularity Analysis of 10 DOF Lower Body of Humanoid Robot

Bharadwaj, Deepak; Mishra, Natraj; Pathak, Maheshwar

doi:10.18280/mmep.090226

Cited by 2 publications

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“…The fuzzy reinforcement control algorithm structure based on the actor critic temporal difference method. The policy represents the set of control parameters [8,9].…”

Section: Reinforcement Learning Algorithmmentioning

confidence: 99%

Simulation of Reinforcement Learning Algorithm for Motion Control of an Autonomous Humanoid

Bharadwaj¹,

Dutt²

2022

JESA

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Autonomy is an issue in robotics systems. Currently the robotics research community is focusing on autonomy in the decision. The pre-programmed humanoid robots perform the operation in a known scenario. The presence of an obscure environment, a lack of awareness of the environment, humanoid robot fails to perform the random task. In such cases, the preprogrammed robot needs to be reprogrammed to enable it to perform in the changing environment. There is very limited success achieved in the autonomy of the decision-making process. This research work considered the problem of an autonomy while the deicion making in th real time interaction. The reinforcement algorithm helps to do the task in such type of unstructured and unknown environment. Reinforcement learning problems are categorized into partial Markov Decision Processes (MDP). The goal of RL agent is to minimize its immediate and expected costs. When the system interacts with the Markov Decision Process, RL agent passes through an intermediate sequence of states that depends on another by transition probabilities. The agents action takes, and the agents experience a sequence of immediate costs incurred. Reinforcement Learning and teaching approach like Queue Learning (Q-Learning) is implemented for humanoid robot for navigation and exploration. The Q-learning expresses the expected costs to go of a state action pair defined, which is meant to express the expected costs arising after having taken action in the state following policy. Based on the optimal policy of the reinforcement algorithm, a reinforcement controller was implemented. The transition probabilities of the controller depend on the randomness of the controller. The random values of the controller decide the action. Simulations were carried out for the different positions of the proposed model, and an interesting result were was observed while the transition from the sitting position to the goal position.

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“…The fuzzy reinforcement control algorithm structure based on the actor critic temporal difference method. The policy represents the set of control parameters [8,9].…”

Section: Reinforcement Learning Algorithmmentioning

confidence: 99%

Simulation of Reinforcement Learning Algorithm for Motion Control of an Autonomous Humanoid

Bharadwaj¹,

Dutt²

2022

JESA

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“…If roots are complex conjugate, then the response of the system is oscillatory. The system response analysis helps in fixing the controller parameters that ensure closest possible trajectory tracking performance of a joint [20]. Depending on the values of the damping ratio, these roots can be real and unequal or real and equal or complex conjugate.…”

Section: Effect Of Damping Factor On the Systemmentioning

confidence: 99%

“…Each joint of a manipulator was viewed as an individual system to be controlled for the simplified and linear controllers based on the PD and PID control scheme were examined for the simplified SISO model [20]. More realistic problem is the control of an n-DOF manipulator as a single system.…”

Section: Computed Torque Controlmentioning

confidence: 99%

Mathematical Modeling and Control Architecture of the Autonomous Lower Body of a Humanoid Robot

Bharadwaj¹,

Dutt²

2022

MMEP

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Joint controlling is n issues in humanoid robotics. Due to large number of joint present in the humanoid robot, nonlinearity causes the problem for a smooth walk. Processor has to do a lot of computation before the execution of the operation. Due to Serial and parallel linkages of Human manipulator structure, controlling is done with the mixed mode of the operation. The Central pattern generator (CPG) controller architecture were adopted for the such type of operation. CPG controller system interact with the environment and generates the task for the joint using the trajectory control. A simple master-slave control architecture was implemented for the controlling of the lower body of a humanoid robot trajectory. The nonlinearity was minimized by selecting the popper gear ratio. The stiffness and damping designed based on the natural frequency of the system. The controller design was optimized at damping factor 1. The structur

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Kinematic and Singularity Analysis of 10 DOF Lower Body of Humanoid Robot

Cited by 2 publications

References 0 publications

Simulation of Reinforcement Learning Algorithm for Motion Control of an Autonomous Humanoid

Simulation of Reinforcement Learning Algorithm for Motion Control of an Autonomous Humanoid

Mathematical Modeling and Control Architecture of the Autonomous Lower Body of a Humanoid Robot

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