Azhar Aulia Saputra

Biologically Inspired Control System for 3-D Locomotion of a Humanoid Biped Robot

This paper proposes the control system for 3-D locomotion of a humanoid biped robot based on a biological approach. The muscular system in the human body and the neural oscillator for generating locomotion signals are adapted in this paper. We extend the neuro-locomotion system for modeling a multiple neuron system, where motoric neurons represent the muscular system and sensoric neurons represent the sensor system inside the human body. The output signals from coupled neurons representing the angle joint level are controlled by gain neurons that represent the energy burst for driving the joint in each motor. The direction and the length of step in robot locomotion can be adjusted by command neurons. In order to form the locomotion pattern, we apply multiobjective evolutionary computation to solve the multiobjective problem when optimizing synapse weights between the motoric neurons. We use recurrent neural network (RNN) for the stabilization system required for supporting locomotion. RNN generates a dynamic weight synapse value between the sensoric neuron and the motoric neuron. The effectiveness of our system is demonstrated in open dynamic engine computer simulation and in a real robot application that has 12 degrees of freedom (DoFs) in legs and four DoFs in hands.

Combining pose control and angular velocity control for motion balance of humanoid robot soccer EROS

This paper proposes a research about the humanoid robot system stability to the basic movements in playing football (walking, running, and kicking a ball). The system controls the stability of the robot body angle in order to remain in an ideal position, using the hand as a function of the feedback that has been controlled the actuator separately with leg function on the robot. The hand has a function as robot body tilt actuator controller and the foot has a function as gait motion control system that controls the robot to walk. This system has deficiency to disorders the high impulse, resulting in added angular velocity control system functions, which can reduce the foot force moment generated when stopping suddenly and unexpectedly ran. System control used PID control while in motion pattern and kinematic control system using Fuzzy algorithm. We applied the combination between the control and speed control angle pose at EROS (EEPIS Robosoccer).

Efficiency energy on humanoid robot walking using evolutionary algorithm

One of the problems in humanoid locomotion generation is energy efficiency. This paper proposes a method for energy efficiency optimization in simple humanoid robot locomotion using single objective genetic algorithm. With the aim to produce walking trajectory system using minimum energy and good stabilization, torque and oscillation analysis are required to calculate the stabilization. The number of desired outputs in this system is 4 parameters and the number of inputs is 9 parameters. We used neural network with back propagation learning mechanism to realize the relationship between input and output data as well as producing fitness function for genetic algorithm. The trajectory system has 2 trajectory equations, which is pelvis trajectory and ankle trajectory. Ankle trajectory is formed from circle function in Cartesian coordinate space and pelvis trajectory is formed from third order polynomial equation. Both of them are influenced by inclination of robot body. In the experiment, we apply this system using Bioloid robot with inertial sensor already installed. The experimental results show the analysis of energy by observing the torque resulted by servomotor in each joint. We observe that using this system, the torque value resulted by servomotors was decreased and has good stabilization.

Adaptive motion pattern generation on balancing of humanoid robot movement

This paper discusses about adaptive trajectory control applied in motion pattern trajectory of humanoid robot movement. The aim of this research is to increase the stabilization of robot during walking and running. In this research, the control system produced the next step of the trajectory based on the current condition and analyzed the center of gravity point from the body of the robot. According to this, robot posed the foot step depend on the location of center of gravity point and stop the swing of its foot when the foot has reached the ground. In order to reduce the vibration effect arised by the swing of robot steps, this system is supported by vibration control. Robot is also supported by hand reaction learning system based on recurrent neural network. The trajectory pattern of robot movement has 2 trajectory equations: ankle trajectory formed by circle function in Cartesian coordinate space and pelvis trajectory formed by the third order polynomial equation. Both of them are influenced by inclination of the body of robot. We used the inverted pendulum approach combined with dynamic step trajectory. By using this system, robot can walk in the different surface and uneven surface. This system is applied on humanoid robot EROS (EEPIS Robot Soccer).

Multi-objective evolutionary algorithm for neural oscillator based robot locomotion

In this paper we present synaptic weight optimization for dynamic locomotion in four-legged robot (cat, dog) based on neural oscillator. We investigate the muscular structure to design the relationship for both extensor neuron and flexor neuron. The robot has 3 joints in each leg and each joint is represented by 2 neurons, extensor and flexor neuron. The robot has 4 main circular neurons as the server neuron and the other neurons are the client neurons. The server neurons generate the oscillator signal to the client neurons. The signal can be dynamically adjusted according to the environmental condition. Not only the synaptic network between the neurons, but the synaptic network between neurons and sensors was also designed to realize dynamical locomotion. Pressure sensor and inclination sensor were installed in the robot. The signal is influenced by ground reaction sensor and body inclination feedback. While the foot touches the ground, the sensory neuron sends the signal to the joint neuron. Negative signal will be sent to flexor neuron and positive signal will be sent to extensor neuron. To optimize the strength of weights in the synaptic neurons we apply the Nondominated Sorting Genetic Algorithm II (NSGA-II). The stability of torso body, the velocity, and the movement direction are the three objectives in the multi-objective NSGA-II. In the experiments, a computer simulation framework, the Open Dynamic Engine (ODE) is applied. The solution is evaluated based mainly on the moving distance of the robot. Experiments were conducted to confirm the proposed technique.

Interconnection Structure Optimization for Neural Oscillator Based Biped Robot Locomotion

One of the problems in neural oscillator based humanoid locomotion is the interconnection structure and its weights. They influence the locomotion performance. This paper proposes an evolutionary algorithm for determining the interconnection structure in humanoid robot locomotion based on neural oscillator. The aim of this paper is to form the interconnection structure of motor neurons in order to produce the locomotion pattern in humanoid biped robot. The evolutionary system forms the connection and determines the synapse weight values of the 12 motor neurons distributed to 6 joint angles (two hip-x joints, two hip-y joints, two knee joints). One chromosome has 53 genes, where 50 genes represent the weight values between motor neurons and 3 genes represent the gain parameters in hip-y, hip-x, and knee joint. Center of gravity and speed walking analysis are required for fitness evaluation. In order to prove the effectiveness of the system model, we realized it in a computer simulation. The experimental result shows the comparison result with our previous model. The stabilization level and speed resulted by using this system are increased.

A neuro-based network for on-line topological map building and dynamic path planning

This paper presents a novel combination method for on-line topological map building and dynamic path planning. The proposed method consists of two main components: Bayesian Adaptive Resonance Associative Memory (Bayesian ARAM) and forward-backward propagation path planner. Bayesian ARAM incrementally clusters sensory information and generates topological map. The explored environment is described as a group of neurons (nodes) and edges. Each neuron (nodes) represents a distinct place and it is defined as multi-dimensional Gaussian distribution which does not require any prior knowledge of what a place is supposed to be to make it works in natural environment. The topological map is incrementally generated by Bayesian ARAM. The forward-backward propagation path planner consists of two process: forward propagation determines the possible path while backward propagation with neuron pruning eliminates inefficient neurons and determines the optimum pathway from current location to target location based on the generated map information. The effectiveness of our proposed method is validated by several standardized benchmark datasets.

Multimodal Recurrent Neural Network (MRNN) Based Self Balancing System: Applied into Two-Wheeled Robot

Biologically inspired control system is necessary to be increased. This paper proposed the new design of multimodal neural network inspired from human learning system which takes different action in different condition. The multimodal neural network consists of some recurrent neural networks (RNNs) those are separated into different condition. There is selector system that decides certain RNN system depending the current condition of the robot. In this paper, we implemented this system in pendulum mobile robot as the basic object of study. Several certain number of RNNs are implemented into certain different condition of tilt robot. RNN works alternately depending on the condition of robot. In order to prove the effectiveness of the proposed model, we simulated in the computer simulation Open Dynamic Engine (ODE) and compared with ordinary RNN. The proposed neural model successfully stabilize the applied robot (2-wheeled robot). This model is developed for implemented into humanoid balancing learning system as the final object of study.

Walking speed control in human behavior inspired gait generation system for biped robot

This paper proposes a gait generation system based on human behavior using biological approach. Humans have different gaits with different levels of speed or step. The proposed gait generator is able to generate the walking transition when the speed and the step length are changing dynamically. Neuron inter-connection structures as the locomotion model are formed. We apply evolutionary computation for each level of walking optimization. In locomotion generator, one joint angle is represented by two coupled neurons. Synaptic weights connected between ten motor neurons represent five joint angles and their gain values required to be optimized during several levels of speed. The optimized walking patterns are combined for acquiring dynamic relationship in one gait generator by using supervised multilayer perceptron (MLP) learning system. This gait generator uses optimized MLP weight parameters to generate synaptic weights transferred to locomotion generator depending on the desired walking speed. In order to prove the effectiveness of the model, we implemented it in computer simulation and in simple humanoid robot. The walking transitions depending on the changes in the walking speed are also shown. The smoothness of walking transition increased compared to previous researches.

Development of Humanoid Robot Locomotion Based on Biological Approach in EEPIS Robot Soccer EROS

In this paper we propose the development of EROS locomotion by using neural oscillator. We investigated muscular structure of human body for designing the neuron structure. Two motoric neurons, extensor neuron and flexor neuron, represent one structure of joint that generating the angle of joint. Sensoric neuron connection also designed for adapting the environment. Three kinds of sensor such as ground reaction sensor, tilt sensor, and angular velocity sensor are utilized for validate the proposed method. Evolutionary algorithm was used for optimizing synapse weight among motoric neuron, while recurrent neural network was used for the dynamical condition learning. The locomotion system of this research was shown using Open Dynamic Engine ODE. The proposed method can generate locomotion pattern and its stability learning system improves the stability of locomotion. The proposed approach formed the walking locomotion that potentially can be developed to become adaptive locomotion.