Indra Adji Sulistijono

Human Head Tracking Based on Particle Swarm Optimization and Genetic Algorithm

This paper compares particle swarm optimization and a genetic algorithm for perception by a partner robot. The robot requires visual perception to interact with human beings. It should basically extract moving objects using visual perception in interaction with human beings. To reduce computational cost and time consumption, we used differential extraction. We propose human head tracking for a partner robot using particle swarm optimization and a genetic algorithm. Experiments involving two maximum iteration numbers show that particle swarm optimization is more effective in solving this problem than genetic algorithm.

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).

Evolutionary robot vision and particle swarm optimization for Multiple human heads tracking of a partner robot

This paper discusses the advantage and disadvantage of evolutionary robot vision and particle swarm optimization for multiple human heads tracking. Evolutionary robot vision combines the technologies of the evolutionary computation and robot vision. Both of evolutionary computation and particle swarm optimization can perform the multiple human heads tracking well for feasible solution in a dynamic movement. This paper compares their performance. Finally, the proposed method is applied to a partner robot, and we discuss the effectiveness of the multiple human heads tracking in the natural communication with humans.

Particle swarm intelligence robot vision for multiple human tracking of a partner robot

This paper discusses the advantage and disadvantage of particle swarm intelligence robot vision for multiple human heads tracking. The particle swarm intelligence robot vision combines the technologies of the swarm intelligence computation and robot vision. Particle swarm optimization can perform the multiple human heads tracking well for feasible solution in a dynamic movement. This paper shows the performance. Finally, the proposed method is applied to a partner robot, and we discuss the effectiveness of the multiple human heads tracking in the natural communication with humans.

Interactive trajectory generation using evolutionary programming for a partner robot

This paper proposes an integrated method for generating a human-friendly trajectory. First of all, the robot detects the position of the facing human, and then, the robot generates the trajectory realizing a hand-to-hand behavior by using evolutionary programming. Basically, human evaluation is very important for generating robotic behavior, but the structure of human evaluation is not clear beforehand. Therefore, a fuzzy state-value function is used for estimating the structure of human evaluation. We apply a profit sharing plan using the human evaluation to update the fuzzy state-value function. Furthermore, we propose a temperature scheduling method of a Boltzmann selection dependent on the time-series of human evaluation in the interactive evolutionary programming. Several experimental results show the proposed method can generate a human-friendly trajectory with few human evaluation times.

Robot Partner Development Using Emotional Model Based on Sensor Network

This paper discusses the development of robot partner that can perform not only static conversation, but also can perform emotion expression by facial expression, gesture, and word expression using emotional model based on sensor network, therefore it can interact naturally with a person. Generally, the robot has sensors equipped inside it, however to express emotion the equipped sensors are not enough to grasp the necessary input information about the surrounding environmental situation. Therefore we propose a sensor network applied to the robot partner for estimating the environment states as input data, after that the acquired data is processed using emotional model to gain the desired emotional expression. In this paper, first we explain the concept of informationally structured space, robot partner that we are developing, and sensor network. Next, we explain the development of emotional model that consists of data acquisition from sensor network as an input and the model output such as face, gesture, and word expression. Finally, we conduct several experiments based on the proposed method, and discuss the ability of emotional model to develop robot partner that can perform emotional expression.

Human clustering for a partner robot based on computational intelligence

This paper proposes computational intelligence for a perceptual system of a partner robot. The robot requires the capability of visual perception to interact with a human. Basically, a robot should perform moving object extraction, clustering, and classification for visual perception used in the interaction with human. In this paper, we propose a total system for human clustering for a partner robot by using long-term memory, k-means, self-organizing map and fuzzy controller is used for the motion output. The experimental results show that the partner robot can perform the human clustering.

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).

Human Clustering for A Partner Robot Based on Particle Swarm Optimization

This paper proposes swarm intelligence for a perceptual system of a partner robot. The robot requires the capability of visual perception to interact with a human. Basically, a robot should perform moving object extraction and clustering for visual perception used in the interaction with a human. In this paper, we propose a total system for human classification for a partner robot by using particle swarm optimization, k-means, self organizing maps and back propagation. The experimental results show that the partner robot can perform the human clustering and classification