Ya Fang Ho

A Biped Gait Learning Algorithm for Humanoid Robots Based on Environmental Impact Assessed Artificial Bee Colony

Gait pattern performance is a very important issue in the field of humanoid robots, and more and more researchers are now engaged in such studies. However, the tuning processes of the parameters or postures are very tedious and time-consuming. In order to solve this problem, an artificial bee colony (ABC) learning algorithm for a central pattern generator (CPG) gait produce method is proposed in this paper. Furthermore, the fitness of the bee colony is considered through environmental impact assessment, and it is also estimated from the cause of colony collapse disorder from the results of recent investigations in areas, such as pesticides, electromagnetic waves, viruses, and the timing confusion of the bee colony caused by climate change. Each environmental disaster can be considered by its adjustable weighting values. In addition, the developed biped gait learning method is called the ABC-CPG algorithm, and it was verified in a self-developed high-integration simulator. The strategy systems, motion control system, and gait learning system of the humanoid robot are also integrated through the proposed 3-D simulator. Finally, the experimental results show that the proposed environmental-impact-assessed ABC-CPG gait learning algorithm is feasible and can also successfully achieve the best gait pattern in the humanoid robot.

Development of an Automatic Emotional Music Accompaniment System by Fuzzy Logic and Adaptive Partition Evolutionary Genetic Algorithm

Music is everywhere in the world, and its applications in commerce are extremely versatile. Generally speaking, in order to create some music for background music, it is necessary to engage sound recordists and instrumental performers. However, the process is very time-consuming and costly. In this paper, a real-time emotion-based music accompaniment system is proposed to solve this issue. For different emotions, a fuzzy logic controller is designed to adjust the tempo of the music, and an adaptive partition evolutionary genetic algorithm is developed to create corresponding melodies. The chord progressions are generated via music theory, and the instrumentation is disposed by the conception of the probability. What is noteworthy is that all the processes can be output by Virtual Studio Technology in real time so that users can listen directly to the composing results from any emotions. From the experimental results, the proposed adaptive partition evolutionary genetic algorithm performs better than other optimal algorithms in such topics.

Development of Humanoid Robot Simulator for Gait Learning by Using Particle Swarm Optimization

The design and implementation of particle swarm optimization (PSO) gait learning method for adult-sized humanoid robots is proposed in this paper. In order to reduce the motor damage and let train motions more convenient, a robotics simulator system for humanoid robots is designed. This robotics simulator system is established by an open source software-Open Dynamics Engine (ODE). The model of David developed by aiRobots laboratory is a combination of rigid bodies and joints. The humanoid robot is trained on the robotics simulator system with PSO method, which chooses the trajectory of robots center of mass as the fitness value to learn faster and stable gait automatically. The results of the experiment show that the motions which play on the robotics simulator system are very similar to the real motions, so it can be utilized as the motion training platform. The result of the PSO gait learning method has great performance on the robotics simulator system. The humanoid robot learns gait pattern from marking time to moving center of mass and swing its legs. Finally, this gait let the real humanoid robot walk forward at 14.5 cm/s.

Parameterized gait pattern generator based on linear inverted pendulum model with natural ZMP references

This paper presents a parameterized gait generator based on linear inverted pendulum model (LIPM) theory, which allows users to generate a natural gait pattern with desired step sizes. Five types of zero moment point (ZMP) components are proposed for formulating a natural ZMP reference, where ZMP moves continuously during single support phases instead of staying at a fixed point in the sagittal and lateral plane. The corresponding center of mass (CoM) trajectories for these components are derived by LIPM theory. To generate a parameterized gait pattern with user-defined parameters, a gait planning algorithm is proposed, which determines related coefficients and boundary conditions of the CoM trajectory for each step. The proposed parameterized gait generator also provides a concept for users to generate gait patterns with self-defined ZMP references by using different components. Finally, the feasibility of the proposed method is validated by the experimental results with a teen-sized humanoid robot, David, which won first place in the sprint event at the 20th Federation of International Robot-soccer Association (FIRA) RoboWorld Cup.

Robots That Think Fast and Slow: An Example of Throwing the Ball Into the Basket

Can a robot think like a human being? Scientists in recent years have been trying to achieve this dream, and we are also committed to this same goal. In this paper, we use an example of throwing the ball into the basket to make the robots process with human-like thinking behavior. Such thinking behavior adopted in this paper is divided into two modes: fast and slow. The fast mode belongs to the intuitional reaction, and the slow mode represents the complicated cogitation in human brain. This fascinating human thinking concept is inspired by the book, Thinking, Fast and Slow, which explains the process of the human brain. In addition, the psychology theories proposed in this book are also adopted to realize the thinking algorithms, and our experiments verify that the thinking mode of human beings is reasonable and effective in robots.

A migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller

Obstacle avoidance is an important issue in robotics. In this paper, the particle swarm optimization (PSO) algorithm, which is inspired by the collective behaviors of birds, has been designed for solving the obstacle avoidance problem. Some animals that travel to the different places at a specific time of the year are called migrants. The migrants also represent the particles of PSO for defining the walking paths in this work. Migrants consider not only the collective behaviors, but also geomagnetic fields during their migration in nature. Therefore, in order to improve the performance and the convergence speed of the PSO algorithm, concepts from the migrant navigation method have been adopted for use in the proposed hybrid particle swarm optimization (H-PSO) algorithm. Moreover, the potential field navigation method and the designed fuzzy logic controller have been combined in H-PSO, which provided a good performance in the simulation and the experimental results. Finally, the Federation of International Robot-soccer Association (FIRA) HuroCup Obstacle Run Event has been chosen for validating the feasibility and the practicability of the proposed method in real time. The designed adult-sized humanoid robot also performed well in the 2015 FIRA HuroCup Obstacle Run Event through utilizing the proposed H-PSO.

PSO and neural network based intelligent posture calibration method for robot arm

Inverse kinematics is a general method for defining the joint angles of the robot arm. This method provides an efficient way to control the robot arm for several tasks. However, the server motors or the mechanism design of the robot arm may not always be ideal. If the motor consumption is existed, the error of the final position of the robot arm will be increased. In order to solve this problem, this paper proposes an intelligent method for the posture calibration of the robot arm. In this paper, the particle swarm optimization (PSO) algorithm and the proposed neural network model are integrated to calibrate the kinematics of the robot arm. The experimental results show that the control error can be reduced by applying the proposed method. The feasibility and practicality of the proposed method are also validated in the experiments.

Design and implementation of a series of small-size humanoid robots for FIRA and RoboCup

The design and implementation of a series of small-sized humanoid robots are proposed in this paper. The series includes three types of small-sized humanoid robots participating in AndroSot and HuroCup tournaments in FIRA 2010 and in RoboCup Japan Open 2011. Firstly, the architecture of the system hardware and software, the design process are addressed in details. Then, the new features of the robots and important principles for designing a robot are addressed in details. Finally, the three types of the robots are discussed and compared with the previous generations. This series of robots won the 3rd place in AndroSot tournament, the 1st place in HuroCup tournament in FIRA 2010, and the 2nd place in the KidSize Humanoid League in RoboCup Japan Open 2011.

Design and implementation of double passing strategy for humanoid robot soccer game

The goal of this paper was to accomplish a technical challenge of double passing soccer game for humanoid soccer robots in RoboCup competition. Using only a vision sensor, the control strategies for the technical challenges of humanoid league in RoboCup are designed and presented. The vision system includes the color space setting, the object recognition, a simplified mean shift algorithm, and the target position derivation. Vision system works on the tasks of object recognition, which includes the goal, landmark poles, and the interval of two black poles. The computational time is reduced greatly by the mean shift algorithm and that time can be utilized to do other control strategies. With the proposed control strategies, humanoid robots can successfully complete the RoboCup double passing task. The successful experiment results demonstrate the feasibility and effectiveness of the proposed foot–eye coordination control scheme.

Control Strategy Design for Throw-in Challenge in a Humanoid Robot Soccer Game

In this paper, a control strategy of RoboCup throw-in technical challenge for small-sized humanoid robot is proposed. Throw-in is one of the technical challenges of RoboCup competition, which is a well-known robot competition held annually in different countries. To complete this challenge, the robot must possess object detection ability, stable walking ability, ball-holding ability, and ball-throwing ability. Hence, a small-sized humanoid robot, aiRobots-V, is presented to accomplish the throw-in challenge. Moreover, the control strategy and the abilities mentioned above are introduced. The experiment results demonstrate the performance of the proposed method. Furthermore, aiRobots-V won the second place in this technical challenge of RoboCup soccer game.