Ping Huan Kuo

Recognition System for Home-Service-Related Sign Language Using Entropy-Based

This paper presents a recognition system for understanding the words of home-service-related sign language. Because the data received from a sensor are sequential, the hidden Markov model (HMM) that has been successfully applied to speech signals is chosen as a classifier. However, the number of states in the HMM model should be decided upon first before constructing the HMM classifier. To solve this problem, an entropy-based K -means algorithm is proposed to evaluate the number of states in the HMM model with an entropy diagram. Four real datasets are utilized to verify the developed entropy-based K -means algorithm. Moreover, a data-driven method is given to combine the artificial bee colony algorithm with the Baum-Welch algorithm to determine the structure of HMM. The database contains 11 home-service-related Taiwan sign language words and each word is performed ten times, five males and five females are invited to perform such words. Finally, the recognition system is established by 11 HMM models, and the cross-validation demonstrates an average recognition rate of 91.3%.

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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.

-Means Algorithm and ABC-Based HMM

Display Omitted A new adaptive PSO method is proposed and verified by simulations and a real robot.Our proposed method has been successful applied to three-dimensional obstacle avoidance with manipulator for the home service robot.Both the free-space and obstacle avoidance states are established for evaluations in computer simulations and real-time experiments. Our PSO-IAC algorithm has achieved outstanding performance compared to other methods in these experiments. This paper presents a new particle swarm optimization (PSO) algorithm, called the PSO-IAC algorithm, to resolve the goal of reaching with the obstacle avoidance problem for a 6-DOF manipulator of the home service robot. The proposed PSO-IAC algorithm integrates the improved adaptive inertia weight and the constriction factor with the standard PSO. Both the free-space and obstacle avoidance states are established for evaluations in computer simulations and real-time experiments. The performance comparisons of the PSO-IAC algorithm with respect to the existing inertia weighted PSO (PSO-W), constriction factor based PSO (PSO-C), constriction factor and inertia weighted PSO (PSO-CW), and adaptive inertia weighted PSO (PSO-A) algorithms are examined. Simulation results indicate that the PSO-IAC algorithm provides the fastest convergence capability. Finally, the proposed control scheme can make the manipulator of the home service robot arrive at the goal position with and without obstacles in all real-time experiments.

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

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.

Integrated particle swarm optimization algorithm based obstacle avoidance control design for home service robot

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.

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

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.

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

With the development of online shopping and the demand for automated packaging systems, we propose an Internet of Things (IoT)-based automated e-fulfillment packaging system and a 3-D adaptive particle swarm optimization (PSO)-based packing algorithm. The proposed system leverages the IoT to connect the data collection and conversion layer, the packaging management layer, the decision-making layer, and the application layer. A cyber network connects each robot, sensor, and smart machine to achieve high velocity, flexibility of procedures, and real-time information exchange. When customers order merchandise online, the orders are received and rearranged, and the deployment of items in a box is planned by the system. The proposed packing algorithm controls the arrangement of items. It compares the size and volume of items and boxes to choose a box of suitable size, as well as deciding on the optimal arrangement of items. This algorithm solves the difficult 3-D Multiple Bin Size Bin Packing Problem (3-DMBSBPP) by integrating an adaptive PSO-based configuration algorithm. Our simulation results show that the packing algorithm can deploy items appropriately, with all items packed inside their box without overlap and with an overall center-of-gravity close to the bottom center of the box. When all the items cannot be packed into a single box, the proposed dividing strategies split the items into groups to pack into two or more boxes of similar size. Furthermore, comparing with the real packages we assessed, the proposed algorithm has a competitive performance. Lastly, our robotic experiments show that the proposed packing algorithm can be implemented and executed by a robot and a manipulator. It also demonstrates the efficiency of this system, in which all devices communicate well with each other and the robots accomplish the packaging task successfully and cooperatively.

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

In this paper, a grasping posture control for a robotic arm is developed based on novel adaptive particle swarm optimization (PSO) for the home service robot. To grasp an object using the robotic arm of the home-service robot, both the spatial coordinates of the target and the appropriate collocation of the grasping posture should be examined. In this paper, we present another method for dealing with this problem, which integrates the artificial bee colony (ABC) algorithm into the adaptive particle swarm optimization (APSO) algorithm, where the mutation concept of the scout bee in the ABC algorithm is used to increase the diversity of the particles. In addition, adaptive acceleration coefficients and adaptive inertia weight are presented to ameliorate the convergence rate of the PSO algorithm. We name this control scheme AIWCPSO-S, which represents Adaptive Inertia Weight and acceleration Coefficients PSO with the aid of the Scout bee. Performance comparisons of existing ABC, global ABC, adaptive inertia weight PSO, low-discrepancy sequence initialized PSO algorithm with high-order nonlinear time-varying inertia weight (LHNPSO), oscillating triangular inertia weight PSO (OTIWPSO) and AIWCPSO-S algorithms are conducted by computer simulations. The experiment results show that the presented algorithm gives the most correct and fastest convergence capability.

A Three-Dimensional Adaptive PSO-Based Packing Algorithm for an IoT-Based Automated e-Fulfillment Packaging System

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.

Grasping Posture Control Design for a Home Service Robot using an ABC-based Adaptive PSO Algorithm

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.