28673252
Dynamically Stable Walking and Kicking Gait Planning for Humanoid Soccer Robots
Humanoid dynamic walk and kick are two main technical challenges for the current Humanoid League. In this paper, we conduct a research aiming at generating dynamically stable walking and kicking gait for humanoid soccer robots with consideration of different constraints. Two methods are presented. One is synthesizing gait based on constraint equations, which has formulated gait synthesis as an optimization problem with consideration of some constraints, e.g. zero-moment point (ZMP) constraints for dynamically stable locomotion, internal forces constraints for smooth transition, geometric constraints for walking on an uneven floor and etc. The other is generating feasible gait based on human kicking motion capture data (HKMCD), which uses periodic joint motion corrections at selected joints to approximately match the desired ZMP trajectory. The effectiveness of the proposed dynamically stable gait planning approach for humanoid walking on a sloping surface and humanoid kicking on an even floor has been successfully tested on our newly developed Robo-Erectus humanoid soccer robots, which won second place in the RoboCup 2002 Humanoid Walk competition and got first place in the RoboCup 2003 Humanoid Free Performance competition.
7697882
Incorporating Perception-Based Information in Reinforcement Learning Using Computing with Words
In this paper, a general fuzzy reinforcement learning (FRL) agent that can utilise not only measurement-based information but also perception-based information by means of computing with words (CW) is proposed. By introducing fuzzy numbers and their arithmetic operations and fuzzy Lyapunov synthesis in the domain of CW, a set of stable fuzzy control rules can be derived from perception-based information. Moreover, based on a neurofuzzy network architecture, the fuzzy rules can be incorporated in the FRL agent to initialise its action network, critic network and evaluation feedback module so as to improve the learning. The performance and applicability of the proposed approach are illustrated through the practical implementation of learning control of an autonomous pole-balancing mobile robot.
9275481
RoboCup 2004 Competitions and Symposium: A Small Kick for Robots, a Giant Score for Science
RoboCup is an international initiative with the main goals of fostering research and education in artificial intelligence and robotics, as well as of promoting science and technology to world citizens. The idea behind RoboCup is to provide a standard problem for which a wide range of technologies can be integrated and examined, as well as being used for project-oriented education, and to organize annual events open to the general public, at which different solutions to the problem are compared. The eighth annual RoboCup -- RoboCup 2004 -- was held in Lisbon, Portugal, from 27 June to 5 July. In this article, a general description of RoboCup 2004 is presented, including summaries concerning teams, participants, distribution into leagues, main research advances, as well as detailed descriptions for each league.
33132446
Fuzzy control rules extraction from perception-based information using computing with words
In this paper, a novel approach to extract fuzzy control rules from perception-based information using computing with words (CW) is firstly presented. The approach is based on applying fuzzy numbers and their arithmetic operations. The fuzzy Lyapunov synthesis, which gives a linguistic description on the plant and the control objective, is used to design a stable fuzzy controller. Secondly, that the traditional fuzzy-control rules can be derived systematically, rather than heuristically, and their stability can be guaranteed is demonstrated. Moreover, by introducing fuzzy numbers and their arithmetic operations, the "words" represented by fuzzy numbers could be efficiently manipulated for fuzzy controller design. The performance and applicability of the proposed method are illustrated through the practical implementation of fuzzy control of a pole-balancing vehicle.
12769414
Robot learning with GA-based fuzzy reinforcement learning agents
Abstract How to learn from both expert knowledge and measurement-based information for a robot to acquire perception and motor skills is a challenging research topic in the field of autonomous robotic systems. For this reason, a general GA (genetic algorithm)-based fuzzy reinforcement learning (GAFRL) agent is proposed in this paper. We first characterize the robot learning problem and point out some major issues that need to be addressed in conjunction with reinforcement learning. Based on a neural fuzzy network architecture of the GAFRL agent, we then discuss how different kinds of expert knowledge and measurement-based information can be incorporated in the GAFRL agent so as to accelerate its learning. By making use of the global optimization capability of GAs, the GAFRL can solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can evaluate the candidate solutions regularly even during the periods without external feedback from the environment. This can guide GAs to perform a more effective global search. Finally, different types of GAFRL agents are constructed and verified using the simulation model of a physical biped robot.
40603933
Integration of linguistic and numerical information for biped control
Bipedal locomotion is an important hallmark of human evolution. Despite of complex control systems, human locomotion is characterized by smooth, regular, and repeating movements. Therefore, there is the potential for applying human locomotion strategies and any knowledge available to the biped control. In order to make the most use of the information available, a linguistic-numerical integration-based biped control method is proposed in this paper. The numerical data from biped measuring instruments, and the linguistic rules obtained from intuitive walking knowledge and biomechanics study have been classified into four categories: direct rules, direct data, indirect rules, and indirect data. Based on inverse learning and data fusion theory, two simple and intuitive integration schemes are proposed to integrate linguistic and numerical information with various forms, such as direct and indirect. One is neurofuzzy-based integration, and another is fuzzy rules extraction-based integration. The simulation results show that the biped gait and joint control performance can be significantly improved by the prescribed synergy method-based neurofuzzy gait synthesis and fuzzy rules extraction-based joint control strategies using linguistic and numerical integrated information.
9173348
Particle Swarm Optimization for Simultaneous Optimization of Design and Machining Tolerances
Tolerance assignment is an important issue in product design and manufacturing. However, this problem is commonly formulated as nonlinear, multi-variable and high constrained model. Most of the heuristics for this problem are based on penalty function strategy which unfortunately suffers from inherent drawbacks. To overcome these drawbacks, this paper presented a new powerful tool-Particle Swarm Optimization algorithm (PSO) and meanwhile proposed a sophisticated constraints handling scheme suitable for the optimization mechanism of PSO. An example involving simultaneously assigning both design and machining tolerances based on optimum total machining cost is employed to demonstrate the efficiency and effectiveness of the proposed approach. The experimental result based on the comparison between PSO and GA show that the new PSO model is a powerful tool.
2612705
Credit Scoring Model Based on Neural Network with Particle Swarm Optimization
Credit scoring has gained more and more attentions both in academic world and the business community today. Many modeling techniques have been developed to tackle the credit scoring tasks. This paper presents a Structure-tuning Particle Swarm Optimization (SPSO) approach for training feed-forward neural networks (NNs). The algorithm is successfully applied to a real credit problem. By simultaneously tuning the structure and connection weights of NNs, the proposed algorithm generates optimized NNs with problem-matched information processing capacity and it also eliminates some ill effects introduced by redundant input features and the corresponding redundant structure. Compared with BP and GA, SPSO can improve the pattern classification accuracy of NNs while speeding up the convergence of training process.
17546380
Web Services Discovery with DAML-QoS Ontology
As more and more Web services are deployed, Web serviceâ€™s discovery mechanisms become essential. Similar services from different sources often exhibit quite different Quality of Service (QoS) levels. For service selection and management purpose, it is necessary to explicitly, precisely, and flexibly specify various constraints and QoS metrics for Web services descriptions. This paper provides a novel ontology as a complement to DARPA Agent Markup Languageâ€”Service (DAML-S) Ontology to provide a better QoS metrics model. Three layers are defined, together with clear role descriptions for development. Cardinality constraints are utilized to describe the QoS property constraints. A matchmaking algorithm with multiple matching degrees is provided accordingly. When incorporated with DAML-S, the multiple service level objectives can be described by assigning multiple QoS profiles to one service profile. A prototype system was developed, which demonstrates the feasibility for small to medium-sized service advertisement repository.
1982773
QoS measurement issues with DAML-QoS ontology
With the industry's efforts in promoting the use of Web services, a huge number of Web services are being developed for the Web. To improve the service provider's competition advantage, it is necessary to support QoS-aware service discovery mechanism as well as the corresponding measurement system. In this paper we present a QoS measurement framework based on the DAML-QoS ontology, together with the measurement code generator. The DAML-QoS ontology is designed to specify QoS constraints for Web services. It works as a complement to DAML-S to provide QoS-aware service discovery. This paper focuses on its measurement framework to check the service provider's runtime compliance with the advertised service level. The code generator helps to generate the measurement code according to the DAML-QoS specification. This ontology level specification tries to reduce the service's integration complexity and achieve better interoperability, automation and extensibility
971957
DAML-QoS ontology for Web services
As more and more Web services are deployed, Web service's discovery mechanisms become essential. Similar services can have quite different QoS levels. For service selection and management purpose, it is necessary to explicitly, precisely, and unambiguously specify various constraints and QoS metrics for Web services descriptions. This paper provides a novel DAML-QoS ontology as a complement for DAML-S ontology to provide a better QoS metrics model. Three layers are defined together with clear role descriptions for developments. Cardinality constraints are utilized to describe the QoS property constraints. Basic profile is presented for general Web service's description and the speed startup of ontology definition. Matchmaking algorithm for QoS property constraints is presented and different matching degrees are described. When incorporated with DAML-S, multiple service levels can be described through attaching multiple QoS profiles to one service profile. Well-defined Metrics can be further utilized by measurement organizations to guarantee the promised service level.