Changjiu Zhou

Dynamically stable gait planning for a humanoid robot to climb sloping surface

In this paper, we formulate gait synthesis of humanoid biped locomotion 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, e.g. sloping surface and etc. In the frame of gait synthesis tied with constraint functions, computational learning methods can be incorporated to further improve the gait. The effectiveness of the proposed dynamically stable gait planning and learning approach for humanoid walking on both even floor and sloping surface has been successfully tested on our humanoid soccer robots named Robo-Erectus, which won first place in the RoboCup 2003 Humanoid League Free Performance competition and got 4 silver awards in the RoboCup Humanoid League 2004.

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.

Experimenting False Alarm Demand for Human Robot Interactions in Humanoid Soccer Robots

Performance of robots in human robot teams has always been a topic of interest for many researchers in human robot interaction community. Traditionally adopted neglect tolerance model for performance measurements assume ideal conditions in which the operator switches control between robots sequentially based on acceptable performance level for each robot ignoring any false alarms due to erroneous interactions. In this paper, we present the false alarm demand, a new metric for measuring effects of false alarms on human robot team performance and extend the neglect tolerance model to situations in which false positives and false negatives are prevalent. Experiments were performed with real and virtual humanoid soccer robots across tele-operated, and point to point modes of autonomy. Measured false alarm demand and robot performances were largely consistent with the proposed extended neglect tolerance model predictions for real and virtual experiments for both autonomy modes. Experiments also showed that extended neglect tolerance model offered better estimation of robot performances as compared to the traditionally adopted neglect tolerance model for situations wherein false alarms are prevalent.

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.

Generating human-like soccer primitives from human data

Recently, interest in analysis and generation of human and human-like motion has increased in various areas. In robotics, in order to operate a humanoid robot, it is necessary to generate motions that have strictly dynamic consistency. Furthermore, human-like motion for robots will bring advantages such as energy optimization. This paper presents a mechanism to generate two human-like motions, walking and kicking, for a biped robot using a simple model based on observation and analysis of human motion. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like motions. The approach presented here rests on the principle that in most biological motor learning scenarios some form of optimization with respect to a physical criterion is taking place. In a similar way, the equations of motion for the humanoid robot systems are formulated in such a way that the resulting optimization problems can be solved reliably and efficiently. The simulation results show that faster and more accurate searching can be achieved to generate an efficient human-like gait. Comparison is made with methods that do not include observation of human gait. The gait has been successfully used to control Robo-Erectus, a soccer-playing humanoid robot, which is one of the foremost leading soccer-playing humanoid robots in the RoboCup Humanoid League.

A MODULAR ARCHITECTURE FOR HUMANOID SOCCER ROBOTS WITH DISTRIBUTED BEHAVIOR CONTROL

This paper presents an embedded control architecture constructed for Robo-Erectus, a soccer-playing humanoid robot developed at the Advanced Robotics and Intelligent Control Centre of Singapore Polytechnic. The Robo-Erectus team has participated in the KidSize category of RoboCups Humanoid League since 2002, collecting different awards. The latest version of Robo-Erectus has many capabilities that can be exploited to improve the robots behavior. The new embedded controller has made possible the first stage of the performance (displayed during RoboCup 2007), including network communication, mapping, and localization. The new mechanical, electronic design, embedded control architecture, and control schemes are described in this paper. In addition to the hardware, the paper presents details of the modules for gait generation, vision, behavior control, and communication.

ON THE REDEFINITION OF FAN OUT METRIC FOR HUMAN ROBOT INTERACTIONS WITH HUMANOID SOCCER ROBOTS

Fan out (FO) is adopted as a general index among human robot interaction researchers in predicting the maximum number of robots a single operator can handle simultaneously while maintaining performance at acceptable levels. Neglect tolerance model forms the basis for FO metric that assumes ideal conditions wherein the operator switches control between robots sequentially based on acceptable performance ignoring any false alarms due to erroneous interactions. In this article, we redefine the FO metric to account for any additional demands due to the occurrence of false alarms, as these additional demands could lead to task failure. Experiments with our virtual and real humanoid soccer robots across tele-operation and semi-autonomous modes of autonomy showed significant drop in FO predictions with inclusion of demands due to false alarms for all experimental cases.

Recognition and generation of motion primitives with humanoid robots

Recently, interest in analysis and generation of human and human-like motion has increased in various areas. In robotics, in order to operate a humanoid robot, it is necessary to generate motions that have strictly dynamic consistency. Furthermore, human-like motion for robots will bring advantages such as energy optimization.

False alarm demand: A new metric for measuring robot performance in human robot teams

Performance of robots in human robot teams has always been a topic of interest for many researchers in human robot interaction community. Traditionally adopted Crandalls model for performance measurements assume ideal conditions in which the operator switches control between robots sequentially based on acceptable performance level ignoring any false alarms due to erroneous interactions. In this paper, we present the false alarm demand, a new metric for measuring effects of false alarms on human robot team performance and extend the Crandalls model to situations in which false positives and false negatives are prevalent. Experiments were performed with real and virtual humanoid soccer robots across tele-operated, and point to point modes of autonomy. Measured false alarms demand and robot performances were largely consistent with the proposed extended Crandalls model predictions for both real and virtual experiments for both autonomy modes.

Robust Monte Carlo Localization for humanoid soccer robot

Most of Monte Carlo Localization (MCL) face kidnap problem. A novel method, called state-driven Monte Carlo localization (SDMCL) is presented to solve kidnap problem so that localization of humanoidrobot can be more efficient. In the proposed SDMCL, Focus and near are feature variables to divide the states of particles into four types: messy, approach, cluster and error. The state ‘error’ denotes some dramatic errors in location, such as kidnap. So the kidnap can be detected on line by monitoring the state of particles if the transition of real location of robot is large enough to be found by sensors. Based on the state detected, a novel strategy is proposed to reset the state of particles to avoid revising the particles gradually. The effectiveness of the proposed SDMCL is verified by RoboCup TeenSize humanoid soccer robot, Robo-Erectus Senior. The experimental results showe that the humanoid robot is able to localize itself accurately to perform humanoid soccer game. It also shows that the proposed SDMCL can recover from the kidnap problem quickly while holding its superior performance in the precision and stability of localization.