Tadashi Naruse

RoboCup 2009: Robot Soccer world cup XIII

Coordinated Action in a Heterogeneous Rescue Team.- Concept Evaluation of a Reflex Inspired Ball Handling Device for Autonomous Soccer Robots.- Development of a Realistic Simulator for Robotic Intelligent Wheelchairs in a Hospital Environment.- Creating Photo Maps with an Aerial Vehicle in USARsim.- Real-Time Hand Gesture Recognition for Human Robot Interaction.- Combining Key Frame Based Motion Design with Controlled Movement Execution.- Applying Dynamic Walking Control for Biped Robots.- Modeling Human Decision Making Using Extended Behavior Networks.- Motion Synthesis through Randomized Exploration on Submanifolds of Configuration Space.- Robust and Computationally Efficient Navigation in Domestic Environments.- Robust Collision Avoidance in Unknown Domestic Environments.- Real-Time Ball Tracking in a Semi-automated Foosball Table.- Three Humanoid Soccer Platforms: Comparison and Synthesis.- Learning Complementary Multiagent Behaviors: A Case Study.- Rollover as a Gait in Legged Autonomous Robots: A Systems Analysis.- Pareto-Optimal Collaborative Defensive Player Positioning in Simulated Soccer.- A Novel Camera Parameters Auto-adjusting Method Based on Image Entropy.- Object Recognition with Statistically Independent Features: A Model Inspired by the Primate Visual Cortex.- Using Genetic Algorithms for Real-Time Object Detection.- An Approximate Computation of the Dominant Region Diagram for the Real-Time Analysis of Group Behaviors.- A Lua-based Behavior Engine for Controlling the Humanoid Robot Nao.- Stable Mapping Using a Hyper Particle Filter.- A Characterization of 3D Sensors for Response Robots.- Multiple Model Kalman Filters: A Localization Technique for RoboCup Soccer.- Integrated Genetic Algorithmic and Fuzzy Logic Approach for Decision Making of Police Force Agents in Rescue Simulation Environment.- IntellWheels MMI: A Flexible Interface for an Intelligent Wheelchair.- Analyzing the Human-Robot Interaction Abilities of a General-Purpose Social Robot in Different Naturalistic Environments.- Communicating among Robots in the RoboCup Middle-Size League.- Multi-robot Cooperative Object Localization.- Evolution of Biped Walking Using Truncated Fourier Series and Particle Swarm Optimization.- Efficient Behavior Learning by Utilizing Estimated State Value of Self and Teammates.- Sensor and Information Fusion Applied to a Robotic Soccer Team.- Omnidirectional Walking Using ZMP and Preview Control for the NAO Humanoid Robot.- RoboCup@Home: Results in Benchmarking Domestic Service Robots.- Connecting the Real World with the Virtual World - Controlling AIBO through Second Life.- A Hybrid Agent Simulation System of Rescue Simulation and USARSim Simulations from Going to Fire-Escape Doors to Evacuation to Shelters.- SSL-Vision: The Shared Vision System for the RoboCup Small Size League.- Heuristic Formation Control in Multi-robot Systems Using Local Communication and Limited Identification.- Cooperative Multi-robot Map Merging Using Fast-SLAM.

High speed line detection by Hough transform in local area

In line detection method first let the candidates of line segment whose positional precision or reliability are guaranteed to some extent be extracted in a local area, then let the line be extracted by a verification process in the existing area of candidate line which is defined by an extension process for the line segment in the pattern space, and finally let all lines be extracted by a shifting operation of the local area in the image appropriately. By these processes, our method realizes the absorption of the quantization errors in Hough transform, as well as the extraction of short line segments. Furthermore, parallel processing is available for the hardware implementation. In this paper, we show theoretically and experimentally that calculation costs for line detection is extremely reduced without any degradation of detectability and reliability in comparison with a conventional Hough transform.

Dynamic Positioning Method Based on Dominant Region Diagram to Realize Successful Cooperative Play

In this paper, we propose a new technique to compute, in real time, the positions of robots in a cooperative play such as the pass-and-shoot play. To evaluate the positioning of the robot, we use the Dominant Region(DR) diagram, which is a kind of a Voronoi diagram. In the DR diagram, the soccer field is divided into regions, each of which shows an area that a robot can reach faster than the other robots. This division is based on the time of arrival while the division by the Voronoi diagram is based on the distance of arrival. Though the DR diagram plays a primary role in the positioning of the robots, it has a serious problem of taking much computation time. To overcome this problem, we show an approximate calculation procedure to obtain the DR diagram, which realizes the real time computation, i.e. a computation within a frame time. Applying the approximate dominant diagram to the positioning of the robots for the pass play, we show, by the simulation study, the DR diagram can be calculated in real time, an appropriate position for the pass play can be obtained.

Cooperative Soccer Play by Real Small-Size Robot

One of the typical cooperative actions is the pass play in RoboCup small-size league. This paper presents three technical key features to realize robust pass play between robots. The first one is the high resolution image processing to detect the positions and orientations of the robots. The second one is the control algorithm to move and adjust the robots for the pass play. The third one is the mechanism to catch the ball moving at high speed. This paper discusses these methods and shows the effectiveness of the methods by experimental results.

An approximate computation of the dominant region diagram for the real-time analysis of group behaviors

This paper describes a method for a real-time calculation of a dominant region diagram (simply, a dominant region). The dominant region is proposed to analyze the features of group behaviors. It draws spheres of influence and is used to analyze a teamwork in the team sports such as soccer and handball. In RoboCup Soccer, particularly in small size league(SSL), the dominant region takes an important role to analyze the current situation in the game, and it is useful for evaluating the suitability of the current strategy. Another advantage of its real-time calculation is that it makes possible to predict a success or failure of passing. To let it work in a real environment, a real-time calculation of the dominant region is necessary. However, it takes 10 to 40 seconds to calculate the dominant region of the SSL’s field by using the algorithm proposed in [3]. Therefore, this paper proposes a real-time calculation algorithm of the dominant region. The proposing algorithm compute an approximate dominant region. The basic idea is (1) to make a reachable polygonal region for each time t1, t2, ... , tn, and (2) to synthesize it incrementally. Experimental result shows that this algorithm achieves about 1/1000 times shorter in computation time and 90% or more approximate accuracy compared with the algorithm proposed in [3]. Moreover, this technique can predict the success or failure of passing in 95% accuracy.

Analyzing and Learning an Opponent’s Strategies in the RoboCup Small Size League

This paper proposes a dissimilarity function that is useful for analyzing and learning the opponent’s strategies implemented in a RoboCup Soccer game. The dissimilarity function presented here identifies the differences between two instances of the opponent’s deployment choices. An extension of this function was developed to further identify the differences between deployment choices over two separate time intervals. The dissimilarity function, which generates a dissimilarity matrix, is then exploited to analyze and classify the opponent’s strategies using cluster analysis. The classification step was implemented by analyzing the opponent’s strategies used in set plays captured in the logged data obtained from the Small Size League’s games played during RoboCup 2012. The experimental results showed that the attacking strategies used in set plays may be effectively classified. A method for learning an opponent’s attacking strategies and deploying teammates in advantageous positions on the fly in actual games is discussed.

Fast Image Processing and Flexible Path Generation System for RoboCup Small Size League

Two key features of successful multi-robot systems in RoboCup are robustness of a vision system and optimization of feedback control in order to reach the goal point and generate the action under the team strategy. This paper proposes two new methods. One is a fast image processing method, which is coped with the spatial variance of color parameters in the field, to extract the positions of robots and ball in 1/30 sec. The separation problem in the interlaced format image is solved. Another one is a path generation method in which the robot approaches the goal by changing its direction convergently. With these two algorithms, the real time processing system is realized by generating a stable path under a low quality input image.

High-speed line detection method using hough transform in local area

In this paper, a high-speed line detection method using Hough transform is proposed. This approach is inspired by features of human foveal vision. In particular, line segment candidates are extracted at sufficient level of accuracy by applying Hough transform inside a local window. After that, greater areas are generated by extension of line segment candidates, and segment verification is executed. Such local window is then moved sequentially until the entire image is covered. Such processing makes possible absorption of quantization errors of Hough transform as well as extraction of shorter segments that were hard to detect by means of standard Hough transform. Generally, the proposed algorithm supports the same level of accuracy as standard Hough transform while featuring acceleration (and computing cost reduction); moreover, further performance boost can be achieved through parallel processing.

Ray tracing using dynamic subtree-algorithm and speed evaluation

For fast ray tracing computation, it is essential to determine efficiently which object is projected on each pixel of the image plane. The data structure plays a major role in this process, and a hierarchical tree data structure of the objects often is used. This paper proposes a very fast algorithm based on use of a subtree of given hierarchical tree. Experimental results show that the number of intersection computations can be reduced greatly by the use of subtrees and that the computation cost for dynamic construction of a subtree is low. Analysis of the algorithm supports the experimental results. Experimental comparison with Arvos algorithm [6], one of the fastest existing algorithms, shows that our algorithm is faster for primary rays.

Detection of Basic Behaviors in Logged Data in RoboCup Small Size League

This paper describes a method that extracts the basic behaviors of robots such as kicking and passing from the history data of the positions and velocities of the robots and the ball in RoboCup Small Size League (SSL). In this paper, as a first step, we propose an offline method that extracts the basic behaviors of robots from the logged data which is a record of the positions and velocities of the robots and the ball as the time series data. First, paying attention to the ball movement, we extract the line segments in the ball trajectory which satisfy our proposed conditions. These segments arise from the kicking actions. Then we classify the extracted line segments into the detailed kicking actions by analysing the intention of the kicked robot. We also propose algorithms that detect and classify the covering actions. Experimental results show that 98% of the kicking actions are correctly detected and more than 80% of the detected kicking actions are correctly classified, and that 90% of the covering actions are also correctly classified.