Jesus Savage

RoboCup 2011: Robot Soccer World Cup XV

This book includes the thoroughly refereed post-conference proceedings of the 15th Annual RoboCup International Symposium, held in Istanbul, Turkey, in July 2011. The 12 revised papers and 32 poster presentation presented were carefully reviewed and selected from 97 submissions. The papers are orginazed on topical sections on robot hardware and software, perception and action, robotic cognition and learning, multi-robot systems, human-robot interaction, education and edutainment and applications.

Animal-machine interfaces

During the last decades much effort have been done to develop human-machine interfaces that has led to several devices that help humans to use them. Our main motivation for this project is that those traditional human-machine interfaces and their advantages can be extended to other living beings, in order to provide them comfort and also to enhance human-animal communication. In this paper we present the results of the first two years of a research project named UNAM-CAN, which is a joint effort of the Engineering and Zoological faculties at UNAM University. The project involves a service dog that carries a computer (Wearable Computer), that gives simple commands through a speaker. The main idea is that a complex task can be decomposed into simple sequential tasks that the dog can execute. One of the main problems to solve is that the dog needs to perform the requested tasks without the presence of a person. We did some experiments that shows that this is feasible.

Optimization of waypoint-guided potential field navigation using evolutionary algorithms

This paper describes a method for optimization of waypoint selection for potential field navigation in autonomous robots. In the method presented here, a genetic algorithm (GA) is used for optimizing the potential field. The chromosome of each individual encodes parametrizations for the potential field generated by waypoints, obstacles, and goals. The waypoints themselves are obtained through a Voronoi tessellation of the environment in which the robot is operating. It is demonstrated that the algorithm allows a robot to navigate safely and efficiently through spaces with many obstacles, even in cases where these are placed in a strongly unfavorable way. Furthermore, the results from simulations were implemented successfully in an actual Khepera robot. Using a slightly simplified navigation procedure, in which the robot comes to a standstill between successive steps in the navigation, the Khepera robot managed to navigate through one of the most difficult environments used in the simulations. Finally, the paper briefly describes a different implementation of potential field navigation, in the path planning adaptation submodule of a more advanced simulated mobile robot (VirBot).

The Use of Scripts Based on Conceptual Dependency Primitives for the Operation of Service Mobile Robots

This paper describes a Human-Robot interaction subsystem that is part of a robotics architecture, the ViRbot, used to control the operation of service mobile robots. The Human/Robot Interface subsystem consists of tree modules: Natural Language Understanding, Speech Generation and Robots Facial Expressions. To demonstrate the utility of this Human-Robot interaction subsystem it is presented a set of applications that allows a user to command a mobile robot through spoken commands. The mobile robot accomplish the required commands using an actions planner and reactive behaviors. In the ViRbot architecture the actions planner module uses Conceptual Dependency (CD) primitives as the base for representing the problem domain. After a command is spoken a CD representation of it is generated, a rule base system takes this CD representation, and using the state of the environment generates other subtasks represented by CDs to accomplish the command. In this paper is also presented how to represent context through scripts. Using scripts it is easy to make inferences about events for which there are incomplete information or are ambiguous. Scripts serve to encode common sense knowledge. Scripts are also used to fill the gaps between seemingly unrelated events.

Robust feature descriptors for efficient vision-based tracking

This paper presents a robust implementation of an object tracker able to tolerate partial occlusions, rotation and scale for a variety of different objects. The objects are represented by collections of interest points which are described in a multi-resolution framework, giving a representation of those points at different scales. Inspired by [1], a stack of descriptors is built only the first time that the interest points are detected and extracted from the region of interest. This provides efficiency of representation and results in faster tracking due to the fact that it can be done off-line. An Unscented Kalman Filter (UKF) using a constant velocity model estimates the position and the scale of the object, with the uncertainty in the position and the scale obtained by the UKF, the search of the object can be constrained only in a specific region in both the image and in scale. The use of this approach shows an improvement in real-time tracking and in the ability to recover from full occlusions.

Odometry-Based Viterbi Localization with Artificial Neural Networks and Laser Range Finders for Mobile Robots

This paper proposes an approach that solves the Robot Localization problem by using a conditional state-transition Hidden Markov Model (HMM). Through the use of Self Organized Maps (SOMs) a Tolerant Observation Model (TOM) is built, while odometer-dependent transition probabilities are used for building an Odometer-Dependent Motion Model (ODMM). By using the Viterbi Algorithm and establishing a trigger value when evaluating the state-transition updates, the presented approach can easily take care of Position Tracking (PT), Global Localization (GL) and Robot Kidnapping (RK) with an ease of implementation difficult to achieve in most of the state-of-the-art localization algorithms. Also, an optimization is presented to allow the algorithm to run in standard microprocessors in real time, without the need of huge probability gridmaps.

Obstacle Avoidance Behaviors for Mobile Robots Using Genetic Algorithms and Recurrent Neural Networks

Abstract This paper discusses how to generate mobile robots’ behaviors using genetic algorithms (GA). The behaviors are built using state machines implemented in recurrent neural networks (RNN), controlling the movements of a humanoid mobile robot. The weights of the RNN are found using a GA, these are evaluated according to a fitness function that grades their performance. Basically, this function evaluates the robots performance when it goes from an origin to a destination, and the grading of the robot evaluates also that the robots behavior using RNN is similar to the behavior generated by a potential fields approach for navigation. Our objective was to prove that GA is a good option as a method for finding behaviors for mobile robots’ navigation and also that these behaviors can be implemented using RNN.

Using Background and Segmentation Algorithms Applied in Mobile Robots

Abstract In this article a segmentation algorithm for detecting moving objects is presented. The aim of the research is to integrate the algorithm in applications such as car parking video surveillance systems. One of the techniques used in this paper to detect motion in a sequence of images is the use of the background model, which is widely used. The technique allows to detect which objects are moving (without identification) which is the first stage for further processing in tasks such as tracking and object recognition. The results from the segmentation algorithm using several parameters are presented that validate the approach.

Development of an object recognition and location system using the Microsoft Kinect™ sensor

This paper presents the development of an object recognition and location system using the Microsoft Kinect™, an off-the-shelf sensor for videogames console Microsoft Xbox 360™ which is formed by a color camera and depth sensor. This sensor is capable of capturing color images and depth information from a scene. This vision system uses a) data fusion of both color camera and depth sensor to segment objects by distance; b) scale-invariant features to characterize and recognize objects; and c) cameras internal parameters combined with depth information to locate objects relative to the camera point of view. The system will be used along with a robotic arm to grab objects.

Identification of Relevant Genes with a Multi-Agent System using Gene Expression Data

In recent years, technology for information extraction from gene activity in cells, has made an important breakthrough with the DNA microarrays. With this technology it is possible for researchers to know which genes are active in a particular cell in particular situation. The comparison of gene expression patterns (which genes are active) of two cells of the same type, one normal and the other belonging to a tumor, can be of great help in understanding what are the genes that might be involved in the tumor formation. Microarray technology is a high throughput information extraction technology; with a single microarray it is possible to extract, at once, information about the expression of thousand of genes. A typical experiment might involve the study of several microarrays and the comparison of the information extracted from them with standardized microarray gene expression databases. From a computing point of view, microarray technology, opens interesting research issues at different levels, like data analysis and statistical information processing, information standardization, and automation of the whole information processes involved in each experiment. This chapter addresses this latter issue. We present a multi-agent platform automating information processing of experimental microarray samples and its comparison with publicly accessible microarray databases. A complete system for gene expression analysis can be based on different agents to solve parts of the problem, and due the diversity of paths that knowledge discovery could find, a system based on coordinated multiple agents can improve performance. Here we describe a multi-agent system that was used for gene expression analysis in samples of cervical cancer for obtaining specific knowledge about the genetic basis of the cancer. Cervical cancer is one of the most common in Females. Its incidence in Mexico (50 per 100,000 inhabitants per year) is among the highest in the world (Lazcano-Ponce, 2009). Section 2 presents an introduction to microarray for gene expression analysis; in section 3 there is information about multi-agent system technology related with gene expression