Design an evaluation of RoboCup humanoid goalie

P. 19-26In this article we describe the ethological inspired architecture we have developed and how it has been used to implement a humanoid goalkeeper according to the regulations of the two-legged Standard Platform League of the RoboCup Federation. We present relevant concepts borrowed from ethology that we have successfully used for generating autonomous behaviours in mobile robotics, such as the use of ethograms in robotic pets or the ideas of schemata, or the use of fixed actions patterns to implement reactivity. Then we discuss the implementation of this architecture on the Nao biped robot. Finally, we propose a method for its evaluation and validation and analyse the results obtained during RoboCup real competition, which allowed us to test first hand how it worked in a real environmentS

Evolution of a Cognitive Architecture for Social Robots: Integrating Behaviors and Symbolic Knowledge

[EN] This paper presents the evolution of a robotic architecture intended for controlling autonomous social robots. The first instance of this architecture was originally designed according to behavior-based principles. The building blocks of this architecture were behaviors designed as a finite state machine and organized in an ethological inspired way. However, the need of managing explicit symbolic knowledge in human–robot interaction required the integration of planning capabilities into the architecture and a symbolic representation of the environment and the internal state of the robot. A major contribution of this paper is the description of the working memory that integrates these two approaches. This working memory has been implemented as a distributed graph. Another contribution is the use of behavior trees instead of state machine for implementing the behavior-based part of the architecture. This late version of the architecture has been tested in robotic competitions (RoboCup or European Robotics League, among others), whose performance is also discussed in this paper.SIEuropean Horizon 2020 research and innovation program under grant agreement No 732410.Ministerio de Ciencia, Innovación y Universidade

Robotics software frameworks for multi-agent robotic systems development

Robotics is an area of research in which the paradigm of Multi-Agent Systems (MAS) can prove to be highly useful. Multi-Agent Systems come in the form of cooperative robots in a team, sensor networks based on mobile robots, and robots in Intelligent Environments, to name but a few. However, the development of Multi-Agent Robotic Systems (MARS) still presents major challenges. Over the past decade, a high number of Robotics Software Frameworks (RSFs) have appeared which propose some solutions to the most recurrent problems in robotics. Some of these frameworks, such as ROS, YARP, OROCOS, ORCA, Open-RTM, and Open-RDK, possess certain characteristics and provide the basic infrastructure necessary for the development of MARS. The contribution of this work is the identification of such characteristics as well as the analysis of these frameworks in comparison with the general-purpose Multi-Agent System Frameworks (MASFs), such as JADE and Mobile-C.Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Junta de Andalucía P06-TIC-2298Junta de Andalucía P08-TIC-0386

Knowledge modeling through computational agents: application to surveillance systems.

In this work the concept of computational agent is located within the methodological framework of levels and domains of description of a calculus in the context of different usual paradigms in Artificial Intelligence (symbolic, situated, connectionist, and hybrid). Emphasis in the computable aspects of agent theory is put, leaving open the possibility to the incorporation of other aspects that are still pure cognitive nomenclature without any computational counterpart of equivalent semantic richness. The ideas presented are shown as currently being implemented on semi-automatic surveillance systems. A case study of a mobile robot application for the detection and following of humans is described

Navigational Strategies for Control of Underwater Robot using AI based Algorithms

Autonomous underwater robots have become indispensable marine tools to perform various tedious and risky oceanic tasks of military, scientific, civil as well as commercial purposes. To execute hazardous naval tasks successfully, underwater robot needs an intelligent controller to manoeuver from one point to another within unknown or partially known three-dimensional environment. This dissertation has proposed and implemented various AI based control strategies for underwater robot navigation. Adaptive versions of neuro-fuzzy network and several stochastic evolutionary algorithms have been employed here to avoid obstacles or to escape from dead end situations while tracing near optimal path from initial point to destination of an impulsive underwater scenario. A proper balance between path optimization and collision avoidance has been considered as major aspects for evaluating performances of proposed navigational strategies of underwater robot. Online sensory information about position and orientation of both target and nearest obstacles with respect to the robot’s current position have been considered as inputs for path planners. To validate the feasibility of proposed control algorithms, numerous simulations have been executed within MATLAB based simulation environment where obstacles of different shapes and sizes are distributed in a chaotic manner. Simulation results have been verified by performing real time experiments of robot in underwater environment. Comparisons with other available underwater navigation approaches have also been accomplished for authentication purpose. Extensive simulation and experimental studies have ensured the obstacle avoidance and path optimization abilities of proposed AI based navigational strategies during motion of underwater robot. Moreover, a comparative study has been performed on navigational performances of proposed path planning approaches regarding path length and travel time to find out most efficient technique for navigation within an impulsive underwater environment

Proceedings of the NASA Conference on Space Telerobotics, volume 3

The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

Technology for the Future: In-Space Technology Experiments Program, part 2

The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiments Program In-STEP 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/ university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part two of two parts and contains the critical technology presentations for the eight theme elements and a summary listing of critical space technology needs for each theme

Visual Attention for Robotic Cognition: A Biologically Inspired Probabilistic Architecture

The human being, the most magnificent autonomous entity in the universe, frequently takes the decision of `what to look at' in their day-to-day life without even realizing the complexities of the underlying process. When it comes to the design of such an attention system for autonomous robots, all of a sudden this apparently simple task appears to be an extremely complex one with highly dynamic interaction among motor skills, knowledge and experience developed throughout the life-time, highly connected circuitry of the visual cortex, and super-fast timing. The most fascinating thing about visual attention system of the primates is that the underlying mechanism is not precisely known yet. Different influential theories and hypothesis regarding this mechanism, however, are being proposed in psychology and neuroscience. These theories and hypothesis have encouraged the research on synthetic modeling of visual attention in computer vision, computational neuroscience and, very recently, in AI robotics. The major motivation behind the computational modeling of visual attention is two-fold: understanding the mechanism underlying the cognition of the primates' and using the principle of focused attention in different real-world applications, e.g. in computer vision, surveillance, and robotics. Accordingly, we observe the rise of two different trends in the computational modeling of visual attention. The first one is mostly focused on developing mathematical models which mimic, as much as possible, the details of the primates' attention system: the structure, the connectivity among visual neurons and different regions of the visual cortex, the flow of information etc. Such models provide a way to test the theories of the primates' visual attention with minimal involvement from the live subjects. This is a magnificent way to use technological advancement for the understanding of human cognition. The second trend in computational modeling, on the other hand, uses the methodological sophistication of the biological processes (like visual attention) to advance the technology. These models are mostly concerned with developing a technical system of visual attention which can be used in real-world applications where the principle of focused attention might play a significant role for redundant information management. This thesis is focused on developing a computational model of visual attention for robotic cognition and, therefore, belongs to the second trend. The design of a visual attention model for robotic systems as a component of their cognition comes with a number of challenges which, generally, do not appear in the traditional computer vision applications of visual attention. The robotic models of visual attention, although heavily inspired by the rich literature of visual attention in computer vision, adopt different measures to cope with these challenges. This thesis proposes a Bayesian model of visual attention designed specifically for robotic systems and, therefore, tackles the challenges involved with robotic visual attention. The operation of the proposed model is guided by the theory of biased competition, a popular theory from cognitive neuroscience describing the mechanism of primates' visual attention. The proposed Bayesian attention model offers a robot-centric approach of visual attention where the head-pose of a robot in the 3D world is estimated recursively such that the robot can focus on the most behaviorally relevant stimuli in its environment. The behavioral relevance of an object determined based on two criteria which are inspired by the postulates of the biased competitive hypothesis of visual attention in the primates. Accordingly, the proposed model encourages a robot to focus on novel stimuli or stimuli that have similarity with a `sought for' object depending on the context. In order to address a number of robot-specific issues of visual attention, the proposed model is further extended to the multi-modal case where speech commands from the human are used to modulate the visual attention behavior of the robot. The Bayes model of visual attention, inherited from the Bayesian sensor fusion characteristic, naturally accommodates multi-modal information during attention selection. This enables the proposed model to be the core component of an attention oriented speech-based human-robot interaction framework. Extensive experiments are performed in the real-world to investigate different aspects of the proposed Bayesian visual attention model