1,463 research outputs found
Evolution of a robotic soccer player
Robotic soccer is a complex domain where, rather than hand-coding computer programs to control
the players, it is possible to create them through evolutionary methods. This has been successfully
done before by using genetic programming with high-level genes. Such an approach is, however,
limiting. This work attempts to reduce that limit by evolving control programs using genetic
programming with low-level nodes
Optimization of rules selection for robot soccer strategies
Mobile embedded systems belong among the
typical applications of distributed systems control in realtime.
An example of a mobile control system is a robotic
system. The proposal and realization of such a distributed
control system represents a demanding and complex task
for real-time control. In the process of robot soccer game
applications, extensive data is accumulated. The reduction
of such data is a possible win in a game strategy. The main
topic of this article is a description of an efficient method
for rule selection from a strategy. The proposed algorithm
is based on the geometric representation of rules. A
described problem and a proposed solution can be applied
to other areas dealing with effective searching of rules in
structures that also represent coordinates of the real world.
Because this construed strategy describes a real space and
the stores physical coordinates of real objects, our method
can be used in strategic planning in the real world where
we know the geographical positions of objects.Web of Science11art. no. 1
The Director: A Composable Behaviour System with Soft Transitions
Software frameworks for behaviour are critical in robotics as they enable the
correct and efficient execution of functions. While modern behaviour systems
have improved their composability, they do not focus on smooth transitions and
often lack functionality. In this work, we present the Director, a novel
behaviour framework and algorithm that addresses these problems. It has
functionality for soft transitions, multiple implementations of the same action
chosen based on conditionals, and strict resource control. This system has
shown success in the Humanoid Kid Size 2022/2023 Virtual Season and the
Humanoid Kid Size RoboCup 2023 Bordeaux competition
Multi-Platform Intelligent System for Multimodal Human-Computer Interaction
We present a flexible human--robot interaction architecture that incorporates emotions and moods to provide a natural experience for humans. To determine the emotional state of the user, information representing eye gaze and facial expression is combined with other contextual information such as whether the user is asking questions or has been quiet for some time. Subsequently, an appropriate robot behaviour is selected from a multi-path scenario. This architecture can be easily adapted to interactions with non-embodied robots such as avatars on a mobile device or a PC. We present the outcome of evaluating an implementation of our proposed architecture as a whole, and also of its modules for detecting emotions and questions. Results are promising and provide a basis for further development
Predicting performance in team games: The automatic coach
This is an electronic version of the paper presented at the 3rd International Conference on Agents and Artificial Intelligence, held in Rome on 2011A wide range of modern videogames involves a number of players collaborating to obtain a common goal.
The way the players are teamed up is usually based on a measure of performance that makes players with a
similar level of performance play together. We propose a novel technique based on clustering over observed
behaviour in the game that seeks to exploit the particular way of playing of every player to find other players
with a gameplay such that in combination will constitute a good team, in a similar way to a human coach.
This paper describes the preliminary results using these techniques for the characterization of player and team
behaviours. Experiments are performed in the domain of Soccerbots.This work has been partly supported by: Spanish
Ministry of Science and Education under grant
TIN2009-13692-C03-03, TIN2010-19872 and Spanish
Ministry of Industry under grant TSI, 020110-
2009-205
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Abstracting Multidimensional Concepts for Multilevel Decision Making in Multirobot Systems
Multirobot control architectures often require robotic tasks to be well defined before allocation. In complex missions, it is often difficult to decompose an objective into a set of well defined tasks; human operators generate a simplified representation based on experience and estimation. The result is a set of robot roles, which are not best suited to accomplishing those objectives. This thesis presents an alternative approach to generating multirobot control algorithms using task abstraction. By carefully analysing data recorded from similar systems a multidimensional and multilevel representation of the mission can be abstracted, which can be subsequently converted into a robotic controller.
This work, which focuses on the control of a team of robots to play the complex game of football, is divided into three sections: In the first section we investigate the use of spatial structures in team games. Experimental results show that cooperative teams beat groups of individuals when competing for space and that controlling space is important in the game of robot football. In the second section, we generate a multilevel representation of robot football based on spatial structures measured in recorded matches. By differentiating between spatial configurations appearing in desirable and undesirable situations, we can abstract a strategy composed of the more desirable structures. In the third section, five partial strategies are generated, based on the abstracted structures, and a suitable controller is devised. A set of experiments shows the success of the method in reproducing those key structures in a multirobot system. Finally, we compile our methods into a formal architecture for task abstraction and control.
The thesis concludes that generating multirobot control algorithms using task abstraction is appropriate for problems which are complex, weakly-defined, multilevel, dynamic, competitive, unpredictable, and which display emergent properties
- …