Real-time Coordinate Estimation for Self-Localization of the Humanoid Robot Soccer BarelangFC

In implementation, of the humanoid robot soccer consists of more than three robots when played soccer on the field. All the robots needed to be played the soccer as human done such as seeking, chasing, dribbling and kicking the ball. To do all of these commands, it is required a real-time localization system so that each robot will understand not only the robot position itself but also the other robots and even the object on the field’s environment. However, in real-time implementation and due to the limited ability of the robot computation, it is necessary to determine a method which has fast computation and able to save much memory. Therefore, in this paper we presented a real-time localization implementation method using the odometry and Monte Carlo Localization (MCL) method. In order to verify the performance of this method, some experiment has been carried out in real-time application. From the experimental result, the proposed method able to estimate the coordinate of each robot position in X and Y position on the field.Dalam implementasinya, robot humanoid soccer terdiri lebih dari tiga robot di lapangan ketika sedang bermain bola. Semua robot diharapkan dapat memainkan sepak bola seperti manusia seperti mencari, mengejar, menggiring bola dan menendang bola. Untuk melakukan semua perintah tersebut, diperlukan sistem lokalisasi real-time sehingga setiap robot tidak hanya memahami posisi robotnya sendiri tetapi juga robot-robot lain bahkan objek yang berada di sekitar lapangan. Namun dalam implementasi real-time dan karena keterbatasan kemampuan komputasi robot, diperlukan suatu metode komputasi yang cepat dan mampu menghemat banyak memori. Oleh karena itu, dalam makalah ini menyajikan metode implementasi lokalisasi real-time dengan menggunakan metode odometry and Monte Carlo Localization (MCL). Untuk memverifikasi kinerja metode ini, beberapa percobaan telah dilakukan dalam aplikasi real-time. Dari hasil percobaan, metode yang diusulkan mampu mengestimasi koordinat posisi robot pada posisi X dan Y di lapangan ketika sedang bermain bola

Decision shaping and strategy learning in multi-robot interactions

Recent developments in robot technology have contributed to the advancement of autonomous behaviours in human-robot systems; for example, in following instructions received from an interacting human partner. Nevertheless, increasingly many systems are moving towards more seamless forms of interaction, where factors such as implicit trust and persuasion between humans and robots are brought to the fore. In this context, the problem of attaining, through suitable computational models and algorithms, more complex strategic behaviours that can influence human decisions and actions during an interaction, remains largely open. To address this issue, this thesis introduces the problem of decision shaping in strategic interactions between humans and robots, where a robot seeks to lead, without however forcing, an interacting human partner to a particular state. Our approach to this problem is based on a combination of statistical modeling and synthesis of demonstrated behaviours, which enables robots to efficiently adapt to novel interacting agents. We primarily focus on interactions between autonomous and teleoperated (i.e. human-controlled) NAO humanoid robots, using the adversarial soccer penalty shooting game as an illustrative example. We begin by describing the various challenges that a robot operating in such complex interactive environments is likely to face. Then, we introduce a procedure through which composable strategy templates can be learned from provided human demonstrations of interactive behaviours. We subsequently present our primary contribution to the shaping problem, a Bayesian learning framework that empirically models and predicts the responses of an interacting agent, and computes action strategies that are likely to influence that agent towards a desired goal. We then address the related issue of factors affecting human decisions in these interactive strategic environments, such as the availability of perceptual information for the human operator. Finally, we describe an information processing algorithm, based on the Orient motion capture platform, which serves to facilitate direct (as opposed to teleoperation-mediated) strategic interactions between humans and robots. Our experiments introduce and evaluate a wide range of novel autonomous behaviours, where robots are shown to (learn to) influence a variety of interacting agents, ranging from other simple autonomous agents, to robots controlled by experienced human subjects. These results demonstrate the benefits of strategic reasoning in human-robot interaction, and constitute an important step towards realistic, practical applications, where robots are expected to be not just passive agents, but active, influencing participants

Scaled Autonomy for Networked Humanoids

Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework. The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment. Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC

Artificial Vision in the Nao Humanoid Robot

Projecte Final de Màster UPC realitzat en col.laboració amb l'Universitat Rovira i Virgili. Departament d'Enginyeria Informàtica i MatemàtiquesRobocup is an international robotic soccer competition held yearly to promote innovative research and application in robotic intelligence. Nao humanoid robot is the new RoboCup Standard Platform robot. This platform is the new Nao robot designed and manufactured by the french company Aldebaran Robotics. The new robot is an advanced platform for developing new computer vision and robotics methods. This Master Thesis is oriented to the study of some fundamental issues for the artificial vision in the Nao humanoid robots. In particular, color representation models, real-time segmentation techniques, object detection and visual sonar approaches are the computer vision techniques applied to Nao robot in this Master Thesis. Also, Nao’s camera model, mathematical robot kinematic and stereo-vision techniques are studied and developed. This thesis also studies the integration between kinematic model and robot perception model to perform RoboCup soccer games and RoboCup technical challenges. This work is focused in the RoboCup environment but all computer vision and robotics algorithms can be easily extended to another robotics fields

Motion planning and reactive control on learnt skill manifolds

We propose a novel framework for motion planning and control that is based on a manifold encoding of the desired solution set. We present an alternate, model-free, approach to path planning, replanning and control. Our approach is founded on the idea of encoding the set of possible trajectories as a skill manifold, which can be learnt from data such as from demonstration. We describe the manifold representation of skills, a technique for learning from data and a method for generating trajectories as geodesics on such manifolds. We extend the trajectory generation method to handle dynamic obstacles and constraints. We show how a state metric naturally arises from the manifold encoding and how this can be used for reactive control in an on-line manner. Our framework tightly integrates learning, planning and control in a computationally efficient representation, suitable for realistic humanoid robotic tasks that are defined by skill specifications involving high-dimensional nonlinear dynamics, kinodynamic constraints and non-trivial cost functions, in an optimal control setting. Although, in principle, such problems can be handled by well understood analytical methods, it is often difficult and expensive to formulate models that enable the analytical approach. We test our framework with various types of robotic systems – ranging from a 3-link arm to a small humanoid robot – and show that the manifold encoding gives significant improvements in performance without loss of accuracy. Furthermore, we evaluate the framework against a state-of-the-art imitation learning method. We show that our approach, by learning manifolds of robotic skills, allows for efficient planning and replanning in changing environments, and for robust and online reactive control

Push Recovery Through Walking Phase Modification for Bipedal Locomotion

Ph.DDOCTOR OF PHILOSOPH