This work has been supported by the Spanish Government TIN2016-76515-R Grant, supported with Feder funds, and by grant of Vicerrectorado de Investigación y Transferencia de Conocimiento para el fomento de la I+D+i en la Universidad de Alicante 2016.
This paper presents an efficient closed-loop locomotion control system for biped robots that operates in the joint space. The robot’s joints are directly driven through control signals generated by a central pattern generator (CPG) network. A genetic algorithm is applied in order to find out an optimal combination of internal parameters of the CPG given a desired walking speed in straight line. Feedback signals generated by the robot’s inertial and force sensors are directly fed into the CPG in order to automatically adjust the locomotion pattern over uneven terrain and to deal with external perturbations in real time. Omnidirectional motion is achieved by controlling the pelvis motion. The performance of the proposed control system has been assessed through simulation experiments on a NAO humanoid robot.
This work has been partially supported by the Spanish Ministerio de Economía y Competitividad Project TIN2015-65686-C5-5-R, by the Extremadura Government fund GR15120 ”Ayudas a Grupos” and FEDER funds.
An orientation estimation algorithm is presented. This algorithm is based on the Extended Kalman Filter, and uses quaternions as the orientation descriptor. For the filter update, we use measurements from an Inertial Measurement Unit (IMU). The IMU consists in a triaxial angular rate sensor, and an also triaxial accelerometer. Quaternions describing orientations live in the unit sphere of R4. Knowing that this space is a manifold, we can apply some basic concepts regarding these mathematical objects, and an algorithm that reminds the also called “Multiplicative Extended Kalman Filter” arises in a natural way. The algorithm is tested in a simulated experiment, and in a real one.
Current modern society is characterized by an increasing level of elderly population. This population group is usually ligated to important physical and cognitive impairments, which implies that older people need the care, attention and supervision by health professionals. In this paper, a new system for supervising rehabilitation therapies using autonomous robots for elderly is presented. The therapy explained in this work is a modified version of the classical ’Simon Says’ game, where a robot executes a list of motions and gestures that the human has to repeat each time with a more level of difficulty. The success of this therapy from the point of view of the software is to provide from an algorithm that detect and classified the gestures that the human is imitating. The algorithm proposed in this paper is based on the analysis of sequences of images acquired by a low cost RGB-D sensor. A set of human body features is detected and characterized during the motion, allowing the robot to classify the different gestures. In addition, this paper describes the human-robot interaction performed by the ’Simon Says’ game implementation. Experimental results demonstrate the robustness and accuracy of the detection and classification method, which is crucial for the development of the therapy.
The Workshop on Physical Agents is a forum for information and experience exchange in different areas regarding the concept of embodied agents, especially applied to the control and coordination of autonomous systems: robots, mobile robots, domotics, agents, industrial applications or complex systems.This special issue brings together a selection of revised and extended papers that were first presented at the XVII Workshop on Physical Agents (WAF'2016), which was held on June 16-17, 2016 at the School of Telecommunication Engineering and Information Technology of the University of Málaga (Spain).
The Workshop on Physical Agents intends to be a forum for information and experience exchange in different areas regarding the concept of embodied agents, especially applied to the control and coordination of autonomous systems: robots, mobile robots, domotics, agents, industrial applications or complex systems.This special issue brings together a selection of revised and extended papers that were first presented at the XIII Workshop on Physical Agents (WAF'2012), which was held the 3rd and 4th of September 2012 at the University of Santiago de Compostela (Spain).
This paper presents the application developed for humanoid robots which are used in therapy of dementia patients, as a cognitive stimulation tool. It has been created using BICA, a component oriented framework for programming robot applications, which is also described. The developed robotherapy application includes the control software onboard the robot and some tools like the visual script generator or several monitoring tools to supervise the robot behavior along the sessions. The behavior of the robot along the therapy sessions is visually programmed in a session script that allows music playing, physical movements (dancing, exercises...), speech synthesis and interaction with the human monitor. The monitoring tools allow the therapist interaction with the robot through its buttons, a tablet or a Wiimote device. Experiments with real dementia patients have been performed in collaboration with a research center in neurological diseases. Initial results show a slight (or mild) improvement in neuropsychiatric symptoms over other traditional therapy methods.
Mobile robots operating in the real world need a very reliable localization system to navigate autonomously for long periods of time.Numerous methods for indoor mobile robot localization have been developed.However, an affordable system covering all environments and situations is not yet available.Therefore, it is very important for mobile robot application developers to be aware of the operation and limitations of the different localization systems in order to obtain the best performance for each case.This paper evaluates two indoor localization systems that are integrated in the RIDE architecture: a commercial (Hagisonic StarGazer) and a low cost localization system based on the popular Wii remote control (WiiMote) with different tag distributions were evaluated.Characteristics that were tested include precision, accuracy, reliability, cost and immunity to interference.
In this article we describe a novel algorithm that allows fast and continuous learning on a physical robot working in a real environment.The learning process is never stopped and new knowledge gained from robot-environment interactions can be incorporated into the controller at any time.Our algorithm lets a human observer control the reward given to the robot, hence avoiding the burden of defining a reward function.Despite the highly-non-deterministic reinforcement, through the experimental results described in this paper, we will see how the learning processes are never stopped and are able to achieve fast robot adaptation to the diversity of different situations the robot encounters while it is moving in several environments.
This work was supported by the Spanish Ministry of Economy and Competitiveness under grants TIN2011-22935 and TIN2009-07737 and by the Galician Government (Consolidation of Competitive Research Groups, Xunta de Galicia ref. 2010/6). Manuel Mucientes is supported by the Ramón y Cajal program of the Spanish Ministry of Economy and Competitiveness.
During the last six years the RoboComp robotics framework has been steadily growing in the number of software components, the variety of robots supported and in new solutions to the maintenance of large robotics software repositories. In this paper we present recent advances in the formal definition of the RoboComp component model and a new set of tools based on Domain Specific Languages that have been created to simplify the whole development cycle of the components. Moreover, a new robot simulation tool has been created providing perfect integration with RoboComp and better control over experiments than current existing simulators. Finally, the paper describes a working solution to the important problem of communications middleware independence, which allows users to decide which middleware the components will be compiled with. Our solution has been validated by the integration of Nerve, a novel middleware for critical robotics tasks, in RoboComp.
This paper presents a new system for recognition and imitation of a set of facial expressions using the visual information acquired by the robot. Besides, the proposed system detects and imitates the interlocutor’s head pose and motion. The approach described in this paper is used for human-robot interaction (HRI), and it consists of two consecutive stages: i) a visual analysis of the human facial expression in order to estimate interlocutor’s emotional state (i.e., happiness, sadness, anger, fear, neutral) using a Bayesian approach, which is achieved in real time; and ii) an estimate of the user’s head pose and motion. This information updates the knowledge of the robot about the people in its field of view, and thus, allows the robot to use it for future actions and interactions. In this paper, both human facial expression and head motion are imitated by Muecas, a 12 degree of freedom (DOF) robotic head. This paper also introduces the concept of human and robot facial expression models, which are included inside of a new cognitive module that builds and updates selective representations of the robot and the agents in its environment for enhancing future HRI. Experimental results show the quality of the detection and imitation using different scenarios with Muecas.
The growing demand for service robots requires a better and more natural human-machine interaction.Given that an important part of human communication is non-verbal, it is necessary to endow robots with gestural communication capabilities similar to humans.This paper describes the design and construction of a realistic, mechatronic head with high gesture capacity.The proposed design is based on the human anatomy and the facial expressions are defined through the Facial Action Coding System (FACS).The paper shows the implementation details of the mechatronic head and the way a set of servomotors can generate the basic action units of FACS as well as the basic and more complex emotional gestures.
Nowadays, deploying service robots and adapting their services to a new environment is a task which might require several days. This is an important problem of robotics in general, but specially when the goal is to bring robots to our everyday life. In this paper we present a multi-agent intelligent space, which consists on intelligent cameras and autonomous guide robots. The deployment of the system does not require expertise and can be done in a short period of time. The cameras detect situations requiring the robots' guiding services, inform the robots accordingly, and support the robots navigation towards the goal areas, without the need of a map of the environment. An example of these situations requiring the robot guide service could be a group of persons entering a museum. In this sense, we also present an adaptive person follower behaviour intended to be the basis of a route learning process, necessary to offer the guide service.
Human-Robot Interaction (HRI) is one of the most important subfields of social robotics.In several applications, text-to-speech (TTS) techniques are used by robots to provide feedback to humans.In this respect, a natural synchronization between the synthetic voice and the mouth of the robot could contribute to improve the interaction experience.This paper presents an algorithm for synchronizing Text-To-Speech systems with robotic mouths.The proposed approach estimates the appropriate aperture of the mouth based on the entropy of the synthetic audio stream provided by the TTS system.The paper also describes the cost-efficient robotic head which has been used in the experiments and introduces the use of conversational gestures for engaging Human-Robot Interaction.The system, which has been implemented in C++ and can perform in realtime, is freely available as part of the RoboComp open-source robotics framework.Finally, the paper presents the results of the opinion poll that has been conducted in order to evaluate the interaction experience.
Underwater gliders have revealed as a valuable scientific platform, with a growing number of successful environmental sampling applications.They are specially suited for long range missions due to their unmatched autonomy level, although their low surge speed makes them strongly affected by ocean currents.Path planning constitutes a real concern for this type of vehicle, as it may reduce the time taken to reach a given waypoint or save power.In such a dynamic environment it is not easy to find an optimal solution or any such requires large computational resources.In this paper, we present a path planning scheme with low computational cost for this kind of underwater vehicle that allows static or dynamic obstacle avoidance, frequently demanded in coastal environments, with land areas, strong currents, shipping routes, etc.The method combines an initialization phase, inspired by a variant of the A* search process and ND algorithm, with an optimization process that embraces the physical vehicle motion pattern.Consequently, our method simulates a glider affected by the ocean currents, while it looks for the path that optimized a given objective.The method is easy to configure and adapt to various optimization problems, including missions in different operational scenarios.This planner shows promising results in realistic simulations, including ocean currents that vary considerably in time, and provides a superior performance over other approaches that are compared in this paper.
This article describes a proposal to achieve fast robot learning from its interaction with the environment.Our proposal will be suitable for continuous learning procedures as it tries to limit the instability that appears every time the robot encounters a new situation it had not seen before.On the other hand, the user will not have to establish a degree of exploration (usual in reinforcement learning) and that would prevent continual learning procedures.Our proposal will use an ensemble of learners able to combine dynamic programming and reinforcement learning to predict when a robot will make a mistake.This information will be used to dynamically evolve a set of control policies that determine the robot actions.
Vision devices are today one of the most often used sensory elements in autonomous robots. Some of their hindrances are the difficulty in extracting useful information from the captured images and the small visual field of regular cameras. Visual attention systems and active vision may help to overcome them. This work proposes a dynamic visual memory to store the information gathered from a continuously moving camera onboard the robot and an attention system to choose where to look at with such mobile camera. The visual memory is a collection of relevant task-oriented objects and 3D segments, and its scope is wider than instantaneous field of view of the camera. The attention system takes into account the need to reobserve objects in the visual memory, explore new areas and test hypothesis about object existence in the robot surroundings. The system has been programmed and validated in a real Pioneer robot that uses the information in the visual memory for navigation tasks.
The Workshop of Physical Agents intends to be a forum for information and experience exchange in different areas regarding the concept of agent in physical environments, especially applied to the control and coordination of autonomous systems: robots, mobile robots, industrial processes or complex systems.This special issue is devoted to the selected papers presented in the WAF11 that took place in Albacete (SPAIN) from 5th to 6th of September.
