Haptic interaction plays a crucial role in enhancing realism and immersion in virtual environments, particularly in applications such as robotic rehabilitation. In these environments, therapy based on serious games that combine VR and haptic feed promise to offer engaging, affordable and reliable treatments. Most 6-DoF haptic methods have focused on precision in the kinematic fidelity of the haptic feedback. However, rehabilitation games for upper limb focus more on the forces exerted by the patient than the kinematic precision of the contacts with the virtual environment, since these exerted forces will be what powers the neuromuscular recovery of lost movement. We believe that a haptic control method with a focus on providing stable compliant and safe collisions is required to further the research into VR rehabilitation gamification. In addition, these solutions often rely on specifically tailored simulators running at very high frequencies. In contrast, commercial game engine that bring the most capabilities for gamification run at very low frequencies in comparison (≃50 Hz). In this work, we present a novel approach that integrates god object and penalty-based methods to achieve a balance between stability and computational efficiency, to enable 6-DoF haptic force generation on a collaborative robot. This is achieved through a relaxed quasi-god object simulation in the game engine and geodesic constraints in the robot’s haptic loop.

Robotic-assisted upper limb rehabilitation has gained significant attention in recent years due to its potential to enhance the recovery process for individuals with motor impairments resulting from neurological conditions and injuries. The main rehabilitation treatments rely on the repetitive execution of a movement of the upper-limb, guided by a therapist to prevent incorrect movements and to provide the necessary support. Many of the exercises performed by therapists can be modeled as a movement in SE(3) space (position and orientation). This movement itself is one-dimensional, as it can be modeled by a one-dimensional curve. To solve a similar problem, some approaches have been proposed in human-robot interaction (HRI) following virtual guides, but are either limited to specific types of curves (e.g. without orientation) or rely on linear control methods with non-intuitive parameters. To address these limitations and enable the use of these methods in physical rehabilitation, this paper extends Cartesian impedance control to splines, which we term path impedance control. It capitalizes on the intrinsic path geometry of end-effector robotic rehabilitation systems. The primary objective of this control algorithm is to emulate the sensation of maneuvering a physical object along a wire, akin to conventional exercise machines; and, in conjunction, provide an intuitive parametrization of rehabilitation exercises. We build on existing virtual guide control strategies using non-linear control and Lie Groups to generalize the control law to any one-parameter SE(3) curve.

Soft links and actuators are nowadays emerging technologies aiming to overcome some problems in robotics such as weight, cost or human interaction. However, the nonlinear nature of their elements can make their characterization challenging and hinder the use of standard control engineering tools. In this paper, we explore different state-of-the-art identification methods for the soft neck, in order to find a reliable plant model. Even though the neck has three Degrees of freedom, in this work we only consider the planar deflection of the link as a starting point for future analysis. Given the nonlinear nature of the soft neck, we consider two identification strategies, i.e., set membership, which is a data driven, nonlinear and nonparametric identification strategy, and Recursive Least Squares at selected linearization points. A neural network identification is also given for comparison purposes. Results show that the explored methods offer a suitable alternative to identify the dynamics of the neck that allows their implementation for simulation and future control.

The maintenance and inspection of the flooded shafts, specially coal ones, is an important environmental problem. There are thousands of shafts of this type in Europe with the danger of pollution, flood and collapse. This paper presents some of the main ongoing works of the EU project STAMS that develop an autonomous underwater robotic system for periodic monitoring of flooded shafts in hazardous and complex conditions. The accurate navigation is very cluttered at 1.000 m depth conditions, where minimum visibility and unexpected obstacles are some of the difficulties to overcome. We are going beyond classical navigation approaches using only few sensor information. Another innovation is the installation of Reference Points (RPs) in the shaft's walls by the robot using a special fixation mechanism. The specially designed cases of the RPs allow to house specific sensors and help in the navigation, and will be used in periodic monitoring and assessment of the mine shafts. The positioning and attachment of these RPs is another contribution of this paper.

Continuous Goal-Directed Actions (CGDA)is a robot imitation framework that encodes actions as the changes they produce on the environment. While it presents numerous advantages with respect to other robot imitation frameworks in terms of generalization and portability, final robot joint trajectories for the execution of actions are not necessarily encoded within the model. This is studied as an optimization problem, and the solution is computed through evolutionary algorithms in simulated environments. Evolutionary algorithms require a large number of evaluations, which had made the use of these algorithms in real world applications very challenging. This paper presents online evolutionary strategies, as a change of paradigm within CGDA execution. Online evolutionary strategies shift and merge motor execution into the planning loop. A concrete online evolutionary strategy, Online Evolved Trajectories (OET), is presented. OET drastically reduces computational times between motor executions, and enables working in real world dynamic environments and/or with human collaboration. Its performance has been measured against Full Trajectory Evolution (FTE)and Incrementally Evolved Trajectories (IET), obtaining the best overall results. Experimental evaluations are performed on the TEO full-sized humanoid robot with “paint” and “iron” actions that together involve vision, kinesthetic and force features.

Dynamic balance control for humanoid robots encounters difficulties such as stability, speed, and smoothness. In most of the previous studies, joints act as controller of the Center of Mass (CoM)supported using a simplified mathematical model. Then, the stability of the motion is guaranteed using the Zero Moment Point (ZMP)stability criterion. In this video, a humanoid robot [1] will carry a tray secured to the wrist and the objects to be transported will be placed on the tray. This condition implies that the object is not grasped and therefore, the robot arm will be the only point of support of the object through the tray. Thus, the manipulation control system must be able to detect the stability of the object and act according to the different perturbations applied to it. A 3D balance control system for non-grasping tasks is presented and it is based on the ZMP criterion and 3D inverted pendulum equations. The perception system required is based on the use of Force-Torque sensors [2], computer vision [3], and their integration. The effectiveness of the proposed approach is being investigated with the humanoid robot TEO.

Robotics can play a significant role in the rehabilitation of patients with spasticity by improving their early diagnosis and reducing the costs associated with care. Spasticity is a muscle control disorder characterized by an increase in muscle tone with exaggerated stretch reflexes, as one component of the upper motor neuron syndrome. Furthermore, spasticity is present in other pathologies, such as cerebral palsy, spina bifida, brain stroke among others. This video shows the ongoing research on developing a platform for the modelling and the assessment of spasticity using collaborative robots as clinical tool. Our aim is to develop methods for non-invasive biomechanical modelling of upper limbs joints using 7-DOF Rosen Kinematics [1], mixed with a non-linear state of Hills force-velocity relation [2], improved by introducing new parameters such as rigidity, viscoelasticity, extensibility and thixotropy. After a learning phase performed by the therapist, the robot replicates the trajectories required to perform the assessment. The video also describes the detailed analysis of passive movement response (force/torque and position/velocity)of the limb. These parameters will be used to determine the degree of spasticity of patients in a fast and objective manner, while simultaneously developing new clinical scales, such as a modified version of Ashworth [3].

This paper presents a novel method for the control of the elbow joint of the humanoid robot TEO, based on a fractional order PD controller. Due to the graphical nature of the proposed method, a few basic operations are enough to tune the controller, offering very competitive results compared to classic methods. The experiments show a robust performance of the system to mass changes at the tip of the humanoid right arm.

One of the most important and complex tasks for a humanoid robot is to avoid overturning during a bipedal gait. This work aims at setting the base for designing a general balance controller to be used with any humanoid robot. In addition, it is based on a strong simplification of humanoid model which attempts to be used in real-time applications. In particular, several “stability zones” are defined as function of the criticalness of balance. The results are presented in simulation and experimentally, using the humanoid platforms HOAP-3 and TEO.

As robotic systems become more popular in household environments, the complexity of required tasks also increases. In this work we focus on a domestic chore deemed dull by a majority of the population, the task of ironing. The presented algorithm improves on the limited number of previous works by joining 3D perception with force/torque sensing, with emphasis on finding a practical solution with a feasible implementation in a domestic setting. Our algorithm obtains a point cloud representation of the working environment. From this point cloud, the garment is segmented and a custom Wrinkleness Local Descriptor (WiLD) is computed to determine the location of the present wrinkles. Using this descriptor, the most suitable ironing path is computed and, based on it, the manipulation algorithm performs the force-controlled ironing operation. Experiments have been performed with a humanoid robot platform, proving that our algorithm is able to detect successfully wrinkles present in garments and iteratively reduce the wrinkleness using an unmodified iron.

Many proposed activities to be carried out by unmanned aerial vehicles (UAVs) in urban environments require a control over the altitude for different purposes. Energy saving and minimization of costs are some of these objectives. This work presents a method to impose a flight level in a mission planning carried out by a UAV in a 3D urban environment. The planning avoids all obstacles encountered in the environment and maintains a fixed flight level in the majority of the trajectory. The method used as planner is the Fast Marching Square (FM2) method, which includes two adjustment parameters. Depending on the values of these parameters, it is possible to introduce into the planning an altitude constraint, as well as to modify the smoothness of the trajectory and the safety margins from the obstacles. Several simulated experiments have been carried out in different situations obtaining very good results.

This article presents the design, implementation and test of a joint mechanical device created to improve the safety and performance of robots interacting physically with human beings. The mechanism consists of a series of linear springs, preloaded and arranged in such a way to take advantage of the benefits of series elastic actuators and to limit the maximum applicable torque. This is a passive mechanism because it does not have a second actuator system on the same joint. However, it involves a joint system of variable stiffness given the three distinct performance phases it has, each one having a different stiffness value, elastic torque-dependent occurring in the joint.

There is an open discussion between those who defend mass-distributed models for humanoid robots and those in favor of simple concentrated models. Even though each of them has its advantages and disadvantages, little research has been conducted analyzing the control performance due to the mismatch between the model and the real robot, and how the simplifications affect the controller’s output. In this article we address this problem by combining a reduced model of the humanoid robot, which has an easier mathematical formulation and implementation, with a fractional order controller, which is robust to changes in the model parameters. This controller is a generalization of the well-known proportional–integral–derivative (PID) structure obtained from the application of Fractional Calculus for control, as will be discussed in this article. This control strategy guarantees the robustness of the system, minimizing the effects from the assumption that the robot has a simple mass distribution. The humanoid robot is modeled and identified as a triple inverted pendulum and, using a gain scheduling strategy, the performances of a classical PID controller and a fractional order PID controller are compared, tuning the controller parameters with a genetic algorithm.

Programming by demonstration (PbD) allows matching the kinematic movements of a robot with those of a human. The presented Continuous Goal-Directed Actions (CGDA) is able to additionally encode the effects of a demonstrated action, which are not encoded in PbD. CGDA allows generalization, recognition and execution of action effects on the environment. In addition to analyzing kinematic parameters (joint positions/velocities, etc.), CGDA focuses on changes produced on the object due to an action (spatial, color, shape, etc.). By tracking object features during action execution, we create a trajectory in an n-dimensional feature space that represents object temporal states. Discretized action repetitions provide us with a cloud of points. Action generalization is accomplished by extracting the average point of each sequential temporal interval of the point cloud. These points are interpolated using Radial Basis Functions, obtaining a generalized multidimensional object feature trajectory. Action recognition is performed by comparing the trajectory of a query sample with the generalizations. The trajectories discrepancy score is obtained by using Dynamic Time Warping (DTW). Robot joint trajectories for execution are computed in a simulator through evolutionary computation. Object features are extracted from sensors, and each evolutionary individual fitness is measured using DTW, comparing the simulated action with the generalization.

The work described here explores a neural network architecture that can be embedded directly in the realtime sensorimotor coordination loop of a developmental robot platform. We take inspiration from the way children are able to learn while interacting with a teacher, in particular the use of prediction of the teacher actions to improve own learning. The architecture is based on two neural networks that operate online, and in parallel, one for learning and one for prediction. A Hebbian learning rule is used to associate the high-dimensional afferent sensor input at different time-delays with the current efferent motor commands corresponding to the teacher demonstration. The predictions of future motor commands are used to limit the growth of the neural network weights, and to enable the robot to smoothly continue movements the teacher has begun. Results on a simulated iCub robot learning object interaction tasks are presented, including an analysis of the sensitivity to changes in the task setup. We also outline the first implementation on the real iCub platform.

In this paper, we study how human-robot interaction can be beneficial on the Continuous Goal-Directed Actions (CGDA) framework. Specifically, a system for robot discovery of motor primitives from random human-guided movements has been developed. These guided motor primitives (GMP) are used as scaffolds to reproduce a goal-directed actions. CGDA encodes goals as the changes produced on object features (color, area, etc) due to actions. This paper focuses on using motor primitives extracted from human-guided random robot movements to execute these goal-directed actions. The human guides the robot joints in random movements, which are later divided in small segments. These segments are compared in terms of joint positions and selected to be diverse. To perform goal-directed actions, the robot must discover an adequate sequence of GMP. To discover these sequences we organize the primitives as a tree with incremental depths (where each node represents a primitive) and use a breadth-first search. In one of the experiments performed, the robot executes a task based on spatial object features. In the other experiment, the goal is to paint a wall by following a color feature trajectory.

In recent years, Automated Planning (AP) has experienced important advances. In this study we apply such advances to the field of Mobile Assistive Robots (MAR). In particular, we propose the use of AP to implement the deliberative step between observation and action execution in MAR. First, we analyze the requirements that allow a MAR to plan navigation and manipulation actions in near real time. The intention is to build the foundation for a planning module within the Simultaneous User Learning and TAsk executioN (SULTAN) architecture, allowing a MAR to perform Daily Life Activities (DLA) in humanlike environments. Second, we apply AP techniques in fully observable, deterministic and static simulated environments with a single MAR. In addition, we analyze and compare the best available satisficing automated planners. The selected planners participate in several experiments to obtain plans for a Planning Domain Definition Language (PDDL) based on the Tidybot domain. Finally, in order to know how competitive the selected planners are, we compare the experimental results in detail.

An approach for adaptive shared control of an assistive manipulator is presented. A set of distributed collision and proximity sensors is used to aid in limiting collisions during direct control by the disabled user. Artificial neural networks adapt the use of the proximity sensors online, which limits movements in the direction of an obstacle before a collision occurs. The system learns by associating the different proximity sensors to the collision sensors where collisions are detected. This enables the user and the robot to adapt simultaneously and in real-time, with the objective of converging on a usage of the proximity sensors that increases performance for a given user, robot implementation and task-set. The system was tested in a controlled setting with a simulated 5 DOF assistive manipulator and showed promising reductions in the mean time on simplified manipulation tasks. It extends earlier work by showing that the approach can be applied to full multi-link manipulators.

This paper presents a robot imagination system that generates models of objects prior to their perception. This is achieved through a feature inference algorithm that enables computing the fusion of keywords which have never been presented to the robot together previously. In this sense, robot imagination is defined as the robot's capability of generating feature parameter values of unknown objects by generalizing characteristics from previously presented objects. The system is first trained with visual information paired with semantic object descriptions from which keywords are extracted. Each keyword creates an instance of the learnt object in an n-dimensional feature space. The core concept behind the robot imagination system presented in this paper is the use of statistically fit hyperplanes in the feature space to represent and simultaneously extend the meaning of grounded words. The inference algorithm allows to determine complete solutions in the feature space. Finally, evolutionary algorithms are used to return these numeric values to the real world, completing an inverse semantic process.

The study of Human Postural Control (HPC) system reveals the existence of an anticipative component for improving its performance. This anticipative subsystem is able to preview future consequences caused by a postural disturbance and, as well, to trigger corrective actions. This paper presents the novel high level architecture for the humanoid robot TEO based on the same principles than the human system. The core of the architecture is an anticipative system for predicting postural corrections based on the evaluation of unexpected events or surprises. Basically, these events are caused by the mismatch between sensory perceptions and the expected. The resulting surprise is used by a decision making system that decides whether a correction is necessary, and selects the adequate graded postural correction from a set of motion patterns or synergies. All the anticipative process is enabled by Neuro-Fuzzy modules that provide the system with human style reasoning behaviour.

The aim of this work is to model and simulate the humanoid robot HOAP-3 in the OpenHRP3 platform. Our purpose is to create a virtual model of the robot so that different motions and tasks can be tested in different environments. This will be the first step before testing the motion patterns in the real HOAP-3. We use the OpenHRP3 platform for the creation and validation of the robot model and tasks. The procedure followed to reach this goal is detailed in this article. In order to validate our experience, different walking motions are tested and the simulation results are compared with the experimental ones.

Facial gesture recognition is one of the main topics in HRI. We have developed a novel algorithm who allows to detect emotional states, like happiness, sadness or emotionless. A humanoid robot is able to detect these states with a ratio of success of 83% and interact in consequence. We use Active Appearance Models (AAMs) to determinate face features and classify the emotions using neural evolution, based on neural networks and differential evolution algorithm.

Assistive robots are increasingly being envisioned as an aid to the elderly and disabled. However controlling a robotic system with a potentially large amount of Degrees of Freedom (DOF) in a safe and reliable way is not an easy task, even without limitations in the mobility of the upper extremities. Shared control has been proposed as a way of aiding disabled users in controlling mobility aids such as assistive wheelchairs, by using the sensors of the robotic platform to predict the user's intent and assist in navigation. Assistive manipulators, that aim to perform physical Daily Life Activities (DLA), is a more complex problem however. This calls for good experimental practices to ensure repeatability, reproducibility, and steady progress. The work presented here attempts to model the complete system for assistive manipulators, and in the context of this model define metrics and good practices for benchmarking shared control for such robots. An adaptive shared control approach for limiting collisions during teleoperation is used as a case study. Improvements in performance are shown, quantified by the trade-off between mean time and number of collisions as well as the controllability from the user's perspective.

This article deals with the design of the full-size humanoid robot TEO, an improved version of its predecessor Rh-1. The whole platform is conceived under the premise of high efficiency in terms of energy consumption and optimization. We will focus mainly on the electromechanical structure of the lower part of the prototype, which is the main component demanding energy during motion. The dimensions and weight of the robotic platform, together with its link configuration and rigidity, will be optimized. Experimental results are presented to show the validity of the design.

Increasingly disabled and elderly people with mobility problems want to live autonomously in their home environment. They are motivated to use robotic aids to perform tasks by themselves, avoiding permanent nurse or family assistant supervision. They must find means to rehabilitate their abilities to perform daily life activities (DLAs), such as eating, shaving, or drinking. These means may be provided by robotic aids that incorporate possibilities and methods to accomplish common tasks, aiding the user in recovery of partial or complete autonomy. Results are highly conditioned by the system's usability and potential. The developed portable assistive robot ASIBOT helps users perform most of these tasks in common living environments. Minimum adaptations are needed to provide the robot with mobility throughout the environment. The robot can autonomously climb from one surface to another, fixing itself to the best place to perform each task. When the robot is attached to its wheelchair, it can move along with it as a bundle. This paper presents the work performed with the ASIBOT in the area of rehabilitation robotics. First, a brief description of the ASIBOT system is given. A description of tests that have been performed with the robot and several impaired users is given. Insight into how these experiences have influenced our research efforts, especially, in home environments, is also included. A description of the test bed that has been developed to continue research on performing DLAs by the use of robotic aids, a kitchen environment, is given. Relevant conclusions are also included.

Assistive robotics HRI has a number of important characteristics that distinguishes it from other forms of HRI. This includes the need for both high flexibility, safety and reliability in controlling the robotic system. Approaching the system as a human-robot binomial, with the user and the robot acting in a closed-loop, may be beneficial to understanding and improving the interaction. This paper investigates the feasibility of modeling and quantifying assistive robotics HRI inside such a human-robot binomial using concepts from Information Theory.

ASIBOT is an assistive climbing robot that is capable of aiding in daily tasks from fixed docking stations in the environment. A task-oriented design process was applied to improve the robot kinematic structure, which was based on the grid method. Twelve different robot designs were optimized for typical kitchen scenarios, followed by a quantitative comparison.

This paper deals with alternative humanoid robot dynamics modelling, using the screw theory and Lie groups called the special Euclidean group (SE(3)). The dynamic models are deduced analitically. The inverse dynamics model is obtained by the Lagrangian formulation under screw theory, when the Jacobian manipulator depends on the respective twist and joint angles; on the other hand, the POE formula drives a very natural and explicit description of the Jacobian manipulator without the drawbacks of local representation. The forward dynamics were solved by propagation method from an end-effector to the center of gravity (COG) always on the SE(3). Many tests for reference dynamic walking patterns have been carried out, which are represented in simulation and experimental results. The results will be discussed in order to validate the proposed algorithms.

This article reports on the results of a user study that investigates the perceived usability of naïve users conducting two navigation tasks with the HOAP-3 robot. In this user study, the robot was controlled via a Graphical User Interface (GUI) that enabled the users to navigate the robot in the physical environment. The main goal of the user study was to gain insights on the question if users assign usability issues mainly to the robot, the GUI, or both. The participants performed two tasks: (1) navigating the robot through a maze to find the exit, (2) navigating the robot through the maze to find an object. The user study was supplemented by the think aloud technique and the SUS questionnaire to investigate usability aspects. Furthermore, task duration and task completion were measured. The results stress that the users rated the overall usability of the navigation rather positive. They identified usability problems for both, the GUI and the robot itself. The participants made more suggestions for improving the interface than for improvement of the robot. A reflection on the think aloud data revealed, however, three underlying principles (“feeling of distance”, “lack of orientation”, and “lack of control” ) for the usability problems that were independent of the assignment to the GUI and the robot.

Efficient methods have so far been proposed for planning dynamically stable walking pattern for humanoid robots. However, to guarantee that the reference joint trajectory will produce a safe movement despite modeling errors and perturbations, a stabilizer needs to be implemented on the robot. Though this stabilizer constitutes an essential part of the control strategy of most advanced humanoid platform, it is usually not open-source and dedicated to the own robot characteristics. The goal of this paper is to propose a general and practical strategy for designing a stabilizer for joint-position controlled humanoid robots. The proposed method is based on a double inverted pendulum model and a decoupling approach thanks to which the position of the ZMP and the center of gravity can be controlled independently through the regulation of the ankle and hip joints. The stabilizer generates the expected stabilizing torques from the admissible joint position input. The resulting control algorithm is fast and can be easily executed on the robot. This algorithm was successfully implemented as real-time plugins for the OpenHRP simulator of the HRP2. Simulations showing the efficiency of the method are presented and discussed.

The full-scale humanoid robot RH-1 has been totally developed in the University Carlos III of Madrid. In this paper we present a novel approach for controlling the motion of the whole body of RH-1 robot, so that it can perform tasks in collaboration with humans. Given the mechanical limitations of the robot platform, it is not possible to achieve completely tasks in collaboration with humans. The lack of degrees of freedom (DOF) in robot arms are compensated using the virtual prismatic joints of the center of mass of the robot. This way, the movements that cannot be accomplished by using only the arms are assisted by the movement of the legs. The whole stability of the robot is also guaranteed by the posture control. All the models and algorithms are verified by several simulations and experimental results.

This paper addresses the problem of generating dynamic motion for a humanoid between two predefined postures. The humanoid robot starts its motion from a statically stable configuration to another known statically stable configuration when it is necessary to make step; that is, the center of mass (COM) is inside the support polygon at the initial and the goal configurations. A dynamic motion generation method is proposed in order to generate the whole-body robot motion. Zero moment point preview control is used for maintaining dynamic stability while the robot is taking the step and suitable splines are used for generating the hands, neck and foot motion because it is a free motion on the field of gravity; the screw theory is applied under Lie groups for kinematics and dynamics humanoid modeling, which allows smooth and natural humanoid motion. Successful results on the HRP-2 humanoid robot are shown and discussed.

This paper describes the control of an automatic overhead crane for assembly of modular building elements. The automatic system was developed using a commercial full-size crane, which was modified by adding adequate sensors, servomotors, and control strategy. The crane, which maintained its original cables, was converted to a robotic system, and is controlled via a computer-based multiaxes control board. The implemented algorithms solve two main problems of module assembly: 1) precision positioning of large and heavy modules in the order of a few centimeters, and 2) anti-swinging transportation of modules, even in difficult weather conditions (not extreme). The implementation of the anti-swinging control is achieved using the on-line two-dimensional inclinometer measurements and an on-line calculation of input impulse trains. The developed input-shaping control is divided into two phases: straight line motion and external perturbances cancellation. Experimental results of the developed algorithms which demonstrate the effectiveness of the new process are presented.

The present paper refers to the design of a robotic hand named RL1 hand. This robotic hand has 3 articulated fingers, which adapt themselves to the object that is being held. The fingers movement and auctioning are made through one direct current motor. The control is done through simple commands and can be sent from any computer, as a string of characters. One special characteristic this robotic hand has is that its mechanism, its electronic control and its 250 gr weight actuator are all embodied in the ASIBOT Robot. This arm was developed to help disable people or people with special needs. Apart from the topics described, the paper also refer to a theoretical study made to optimize the size and localization of the underactuated fingers' pulleys in order to exploit the most of the strength made by the tendon. Thanks to this study, better results would significantly improve these fingers' design

Human care and service demands need an innovative robotic solution to make easier the everyday life of elderly and disabled people in home and workplace environments. The main objective of this project is to develop a new concept of climbing robot for this type of service applications. The robot is a 5 DOF self-containing manipulator, that includes on-board all the control system. The main advantage of the robot is its light weight, about 11 kg for a 1.3m reach. The robot is totally autonomous and needs only power supply to be operated. The robot is a symmetrical arm able to move (or to climb) between different points (docking stations) of the rooms and, if it is necessary, "jump" to (or from) the environment to the wheelchair. In this way the MATS robot should become a home companion and assistance for numerous persons. This paper presents the live experimentation of the robot. Different tasks have been tested such as: eating, shaving, making-up, etc.

At the present time, the robotics hand research field is searching for those robots that could imitate the human hand, both in shape and in skill. As another option, some manipulator are developed, even though they do not imitate the human hand, they try to fulfil certain specific tasks with the biggest precision and skill. Some keys to make the robotic hands integrable in any manipulator are: built them lighter, a minimum amount of actuators and a simple sensorial system to simplify its control. The present work will describe the design of the RL1 Hand developed in the Robotic's Lab at the Carlos III University of Madrid. This robotic hand has 3 articulated fingers, which adapt themselves to the object which is being held. The fingers movement and auctioning are made through one direct current motor. The control is done through simple commands and can be sent from any computer, as a string of characters. To prove its ability the RL1 Hand was mounted in a robotic arm, ASIBOT Robot. This arm was developed to help disabled people or people with special needs. The RL1 Hand, its mechanisms, its electronic control and its actuator weight 250gr and they are embodied in the ASIBOT Robot.

The humanoid bipedal locomotion requires computationally efficient solutions of the navigation and inverse kinematics problems. This paper presents analytic methods, using tools from computational geometry and techniques from the theory of Lie groups, to develop new geometric algorithms for the navigation path planning, locomotion movement, and kinematics modeling of humanoid robots. To solve the global navigation problem, we introduce the new fast marching method modified (FM3) algorithm, based on the fast marching methods (FMM) used to study interface motion, that gives a close-form solution for the humanoid collision-free whole body trajectory (WBT) calculation. For the bipedal locomotion, we build the new geometric algorithm one step to goal (OSG), to produce a general solution for the body and footstep planning which make the humanoid to move a single step towards a defined objective. We develop the new approach called sagittal kinematics division (SKD), for the humanoid modeling analysis, to allow us to solve the humanoid inverse kinematics problem using the mathematical techniques of Lie groups, like the product of exponentials (POE). The works are presented along with computed examples of the humanoid robot RHO at the University Carlos III of Madrid. We remark that this paper introduces only closed-form solutions, numerically stable and geometrically meaningful, suitable for real-time applications.

This paper presents new methodology to solve the inverse dynamics of the humanoid robots. The objective is to calculate the joints' torque in order to select in optimal way the robot's gait and actuators. The main advantage of the method is its simplicity that everyone can implement it easily using common software packages which are not prepared for the humanoid robot modeling, like Matlab robotics toolbox. The inverse dynamics solving is divided in two parts, the upper and lower extremities (including the body). At the same time it is necessary to analyze separately the phases on simple, and double support. The first one doesn't present so much difficulty by using conventional manipulator dynamic modeling. But during the double support phase the modeling is performing for each leg separately by using balanced mass distribution between them. This distribution is based on the reaction forces calculation for each supported leg. The developed method had been applied to the 21 DOF and 1.4 m height Rh-0 humanoid robot under development at the Robotics Lab. of the University Carlos III of Madrid.

This paper reviews a new approach in the area of rehabilitation robotics. The service robot MATS helps disable and elderly people in their day life activities in common living environment like kitchen, bathroom, bedroom, etc. In this way the quality of life of the important part of population improves toward their social integration. This new prototype has new abilities like climb from one wall to another or from the table to the wheelchair, and at the same time to be attached and move with the wheelchair. The robot is totally autonomous and needs only power supply to be operated. This paper presents the distributed software architecture and the concept design of the HMI which handles the robotic system.

A human-mobile manipulator cooperation module is designed to support a target task consisting of the transportation of a rigid object between a mobile manipulator and a master human worker. Our approach introduces an intention recognition capability in the robot, based on the search for spectral patterns in the force signal measured at the arm gripper. The mobile manipulator takes advantage of this capability by generating its own motion plans in order to collaborate in the execution of the task. This has been designated as active cooperation.

There has been an increasing interest in developing and employing special robots in construction in order to assist the human operator as well as to limit the damage caused to him. For this end, the ROMA robot, a specially developed self-supported robot has been built. The robot is designed to perform 3D complex movements and navigate through metallic structures using the "caterpillar" concept. The control and monitoring of the robot is achieved through an advanced graphical user interface (GUI), to allow an effective and user friendly operation of the robot with a minimum operator training. The GUI has been designed with an open and modular structure allowing future reusability and scalability when necessary. This paper describes the design of the ROMA robot with a special emphasis on its GUI.

This paper presents a robot assembly system for the construction industry based on an articulated robot placed over a mobile platform. The assembly process and the robot has been developed under the computer integrated construction concept. Its main task is the assembly of blocks for the erection of walls in industrial buildings.