Carlos Soria

Stable AGV corridor navigation with fused vision-based control signals

This work presents a control strategy for mobile robots navigating in corridors, using the fusion of the control signals from vision based controllers. To this aim two controllers are proposed to generate the control signals to be fused: one is based on the optical flow calculation and the other is based on the perspective lines in the corridor. Both controllers generate angular velocity commands to keep the robot navigating along the corridor, and compensate for the dynamics of the robot. The fusion of both control signals is made by using a Kalman filter. Stability of the resulting control system in analyzed. Experiments on a laboratory robot are presented to show the feasibility and performance of the proposed controller.

Towards a New Modality-Independent Interface for a Robotic Wheelchair

This work presents the development of a robotic wheelchair that can be commanded by users in a supervised way or by a fully automatic unsupervised navigation system. It provides flexibility to choose different modalities to command the wheelchair, in addition to be suitable for people with different levels of disabilities. Users can command the wheelchair based on their eye blinks, eye movements, head movements, by sip-and-puff and through brain signals. The wheelchair can also operate like an auto-guided vehicle, following metallic tapes, or in an autonomous way. The system is provided with an easy to use and flexible graphical user interface onboard a personal digital assistant, which is used to allow users to choose commands to be sent to the robotic wheelchair. Several experiments were carried out with people with disabilities, and the results validate the developed system as an assistive tool for people with distinct levels of disability.

Supervisory control for a telerobotic system: a hybrid control approach

A supervisory controller for a robotic teleoperation system is presented in this paper. The low-level control structure was designed to control both the position and the interaction force between the remote manipulator and its environment. Hybrid systems theory is applied to design the supervisory control in order to detect when force and position thresholds are overcome and when the communication between the local and the remote stations are interrupted or returned. On the occurrence of such events, the controller modifies the references sent from the local station to the remote robot in order to improve performance and operation safety. Simulations as well as experimental results show the performance of the hybrid system incorporating the designed supervisory controller.

Robust EMG sensing system based on data fusion for myoelectric control of a robotic arm

BackgroundMyoelectric control of a robotic manipulator may be disturbed by failures due to disconnected electrodes, interface impedance changes caused by movements, problems in the recording channel and other various noise sources. To correct these problems, this paper presents two fusing techniques, Variance Weighted Average (VWA) and Decentralized Kalman Filter (DKF), both based on the myoelectric signal variance as selecting criterion.MethodsTested in five volunteers, a redundant arrangement was obtained with two pairs of electrodes for each recording channel. The myoelectric signals were electronically amplified, filtered and digitalized, while the processing, fusion algorithms and control were implemented in a personal computer under MATLAB® environment and in a Digital Signal Processor (DSP). The experiments used an industrial robotic manipulator BOSCH SR-800, type SCARA, with four degrees of freedom; however, only the first joint was used to move the end effector to a desired position, the latter obtained as proportional to the EMG amplitude.ResultsSeveral trials, including disconnecting and reconnecting one electrode and disturbing the signal with synthetic noise, were performed to test the fusion techniques. The results given by VWA and DKF were transformed into joint coordinates and used as command signals to the robotic arm. Even though the resultant signal was not exact, the failure was ignored and the joint reference signal never exceeded the workspace limits.ConclusionThe fault robustness and safety characteristics of a myoelectric controlled manipulator system were substantially improved. The proposed scheme prevents potential risks for the operator, the equipment and the environment. Both algorithms showed efficient behavior. This outline could be applied to myoelectric control of prosthesis, or assistive manipulators to better assure the system functionality when electrode faults or noisy environment are present.

Sliding Mode Neuro Adaptive Control in Trajectory Tracking for Mobile Robots

In this work a neural indirect sliding mode control method for mobile robots is proposed. Due to the nonholonomic property and restricted mobility, the trajectory tracking of this system has been one of the research topics for the last ten years. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a practical design that combines an indirect neural adaptation technique with sliding mode control to compensate the dynamics of the robot. Using an online adaptation scheme, a neural sliding mode controller is used to approximate the equivalent control in the neighbourhood of the sliding manifold. A sliding control is appended to ensure that the neural sliding mode control can achieve a stable closed-loop system for the trajectory-tracking control of a mobile robot with unknown nonlinear dynamics. The proposed design simultaneously guarantees the stability of the adaptation of the neural nets and obtains suitable equivalent control when the parameters of the robot model are unknown in advance. The robust adaptive scheme is applied to a mobile robot and shown to be able to guarantee that the output tracking error will converge to zero.

SLAM algorithm applied to robotics assistance for navigation in unknown environments.

BackgroundThe combination of robotic tools with assistance technology determines a slightly explored area of applications and advantages for disability or elder people in their daily tasks. Autonomous motorized wheelchair navigation inside an environment, behaviour based control of orthopaedic arms or users preference learning from a friendly interface are some examples of this new field. In this paper, a Simultaneous Localization and Mapping (SLAM) algorithm is implemented to allow the environmental learning by a mobile robot while its navigation is governed by electromyographic signals. The entire system is part autonomous and part user-decision dependent (semi-autonomous). The environmental learning executed by the SLAM algorithm and the low level behaviour-based reactions of the mobile robot are robotic autonomous tasks, whereas the mobile robot navigation inside an environment is commanded by a Muscle-Computer Interface (MCI).MethodsIn this paper, a sequential Extended Kalman Filter (EKF) feature-based SLAM algorithm is implemented. The features correspond to lines and corners -concave and convex- of the environment. From the SLAM architecture, a global metric map of the environment is derived. The electromyographic signals that command the robots movements can be adapted to the patients disabilities. For mobile robot navigation purposes, five commands were obtained from the MCI: turn to the left, turn to the right, stop, start and exit. A kinematic controller to control the mobile robot was implemented. A low level behavior strategy was also implemented to avoid robots collisions with the environment and moving agents.ResultsThe entire system was tested in a population of seven volunteers: three elder, two below-elbow amputees and two young normally limbed patients. The experiments were performed within a closed low dynamic environment. Subjects took an average time of 35 minutes to navigate the environment and to learn how to use the MCI. The SLAM results have shown a consistent reconstruction of the environment. The obtained map was stored inside the Muscle-Computer Interface.ConclusionsThe integration of a highly demanding processing algorithm (SLAM) with a MCI and the communication between both in real time have shown to be consistent and successful. The metric map generated by the mobile robot would allow possible future autonomous navigation without direct control of the user, whose function could be relegated to choose robot destinations. Also, the mobile robot shares the same kinematic model of a motorized wheelchair. This advantage can be exploited for wheelchair autonomous navigation.

Vision-based tracking control for mobile robots

This work presents a control strategy that allows a follower robot to track a target vehicle moving along an unknown trajectory with unknown velocity. It uses only artificial vision to establish both the robots position and orientation relative to the target, so as to maintain a specified formation at a given distance. The control system is proved to be asymptotically stable at the equilibrium point, which corresponds to the navigation objective. Experimental results with two robots, a leader and a follower, are included to show the performance of the proposed vision-based tracking control system

Combined force and visual control of an industrial robot

This work proposes control structures that efficiently combine force control with vision servo control of robot manipulators. Impedance controllers are considered which are based both on visual servoing and on physical or fictitious force feedback, the force and visual information being combined in the image space. Force and visual servo controllers included in extended hybrid control structures are also considered. The combination of both force and vision based control allows the tasks range of the robot to be extended to partially structured environments. The proposed controllers, implemented on an industrial SCARA-type robot, are tested in tasks involving physical and virtual contact with the environment.

Two-dimensional myoelectric control of a robotic arm for upper limb amputees

Rehabilitation engineering and medicine have become integral and significant parts of health care services, particularly and unfortunately in the last three or four decades, because of wars, terrorism and large number of car accidents. Amputees show a high rate of rejection to wear prosthetic devices, often because of lack of an adequate period of adaptation. A robotic arm may appear as a good preliminary stage. To test the hypothesis, myoelectric signals from two upper limb amputees and from four normal volunteers were fed, via adequate electronic conditioning and using MATLAB, to an industrial robotic arm. Proportional strength control was used for two degrees of freedom (x-y plane) by means of eight signal features of control (four traditional statistics plus energy, integral of the absolute value, Willisons amplitude, waveform length and envelope) for comparison purposes, and selecting the best of them as final reference. Patients easily accepted the system and learned in short time how to operate it. Results were encouraging so that valuable training, before prosthesis is implanted, appears as good feedback; besides, these patients can be hired as specialized operators in semi-automatized industry.

Identification and control of nonlinear dynamics of a mobile robot in discrete time using an adaptive technique based on neural PID

In this work, original results, concerning the application of a discrete-time adaptive PID neural controller in mobile robots for trajectory tracking control, are reported. In this control strategy, the exact dynamical model of the robot does not need to be known, but a neural network is used to identify the dynamic model. To implement this strategy, two controllers are implemented separately: a kinematic controller and an adaptive neural PID controller. The uncertainty and variations in the robot dynamics are compensated by an adaptive neural PID controller. It is efficient and robust in order to achieve a good tracking performance. The stability of the proposed technique, based on the discrete-time Lyapunovs theory, is proven. Finally, experiments on the mobile robot have been developed to show the performance of the proposed technique, including the comparison with a classical PID.