There are some industrial tasks that are still mainly performed manually by human workers due to their complexity, which is the case of surface treatment operations (such as sanding, deburring, finishing, grinding, polishing, etc.) used to repair defects. This work develops an advanced teleoperation and control system for industrial robots in order to assist the human operator to perform the mentioned tasks. On the one hand, the controlled robotic system provides strength and accuracy, holding the tool, keeping the right tool orientation and guaranteeing a smooth approach to the workpiece. On the other hand, the advanced teleoperation provides security and comfort to the user when performing the task. In particular, the proposed teleoperation uses augmented virtuality (i.e., a virtual world that includes non-modeled real-world data) and haptic feedback to provide the user an immersive virtual experience when remotely teleoperating the tool of the robot system to treat arbitrary regions of the workpiece surface. The method is illustrated with a car body surface treatment operation, although it can be easily extended to other surface treatment applications or even to other industrial tasks where the human operator may benefit from robotic assistance. The effectiveness of the proposed approach is shown with several experiments using a 6R robotic arm.

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Contact driven tasks, such as surface conditioning operations (wiping, polishing, sanding, etc.), are difficult to program in advance to be performed autonomously by a robotic system, specially when the objects involved are moving. In many applications, human-robot physical interaction can be used for the teaching, specially in learning from demonstrations frameworks, but this solution is not always available. Robot teleoperation is very useful when user and robot cannot share the same workspace due to hazardous environments, inaccessible locations, or because of ergonomic issues. In this sense, this paper introduces a novel dual-arm teleoperation architecture with haptic and visual feedback to enhance the operator immersion in surface treatment tasks. Two task-based assistance systems are also proposed to control each robotic manipulator individually. To validate the remote assisted control, some usability tests have been carried out using Baxter, a dual-arm collaborative robot. After analysing several benchmark metrics, the results show that the proposed assistance method helps to reduce the task duration and improves the overall performance of the teleoperation.

Directive (EU) 2015/653 on driving licenses has involved the modification of different codes that must appear on driver’s licenses. The definition of specific codes (20.07 and 40.01) compels measurement of the braking and steering forces. Performing practical tests to assess the driving fitness of special drivers will help to determine the maximum force that a driver can apply on primary controls when driving. From that point, definition of car control adaptations required to supply their functional deficiencies can be stated. This article describes a data acquisition system designed and developed for obtaining data from experimental tests based on the execution of habitual driving manoeuvres (braking, lane change and roundabouts). The data gathered will allow for definition of the thresholds of biomechanical values (forces on the steering wheel and brake pedal) and ergonomic values (driver’s upper extremity mobility ranges) necessary for driving motor vehicles. The results have shown that application in real driving tests of the data acquisition system designed provides valid and suitable results for the case studied. Therefore, it will contribute to substantially improving the assessment procedure for drivers in general and for disabled people in particular when obtaining or renewing their driving licenses.

In heavy-duty vehicles multiple signals are available to estimate the vehicle's kinematics, such as inertial, GPS and CAN linear and angular speed readings. These signals have different noise variance, bandwidth and sampling rate. In this paper we present a non-linear sensor fusion algorithm allowing asynchronous sampling and non-causal smoothing, and we apply it to study the accuracy improvements when incorporating CAN measurements to standard GPS+IMU kinematic estimation, as well as the robustness against missing data. Our results show that these asynchronous CAN+GPS+IMU sensor fusion is advantageous in low-speed manoeuvres and GPS-denial environments. Accuracy and robustness to missing data is of course improved with non-causal filtering. The proposed algorithm is based on Extended Kalman Filter and Smoother with exponential discretization of continuous-time stochastic differential equations, in order to process measurements at arbitrary time instants.