Abstract. The paper focuses on a closed-loop hybrid controller (kinematic and dynamic) for path following approaches with industrial forklifts carrying heavy loads at high speeds, where aspects such as vehicle stability, safety, slippage and comfort are considered. The paper first describes a method for generating Double Continuous Curvature (DCC) paths for non-holonomic wheeled mobile robots, which is the basis of the proposed kinematic controller. The kinematic controller generates a speed profile, based on "slow-in" and "fast-out" policy, and a curvature profile recomputing DCC paths in closed-loop. The dynamic controller determines maximum values for decelerations and curvatures, as well as bounded sharpness so that instantaneous vehicle stability conditions can be guaranteed against lateral and frontal tip-overs. One of the advantages of the proposed method, with respect to full dynamic controllers, is that it does not require dynamic parameters to be estimated for modelling, which in general can be a difficult task. The proposed kinematic-dynamic controller is afterwards compared with a classic kinematic controller like Pure-Pursuit. For that purpose, in our hybrid control structure we have just replaced the proposed kinematic controller with Pure-Pursuit. Several metrics, such as settling time, overshoot, safety and comfort have been analysed.

Leopoldo Armesto; Girbés, V.; Sala, A.; Miroslav Zima; Václav mídl (2015). Duality Abstract-This paper presents non-iterative linearization-based controllers for nonlinear unconstrained systems, coined as Extended Rauch-Tung-Striebel (ERTS) and Unscented RauchTung-Striebel (URTS) controllers, derived from the duality between optimal control and estimation. The proposed controllers use a Rauch-Tung-Striebel forward-backward smoother as an state estimator in order to compute the original optimal control problem. The new controllers are applied to trajectory-following problems of differential-drive mobile robots and compared with iterative iLQR controller, nonlinear model predictive control and approximate inference approaches. Simulations show that ERTS and URTS controllers produce almost-optimal solutions with a significantly lower computing time, avoiding initialization issues in the other algorithms (in fact, they can be used to initialize them). The paper validates ERTS controller with an experiment of a Pioneer 3DX mobile robot. I. INTRODUCTIONOptimal control is widely used in control practice due to its advantages regarding the individual tuning of actuator amplitudes and control goals for each output, with wellknown solutions for the linear case, both unconstrained (LQR) and constrained [1], [2]. However, it is limited to a narrow spectrum of applications because many systems in practice are inherently nonlinear. Nonlinear optimal control strategies are computationally more demanding, see [3]-[5] for some modelbased approaches to handling it.The goal of model-based optimal control is designing a stabilizing control while minimizing a given performance criterion, usually in a quadratic form, assuming a deterministic plant model is available. Closed-loop solutions can not be found analytically in a general nonlinear case since it involves obtaining the solution of the corresponding Hamilton JacobiBellman equations [6]. One approach to avoid this problem is the iterative solution of a finite-horizon optimal control problem for a given state with a receding horizon implementation; control approaches using this strategy are referred to as model predictive control (MPC,[1]) and nonlinear model predictive control (NMPC,[5]). These approaches can deal with the unconstrained and constrained problems, where both states and control inputs must satisfy particular conditions. MPC is restricted to quadratic cost functions, linear systems and linear constraints, while NMPC can optimize non-quadratic

This research develops a novel teleoperation for robot manipulators based on augmented reality. The proposed interface is equipped with full capabilities in order to replace the classical teach pendant of the robot for carrying out teleoperation tasks. The proposed interface is based on an augmented reality headset for projecting computer-generated graphics onto the real environment and a gamepad to interact with the computer-generated graphics and provide robot commands. In order to demonstrate the benefits of the proposed method, several usability tests were conducted using a 6R industrial robot manipulator in order to compare the proposed interface and the conventional teach pendant interface for teleoperation tasks. In particular, the results of these usability tests show that the proposed approach is more intuitive, ergonomic and easy-to-use. Furthermore, the comparison results also show that the proposed method clearly improves the velocity of the teleoperation task, regardless of the user's previous experience in robotics and augmented reality technology.

This work presents a human-robot closely collaborative solution to cooperatively perform surface treatment tasks such as polishing, grinding, deburring, etc. The method considers two force sensors attached to the manipulator end-effector and tool: one sensor is used to properly accomplish the surface treatment task, while the second one is used by the operator to guide the robot tool. The proposed scheme is based on task priority and adaptive non-conventional sliding mode control. The applicability of the proposed approach is substantiated by experimental results using a redundant 7R manipulator: the Sawyer cobot.

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