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Haptic technology has become a viable way to support operators in vehicular control. This paper investigates two different design philosophies for continuous haptic feedback to support drivers with curve negotiation. The first system, 'direct haptic assistance', is designed to yield best results when the driver gives way to the guidance forces on the steering wheel. The second, 'indirect haptic assistance', is designed to yield best results when the driver counter-acts the forces. The two designs were compared in a driving simulator experiment in which 27 subjects participated. Results show that both systems are helpful in case of low visibility, where the driver lacks sufficient preview of the curves. With normal visibility no improvements on performance were found, for either system. Further experiments are required to investigate the difference between the two approaches

Abstract-This paper considers the problem of realizing a 6-DOF closed-loop motion simulator by exploiting an anthropomorphic serial manipulator as motion platform. Contrary to standard Stewart platforms, an industrial anthropomorphic manipulator offers a considerably larger motion envelope and higher dexterity that let envisage it as a viable and superior alternative. Our work is divided in two papers. In this Part I, we discuss the main challenges in adopting a serial manipulator as motion platform, and thoroughly analyze one key issue: the design of a suitable inverse kinematics scheme for online motion reproduction. Experimental results are proposed to analyze the effectiveness of our approach. Part II [1] will address the design of a motion cueing algorithm tailored to the robot kinematics, and will provide an experimental evaluation on the chosen scenario: closed-loop simulation of a Formula 1 racing car.

The goal of this paper is to better understand how the neuromuscular system of a pilot, or more generally an operator, adapts itself to different types of haptic aids during a pitch control task. A multi-loop pilot model, capable of describing the human behaviour during a tracking task, is presented. Three different identification techniques were investigated in order to simultaneously identify neuromuscular admittance and the visual response of a human pilot. In one of them, the various frequency response functions that build up the pilot model are identified using multi-inputs linear time-invariant models in ARX form. A second method makes use of cross-spectral densities and diagram block algebra to obtain the desired frequency response estimates. The identification techniques were validated using Monte Carlo simulations of a closed-loop control task. Both techniques were compared with the results of another identification method well known in literature and based on crossspectral density estimates. All those methods were applied in an experimental setup in which pilots performed a pitch control task with different haptic aids. Two different haptic aids for tracking task are presented, a Direct Haptic Aid and an Indirect Haptic Aid. The two haptic aids were compared with a baseline condition in which no haptic force was used. The data obtained with the proposed method provide insight in how the pilot adapts his control behavior in relation to different haptic feedback schemes. From the experimental results it can be concluded that humans adapt their neuromuscular admittance in relation with different haptic aids. Furthermore, the two new identification techniques seemed to give more reliable admittance estimates.

This paper, divided in two Parts, considers the problem of realizing a 6-DOF closed-loop motion simulator by exploiting an anthropomorphic serial manipulator as motion platform. After having proposed a suitable inverse kinematics scheme in Part I [1], we address here the other key issue, i.e., devising a motion cueing algorithm tailored to the specific robot motion envelope. An extension of the well-known classical washout filter designed in cylindrical coordinates will provide an effective solution to this problem. The paper will then present a thorough experimental evaluation of the overall architecture (inverse kinematics + motion cueing) on the chosen scenario: closed-loop simulation of a Formula 1 racing car. This will prove the feasibility of our approach in fully exploiting the robot motion capabilities as a motion simulator.