Predictive haptic guidance: intelligent user assistance for the control of dynamic tasks

Abstract

Intelligent systems are increasingly able to offer real-time information relevant to a user's manual control of an interactive system, such as dynamic system control space constraints for animation control and driving. However, it is difficult to present this information in a usable manner and other approaches which have employed haptic cues for manual control in "slow" systems often lead to instabilities in highly dynamic tasks. We present a predictive haptic guidance method based on a look-ahead algorithm, along with a user evaluation which compares it with other approaches (no guidance and a standard potential-field method) in a 1-DoF steered path-following scenario. Look-ahead guidance outperformed the other methods in both quantitative performance and subjective preference across a range of path complexity and visibility and a force analysis demonstrated that it applied smaller and fewer forces to users. These results (which appear to derive from the predictive guidance's supporting users in taking earlier and more subtle corrective action) suggest the potential of predictive methods in aiding manual control of dynamic interactive tasks where intelligent support is available.

Quantifying perception of nonlinear elastic tissue models using multidimensional scaling

“…The rendered shear and normal forces were generated based on the stress equations of the Mooney-Rivlin model given in (6) and (7), respectively. Three different model categories with C 1 = 100 Pa,C 1 = 200 Pa, and C 1 = 400 Pa, and 10 different ratios of C 2 C 1 Table 1: Parameters of virtual models used for force discrimination study.…”

“…Other work has focused on both qualitatively and quantitatively characterizing the efficiency, accuracy, and realism of haptic virtual environments, e.g. [5,6,22].…”

Preparing drivers for dangerous situations: A critical reflection on continuous shared control

“…The following advantages of shared control in comparison to manual control have been empirically demonstrated in driving simulator studies: more accurate path tracking [12], [14], [15], [23]- [25], reduced distance variability during car following [32], [33], reduced control activity [33], and reduced workload as demonstrated by faster reaction times and better secondary task performance [12], [16], [21], [22], [32], [34]. These effects are self-evident in some steering systems: When the driver conforms to the guiding forces (or even releases the hands from the steering wheel), the machine will stay in control and accurately steer the car along the target path (e.g., [35]).…”

Neuromuscular Analysis as a Guideline in designing Shared Control

“…These systems are sometimes called haptic guidance systems, or force feedback systems and are essentially dynamic extensions from the static 'virtual fixtures' approach (Rosenberg, 1993). A good example is a lane-keeping assistance system in which a motor acts on the steering wheel by adding torques (Griffiths & Gillespie, 2005;Forsyth & MacLean, 2006;Brandt et al, 2008;Mulder et al, 2008b). Note that the relationship between steering angle and tyre angle remains constant, which has been found to be important for vehicular steering (Toffin et al, 2007).…”

“…Although in general the reported shared control systems provided beneficial results such as improved performance and reduced mental load, negative effects were reported as well that seem to indicate that forces were tuned too high. Many subjects did not feel completely in control (e.g., Forsyth & MacLean, 2006), and it was somewhat difficult for subjects to avoid collisions not foreseen by the system (e.g., Griffith & Gillespie, 2005). Lacking quantitative knowledge of neuromuscular response to forces, it is quite difficult to optimally design the feedback forces.…”