We conducted a series of experiments to investigate effects of scale changes on distance perception in virtual environments. All experiments were carried out in an HMD. Participants first made distance estimates with feedback in a virtual tunnel (adaptation) and then made distance estimates without feedback in a differently-scaled virtual environment (test). We examined several types of scale changes, including changing the size of (1) the tunnel, (2) the targets, and (3) the separation of the two targets. Changes in target size always affected distance estimates at test. When the targets became smaller, participants overshot distance and when the targets became larger, participants undershot distance. Changes in the size of the tunnel or the separation between the targets (without a change in the size of the targets) had a minimal effect on distance estimates. These results indicate that distance estimates at test were strongly influenced by familiar size cues for distance. The discussion focuses on the stability of calibration processes and mechanisms for cue integration for perceiving distance in virtual environments.

The goal of our work is to develop a programmatically controlled peer to bicycle with a human subject for the purpose of studying how social interactions influence road-crossing behavior. The peer is controlled through a combination of reactive controllers that determine the gross motion of the virtual bicycle, action-based controllers that animate the virtual bicyclist and generate verbal behaviors, and a keyboard interface that allows an experimenter to initiate the virtual bicyclist's actions during the course of an experiment. The virtual bicyclist's repertoire of behaviors includes road following, riding alongside the human rider, stopping at intersections, and crossing intersections through specified gaps in traffic. The virtual cyclist engages the human subject through gaze, gesture, and verbal interactions. We describe the structure of the behavior code and report the results of a study examining how 10- and 12-year-old children interact with a peer cyclist that makes either risky or safe choices in selecting gaps in traffic. Results of our study revealed that children who rode with a risky peer were more likely to cross intermediate-sized gaps than children who rode with a safe peer. In addition, children were significantly less likely to stop at the last six intersections after the experience of riding with the risky than the safe peer during the first six intersections. The results of the study and children's reactions to the virtual peer indicate that our virtual peer framework is a promising platform for future behavioral studies of peer influences on children's bicycle riding behavior.

We conducted two experiments that compared distance perception in real and virtual environments in six visual presentation methods using either timed imagined walking or direct blindfolded walking, while controlling for several other factors that could potentially impact distance perception. Our presentation conditions included unencumbered real world, real world seen through an HMD, virtual world seen through an HMD, augmented reality seen through an HMD, virtual world seen on multiple, large immersive screens, and photo-based presentation of the real world seen on multiple, large immersive screens. We found that there was a similar degree of underestimation of distance in the HMD and large-screen presentations of virtual environments. We also found that while wearing the HMD can cause some degree of distance underestimation, this effect depends on the measurement protocol used. Finally, we found that photo-based presentation did not help to improve distance perception in a large-screen immersive display system. The discussion focuses on points of similarity and difference with previous work on distance estimation in real and virtual environments.

The goal of our work is to develop a programmatically controlled peer to ride with a human subject for the purpose of studying how social interactions influence riding behavior. The peer is controlled through a combination of reactive controllers that determine the gross motion of the virtual bicycle, action-based controllers that animate the virtual bicyclist and generate verbal behaviors, and a keyboard interface that allows an experimenter to initiate the virtual bicyclist's actions during the course of an experiment. The virtual bicyclist's repertoire of behaviors includes road following, riding alongside the human rider, stopping at intersections, and crossing intersections through specified gaps. The virtual cyclist engages the human subject through gaze, gesture, and verbal interactions. We describe the structure of the behavior code and report the results of a pilot study examining how 10- and 12-year-old children interact with a peer cyclist. Results of the pilot study showed that the presence of the peer had a significant influence on the size of the gaps taken as well as time left to spare between the participant and the trailing car in the crossed gap.

This paper presents steering behaviors that control autonomous vehicles populating roadways in virtual urban environments. Behavior programming is facilitated by a set of representations of the environment that use convenient frames of reference in natural coordinate systems. Roadway surfaces are modeled as three-dimensional ribbons that make the local orientation of the road explicit and allow relative distances on the road to be simply computed. Roads and intersections are connected to form a ribbon network. An egocentric representation called a path melds road and intersection segments into a single, continuous ribbon that captures the vehicle’s shortterm plan of navigation. A topological structure called a route supports wayfinding. We describe how the interrelated ribbon, path, and route representations are used to build multi-component behaviors that plan routes and safely navigate through traffic filled road networks – tracking lanes, shifting lanes to avoid congestion, anticipating lane changes needed to make turns dictated by the route, negotiating intersections, and respecting the rules of the road.

We conducted three experiments to compare distance perception in real and virtual environments. In Experiment 1, adults estimated how long it would take to walk to targets in real and virtual environments by starting and stopping a stopwatch while looking at a target person standing between 20 and 120 ft away. The real environment was a large grassy lawn in front of a university building. We replicated this scene in our virtual environment using a nonstereoscopic, large-screen immersive display system. We found that people underestimated time to walk in both environments for distances of 40 to 60 ft and beyond. However, time-to-walk estimates were virtually identical across the two environments, particularly when people made real environment estimates first. In Experiment 2, 10- and 12-year-old children and adults estimated time to walk in real and virtual environments both with and without vision. Adults underestimated time to walk in both environments for distances of 60 to 80 ft and beyond. Again, their estimates were virtually identical in the real and virtual environment both with and without vision. Twelve-year-olds' time-to-walk estimates were also very similar across the two environments under both viewing conditions, but 10-year-olds exhibited greater underestimation in the virtual than in the real environment. A third experiment showed that adults' time-to-walk estimates were virtually identical to walking without vision. We conclude that distance perception may be better in virtual environments involving large-screen immersive displays than in those involving head-mounted displays (HMDS).

This paper describes formulation and management of constraints, and a nonlinear optimization algorithm that together enable interactive geometrically aware manipulation of articulated objects. Going beyond purely kinematic or dynamic approaches, our solution method directly employs geometric constraints to ensure non‐interpenetration during object manipulation. We present the formulation of the inequality constraints used to ensure nonpenetration, describe how to manage the set of active inequality constraints as objects move, and show how these results are combined with a nonlinear optimization algorithm to achieve interactive geometrically aware object manipulation. Our optimization algorithm handles equality and inequality constraints and does not restrict object topology. It is an efficient iterative algorithm, quadratically convergent, with each iteration bounded by O(nnz(L)), where nnz(L) is the number of non‐zeros in L, a Cholesky factor of a sparse matrix.

Techniques for interactive 3D manipulation of articulated objects in cluttered environments should be geometrically aware, going beyond basic inverse or forward kinematics to allow contact while preventing interpenetration. This paper describes a general purpose interactive object manipulation technique using nonlinear optimization. The method converts geometry awareness into sets of inequality constraints and handles nonlinear equality and inequality constraints efficiently without restricting object topology. Our iterative algorithm has a quadratic convergence rate and each iteration can be solved in O(n/sub nz/(L)), where n/sub nz/(L) is the number of non-zeros in L, a Cholesky factor of a sparse matrix. To promote additional speedup, symbolic factorization is separated from numerical computation. Our approach provides a framework for using optimization techniques in interactive tools for building and manipulating models in constrained, cluttered environments.

Abstract This paper examines the control of complex physical objects in simulation. We introduce a programming paradigm that allows a simulation to be treated as a multi‐level constraint solver. The control programmer is given the ability to specify constraints on the controlled response of mechanisms and to conditionally change these constraints dependent on the state of system. The approach facilitates the development of model‐based, event‐driven control programs. The usefulness of the paradigm is demonstrated through the simulation of a hopping robot.

The architecture of Newton, a general-purpose system for simulating the dynamics of complex physical objects, is described. The system automatically formulates and analyzes equations of motion, and performs automatic modification of this system of equations when necessitated by changes in kinematic relationships between objects. Impact and temporary contact are handled, although only using simple models. User-directed influence of simulations is achieved using Newton's module, which can be used to experiment with the control of many-degree-of-freedom articulated objects. >

The authors suggest that there are two components required in most control schemes: an algorithmic component which takes a high-level goal and generates joint trajectories, and a dynamics component which takes these joint trajectories and generates the required joint torques. The joint torques are then sent to the mechanism, which (it is hoped) achieves the high-level goal. An approach is presented which simplifies the programming of the algorithmic component for high-degree-of-freedom objects. Instead of supplying a complete set of joint trajectories as a function of time, the algorithm controls other, more intuitive, degrees of freedom. The degrees of freedom are automatically converted to the more conventional joint trajectories. The algorithm can underconstrain the object, in which case constrained optimization is used in converting to joint trajectories. This approach is applied to generate joint trajectories for a walking biped. The walking algorithm, is presented along with the results from testing with the authors' Newton simulation system (ibid., vol.3, p.1806-11).>