Biped robots have better mobility than conventional wheeled robots, but they tend to tip over easily. To be able to walk stably in various environments, such as on rough terrain, up and down slopes, or in regions containing obstacles, it is necessary for the robot to adapt to the ground conditions with a foot motion, and maintain its stability with a torso motion. When the ground conditions and stability constraint are satisfied, it is desirable to select a walking pattern that requires small torque and velocity of the joint actuators. We first formulate the constraints of the foot motion parameters. By varying the values of the constraint parameters, we can produce different types of foot motion to adapt to ground conditions. We then propose a method for formulating the problem of the smooth hip motion with the largest stability margin using only two parameters, and derive the hip trajectory by iterative computation. Finally, the correlation between the actuator specifications and the walking patterns is described through simulation studies, and the effectiveness of the proposed methods is confirmed by simulation examples and experimental results.

/pdf/planning-walking-patterns-for-a-biped-robot-18hxylrb20.pdf

Planning walking patterns for a biped robot

Contents: Foreword. Preface. What Are Statistical Decisions? Non-Parametric Measures of Sensitivity. Gaussian Distributions of Signal and Noise With Equal Variances. Gaussian Distributions of Signal and Noise With Unequal Variances. Conducting a Rating Scale Experiment. Choice Theory Approximations to Signal Detection Theory. Threshold Theory. The Laws of Categorical and Comparative Judgement. Appendices: Answers to Problems. Logarithms. Integration of the Expression for the Logistic Curve. Computer Programmes for Signal Detection Analysis. Tables.

A Primer of Signal Detection Theory; Table of D' and β

Referring as a Collaborative Process

Transportation research. part c, emerging technologies

The Lean Startup: How today's entrepreneurs use continuous innovation to create radically successful businesses

We report on the results of a study in which pairs of subjects were involved in spoken dialogues and one of the subjects also operated a simulated vehicle. We estimated the driver's cognitive load based on pupil size measurements from a remote eye tracker. We compared the cognitive load estimates based on the physiological pupillometric data and driving performance data. The physiological and performance measures show high correspondence suggesting that remote eye tracking might provide reliable driver cognitive load estimation, especially in simulators. We also introduced a new pupillometric cognitive load measure that shows promise in tracking cognitive load changes on time scales of several seconds.

http://www.andrewkun.com/papers/2010/ETRA10_Palinko_Estimating_Cognitive_Load_final.pdf

Estimating cognitive load using remote eye tracking in a driving simulator

The field of automotive user interfaces has developed rapidly over the last several years. To date, the field has primarily focused on creating user interfaces that promote safe driving, including when the driver is engaged in a secondary task in addition to operating the vehicle. However, researchers now need to prepare for a major change in the automotive domain: the automated driving revolution. The authors argue for a new research agenda that focuses on four challenges for automotive user interfaces: assuring safety in the age of automation, transforming vehicles into places for productivity and play, taking advantage of new mobility options made possible by automated vehicles, while throughout all this preserving user privacy and data security. This article is part of a special issue on smart vehicle spaces.

Shifting Gears: User Interfaces in the Age of Autonomous Driving

Prior research has shown that when drivers look away from the road to view a personal navigation device (PND), driving performance is affected. To keep visual attention on the road, an augmented reality (AR) PND using a heads-up display could overlay a navigation route. In this paper, we compare the AR PND, a technology that does not currently exist but can be simulated, with two PND technologies that are popular today: an egocentric street view PND and the standard map-based PND. Using a high-fidelity driving simulator, we examine the effect of all three PNDs on driving performance in a city traffic environment where constant, alert attention is required. Based on both objective and subjective measures, experimental results show that the AR PND exhibits the least negative impact on driving. We discuss the implications of these findings on PND design as well as methods for potential improvement.

/pdf/augmented-reality-vs-street-views-a-driving-simulator-study-40646otff4.pdf

Augmented reality vs. street views: a driving simulator study comparing two emerging navigation aids

In this paper, we present a proof-of-concept approach to estimating mental workload by measuring the user's pupil diameter under various controlled lighting conditions. Knowing the user's mental workload is desirable for many application scenarios, ranging from driving a car, to adaptive workplace setups. Typically, physiological sensors allow inferring mental workload, but these sensors might be rather uncomfortable to wear. Measuring pupil diameter through remote eye-tracking instead is an unobtrusive method. However, a practical eye-tracking-based system must also account for pupil changes due to variable lighting conditions. Based on the results of a study with tasks of varying mental demand and six different lighting conditions, we built a simple model that is able to infer the workload independently of the lighting condition in 75% of the tested conditions.

/pdf/a-model-relating-pupil-diameter-to-mental-workload-and-e5f08npsaz.pdf

A Model Relating Pupil Diameter to Mental Workload and Lighting Conditions

An adaptive dynamic balance scheme was implemented and tested on an experimental biped. The control scheme used pre-planned but adaptive motion sequences. CMAC neural networks were responsible for the adaptive control of side-to-side and front-to-back balance, as well as for maintaining good foot contact. Qualitative and quantitative test results show that the biped performance improved with neural network training. The biped is able to start and stop on demand, and to walk with continuous motion on flat surfaces at a rate of up to 100 steps per minute, with up to 6 cm long step.

Andrew L. Kun

Papers

Estimating cognitive load using remote eye tracking in a driving simulator

Shifting Gears: User Interfaces in the Age of Autonomous Driving

Augmented reality vs. street views: a driving simulator study comparing two emerging navigation aids

A Model Relating Pupil Diameter to Mental Workload and Lighting Conditions

Adaptive dynamic balance of a biped robot using neural networks