On the kinematics of robot heads

Sharkey, Paul; Murray, David; Heuring, J.J.

doi:10.1109/70.585904

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…Our work extends past results we employed for creating realistic interactions between a human and a virtual actor [1]. Unlike other published work in this area [6], a straightforward, approximate kinematic model of humanoid robot head was used by our algorithm, rather than an accurate one. Moreover, the objective function in our algorithm is formed based on kinesiological properties of real human eyes (Listing's Law) [7,8] so that the resulting motion of the humanoid robot actor is similar to human's head-eye motion.…”

Section: Introductionmentioning

confidence: 90%

Realistic and robust head-eye coordination of conversational robot actors in human tracking applications

Rajruangrabin

Popa

2009

Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments

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Recent advances in computing and robot technology create new opportunities for building robots with increasingly more sophisticated interactivity. One such application is the visual interaction between humans and humanoid in tasks such as mimicking and following. Achieving realistic head-eye motion of the humanoid requires understanding of human kinesiology that dictates the way human coordinate head-eye motion and the ability to control the motion of humanoid to move in the same manner that humans do. In this paper we propose an efficient head-eye motion coordination scheme using an optimization approach -an objective function is formed based on human kinesiology and then optimized for obtaining a realistic head-eye trajectory. The tracking robustness during conversational interaction with a human is further enhanced through a visual feedback scheme, which reduces modelling errors of the humanoid hardware. Experimental results show the tracking efficiency and realism of the motion generated by the proposed scheme with Lilly, a humanoid under development in our lab.

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Section: Introductionmentioning

confidence: 90%

Realistic and robust head-eye coordination of conversational robot actors in human tracking applications

Rajruangrabin

Popa

2009

Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments

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“…Specifically, in order to simulate a human neck, a two-degree-of-freedom rotary platform was proposed, built with joints and links based on head kinematics. Therefore, the motion at the end-effector could be estimated through the motion in each joint based on forward kinematics [10,11], as shown in Figure 4.…”

Section: Motion Generator-based On Human Neck Movementmentioning

confidence: 99%

Time Sequential Motion-to-Photon Latency Measurement System for Virtual Reality Head-Mounted Displays

et al. 2018

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Because the interest in virtual reality (VR) has increased recently, studies on head-mounted displays (HMDs) have been actively conducted. However, HMD causes motion sickness and dizziness to the user, who is most affected by motion-to-photon latency. Therefore, equipment for measuring and quantifying this occurrence is very necessary. This paper proposes a novel system to measure and visualize the time sequential motion-to-photon latency in real time for HMDs. Conventional motion-to-photon latency measurement methods can measure the latency only at the beginning of the physical motion. On the other hand, the proposed method can measure the latency in real time at every input time. Specifically, it generates the rotation data with intensity levels of pixels on the measurement area, and it can obtain the motion-to-photon latency data in all temporal ranges. Concurrently, encoders measure the actual motion from a motion generator designed to control the actual posture of the HMD device. The proposed system conducts a comparison between two motions from encoders and the output image on a display. Finally, it calculates the motion-to-photon latency for all time points. The experiment shows that the latency increases from a minimum of 46.55 ms to a maximum of 154.63 ms according to the workload levels.

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“…Another logmap device consists of an emulated sensor based on a conventional CCD and an image warp algorithm embedded on a microcontroller [8]. More recently, a new trend towards smaller active vision systems comparable in size to the human head is pushing the limit of motor, gearbox and camera design [19,23,25].…”

Section: Pmentioning

confidence: 99%