Estimation of Time-to-Contact for Navigation of Autonomous Robots Using Parallel Processing

2016 ◽  
Author(s):  
Angel Juan Sanchez Garcia ◽  
Homero Vladimir Rios Figueroa ◽  
Guillermo De Jesus Hoyos Rivera ◽  
Antonio Marin Hernandez
Keyword(s):  
Parallel Processing ◽  
Autonomous Robots ◽  
Time To Contact ◽  
Estimation Of Time

Estimation of Time-to-Contact from Retinal Flow

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 169-169
Author(s):  
M G Harris
Keyword(s):  
Prior Knowledge ◽  
Human Performance ◽  
Rotational Component ◽  
Time To Contact ◽  
Focus Of Expansion ◽  
The World ◽  
New Models ◽  
Smooth Planes ◽  
Estimation Of Time ◽  
Rotation Component

We investigated four models for estimating time-to-contact (TTC) from retinal flow. Lee's model can deal with sparse flow but fails if the flow contains a rotational component. Koenderink's model, based on div, can deal with rotation but fails if the flow is sparse or if the world does not vary coherently in depth. Two new models were developed by representing retinal flow as the sum of an expansion and a rotation component. The first operates on pairs of points and can deal with sparse flow but fails if the world does not vary coherently in depth. Uniquely, this model provides TTC estimates without prior knowledge of either the focus of expansion (FOE) or focus of rotation (FOR). The second model estimates both the FOE and the FOR and then operates on a point-by-point basis. This model can deal with incoherent depth variations. We compared human performance with these different model properties by requiring subjects to estimate FOE and TTC from random-dot kinematograms. We used kinematograms depicting smooth planes and random 3-D clouds of points, and systematically varied the density of the flow. Performance was not substantially reduced by sparse flow or by incoherent depth, which argues against Koenderink's and the first of our own models. Performance remained good when rotation was added to the flow, which argues against Lee's model. Overall, the data favour a model that first decomposes flow into expansion and rotation components and then estimates TTC on a point-by-point basis.

Estimation of time-to-contact from Tau-margin and statistical analysis of behavior

2016 ◽  
Author(s):  
Angel Juan Sanchez Garcia ◽  
Homero Vladimir Rios Figueroa ◽  
Antonio Marin Hernandez ◽  
Maria Karen Cortes Verdin ◽  
Gerardo Contreras Vega
Keyword(s):  
Statistical Analysis ◽  
Time To Contact ◽  
Estimation Of Time

scholarly journals Estimation of time to contact in lateral motion and approach motion

2018 ◽  
Author(s):  
Asieh Daneshi
Keyword(s):  
Mathematical Analysis ◽  
Human Subjects ◽  
Dynamic Environments ◽  
Lateral Motion ◽  
Movement Initiation ◽  
Time To Contact ◽  
Estimation Of Time

The ability to estimate precisely the time to contact (TTC) of the objects is necessary for planning actions in dynamic environments. However, this ability is not the same for all kinds of movement. Sometimes tracking an object and estimating its TTC is easy and accurate and sometimes it is not. In this study, we asked human subjects to estimate TTC of an object in lateral motion and approach motion. The object became invisible shortly after movement initiation. The results proved that TTC estimation for lateral motion is more accurate than for approach motion. We used mathematical analysis to show why humans are better in estimating TTC for lateral motion than for approach motion.

scholarly journals Integration of speed and time for estimating time to contact

2018 ◽  
Vol 115 (12) ◽  
pp. E2879-E2887 ◽  
Author(s):  
Chia-Jung Chang ◽  
Mehrdad Jazayeri
Keyword(s):  
Dynamic Environment ◽  
Tennis Player ◽  
Sources Of Information ◽  
Time To Contact ◽  
Dynamic Stimuli ◽  
Time Estimates ◽  
Temporal Cues ◽  
Timing Mechanisms ◽  
Estimation Of Time ◽  
Different Sources

To coordinate movements with events in a dynamic environment the brain has to anticipate when those events occur. A classic example is the estimation of time to contact (TTC), that is, when an object reaches a target. It is thought that TTC is estimated from kinematic variables. For example, a tennis player might use an estimate of distance (d) and speed (v) to estimate TTC (TTC = d/v). However, the tennis player may instead estimate TTC as twice the time it takes for the ball to move from the serve line to the net line. This latter strategy does not rely on kinematics and instead computes TTC solely from temporal cues. Which of these two strategies do humans use to estimate TTC? Considering that both speed and time estimates are inherently uncertain and the ability of the human brain to combine different sources of information, we hypothesized that humans estimate TTC by integrating speed information with temporal cues. We evaluated this hypothesis systematically using psychophysics and Bayesian modeling. Results indicated that humans rely on both speed information and temporal cues and integrate them to optimize their TTC estimates when both cues are present. These findings suggest that the brain’s timing mechanisms are actively engaged when interacting with dynamic stimuli.

Eye movements influence estimation of time-to-contact in prediction motion

2010 ◽  
Vol 206 (4) ◽  
pp. 399-407 ◽  
Author(s):  
Simon J. Bennett ◽  
Robin Baures ◽  
Heiko Hecht ◽  
Nicolas Benguigui
Keyword(s):  
Eye Movements ◽  
Time To Contact ◽  
Prediction Motion ◽  
Estimation Of Time
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