Trajectory Tracking
Trajectory Tracking - Related Videos
16:14
Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Cited by 15 •
2013
A suite of spatiotemporal processing methods are presented to analyze human trajectory data, such as that collected using a GPS device, for the purpose of modeling pedestrian space-time activities.
04:24
Studying Cell Rolling Trajectories on Asymmetric Receptor Patterns
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Cited by 3 •
2011
We describe a protocol to observe and analyze cell rolling trajectories on asymmetric receptor-patterned substrates. The resulting data are useful for engineering of receptor-patterned substrates for label-free cell separation and analysis.
01:26
Orthogonal Trajectories
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2026
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
07:36
C. elegans Tracking and Behavioral Measurement
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Cited by 14 •
2012
We have developed a video-rate tracking microscope system that can record and quantify C. elegans behavior at high resolution and high speeds. We have also developed computational methods to reduce the dimensionality of the worm images to a fundamental set of measurements that completely describe the shape of the worm.
12:58
In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology
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Cited by 11 •
2015
Particle-tracking microrheology investigates the viscoelasticity of materials. Here, the technique is used to determine the viscoelasticity, creep compliance and effective crosslinking roles of different matrix components of a bacterial biofilm. The matrix consists of polymeric substances secreted by the bacteria and its components determine biofilm structure and mechanical properties.
FAQs
A controller continuously compares the desired position, velocity, or orientation with measured values to identify the current tracking error. It then modifies an available input, such as force, torque, or steering, in response to that discrepancy. This feedback loop helps the system correct deviations while maintaining stable motion as the reference changes over time.
The system’s dynamics determine how changes in force, torque, or steering affect its motion, so controller adjustments must account for that behavior. Disturbances can create additional deviations from the desired path even when the system model is adequate. Designing for both effects improves robustness, meaning the system can continue tracking reliably under changing conditions.
Response time, tracking accuracy, and robustness provide complementary evidence about controller performance. Accuracy indicates how closely the system follows the desired motion, while response time describes how quickly it reacts to changes or errors. Robustness reflects its ability to preserve acceptable behavior despite system dynamics and disturbances. Together, these measures support balanced controller assessment.
The process begins by specifying a time-varying reference and identifying measurable system variables such as position, velocity, or orientation. Engineers then use a mathematical model to relate control inputs to system motion, compare measurements with the reference, and design feedback adjustments. Finally, they assess response time, accuracy, and robustness to judge whether the controller meets its requirements.
Trajectory tracking can use measurements of position, velocity, and orientation to determine how the system differs from its desired motion. Depending on the engineering system, corrective inputs may include force, torque, or steering. Matching the measured variables and available inputs to the system’s motion allows the controller to reduce error through appropriate feedback adjustments.
The approach supports precise motion and reliable task execution in robotics, autonomous vehicles, aerospace systems, and industrial automation. In each setting, the desired motion may change with time, while the controller must maintain accuracy and stability. Its value is especially clear when deviations could reduce operational performance, compromise safe operation, or prevent completion of a planned task.