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Related Concept Videos

Sight Distance in a Vertical Curve01:29

Sight Distance in a Vertical Curve

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Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
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Horizontal Curve: Problem Solving01:03

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A horizontal curve is characterized by its radius, intersection angle, and stationing of key points. In this case, the radius is 400 meters, and the angle of intersection is 30 degrees, with the station of the point of curvature (P.C.) at 0 + 150 meters. The goal is to determine the station values at the point of intersection (P.I.), point of tangency (P.T.), and midpoint of the curve, as well as the length of the long chord.The process begins with calculating the tangent distance (T) and the...
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Design Example: Measuring Distance Between Two Points with Obstructions01:10

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When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
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Vertical Curve: Problem Solving01:23

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Vertical curves provide the transition between two roadway grades, ensuring safety, comfort, and functionality. Calculating elevations at specific stations along the curve involves several systematic steps based on the curve's geometry and provided design parameters.The vertical curve is defined by its length, grades, Point of Vertical Intersection (P.V.I.) location, and P.V.I. elevation. The stations of the Point of Vertical Curvature (P.V.C.), where the curve begins, and the Point of Vertical...
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Uniform Depth Channel Flow: Problem Solving01:18

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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The Curved Openspace Algorithm and a Spike-Latency Model for Sonar-Based Obstacle Avoidance.

Chenxi Wen1, Timothy K Horiuchi1,2

  • 1Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States.

Frontiers in Neurorobotics
|June 20, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neural network model for autonomous robot navigation, enabling obstacle avoidance and goal pursuit using sonar data. The model efficiently processes sensory information for effective real-time control.

Keywords:
attentionbat echolocationcollision avoidanceneural modelroboticsspike latencywinner-take-all

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Autonomous vehicle navigation, particularly with sonar, faces challenges due to limited sensor fields-of-view and complex vehicle dynamics.
  • Effective obstacle avoidance and goal-directed movement are critical for mobile robot applications.

Purpose of the Study:

  • To develop and evaluate a neural model for real-time obstacle avoidance in sonar-guided vehicles.
  • To enhance sensory data collection efficiency using an attentional system for sonar ping control.

Main Methods:

  • A neural model was proposed that maps 2-D sonar sensory space to 1-D motor space, integrating obstacle and goal information.
  • A two-stage winner-take-all (WTA) mechanism was employed for steering action selection.
  • An attentional system was developed to optimize sonar ping directions, minimizing environmental scanning.

Main Results:

  • The proposed neural model successfully enabled a mobile robot to navigate cluttered environments using a narrow field-of-view sonar system.
  • The attentional system improved the efficiency of sensory data collection for task-driven navigation.
  • A spiking neural model variant was introduced, incorporating spike-timing representations and a race-to-first-spike WTA circuit.

Conclusions:

  • The developed neural model provides an effective solution for obstacle avoidance and goal pursuit in sonar-guided robots with limited sensing capabilities.
  • The attentional system enhances navigation efficiency by optimizing sensor data acquisition.
  • The spiking neural network approach offers a biologically plausible and potentially more efficient alternative for future robotic control systems.