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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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In the site survey of a four-sided traverse, internal angles are essential to ensure geometric accuracy. The survey revealed that the sum of the measured internal angles was 359 degrees and 48 minutes, which is 12 minutes less than the expected 360 degrees. This discrepancy signals an error likely arising from measurement inaccuracies during the fieldwork.To rectify this error, the adjustment process involved distributing the 12-minute shortfall equally across the four internal angles. By...
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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
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An Improved Rapidly-Exploring Random Trees Algorithm Combining Parent Point Priority Determination Strategy and

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  • 1School of Technology, Beijing Forestry University, Beijing 100083, China.

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|October 26, 2021
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Summary

This study introduces an improved algorithm for rapid path planning, significantly reducing planning time and redundant points. The enhanced method achieves near 100% success rates for robot path planning.

Keywords:
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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Rapidly-exploring Random Trees (RRT) algorithm faces challenges with long path planning times and numerous redundant points.
  • Efficient path planning is crucial for autonomous systems and robotics.

Purpose of the Study:

  • To optimize the RRT algorithm by reducing path planning time and the number of redundant points.
  • To enhance the success rate and efficiency of path planning for robotic manipulators.

Main Methods:

  • Implemented a parent point priority determination strategy to expedite parent point selection.
  • Integrated a real-time optimization strategy comparing distances to the target point to minimize redundant points.
  • Validated the improved algorithm through 3D path planning simulations and a physical IRB1410 manipulator testbed.

Main Results:

  • Achieved a success rate close to 100% in 3D path planning simulations.
  • Reduced the number of points by over 93.25% compared to the standard RRT algorithm.
  • Decreased path planning time by more than 91.49% and path length by over 7.88%.

Conclusions:

  • The proposed algorithm significantly outperforms the standard RRT in terms of success rate, point reduction, planning time, and path length.
  • The optimized RRT algorithm enables robotic manipulators to safely navigate and reach target points while avoiding obstacles.
  • This enhanced path planning strategy offers substantial improvements for real-world robotic applications.