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Endpoint Distribution Modeling-Based Capture Algorithm for Interfering Multi-Target.

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  • 1The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.

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Summary
This summary is machine-generated.

New models for physical pointing interactions improve target selection accuracy. The proposed algorithms enhance precision for handheld devices and drone control systems, overcoming limitations of current methods.

Keywords:
human–computer interactionpointing interactionremote controlstarget capturetarget distribution modeltrajectory planning

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Area of Science:

  • Human-Computer Interaction
  • Robotics and Control Systems

Background:

  • Pointing interactions in physical spaces lack virtual feedback, relying on user perception.
  • Existing models for target distribution in physical pointing are sparse and simplistic.

Purpose of the Study:

  • To propose novel theoretical models for target distribution in physical pointing interactions.
  • To develop and validate region partitioning algorithms for enhanced pointing accuracy.

Main Methods:

  • Introduced the single-target pointing endpoint distribution model (ST-PEDM) and multi-target pointing endpoint distribution model (MT-PEDM).
  • Developed the basic region partitioning algorithm (BRPA) and enhanced region partitioning algorithm (ERPA).
  • Conducted experiments with 15 participants to validate the models and algorithms.

Main Results:

  • The proposed distribution models accurately describe target areas.
  • ERPA algorithm significantly improved pointing accuracy, reaching 84.11% at 200 cm and 82.74% at 300 cm.
  • BRPA and ERPA algorithms demonstrated high precision and efficiency in determining user intentions.

Conclusions:

  • The developed models and algorithms offer a robust solution for physical pointing interactions.
  • This technology has potential applications in handheld devices and drone swarm control.
  • Improved pointing accuracy facilitates rapid target capture and trajectory planning in complex scenarios.