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Aerial manipulation of long objects using adaptive neuro-fuzzy controller under battery variability.

Praveen Kumar Muthusamy1,2, Mohammed Basheer Mohiuddin3,4, Anees Peringal5,6

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Summary
This summary is machine-generated.

This study introduces a modular aerial manipulation system for precise long object handling in challenging environments. The SO-BFBEL controller significantly improves stability and reduces errors, enhancing operational efficiency.

Keywords:
Brain emotional learningFlight controlFuzzy neural networkQuadrotorTwo finger gripperWind disturbance

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Area of Science:

  • Robotics and Automation
  • Civil Engineering Applications
  • Aerial Systems

Background:

  • Aerial manipulation offers versatile solutions for tasks in confined spaces, crucial for civil engineering and disaster response.
  • Existing single- or dual-arm systems face scalability and maintenance challenges.
  • Precise handling of long objects like pipes in uncertain conditions remains a significant challenge.

Purpose of the Study:

  • To present a Unmanned Aerial Vehicle (UAV)-based aerial manipulation system for precise handling and transportation of long objects.
  • To introduce a modular two-finger gripper design for enhanced scalability and reliability.
  • To evaluate the performance of a SO-BFBEL controller against conventional methods for improved stability and precision.

Main Methods:

  • Development of a modular two-finger gripper for UAV-based aerial manipulation.
  • Implementation of a SO-BFBEL (Sliding Mode Based Feedback Error Learning) controller for enhanced stability and precision.
  • Comparative analysis of the SO-BFBEL controller with DNN-MRFT-based PID and Fuzzy SMC controllers.

Main Results:

  • The SO-BFBEL controller reduced position tracking error by up to 50% compared to conventional controllers.
  • The SO-BFBEL controller demonstrated superior compensation for wind disturbances and battery discharge fluctuations.
  • The SO-BFBEL controller contributed to conserving battery life during manipulation tasks.

Conclusions:

  • The proposed UAV-based aerial manipulation system with a modular gripper and SO-BFBEL controller offers a scalable and reliable solution for precise long object handling.
  • The SO-BFBEL controller significantly enhances system stability, precision, and robustness against environmental disturbances.
  • The system improves operational efficiency and conserves battery life, providing cost-effective advantages for civil engineering and disaster response applications.