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Related Experiment Video

Updated: Feb 2, 2026

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect
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Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown

Chun-Hui Lin1, Shyh-Hau Wang2, Cheng-Jian Lin3

  • 1Department of Computer Science & Information Engineering, Nation Cheng Kung University, Tainan 701, Taiwan. P78071044@mail.ncku.edu.tw.

Sensors (Basel, Switzerland)
|November 30, 2018
PubMed
Summary

This study introduces a novel navigation method for cooperative mobile robots using a behavior mode manager. The dynamic group artificial bee colony-enhanced fuzzy controller enables efficient load carrying and navigation in complex environments.

Keywords:
artificial bee colony algorithmcooperative carryingevolutionary robotfuzzy controlgrouping strategyinterval type-2 neural fuzzy controllernavigation controlwall-following control

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Cooperative mobile robots require sophisticated navigation strategies for tasks like load carrying.
  • Existing methods often struggle with dynamic environmental changes and efficient task completion.

Purpose of the Study:

  • To propose an effective navigation method for cooperative load-carrying mobile robots.
  • To enhance robot adaptability and efficiency in diverse environmental conditions.

Main Methods:

  • A behavior mode manager switching between wall-following mode (WFM) and goal-oriented mode (GOM).
  • An interval type-2 neural fuzzy controller optimized by a dynamic group artificial bee colony (DGABC) algorithm.
  • Adaptive WFM development using reinforcement learning, initially for a single robot, then extended to cooperative robots.

Main Results:

  • The DGABC algorithm demonstrated superior performance in WFM learning compared to standard and improved artificial bee colony algorithms.
  • Cooperative navigation tests confirmed the method's efficiency in transporting task items to the goal.
  • The proposed navigation system successfully enabled robots to complete missions efficiently.

Conclusions:

  • The developed navigation method effectively manages cooperative load-carrying mobile robots.
  • The integration of DGABC and adaptive WFM learning enhances robotic navigation capabilities.
  • This approach offers a robust solution for efficient cooperative robotic missions.