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Model-Predictive Control for Omnidirectional Mobile Robots in Logistic Environments Based on Object Detection Using

Stefan-Daniel Achirei1, Razvan Mocanu2, Alexandru-Tudor Popovici1

  • 1Department of Computer Engineering, "Gheorghe Asachi" Technical University of Iasi, 700050 Iasi, Romania.

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Summary
This summary is machine-generated.

This study presents a custom-trained convolutional neural network (CNN) for object detection in logistic environments, optimized for mobile robots. It also introduces a predictive controller for efficient navigation using CNN-detected objects and LIDAR data.

Keywords:
computer visionconvolutional neural networksdepth sensingdiscretized-time modelnavigationobject detectionomnidirectional mobile robotspredictive control algorithm

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Control Systems Engineering

Background:

  • Object detection is crucial for autonomous mobile robots to perceive and interact with their surroundings.
  • Convolutional Neural Networks (CNNs) have advanced object recognition capabilities, particularly in complex environments like logistics.
  • Integrating environment perception with motion control is a key research area for robotic navigation.

Purpose of the Study:

  • To develop and optimize an object detector for mobile robot platforms in logistic settings.
  • To introduce a model-based predictive controller for guiding omnidirectional robots using CNN-derived object maps and LIDAR data.
  • To enhance the safety, optimality, and efficiency of mobile robot pathfinding in warehouses.

Main Methods:

  • A custom CNN model was trained using an in-house acquired dataset for object detection in a warehouse environment.
  • The CNN model was optimized for deployment on a mobile robot's onboard platform.
  • A model-based predictive controller was simulated to navigate an omnidirectional robot using object detection outputs and LIDAR data.

Main Results:

  • Successful object detection was achieved using the custom-trained CNN on a mobile platform.
  • The predictive control approach demonstrated effective navigation based on detected objects.
  • The integrated system showed potential for safe and efficient path planning in logistic environments.

Conclusions:

  • The custom-trained and optimized CNN model effectively performs object detection for mobile robots in logistic scenarios.
  • The predictive controller, utilizing CNN object detection and LIDAR, enables efficient robot navigation.
  • This research contributes to advancing autonomous capabilities for mobile robots in complex operational environments.