Obstacle Avoidance Technique for Mobile Robots at Autonomous Human-Robot Collaborative Warehouse Environments

This paper presents an obstacle avoidance technique for a mobile robot in human-robot collaborative (HRC) tasks. The proposed solution uses fuzzy logic rules and a convolutional neural network (CNN) in an integrated approach to detect objects during vehicle movement. The goal is to improve the robot...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 25; číslo 8; s. 2387
Hlavní autoři: Sousa, Lucas C., Silva, Yago M. R., Schettino, Vinícius B., Santos, Tatiana M. B., Zachi, Alessandro R. L., Gouvêa, Josiel A., Pinto, Milena F.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 09.04.2025
MDPI
Témata:
Algorithms
Artificial intelligence
Cameras
Collaboration
Comparative analysis
Control
Coordinate transformations
Decision making
Efficiency
Fuzzy algorithms
Fuzzy Logic
Fuzzy systems
human-robot coexistence
Humans
industrial environment
Manufacturing
Methods
Mobile robots
Navigation
Navigation systems
Neural networks
Neural Networks, Computer
Productivity
robot navigation
Robotics
Robotics - methods
Robots
safety collaboration
Sensors
shareable workspace
Technology application
Workers
Brazil
robot navigation
industrial environment
shareable workspace
human-robot coexistence
safety collaboration
ISSN:1424-8220, 1424-8220
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Shrnutí:This paper presents an obstacle avoidance technique for a mobile robot in human-robot collaborative (HRC) tasks. The proposed solution uses fuzzy logic rules and a convolutional neural network (CNN) in an integrated approach to detect objects during vehicle movement. The goal is to improve the robot’s navigation autonomously and ensure the safety of people and equipment in dynamic environments. Using this technique, it is possible to provide important references to the robot’s internal control system, guiding it to continuously adjust its velocity and yaw in order to avoid obstacles (humans and moving objects) while following the path planned for its task. The approach aims to improve operational safety without compromising productivity, addressing critical challenges in collaborative robotics. The system was tested in a simulated environment using the Robot Operating System (ROS) and Gazebo to demonstrate the effectiveness of navigation and obstacle avoidance. The results obtained with the application of the proposed technique indicate that the framework allows real-time adaptation and safe interaction between robot and obstacles in complex and changing industrial workspaces.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25082387