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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Machine Learning

Background:

  • Deep Reinforcement Learning (DRL) offers advanced AI capabilities but faces training challenges in real-world environments due to time, cost, and safety concerns.
  • Training DRL agents in virtual environments is a common solution for real-world applications, bridging the gap between simulation and physical deployment.
  • Path planning for mobile robots in real-world scenarios presents significant challenges, necessitating robust and efficient navigation strategies.

Purpose of the Study:

  • To address the challenges of training Deep Reinforcement Learning (DRL) agents for mobile robot path planning in real-world environments.
  • To develop and implement a novel DRL algorithm that surpasses traditional methods like the Deep Q-network.
  • To create the first real-world implementation of a DRL agent trained in the Minimalistic Gridworld virtual environment for mobile robot navigation.

Main Methods:

  • Utilized the Minimalistic Gridworld as a virtual training environment for the DRL agent.
  • Developed an enhanced DRL algorithm with superior performance compared to the standard Deep Q-network.
  • Designed a mobile robot equipped with algorithms for real-time position and rotation detection to align virtual training with the real world.

Main Results:

  • Successfully trained a DRL agent in a virtual environment for mobile robot path planning.
  • Implemented algorithms to accurately match the virtual environment with real-world conditions, including robot and target localization and orientation.
  • Developed a DRL-based mobile robot capable of reaching its target from any initial position and rotation using only top-view environmental data.

Conclusions:

  • The developed DRL-based mobile robot effectively navigates and reaches its target in real-world environments.
  • This study demonstrates the feasibility and success of training DRL agents in virtual environments for complex real-world robotic tasks.
  • The approach offers a robust solution for mobile robot path planning, overcoming limitations of traditional methods and virtual-to-real-world transfer challenges.