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A Comparison Study between Traditional and Deep-Reinforcement-Learning-Based Algorithms for Indoor Autonomous

Diego Arce1, Jans Solano1, Cesar Beltrán1

  • 1Engineering Department, Pontificia Universidad Católica del Perú, San Miguel, Lima 15088, Peru.

Sensors (Basel, Switzerland)
|December 23, 2023
PubMed
Summary
This summary is machine-generated.

Choosing between traditional and artificial intelligence (AI) algorithms for mobile robot autonomous navigation in dynamic environments requires careful consideration. This study compares Dynamic Window Approach (DWA), Timed Elastic Band (TEB), Deep Reinforcement Learning (DRL) based CADRL, and Soft Actor-Critic (SAC) algorithms.

Keywords:
DRL-based navigationcomparison studydynamic scenariosindoor autonomous navigationmobile robotstraditional navigation

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Autonomous navigation for mobile robots necessitates a choice between traditional control and artificial intelligence (AI) algorithms.
  • This decision is complex, influenced by robot computational capacity, sensor data, and environmental dynamics.
  • Selecting appropriate algorithms is crucial for effective navigation, especially in complex, changing environments.

Purpose of the Study:

  • To review and identify suitable autonomous navigation algorithms for mobile robots operating in dynamic environments.
  • To compare traditional algorithms with Deep Reinforcement Learning (DRL)-based approaches.
  • To provide recommendations for algorithm selection based on robot development requirements.

Main Methods:

  • A comprehensive review of autonomous navigation algorithms was conducted.
  • Selected algorithms, including Dynamic Window Approach (DWA), Timed Elastic Band (TEB), CADRL, and Soft Actor-Critic (SAC), were compared.
  • Performance evaluation was carried out on a robotic platform to assess advantages and disadvantages.

Main Results:

  • The study identified specific traditional and DRL-based algorithms best suited for dynamic environments.
  • Comparative analysis revealed distinct performance characteristics, advantages, and disadvantages for DWA, TEB, CADRL, and SAC.
  • Results indicate that the optimal algorithm choice is contingent upon the specific application and robot characteristics.

Conclusions:

  • No single algorithm is universally superior; selection depends on application-specific needs and robot capabilities.
  • Traditional algorithms like DWA and TEB offer viable solutions under certain conditions.
  • DRL-based algorithms such as CADRL and SAC demonstrate significant potential for complex dynamic navigation tasks.