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April 5, 2026Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science0 citations

Mobile Robot Navigation Using Deep Reinforcement Learning: Algorithms and Challenges

Mobile robot navigation using deep reinforcement learning: Algorithms, challenges, and future directions

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Authors

BSBagu Ramananda SagarKAKiran Kumar AbbiliRKRama Krishna Konjeti

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Overview

Review explores deep reinforcement learning algorithms for robot navigation, highlighting challenges and future directions.

Key Points

  • The aim is to review deep reinforcement learning methods for mobile robot navigation in unstructured environments.
  • Analysis of value-based, policy-based, hybrid, hierarchical, and multi-agent DRL algorithms.
  • Evaluation of applicability to real-world navigation tasks.
  • Discussion of challenges like sample inefficiency and safety constraints.
  • Identified key algorithms and their effectiveness for robotic navigation tasks.
  • Highlighted ongoing challenges in current approaches, such as limited generalization.
  • Outlined future research avenues like sim-to-real transfer and multi-agent collaboration.

Cite This Study

Sagar et al. (2026) studied this question.

synapsesocial.com/papers/69d1fca7a79560c99a0a2522https://doi.org/10.1177/09544062261431870
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