mlsanityData to 14 Oct 2026

Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation

arXiv 2405.16266 ↗First posted 25 May 2024cs.RO cs.LG cs.SYcited by 7

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Collision-free motion is essential for mobile robots. Most approaches to collision-free and efficient navigation with wheeled robots require parameter tuning by experts to obtain good navigation behavior. This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. The robot utilizes LiDAR sensor data and a deep neural network to generate control signals guiding it toward a specified target while avoiding obstacles. We employ two reinforcement learning algorithms in the Gazebo simulation environment: Deep Deterministic Policy Gradient and proximal policy optimization. The study introduces an enhanced neural network structure in the Proximal Policy Optimization algorithm to boost performance, accompanied by a well-designed reward function to improve algorithm efficacy. Experimental results conducted in both obstacle and obstacle-free environments underscore the effectiveness of the proposed approach. This research significantly contributes to the advancement of autonomous robotics in complex environments through the application of deep reinforcement learning.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 15 references that are in the map. First posted on arXiv 25 May 2024.

Builds on

7 of 15 references.

References · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2015Trust Region Policy Optimizationcited by 2,580
  4. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  5. 2016Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Agecited by 411
  6. 2016Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robotscited by 11
  7. 2024ENet-21: An Optimized light CNN Structure for Lane Detectioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 2.

7 papers.

  1. 2025Multi-Agent Systems for Robotic Autonomy with LLMscited by 9
  2. 2024CANVAS: Commonsense-Aware Navigation System for Intuitive Human-Robot Interactioncited by 3
  3. 2025RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigationcited by 3
  4. 2026TADPO: Reinforcement Learning Goes Off-roadcited by 3
  5. 2026AsyncShield: A Plug-and-Play Edge Adapter for Asynchronous Cloud-based VLA Navigationcited by 2
  6. 2025MAER-Nav: Bidirectional Motion Learning Through Mirror-Augmented Experience Replay for Robot Navigationcited by 0
  7. 2025Hybrid DQN-TD3 Reinforcement Learning for Autonomous Navigation in Dynamic Environmentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Research on Autonomous Robots Navigation based on Reinforcement Learning
  2. 2024Collision-Free Robot Navigation in Crowded Environments using Learning based Convex Model Predictive Control
  3. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  4. 2024Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning
  5. 2024Deep Reinforcement Learning-based Large-scale Robot Exploration
  6. 2024Self-Supervised Learning-Based Path Planning and Obstacle Avoidance Using PPO and B-Splines in Unknown Environments
  7. 2024Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
  8. 2024Mobile Robot Exploration Without Maps via Out-of-Distribution Deep Reinforcement Learning