# Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation

arXiv: https://arxiv.org/abs/2405.16266 · HTML: https://mlsanity.com/paper/2405.16266/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots](https://mlsanity.com/paper/1610.01733/) (2016)
- [ENet-21: An Optimized light CNN Structure for Lane Detection](https://mlsanity.com/paper/2403.19782/) (2024)

## Built on by (most cited first, 7 of 7)

- [Multi-Agent Systems for Robotic Autonomy with LLMs](https://mlsanity.com/paper/2505.05762/) (2025)
- [CANVAS: Commonsense-Aware Navigation System for Intuitive Human-Robot Interaction](https://mlsanity.com/paper/2410.01273/) (2024)
- [RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation](https://mlsanity.com/paper/2505.14526/) (2025)
- [TADPO: Reinforcement Learning Goes Off-road](https://mlsanity.com/paper/2603.05995/) (2026)
- [AsyncShield: A Plug-and-Play Edge Adapter for Asynchronous Cloud-based VLA Navigation](https://mlsanity.com/paper/2604.24086/) (2026)
- [MAER-Nav: Bidirectional Motion Learning Through Mirror-Augmented Experience Replay for Robot Navigation](https://mlsanity.com/paper/2503.23908/) (2025)
- [Hybrid DQN-TD3 Reinforcement Learning for Autonomous Navigation in Dynamic Environments](https://mlsanity.com/paper/2510.26646/) (2025)

## 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.