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End-to-End Autonomous Navigation Based on Deep Reinforcement Learning with a Survival Penalty Function.

Shyr-Long Jeng¹, Chienhsun Chiang²

¹Department of Mechanical Engineering, Lunghwa University of Science and Technology, Taoyuan City 333326, Taiwan.

Sensors (Basel, Switzerland)

|October 28, 2023

Summary

This study introduces a deep reinforcement learning approach for autonomous navigation in unknown dynamic environments. The method enhances robot survival and target achievement using a novel reward function, enabling collision-free path planning.

Keywords:

actor–critic (AC) method autonomous reinforcement learning (RL)wheeled mobile robots (WMRs)

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Autonomous navigation in dynamic, map-less environments presents significant challenges.
Traditional methods struggle with sparse rewards and complex obstacle avoidance.

Purpose of the Study:

To propose an end-to-end deep reinforcement learning (DRL) approach for autonomous navigation.
To enable nonholonomic wheeled mobile robots (WMRs) to navigate dynamic, map-less environments effectively.
To address the sparse reward problem and ensure collision-free path planning.

Main Methods:

Utilized two actor-critic (AC) frameworks: deep deterministic policy gradient (DDPG) and twin-delayed DDPG (TD3).
Introduced a comprehensive reward function incorporating a survival penalty to guide the WMR towards its target.
Connected consecutive episodes to increase cumulative penalties for obstacle scenarios, preventing training failure.

Main Results:

Simulations in various scenarios (obstacle-free, parking lot, intersections, multiple obstacles) demonstrated the method's efficiency and safety.
The TD3 algorithm showed faster convergence and greater stability during training compared to DDPG.
TD3 achieved a higher task execution success rate during evaluation.

Conclusions:

The proposed DRL approach with a survival penalty function effectively enables autonomous navigation in challenging environments.
The TD3 algorithm offers superior performance in terms of training efficiency and navigation success rate over DDPG.
This method provides a robust solution for collision-free path planning for WMRs.