Autonomous navigation in unstructured outdoor environments using semantic segmentation guided reinforcement learning

TL;DR

This paper introduces a new system for autonomous robots to navigate through complex forest environments using vision and machine learning.

Contribution

The novel framework combines semantic segmentation with reinforcement learning for GPS-free navigation in unstructured outdoor settings.

Findings

The system achieves an 86.7% success rate in following forest trails.

It demonstrates low collision frequency and precise path tracking in complex scenarios.

Experiments confirm the effectiveness of combining perception and control modules.

Abstract

Robust autonomous navigation in dense, unstructured environments such as forests presents a longstanding challenge in robotics due to complex terrain geometry, dynamic occlusions, and unreliable global positioning signals. This paper proposes a hybrid perception-and-control framework that integrates deep semantic segmentation with reinforcement learning to enable intelligent, vision-driven navigation in visually cluttered forest trails. The system combines Mask R-CNN for pixel-level trail segmentation with a Soft Actor-Critic (SAC) agent that learns adaptive navigation policies under continuous action spaces. A Pure Pursuit controller translates visual predictions into smooth motor commands, ensuring path adherence and stability. The model is trained and evaluated in a high-fidelity forest simulation environment featuring natural obstacles, variable lighting, and randomized trail geometries. Extensive experiments demonstrate that our approach achieves a high trail-following success rate (86.7%), low collision frequency, and precise path tracking in challenging navigation scenarios. Comparative and ablation studies further highlight the synergy between learning-based perception and control. The proposed framework offers a scalable and modular solution for deploying autonomous robots in natural terrains without relying on GPS or prior maps, paving the way for applications in environmental monitoring and field robotics.

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