# CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2308.06594 · HTML: https://mlsanity.com/paper/2308.06594/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 12 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments](https://mlsanity.com/paper/2103.04233/) (2021)
- [TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models](https://mlsanity.com/paper/2205.01791/) (2022)
- [GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation](https://mlsanity.com/paper/1803.03254/) (2018)
- [OFFSEG: A Semantic Segmentation Framework For Off-Road Driving](https://mlsanity.com/paper/2103.12417/) (2021)
- [TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning](https://mlsanity.com/paper/2109.05120/) (2021)
- [Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning](https://mlsanity.com/paper/2103.03651/) (2021)
- [Control of rough terrain vehicles using deep reinforcement learning](https://mlsanity.com/paper/2107.01867/) (2021)
- [OffRoadTranSeg: Semi-Supervised Segmentation using Transformers on OffRoad environments](https://mlsanity.com/paper/2106.13963/) (2021)
- [SynchroSim: An Integrated Co-simulation Middleware for Heterogeneous Multi-robot System](https://mlsanity.com/paper/2208.06569/) (2022)

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

- [TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments](https://mlsanity.com/paper/2402.04061/) (2024)
- [Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey](https://mlsanity.com/paper/2404.00340/) (2024)
- [Joint Retrieval of Cloud properties using Attention-based Deep Learning Models](https://mlsanity.com/paper/2504.03133/) (2025)
- [EnCoMP: Enhanced Covert Maneuver Planning with Adaptive Threat-Aware Visibility Estimation using Offline Reinforcement Learning](https://mlsanity.com/paper/2403.20016/) (2024)
- [Enhancing Robotic Navigation: An Evaluation of Single and Multi-Objective Reinforcement Learning Strategies](https://mlsanity.com/paper/2312.07953/) (2023)
- [QuasiNav: Asymmetric Cost-Aware Navigation Planning with Constrained Quasimetric Reinforcement Learning](https://mlsanity.com/paper/2410.16666/) (2024)
- [Explaining Reinforcement Learning Decisions in Self-adaptive Systems](https://mlsanity.com/paper/2608.14620/) (2026)

## Abstract (arXiv)

Autonomous navigation in offroad environments has been extensively studied in the robotics field. However, navigation in covert situations where an autonomous vehicle needs to remain hidden from outside observers remains an underexplored area. In this paper, we propose a novel Deep Reinforcement Learning (DRL) based algorithm, called CoverNav, for identifying covert and navigable trajectories with minimal cost in offroad terrains and jungle environments in the presence of observers. CoverNav focuses on unmanned ground vehicles seeking shelters and taking covers while safely navigating to a predefined destination. Our proposed DRL method computes a local cost map that helps distinguish which path will grant the maximal covertness while maintaining a low cost trajectory using an elevation map generated from 3D point cloud data, the robot's pose, and directed goal information. CoverNav helps robot agents to learn the low elevation terrain using a reward function while penalizing it proportionately when it experiences high elevation. If an observer is spotted, CoverNav enables the robot to select natural obstacles (e.g., rocks, houses, disabled vehicles, trees, etc.) and use them as shelters to hide behind. We evaluate CoverNav using the Unity simulation environment and show that it guarantees dynamically feasible velocities in the terrain when fed with an elevation map generated by another DRL based navigation algorithm. Additionally, we evaluate CoverNav's effectiveness in achieving a maximum goal distance of 12 meters and its success rate in different elevation scenarios with and without cover objects. We observe competitive performance comparable to state of the art (SOTA) methods without compromising accuracy.