# A Comprehensive Review on Autonomous Navigation

arXiv: https://arxiv.org/abs/2212.12808 · HTML: https://mlsanity.com/paper/2212.12808/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2022.

## Built on by (most cited first, 19 of 19)

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- [3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots](https://mlsanity.com/paper/2605.12689/) (2026)
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- [Path-conditioned Reinforcement Learning-based Local Planning for Long-Range Navigation](https://mlsanity.com/paper/2603.13888/) (2026)
- [GaussianSSC: Triplane-Guided Directional Gaussian Fields for 3D Semantic Completion](https://mlsanity.com/paper/2603.21487/) (2026)
- [Plan First, Diffuse Later: Extrinsic Graph Guidance for Long-Horizon Diffusion Planning](https://mlsanity.com/paper/2605.16863/) (2026)
- [Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation](https://mlsanity.com/paper/2605.20801/) (2026)
- [Dual-Pathway Geometry-Aware MLLM for Spatial Intelligence](https://mlsanity.com/paper/2605.25334/) (2026)
- [PIER-Flow: Physics-Informed Efficient Rectified Flow for Real-Time Mobile Robot Navigation](https://mlsanity.com/paper/2607.10288/) (2026)
- [DroneWAM: Efficient World Action Model for Drone Visual Navigation](https://mlsanity.com/paper/2609.33148/) (2026)

## Abstract (arXiv)

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed.