# Near-Driven Autonomous Rover Navigation in Complex Environments: Extensions to Urban Search-and-Rescue and Industrial Inspection

arXiv: https://arxiv.org/abs/2504.17794 · HTML: https://mlsanity.com/paper/2504.17794/

## Key facts

- Cited by 1 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 4 of its 25 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## 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, 4 of 4)

- [Evolutionary Reinforcement Learning: A Survey](https://mlsanity.com/paper/2303.04150/) (2023)
- [Safe Crossover of Neural Networks Through Neuron Alignment](https://mlsanity.com/paper/2003.10306/) (2020)
- [NEORL: NeuroEvolution Optimization with Reinforcement Learning](https://mlsanity.com/paper/2112.07057/) (2021)
- [GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEA](https://mlsanity.com/paper/2404.01159/) (2024)

## Built on by (most cited first, 1 of 1)

- [NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning](https://mlsanity.com/paper/2604.15076/) (2026)

## Abstract (arXiv)

This paper explores the use of an extended neuroevolutionary approach, based on NeuroEvolution of Augmenting Topologies (NEAT), for autonomous robots in dynamic environments associated with hazardous tasks like firefighting, urban search-and-rescue (USAR), and industrial inspections. Building on previous research, it expands the simulation environment to larger and more complex settings, demonstrating NEAT's adaptability across different applications. By integrating recent advancements in NEAT and reinforcement learning, the study uses modern simulation frameworks for realism and hybrid algorithms for optimization. Experimental results show that NEAT-evolved controllers achieve success rates comparable to state-of-the-art deep reinforcement learning methods, with superior structural adaptability. The agents reached ~80% success in outdoor tests, surpassing baseline models. The paper also highlights the benefits of transfer learning among tasks and evaluates the effectiveness of NEAT in complex 3D navigation. Contributions include evaluating NEAT for diverse autonomous applications and discussing real-world deployment considerations, emphasizing the approach's potential as an alternative or complement to deep reinforcement learning in autonomous navigation tasks.