# Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems

arXiv: https://arxiv.org/abs/2510.09396 · HTML: https://mlsanity.com/paper/2510.09396/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2025.

## Built on by (most cited first, 2 of 2)

- [MR-SLAM: Immersive Spatial Supervision for Multi-Robot Mapping via Mixed Reality](https://mlsanity.com/paper/2605.16432/) (2026)
- [A Local Agentic AI Framework to Generate Edge Case Scenarios for Safety Assurance](https://mlsanity.com/paper/2610.13479/) (2026)

## Abstract (arXiv)

Ensuring robust robotic navigation in dynamic environments is a key challenge, as traditional testing methods often struggle to cover the full spectrum of operational requirements. This paper presents the industrial adoption of Surrealist, a simulation-based test generation framework originally for UAVs, now applied to the ANYmal quadrupedal robot for industrial inspection. Our method uses a search-based algorithm to automatically generate challenging obstacle avoidance scenarios, uncovering failures often missed by manual testing. In a pilot phase, generated test suites revealed critical weaknesses in one experimental algorithm (40.3% success rate) and served as an effective benchmark to prove the superior robustness of another (71.2% success rate). The framework was then integrated into the ANYbotics workflow for a six-month industrial evaluation, where it was used to test five proprietary algorithms. A formal survey confirmed its value, showing it enhances the development process, uncovers critical failures, provides objective benchmarks, and strengthens the overall verification pipeline.