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arXiv · 2510.09396

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

Sajad Khatiri·Francisco Eli Vina Barrientos·Maximilian Wulf·Paolo Tonella·Sebastiano Panichella
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Abstract

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.

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Sajad Khatiri, Francisco Eli Vina Barrientos, Maximilian Wulf, Paolo Tonella, Sebastiano Panichella. 2025-10-10. Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems. https://arxiv.org/abs/2510.09396

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