Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914086915145728 |
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| author | Khatiri, Sajad Barrientos, Francisco Eli Vina Wulf, Maximilian Tonella, Paolo Panichella, Sebastiano |
| author_facet | Khatiri, Sajad Barrientos, Francisco Eli Vina Wulf, Maximilian Tonella, Paolo Panichella, Sebastiano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems Khatiri, Sajad Barrientos, Francisco Eli Vina Wulf, Maximilian Tonella, Paolo Panichella, Sebastiano Robotics Software Engineering 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. |
| title | Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems |
| topic | Robotics Software Engineering |
| url | https://arxiv.org/abs/2510.09396 |