LLM-Agents Driven Automated Simulation Testing and Analysis of small Uncrewed Aerial Systems
Thorough simulation testing is crucial for validating the correct behavior of small Uncrewed Aerial Systems (sUAS) across multiple scenarios, including adverse weather conditions (such as wind, and fog), diverse settings (hilly terrain, or urban areas), and varying mission profiles (surveillance, tracking). While various sUAS simulation tools exist to support developers, the entire process of creating, executing, and analyzing simulation tests remains a largely manual and cumbersome task. Developers must identify test scenarios, set up the simulation environment, integrate the System under Test (SuT) with simulation tools, formulate mission plans, and collect and analyze results. These labor-intensive tasks limit the ability of developers to conduct exhaustive testing across a wide range of scenarios. To alleviate this problem, in this paper, we propose AUTOSIMTEST, a Large Language Model (LLM)-driven framework, where multiple LLM agents collaborate to support the sUAS simulation testing process. This includes: (1) creating test scenarios that subject the SuT to unique environmental contexts; (2) preparing the simulation environment as per the test scenario; (3) generating diverse sUAS missions for the SuT to execute; and (4) automatically analyzing simulation results and providing an interactive analytics interface. Further, the design of the framework is flexible for creating and testing scenarios for a variety of sUAS use cases, simulation tools, and SuT input requirements. We evaluated our approach by (a) conducting simulation testing of PX4 and ArduPilot flight-controller-based SuTs, (b) analyzing the performance of each agent, and (c) gathering feedback from sUAS developers. Our findings indicate that AUTOSIMTEST significantly improves the efficiency and scope of the sUAS testing process, allowing for more comprehensive and varied scenario evaluations while reducing the manual effort.
Wed 30 AprDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | AutonomyResearch Track at 213 Chair(s): Lionel Briand University of Ottawa, Canada; Lero centre, University of Limerick, Ireland | ||
11:00 15mTalk | A Differential Testing Framework to Identify Critical AV Failures Leveraging Arbitrary Inputs Research Track Trey Woodlief University of Virginia, Carl Hildebrandt University of Virginia, Sebastian Elbaum University of Virginia | ||
11:15 15mTalk | Automating a Complete Software Test Process Using LLMs: An Automotive Case Study Research Track Shuai Wang , Yinan Yu Chalmers University of Technology, Robert Feldt Chalmers | University of Gothenburg, Dhasarathy Parthasarathy Volvo Group Pre-print | ||
11:30 15mTalk | LLM-Agents Driven Automated Simulation Testing and Analysis of small Uncrewed Aerial Systems Research Track Venkata Sai Aswath Duvvuru Saint Louis University, Bohan Zhang Saint Louis University, Missouri, Michael Vierhauser University of Innsbruck, Ankit Agrawal Saint Louis University, Missouri Pre-print Media Attached | ||
11:45 15mTalk | Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models Research Track Luciano Baresi Politecnico di Milano, Davide Yi Xian Hu Politecnico di Milano, Andrea Stocco Technical University of Munich, fortiss, Paolo Tonella USI Lugano Pre-print | ||
12:00 15mTalk | GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems Research Track Linfeng Liang Macquarie University, Yao Deng Macquarie University, Kye Morton Skyy Network, Valtteri Kallinen Skyy Network, Alice James Macquarie University, Avishkar Seth Macquarie University, Endrowednes Kuantama Macquarie University, Subhas Mukhopadhyay Macquarie University, Richard Han Macquarie University, Xi Zheng Macquarie University | ||
12:15 15mTalk | Decictor: Towards Evaluating the Robustness of Decision-Making in Autonomous Driving Systems Research Track Mingfei Cheng Singapore Management University, Xiaofei Xie Singapore Management University, Yuan Zhou Zhejiang Sci-Tech University, Junjie Wang Tianjin University, Guozhu Meng Institute of Information Engineering, Chinese Academy of Sciences, Kairui Yang DAMO Academy, Alibaba Group, China |