Open Access Publications
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Open access publications by faculty, postdocs, and graduate students in the Department of Mechanical Engineering.
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Browsing Open Access Publications by Author "Baxevani, Kleio"
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Item Resilient Ground Vehicle Autonomous Navigation in GPS-denied Environments(Guidance, Navigation and Control, 2022-11-23) Baxevani, Kleio; Yadav, Indrajeet; Yang, Yulin; Sebok, Michael; Tanner, Herbert G.; Huang, GuoquanCo-design and integration of vehicle navigation and control and state estimation is key for enabling field deployment of mobile robots in GPS-denied cluttered environments, and sensor calibration is critical for successful operation of both subsystems. This paper demonstrates the potential of this co-design approach with field tests of the integration of a reactive receding horizon-based motion planner and controller with an inertial aided multi-sensor calibration scheme. The reported method provides accurate calibration parameters that improve the performance of the state estimator, and enable the motion controller to generate smooth and continuous minimal-jerk trajectories based on local LiDAR data. Numerical simulations in Unity, and real-world experimental results from the field corroborate the claims of efficacy for the reported autonomous navigation computational pipeline.Item Resilient Supervisory Multiagent Systems(IEEE Transactions on Robotics, 2021-09-28) Baxevani, Kleio; Zehfroosh, Ashkan; Tanner, Herbert G.Accidental or deliberate disruption of the coordination function in a multiagent system has been discussed and referred to in the social sciences literature as leader decapitation; this article outlines a methodology for making multiagent networks resilient to this type of failure, enabling a timely restoration of operation normalcy by leveraging machine learning techniques. The approach involves endowing the agents with a cascade of independent learning modules that enable them to discover over time their role in the overall system coordinating strategy, so that they are able to autonomously implement it when central coordination seizes to function. Through these machine learning algorithms, the agents incrementally identify the overall system’s task specification and simultaneously optimize their strategy to serve the common goal.