Search Results for author: Arun Lakshmanan

Found 6 papers, 3 papers with code

$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees

no code implementations14 Feb 2023 Zhuohuan Wu, Sheng Cheng, Pan Zhao, Aditya Gahlawat, Kasey A. Ackerman, Arun Lakshmanan, Chengyu Yang, Jiahao Yu, Naira Hovakimyan

Quadrotors that can operate safely in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications.

$\mathcal{L}_1$ Adaptive Augmentation for Geometric Tracking Control of Quadrotors

2 code implementations14 Sep 2021 Zhuohuan Wu, Sheng Cheng, Kasey A. Ackerman, Aditya Gahlawat, Arun Lakshmanan, Pan Zhao, Naira Hovakimyan

This paper introduces an $\mathcal{L}_1$ adaptive control augmentation for geometric tracking control of quadrotors.

Tube-Certified Trajectory Tracking for Nonlinear Systems With Robust Control Contraction Metrics

1 code implementation9 Sep 2021 Pan Zhao, Arun Lakshmanan, Kasey Ackerman, Aditya Gahlawat, Marco Pavone, Naira Hovakimyan

This paper presents an approach towards guaranteed trajectory tracking for nonlinear control-affine systems subject to external disturbances based on robust control contraction metrics (CCM) that aims to minimize the $\mathcal L_\infty$ gain from the disturbances to nominal-actual trajectory deviations.

Motion Planning valid

Contraction $\mathcal{L}_1$-Adaptive Control using Gaussian Processes

no code implementations8 Sep 2020 Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos Theodorou

We present $\mathcal{CL}_1$-$\mathcal{GP}$, a control framework that enables safe simultaneous learning and control for systems subject to uncertainties.

Gaussian Processes

Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification

no code implementations4 Apr 2019 Andrew Patterson, Arun Lakshmanan, Naira Hovakimyan

We show that the uncertainty region for obstacle positions can be expressed in terms of a combination of polynomials generated with Gaussian process regression.

Uncertainty Quantification

Proximity Queries for Absolutely Continuous Parametric Curves

3 code implementations13 Feb 2019 Arun Lakshmanan, Andrew Patterson, Venanzio Cichella, Naira Hovakimyan

In motion planning problems for autonomous robots, such as self-driving cars, the robot must ensure that its planned path is not in close proximity to obstacles in the environment.

Robotics Computational Geometry Graphics

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