no code implementations • 22 Mar 2024 • Jacob W. Knaup, Panagiotis Tsiotras
A dual control problem is formulated in which the effect of the planned control policy on the parameter estimates is modeled and optimized for.
1 code implementation • 11 Dec 2023 • Travis Driver, Andrew Vaughan, Yang Cheng, Adnan Ansar, John Christian, Panagiotis Tsiotras
This paper proposes the incorporation of techniques from stereophotoclinometry (SPC) into a keypoint-based structure-from-motion (SfM) system to estimate the surface normal and albedo at detected landmarks to improve autonomous surface and shape characterization of small celestial bodies from in-situ imagery.
no code implementations • 10 Dec 2023 • Hemanth Manjunatha, Panagiotis Tsiotras
Moreover, by exploring the significance of each modality, this study offers a roadmap for future research in autonomous driving, emphasizing the importance of leveraging multiple models to achieve robust performance.
no code implementations • 10 Dec 2023 • Joshua Pilipovsky, Panagiotis Tsiotras
The theory of covariance control and covariance steering (CS) deals with controlling the dispersion of trajectories of a dynamical system, under the implicit assumption that accurate prior knowledge of the system being controlled is available.
no code implementations • 3 Oct 2023 • Joshua Pilipovsky, Panagiotis Tsiotras
This paper studies the problem of developing computationally efficient solutions for steering the distribution of the state of a stochastic, linear dynamical system between two boundary Gaussian distributions in the presence of chance-constraints on the state and control input.
1 code implementation • 19 Sep 2023 • Dongliang Zheng, Panagiotis Tsiotras
LEA* is simple and easy to implement with minimum modification to A*, resulting in a very small overhead compared to previous lazy search algorithms.
no code implementations • 18 Sep 2023 • Vinodhini Comandur, Tulasi Ram Vechalapu, Venkata Ramana Makkapati, Panagiotis Tsiotras, Seth Hutchinson
The proposed feedback strategy is evaluated for instances involving a single pursuer and a single evader with an uncertain moving obstacle, where the pursuer is assumed to only know the nominal value of the obstacle's speed.
no code implementations • 24 May 2023 • Hemanth Manjunatha, Andrey Pak, Dimitar Filev, Panagiotis Tsiotras
For this purpose, deep learning models can be used to learn compact latent representations from a stream of incoming data.
no code implementations • 21 Apr 2023 • Dongliang Zheng, Panagiotis Tsiotras
In this work, we propose the Informed Batch Belief Trees (IBBT) algorithm for motion planning under motion and sensing uncertainties.
1 code implementation • 11 Apr 2023 • Travis Driver, Panagiotis Tsiotras
We train and test our models on real images of small bodies from legacy and ongoing missions and demonstrate increased performance relative to traditional handcrafted methods.
no code implementations • 30 Mar 2023 • Joshua Pilipovsky, Panagiotis Tsiotras
This paper studies the problem of steering the distribution of a linear time-invariant system from an initial normal distribution to a terminal normal distribution under no knowledge of the system dynamics.
1 code implementation • 22 Mar 2023 • Yue Guan, Mohammad Afshari, Panagiotis Tsiotras
This work studies the behaviors of two large-population teams competing in a discrete environment.
no code implementations • 18 Mar 2023 • Jacob Knaup, Panagiotis Tsiotras
This work considers the optimal covariance steering problem for systems subject to both additive noise and uncertain parameters which may enter multiplicatively with the state and the control.
no code implementations • 28 Feb 2023 • George Rapakoulias, Panagiotis Tsiotras
Finally, a comparative study is performed in systems of various sizes and steering horizons to illustrate the advantages of the proposed method in terms of computational resources compared to the state of the art.
1 code implementation • 30 Jan 2023 • Travis Driver, Kento Tomita, Koki Ho, Panagiotis Tsiotras
Hazard detection and avoidance is a key technology for future robotic small body sample return and lander missions.
no code implementations • 3 Dec 2022 • Joshua Pilipovsky, Vignesh Sivaramakrishnan, Meeko M. K. Oishi, Panagiotis Tsiotras
Verifying the input-output relationships of a neural network so as to achieve some desired performance specification is a difficult, yet important, problem due to the growing ubiquity of neural nets in many engineering applications.
no code implementations • 1 Dec 2022 • Mehregan Dor, Travis Driver, Kenneth Getzandanner, Panagiotis Tsiotras
We propose AstroSLAM, a standalone vision-based solution for autonomous online navigation around an unknown target small celestial body.
no code implementations • 1 Nov 2022 • Fengjiao Liu, George Rapakoulias, Panagiotis Tsiotras
In this paper, we study the optimal control problem for steering the state covariance of a discrete-time linear stochastic system over a finite time horizon.
no code implementations • 27 Sep 2022 • Yue Guan, Longxu Pan, Daigo Shishika, Panagiotis Tsiotras
In this work, we extend the convex bodies chasing problem (CBC) to an adversarial setting, where an agent (the Player) is tasked with chasing a sequence of convex bodies generated adversarially by another agent (the Opponent).
no code implementations • 8 Aug 2022 • Daniel T. Larsson, Dipankar Maity, Panagiotis Tsiotras
It is shown that the resulting information-theoretic abstraction problem over the space of multi-resolution trees can be formulated as a integer linear programming (ILP) problem.
1 code implementation • 3 Aug 2022 • Travis Driver, Katherine Skinner, Mehregan Dor, Panagiotis Tsiotras
Missions to small celestial bodies rely heavily on optical feature tracking for characterization of and relative navigation around the target body.
no code implementations • 22 Jun 2022 • Fengjiao Liu, Panagiotis Tsiotras
In this paper, we study the problem of how to optimally steer the state covariance of a general continuous-time linear stochastic system over a finite time interval subject to additive noise.
no code implementations • 18 May 2022 • Andrey Pak, Hemanth Manjunatha, Dimitar Filev, Panagiotis Tsiotras
Thus, there is a need for deep learning models that explicitly consider the temporal dependence of the data in their architecture.
no code implementations • 9 Mar 2021 • Jack Ridderhof, Panagiotis Tsiotras, Breanna J. Johnson
In this paper, closed-loop entry guidance in a randomly perturbed atmosphere, using bank angle control, is posed as a stochastic optimal control problem.
no code implementations • 25 Feb 2021 • Sagar Suhas Joshi, Seth Hutchinson, Panagiotis Tsiotras
Sampling-based algorithms solve the path planning problem by generating random samples in the search-space and incrementally growing a connectivity graph or a tree.
Robotics
no code implementations • 21 Feb 2021 • Dipankar Maity, Panagiotis Tsiotras
The adversaries are able to listen to the inter-agent communications and try to estimate the state of the agents.
no code implementations • 19 Feb 2021 • Daniel T. Larsson, Dipankar Maity, Panagiotis Tsiotras
In this paper, a mixed-integer linear programming formulation for the problem of obtaining task-relevant, multi-resolution, graph abstractions for resource-constrained agents is presented.
no code implementations • 24 Jan 2021 • Jack Ridderhof, Panagiotis Tsiotras
The problem of optimizing affine feedback laws that explicitly steer the mean and covariance of an uncertain system state in the presence of a Gaussian random field is considered.
no code implementations • 24 Dec 2020 • Jaein Lim, Panagiotis Tsiotras
We encode those properties in a weighted colored graph (geometric information in terms of edge weight and semantic information in terms of edge and vertex color), and propose a generalized A* to find the shortest path among the set of paths with minimal inclusion of low-ranked color edges.
no code implementations • 1 Sep 2020 • Yue Guan, Qifan Zhang, Panagiotis Tsiotras
We explore the use of policy approximations to reduce the computational cost of learning Nash equilibria in zero-sum stochastic games.
no code implementations • 19 May 2020 • Daniel T. Larsson, Dipankar Maity, Panagiotis Tsiotras
In this paper, we develop a framework for path-planning on abstractions that are not provided to the agent a priori but instead emerge as a function of the available computational resources.
no code implementations • 30 Sep 2019 • Dipankar Maity, Panagiotis Tsiotras
In this paper, we consider joint optimal controller synthesis and quantizer scheduling for a partially observed Quantized-Feedback Linear-Quadratic-Gaussian (QF-LQG) system, where the measurements are quantized before being sent to the controller.
no code implementations • 30 Sep 2019 • Daniel T. Larsson, Dipankar Maity, Panagiotis Tsiotras
In this paper, we develop a framework to obtain graph abstractions for decision-making by an agent where the abstractions emerge as a function of the agent's limited computational resources.
no code implementations • 29 Sep 2019 • Justin Zheng, Kazuhide Okamoto, Panagiotis Tsiotras
This work proposes a new self-driving framework that uses a human driver control model, whose feature-input values are extracted from images using deep convolutional neural networks (CNNs).
no code implementations • 22 Oct 2017 • Daniel T. Larsson, Daniel Braun, Panagiotis Tsiotras
In this semi-tutorial paper, we first review the information-theoretic approach to account for the computational costs incurred during the search for optimal actions in a sequential decision-making problem.
no code implementations • 19 Sep 2016 • Oktay Arslan, Panagiotis Tsiotras
Contrary to the RRT* algorithm, the policy improvement during the rewiring step is not performed only locally but rather on a set of vertices that are classified as "promising" during the current iteration.
no code implementations • 23 Jan 2016 • Oktay Arslan, Karl Berntorp, Panagiotis Tsiotras
We describe a new sampling-based algorithm, called CL-RRT#, which leverages ideas from the RRT# algorithm and a variant of the RRT algorithm that generates trajectories using closed-loop prediction.
Robotics