Zobrazeno 1 - 10
of 899
pro vyhledávání: '"Bakolas, A"'
To deploy safe and agile robots in cluttered environments, there is a need to develop fully decentralized controllers that guarantee safety, respect actuation limits, prevent deadlocks, and scale to thousands of agents. Current approaches fall short
Externí odkaz:
http://arxiv.org/abs/2409.09573
In this paper, we address the problem of reducing the computational burden of Model Predictive Control (MPC) for real-time robotic applications. We propose TransformerMPC, a method that enhances the computational efficiency of MPC algorithms by lever
Externí odkaz:
http://arxiv.org/abs/2409.09266
In this paper, we investigate a multi-agent target guarding problem in which a single defender seeks to capture multiple attackers aiming to reach a high-value target area. In contrast to previous studies, the attackers herein are assumed to be heter
Externí odkaz:
http://arxiv.org/abs/2407.00762
Leveraging Gated Recurrent Units for Iterative Online Precise Attitude Control for Geodetic Missions
In this paper, we consider the problem of precise attitude control for geodetic missions, such as the GRACE Follow-on (GRACE-FO) mission. Traditional and well-established control methods, such as Proportional-Integral-Derivative (PID) controllers, ha
Externí odkaz:
http://arxiv.org/abs/2405.15159
Autor:
Patrick, Steven, Bakolas, Efstathios
Model Predictive Path Integral (MPPI) control has proven to be a powerful tool for the control of uncertain systems (such as systems subject to disturbances and systems with unmodeled dynamics). One important limitation of the baseline MPPI algorithm
Externí odkaz:
http://arxiv.org/abs/2403.18066
Autor:
James, Anegi, Bakolas, Efstathios
This paper presents a method for future motion prediction of multi-agent systems by including group formation information and future intent. Formation of groups depends on a physics-based clustering method that follows the agglomerative hierarchical
Externí odkaz:
http://arxiv.org/abs/2403.13905
In this paper, we propose a deep learning based control synthesis framework for fast and online computation of controllers that guarantees the safety of general nonlinear control systems with unknown dynamics in the presence of input constraints. Tow
Externí odkaz:
http://arxiv.org/abs/2312.07345
Autor:
Balci, Isin M., Bakolas, Efstathios
In this paper, we study the finite-horizon optimal density steering problem for discrete-time stochastic linear dynamical systems. Specifically, we focus on steering probability densities represented as Gaussian mixture models which are known to give
Externí odkaz:
http://arxiv.org/abs/2311.08500
In this paper, we consider the problem of safe control synthesis of general controlled nonlinear systems in the presence of bounded additive disturbances. Towards this aim, we first construct a governing augmented state space model consisting of the
Externí odkaz:
http://arxiv.org/abs/2309.16945
We present an approach to ensure safe and deadlock-free navigation for decentralized multi-robot systems operating in constrained environments, including doorways and intersections. Although many solutions have been proposed that ensure safety and re
Externí odkaz:
http://arxiv.org/abs/2308.10966