no code implementations • 16 Aug 2024 • Clinton Enwerem, Erfaun Noorani, John S. Baras, Brian M. Sadler
With the pervasiveness of Stochastic Shortest-Path (SSP) problems in high-risk industries, such as last-mile autonomous delivery and supply chain management, robust planning algorithms are crucial for ensuring successful task completion while mitigating hazardous outcomes.
no code implementations • 13 Mar 2024 • Rui Liu, Erfaun Noorani, Pratap Tokekar, John S. Baras
In this study, we conduct a thorough iteration complexity analysis for the risk-sensitive policy gradient method, focusing on the REINFORCE algorithm and employing the exponential utility function.
no code implementations • 6 Nov 2023 • Clinton Enwerem, John S. Baras
We consider the problem of safely coordinating ensembles of identical autonomous agents to conduct complex missions with conflicting safety requirements and under noisy control inputs.
no code implementations • 2 Oct 2023 • Armin Lederer, Erfaun Noorani, John S. Baras, Sandra Hirche
We propose a method for learning these value functions using common techniques from reinforcement learning and derive sufficient conditions for its success.
no code implementations • 17 Sep 2023 • Clinton Enwerem, John S. Baras
To this end, we present consensus-based control laws for multiagent formation tracking in finite-dimensional state space, with the agents represented by a more general class of dynamics: control-affine nonlinear systems.
no code implementations • 31 Aug 2023 • Nariman Torkzaban, Anousheh Gholami, John S. Baras, Bruce Golden
While the proposed a priori splitting rule in Chen et al. (2017) is fixed for all customers regardless of their demand and location, we suggest an adaptive splitting rule that takes into account the distance of the customers to the depot and their demand values.
no code implementations • 18 May 2023 • Amoolya Tirumalai, Christos N. Mavridis, John S. Baras
In this work, we study the inverse problem of identifying complex flocking dynamics in a domain cluttered with obstacles.
no code implementations • 27 Apr 2023 • Anousheh Gholami, Nariman Torkzaban, John S. Baras
At each microscale instance, utilizing the exact slice demand profiles, a linear program is solved to jointly minimize the unsupported traffic and the resource cost at the RAN.
no code implementations • 18 Jan 2023 • Nariman Torkzaban, Asim Zoulkarni, Anousheh Gholami, John S. Baras
Non-geostationary (NGSO) satellite communications systems have attracted a lot of attention both from industry and academia, over the past several years.
no code implementations • 17 Oct 2021 • Dipankar Maity, David Hartman, John S. Baras
We propose a convex relaxation to the sensor design problem and a reference covariance trajectory is obtained from solving the relaxed sensor design problem.
no code implementations • 16 Apr 2021 • Anousheh Gholami, Nariman Torkzaban, John S. Baras
In the first example, we show how utilizing the trust evidence can improve the performance and the security of Federated Learning.
no code implementations • 7 Apr 2021 • Siyi Wang, Qingchen Liu, Precious Ugo Abara, John S. Baras, Sandra Hirche
In this paper, we study the trade-off between the transmission cost and the control performance of the multi-loop networked control system subject to network-induced delay.
no code implementations • 15 Mar 2021 • Nariman Torkzaban, John S. Baras
Several challenging optimization problems arise while considering the deployment of the space-air-ground integrated networks (SAGINs), among which the optimal satellite gateway deployment problem is of significant importance.
no code implementations • 25 Mar 2020 • Aneesh Raghavan, John S. Baras
There are two problems to be solved by the observers: (i) true state of nature is known: find the distribution of the local information collected; (ii) true state of nature is unknown: collaboratively estimate the same using the distributions found by solving the first problem.
no code implementations • 22 Mar 2020 • Ion Matei, Johan de Kleer, Christoforos Somarakis, Rahul Rai, John S. Baras
We describe how we can build models out of the p-H constructs and how we can train them.
no code implementations • 14 Nov 2016 • Went Luan, Yezhou Yang, Cornelia Fermuller, John S. Baras
In this work, we present a fast target detection framework for real-world robotics applications.
no code implementations • 12 Sep 2016 • Ren Mao, John S. Baras, Yezhou Yang, Cornelia Fermuller
It is designed to adapt the original imitation trajectories, which are learned from demonstrations, to novel situations with various constraints.