Search Results for author: Brent Schlotfeldt

Found 4 papers, 2 papers with code

Feedback Enhanced Motion Planning for Autonomous Vehicles

1 code implementation11 Jul 2020 Ke Sun, Brent Schlotfeldt, Stephen Chaves, Paul Martin, Gulshan Mandhyan, Vijay Kumar

In this work, we address the motion planning problem for autonomous vehicles through a new lattice planning approach, called Feedback Enhanced Lattice Planner (FELP).

Robotics

Learning Q-network for Active Information Acquisition

2 code implementations23 Oct 2019 Heejin Jeong, Brent Schlotfeldt, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas

In this paper, we propose a novel Reinforcement Learning approach for solving the Active Information Acquisition problem, which requires an agent to choose a sequence of actions in order to acquire information about a process of interest using on-board sensors.

reinforcement-learning

Optimal Algorithms for Submodular Maximization with Distributed Constraints

no code implementations30 Sep 2019 Alexander Robey, Arman Adibi, Brent Schlotfeldt, George J. Pappas, Hamed Hassani

Given this distributed setting, we develop Constraint-Distributed Continuous Greedy (CDCG), a message passing algorithm that converges to the tight $(1-1/e)$ approximation factor of the optimum global solution using only local computation and communication.

Resilient Active Information Gathering with Mobile Robots

no code implementations26 Mar 2018 Brent Schlotfeldt, Vasileios Tzoumas, Dinesh Thakur, George J. Pappas

In this paper, we provide the first algorithm, enabling the following capabilities: minimal communication, i. e., the algorithm is executed by the robots based only on minimal communication between them; system-wide resiliency, i. e., the algorithm is valid for any number of denial-of-service attacks and failures; and provable approximation performance, i. e., the algorithm ensures for all monotone (and not necessarily submodular) objective functions a solution that is finitely close to the optimal.

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