activity
20122022
most citedNetworked Decision Making for Poisson Processes: Application to nuclear detection

8 citations · 15 across the 8 of their papers we have counts for

collaborators

8 papers

cs.RO2022

Receding Horizon Navigation and Target Tracking for Aerial Detection of Transient Radioactivity

Indrajeet Yadav, Micheal Sebok, Herbert G Tanner

The paper presents a receding horizon planning and control strategy for quadrotor-type \ac{mav}s to navigate reactively and intercept a moving target in a cluttered unknown and dyn…

cs.GT2020

PAC Reinforcement Learning Algorithm for General-Sum Markov Games

Ashkan Zehfroosh, Herbert G. Tanner

This paper presents a theoretical framework for probably approximately correct (PAC) multi-agent reinforcement learning (MARL) algorithms for Markov games. The paper offers an exte…

cs.LG2020

A Hybrid PAC Reinforcement Learning Algorithm

Ashkan Zehfroosh, Herbert G. Tanner

This paper offers a new hybrid probably approximately correct (PAC) reinforcement learning (RL) algorithm for Markov decision processes (MDPs) that intelligently maintains favorabl…

cs.RO2019★ 4 cited

Design and Construction of Unmanned Ground Vehicles for Sub-Canopy Plant Phenotyping

Adam Stager, Herbert G. Tanner, Erin E. Sparks

Unmanned ground vehicles can capture a sub-canopy perspective for plant phenotyping, but their design and construction can be a challenge for scientists unfamiliar with robotics. H…

cs.RO2018★ 2 cited

Visual-Inertial Target Tracking and Motion Planning for UAV-based Radiation Detection

Indrajeet Yadav, Kevin Eckenhoff, Guoquan Huang +1

This paper addresses the problem of detecting radioactive material in transit using an UAV of minimal sensing capability, where the objective is to classify the target's radioactiv…

eess.SY2012★ 1 cited

Stochastic receding horizon control of nonlinear stochastic systems with probabilistic state constraints

Shridhar K. Shah, Herbert G. Tanner, Chetan D. Pahlajani

The paper describes a receding horizon control design framework for continuous-time stochastic nonlinear systems subject to probabilistic state constraints. The intention is to der…