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20172021
most citedAutonomous UAV Navigation Using Reinforcement Learning

49 citations · 52 across the 3 of their papers we have counts for

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7 papers · 1 filter

cs.RO2021

A Deep Learning Approach To Multi-Context Socially-Aware Navigation

Santosh Balajee Banisetty, Vineeth Rajamohan, Fausto Vega +1

We present a context classification pipeline to allow a robot to change its navigation strategy based on the observed social scenario. Socially-Aware Navigation considers social be…

cs.RO2019

Socially-Aware Navigation: A Non-linear Multi-Objective Optimization Approach

Santosh Balajee Banisetty, Scott Forer, Logan Yliniemi +2

Mobile robots are increasingly populating homes, hospitals, shopping malls, factory floors, and other human environments. Human society has social norms that people mutually accept…

cs.RO2018

Towards a Unified Planner For Socially-Aware Navigation

Santosh Balajee Banisetty, David Feil-Seifer

This paper presents the framework for a novel Unified Socially-Aware Navigation (USAN) architecture and explains its need in Socially Assistive Robotics (SAR) applications. Our app…

cs.RO2018

A Distributed Control Framework of Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking

Huy Xuan Pham, Hung Manh La, David Feil-Seifer +1

Wild-land fire fighting is a hazardous job. A key task for firefighters is to observe the "fire front" to chart the progress of the fire and areas that will likely spread next. Lac…

cs.RO2018

Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage

Huy Xuan Pham, Hung Manh La, David Feil-Seifer +1

This paper proposes a distributed Multi-Agent Reinforcement Learning (MARL) algorithm for a team of Unmanned Aerial Vehicles (UAVs). The proposed MARL algorithm allows UAVs to lear…

cs.RO201849 cited

Autonomous UAV Navigation Using Reinforcement Learning

Huy X. Pham, Hung M. La, David Feil-Seifer +1

Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of the environment may not be available. This paper provide…