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Utsav Patel

7 accepted papers

2023

GrASPE: Graph Based Multimodal Fusion for Robot Navigation in Outdoor Environments

RA-L 2023

We present a novel trajectory traversability estimation and planning algorithm for robot navigation in complex outdoor environments. We incorporate multimodal sensory inputs from an RGB camera, 3D LiDAR, and the robot's odometry sensor to train a prediction model to estimate candidate trajectories'

Cited by 78SourceScholar
2022

CoMet: Modeling Group Cohesion for Socially Compliant Robot Navigation in Crowded Scenes

RA-L 2022

We present CoMet, a novel approach for computing a group’s cohesion and using that to improve a robot’s navigation in crowded scenes. Our approach uses a novel cohesion-metric that builds on prior work in social psychology. We compute this metric by utilizing various visual features of pedestrians f

Cited by 24SourceScholar
2022

TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning

ICRA 2022poster

We present a novel method for reliable robot navigation in uneven outdoor terrains. Our approach employs a fully-trained Deep Reinforcement Learning (DRL) network that uses elevation maps of the environment, robot pose, and goal as inputs to compute an attention mask of the environment. The attentio…

Cited by 82SourceScholar
2021

DWA-RL: Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation among Mobile Obstacles

ICRA 2021poster

We present a novel Deep Reinforcement Learning (DRL) based policy to compute dynamically feasible and spatially aware velocities for a robot navigating among mobile obstacles. Our approach combines the benefits of the Dynamic Window Approach (DWA) in terms of satisfying the robot’s dynamics constrai…

Cited by 88SourceScholar
2020

Crowd-Steer: Realtime Smooth and Collision-Free Robot Navigation in Densely Crowded Scenarios Trained using High-Fidelity Simulation

IJCAI 2020poster

We present a novel high fidelity 3-D simulator that significantly reduces the sim-to-real gap for collision avoidance in dense crowds using Deep Reinforcement Learning (DRL). Our simulator models realistic crowd and pedestrian behaviors, along with friction, sensor noise and delays in the simulated…

Cited by 0SourcePDFScholar
2020

DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviors

ICRA 2020poster

We present DenseCAvoid, a novel algorithm for navigating a robot through dense crowds and avoiding collisions by anticipating pedestrian behaviors. Our formulation uses visual sensors and a pedestrian trajectory prediction algorithm to track pedestrians in a set of input frames and compute bounding…

Cited by 107SourceScholar
2020

Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds

RA-L 2020

We present Frozone, a novel algorithm to deal with the Freezing Robot Problem (FRP) that arises when a robot navigates through dense scenarios and crowds. Our method senses and explicitly predicts the trajectories of pedestrians and constructs a Potential Freezing Zone (PFZ); a spatial zone where th

Cited by 87SourceScholar