5 citations · 9 across the 5 of their papers we have counts for
5 papers · 1 filter
Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
Neşet Ünver Akmandor, Sarvesh Prajapati, Mark Zolotas +1
Traditional motion planning methods for robots with many degrees-of-freedom, such as mobile manipulators, are often computationally prohibitive for real-world settings. In this pap…
StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks
Hongyu Li, Zhengang Li, Neset Unver Akmandor +3
Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive resu…
Deep Reinforcement Learning based Robot Navigation in Dynamic Environments using Occupancy Values of Motion Primitives
Neşet Ünver Akmandor, Hongyu Li, Gary Lvov +2
This paper presents a Deep Reinforcement Learning based navigation approach in which we define the occupancy observations as heuristic evaluations of motion primitives, rather than…
Reactive Navigation Framework for Mobile Robots by Heuristically Evaluated Pre-sampled Trajectories
Neşet Ünver Akmandor, Taşkın Padır
This paper describes and analyzes a reactive navigation framework for mobile robots in unknown environments. The approach does not rely on a global map and only considers the local…
A 3D Reactive Navigation Algorithm for Mobile Robots by Using Tentacle-Based Sampling
Neşet Ünver Akmandor, Taşkın Padır
This paper introduces a reactive navigation framework for mobile robots in 3-dimensional (3D) space. The proposed approach does not rely on the global map information and achieves…