Reinforcement learning for mobile robotics exploration: A survey
LC Garaffa, M Basso, AA Konzen… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Efficient exploration of unknown environments is a fundamental precondition for modern
autonomous mobile robot applications. Aiming to design robust and effective robotic …
autonomous mobile robot applications. Aiming to design robust and effective robotic …
Learning-based methods of perception and navigation for ground vehicles in unstructured environments: A review
DC Guastella, G Muscato - Sensors, 2020 - mdpi.com
The problem of autonomous navigation of a ground vehicle in unstructured environments is
both challenging and crucial for the deployment of this type of vehicle in real-world …
both challenging and crucial for the deployment of this type of vehicle in real-world …
Edge technologies for disaster management: A survey of social media and artificial intelligence integration
Within the paradigm of smart cities, smart devices can be considered as a tool to enhance
safety. Edge sensing, Internet of Things (IoT), big data, social media analytics, edge …
safety. Edge sensing, Internet of Things (IoT), big data, social media analytics, edge …
A sim-to-real pipeline for deep reinforcement learning for autonomous robot navigation in cluttered rough terrain
Robots that autonomously navigate real-world 3D cluttered environments need to safely
traverse terrain with abrupt changes in surface normals and elevations. In this letter, we …
traverse terrain with abrupt changes in surface normals and elevations. In this letter, we …
Terp: Reliable planning in uneven outdoor environments using deep reinforcement learning
K Weerakoon, AJ Sathyamoorthy… - … on Robotics and …, 2022 - ieeexplore.ieee.org
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 …
approach employs a fully-trained Deep Reinforcement Learning (DRL) network that uses …
Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning
In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …
vehicles plan future trajectories. We develop a partially observable Markov decision process …
Path planning for wheeled mobile robot in partially known uneven terrain
Path planning for wheeled mobile robots on partially known uneven terrain is an open
challenge since robot motions can be strongly influenced by terrain with incomplete …
challenge since robot motions can be strongly influenced by terrain with incomplete …
Cybersecurity of autonomous vehicles: A systematic literature review of adversarial attacks and defense models
Autonomous driving (AD) has developed tremendously in parallel with the ongoing
development and improvement of deep learning (DL) technology. However, the uptake of …
development and improvement of deep learning (DL) technology. However, the uptake of …
Explainability in deep reinforcement learning: A review into current methods and applications
T Hickling, A Zenati, N Aouf, P Spencer - ACM Computing Surveys, 2023 - dl.acm.org
The use of Deep Reinforcement Learning (DRL) schemes has increased dramatically since
their first introduction in 2015. Though uses in many different applications are being found …
their first introduction in 2015. Though uses in many different applications are being found …
[HTML][HTML] A comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insights
This article provides a detailed analysis of the assessment of unmanned ground vehicle
terrain traversability. The analysis is categorized into terrain classification, terrain mapping …
terrain traversability. The analysis is categorized into terrain classification, terrain mapping …