A dynamically feasible fast replanning strategy with deep reinforcement learning
M Hasanzade, E Koyuncu - Journal of Intelligent & Robotic Systems, 2021 - Springer
In this work, we aim to develop a fast trajectory replanning methodology enabling highly
agile aerial vehicles to navigate in cluttered environments. By focusing on reducing …
agile aerial vehicles to navigate in cluttered environments. By focusing on reducing …
Sensing-aware deep reinforcement learning with HCI-based human-in-the-loop feedback for autonomous nonlinear drone mobility control
H Lee, S Park - IEEE Access, 2024 - ieeexplore.ieee.org
This paper presents a novel approach for enhancing autonomous drone mobility control
using deep reinforcement learning (DRL), primarily aimed at improving autonomous …
using deep reinforcement learning (DRL), primarily aimed at improving autonomous …
Cooperative Sensing Enhanced UAV Path-Following and Obstacle Avoidance with Variable Formation
The high mobility of unmanned aerial vehicles (UAVs) enables them to be used in various
civilian fields, such as rescue and cargo transport. Path-following is a crucial way to perform …
civilian fields, such as rescue and cargo transport. Path-following is a crucial way to perform …
Robust Planning System for Fast Autonomous Flight in Complex Unknown Environment Using Sparse Directed Frontier Points
Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous
flight in cluttered environments. However, it remains a challenge to efficiently generate a …
flight in cluttered environments. However, it remains a challenge to efficiently generate a …
Memory-based deep reinforcement learning for obstacle avoidance in UAV with limited environment knowledge
A Singla, S Padakandla… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
This paper presents our method for enabling a UAV quadrotor, equipped with a monocular
camera, to autonomously avoid collisions with obstacles in unstructured and unknown …
camera, to autonomously avoid collisions with obstacles in unstructured and unknown …
UAV obstacle avoidance by human-in-the-loop reinforcement in arbitrary 3D environment
This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based
on a deep reinforcement learning method for a large-scale 3D complex environment. The …
on a deep reinforcement learning method for a large-scale 3D complex environment. The …
UAS conflict resolution in continuous action space using deep reinforcement learning
Ensuring safety and providing obstacle conflict alerts to small unmanned aircraft is vital to
their integration into civil airspace. There are many techniques for real-time robust drone …
their integration into civil airspace. There are many techniques for real-time robust drone …
FED-UP: Federated deep reinforcement learning-based UAV path planning against hostile defense system
In military operations, unmanned aerial vehicles (UAVs) have been heavily utilized in recent
years. However, due to the antenna installment regulation, UAVs cannot be controlled by …
years. However, due to the antenna installment regulation, UAVs cannot be controlled by …
Mavrl: Learn to fly in cluttered environments with varying speed
Many existing obstacle avoidance algorithms overlook the crucial balance between safety
and agility, especially in environments of varying complexity. In our study, we introduce an …
and agility, especially in environments of varying complexity. In our study, we introduce an …
Deep reinforcement learning for end-to-end local motion planning of autonomous aerial robots in unknown outdoor environments: Real-time flight experiments
O Doukhi, DJ Lee - Sensors, 2021 - mdpi.com
Autonomous navigation and collision avoidance missions represent a significant challenge
for robotics systems as they generally operate in dynamic environments that require a high …
for robotics systems as they generally operate in dynamic environments that require a high …