A survey on large-population systems and scalable multi-agent reinforcement learning

K Cui, A Tahir, G Ekinci, A Elshamanhory… - arxiv preprint arxiv …, 2022 - arxiv.org
The analysis and control of large-population systems is of great interest to diverse areas of
research and engineering, ranging from epidemiology over robotic swarms to economics …

Drones in the Big City: Autonomous Collision Avoidance for Aerial Delivery Services

FMC de Oliveira, LF Bittencourt… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
As delivery companies continue to explore the use of drones, the need for efficient and safe
operation in urban environments becomes increasingly critical. Market-wide versions of …

Get your cyber-physical tests done! data-driven vulnerability assessment of robotic aerial vehicles

A Ding, M Chan, A Hass… - 2023 53rd Annual …, 2023 - ieeexplore.ieee.org
The rapid growth of robotic aerial vehicles (RAVs) has attracted extensive interest in
numerous public and civilian applications, from flying drones to quadrotors. Security of RAV …

Safe and efficient multi-agent collision avoidance with physics-informed reinforcement learning

P Feng, R Shi, S Wang, J Liang, X Yu… - IEEE Robotics and …, 2024 - ieeexplore.ieee.org
Reinforcement learning (RL) has shown great promise in addressing multi-agent collision
avoidance challenges. However, existing RL-based methods often suffer from low training …

Benchmarking off-policy deep reinforcement learning algorithms for uav path planning

S Garg, H Masnavi, B Fidan… - 2024 International …, 2024 - ieeexplore.ieee.org
This paper presents a benchmarking of off-policy Reinforcement Learning (RL) algorithms
for Unmanned Aerial Vehicle (UAV) path planning. The focus is on assessing the …

Scalable task-driven robotic swarm control via collision avoidance and learning mean-field control

K Cui, M Li, C Fabian, H Koeppl - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
In recent years, reinforcement learning and its multi-agent analogue have achieved great
success in solving various complex control problems. However, multi-agent rein-forcement …

Bio-Inspired Self-Organized Fission–Fusion Control Algorithm for UAV Swarm

X Zhang, W Ding, Y Wang, Y Luo, Z Zhang, J **ao - Aerospace, 2022 - mdpi.com
Swarm control has become a challenging topic for the current unmanned aerial vehicle
(UAV) swarm due to its conflicting individual behaviors and high external interference …

AVOCADO: Adaptive Optimal Collision Avoidance driven by Opinion

D Martinez-Baselga, E Sebastián, E Montijano… - arxiv preprint arxiv …, 2024 - arxiv.org
We present AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion), a novel
navigation approach to address holonomic robot collision avoidance when the degree of …

Get Your Cyber-Physical Tests Done! Data-Driven Vulnerability Assessment of Robotic Vehicle

A Ding, M Chan, A Hassanzadeh, NO Tippenhauer… - 2023 - publications.cispa.de
The rapid growth of robotic aerial vehicles (RAVs) has attracted extensive interest in
numerous public and civilian applications, from flying drones to quadrotors. Security of RAV …

Multi-Agent Reinforcement Learning for the Low-Level Control of a Quadrotor UAV

B Yu, T Lee - 2024 American Control Conference (ACC), 2024 - ieeexplore.ieee.org
By leveraging the underlying structures of the quadrotor dynamics, we propose multi-agent
reinforcement learning frameworks to innovate the low-level control of a quadrotor, where …