Constrained 3-D trajectory planning for aerial vehicles without range measurement
A Line of Sight (LOS) angular acceleration-based three-dimensional (3-D) trajectory
planning method is designed for aerial vehicles under constrained arrival without range …
planning method is designed for aerial vehicles under constrained arrival without range …
[HTML][HTML] Towards an integrated swarm intelligence framework for urban mobility: A systematic review and proposed theoretical model
In recent years, autonomous electric vehicles (A-EVs) have attracted the attention of
academia and industry. In urban mobility, this topology requires consensus to control …
academia and industry. In urban mobility, this topology requires consensus to control …
Collision-free trajectory planning for UAVs based on sequential convex programming
P Zhang, Y Mei, H Wang, W Wang, J Liu - Aerospace Science and …, 2024 - Elsevier
This paper proposes an optimization-based approach to solve the collision-free trajectory
planning problem for unmanned aerial vehicles (UAVs) with nonlinear dynamical systems …
planning problem for unmanned aerial vehicles (UAVs) with nonlinear dynamical systems …
[PDF][PDF] UAV maneuver decision-making via deep reinforcement learning for short-range air combat
Z Zheng, H Duan - Intell Robot, 2023 - f.oaes.cc
Theunmannedaerialvehicle (UAV) hasbeenappliedinunmannedaircombatbec…. The short-
range air combat situation is rapidly changing, and the UAV has to make the autonomous …
range air combat situation is rapidly changing, and the UAV has to make the autonomous …
A new evolutionary strategy for reinforcement learning
The detection of traffic signs in the natural scene requires a great deal of information due to
the variations it undergoes over time for the output of the detection model to be effective and …
the variations it undergoes over time for the output of the detection model to be effective and …
Real-Time On-the-Fly Motion Planning for Urban Air Mobility via Updating Tree Data of Sampling-Based Algorithms Using Neural Network Inference
In this study, we consider the problem of motion planning for urban air mobility applications
to generate a minimal snap trajectory and trajectory that cost minimal time to reach a goal …
to generate a minimal snap trajectory and trajectory that cost minimal time to reach a goal …
Distributed unmanned aerial vehicle positioning for multitarget tracking with bearing-only information
This paper proposes a distributed positioning algorithm for a swarm of unmanned aerial
vehicles (UAVs) to track multiple moving targets with bearing-only measurements. An …
vehicles (UAVs) to track multiple moving targets with bearing-only measurements. An …
Cooperative Mission Planning Using Rollout Policy Optimization
This paper proposes a novel cooperative mission planning for uncrewed aerial systems
(UAS), using roll-out policy optimization and a blend of straight and double-Archimedean …
(UAS), using roll-out policy optimization and a blend of straight and double-Archimedean …
Safe motion planning and learning for unmanned aerial systems
BE Perk, G Inalhan - Aerospace, 2022 - mdpi.com
To control unmanned aerial systems, we rarely have a perfect system model. Safe and
aggressive planning is also challenging for nonlinear and under-actuated systems. Expert …
aggressive planning is also challenging for nonlinear and under-actuated systems. Expert …
Expert Experience Soft Actor–Critic for Unmanned Aerial Vehicle Dynamic Target Assignment
Deep reinforcement learning has proven highly effective in addressing sequential decision-
making challenges, particularly in aerial combat involving unmanned aerial vehicles (UAVs) …
making challenges, particularly in aerial combat involving unmanned aerial vehicles (UAVs) …