scholarly journals A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments

2019 ◽  
Vol 9 (22) ◽  
pp. 4964 ◽  
Author(s):  
Yue ◽  
Guan ◽  
Wang
Keyword(s):  
Reinforcement Learning ◽  
Prior Information ◽  
Search Task ◽  
Efficiency Function ◽  
Awareness Information ◽  
Simulation Results ◽  
Searching Method ◽  
Sea Area ◽  
Sensor Detection ◽  
Multi Uav

In this paper, the important topic of cooperative searches for multi-dynamic targets in unknown sea areas by unmanned aerial vehicles (UAVs) is studied based on a reinforcement learning (RL) algorithm. A novel multi-UAV sea area search map is established, in which models of the environment, UAV dynamics, target dynamics, and sensor detection are involved. Then, the search map is updated and extended using the concept of the territory awareness information map. Finally, according to the search efficiency function, a reward and punishment function is designed, and an RL method is used to generate a multi-UAV cooperative search path online. The simulation results show that the proposed algorithm could effectively perform the search task in the sea area with no prior information.

QoE-driven Adaptive Deployment Strategy of Multi-UAV Networks Based on Hybrid Deep Reinforcement Learning

2021 ◽  
pp. 1-1
Author(s):  
Yi Zhou ◽  
Xiaoyong Ma ◽  
Shuting Hu ◽  
Danyang Zhou ◽  
Nan Cheng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Deployment Strategy ◽  
Multi Uav

scholarly journals A Comparison of Different Data-driven Procedures to Determine the Bunching Window

2021 ◽  
Vol 49 (2) ◽  
pp. 262-293
Author(s):  
Vincent Dekker ◽  
Karsten Schweikert
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Income Distribution ◽  
Prior Information ◽  
Structural Breaks ◽  
Data Driven ◽  
Taxable Income ◽  
Counterfactual Models ◽  
Simulation Results ◽  
Second Procedure

In this article, we compare three data-driven procedures to determine the bunching window in a Monte Carlo simulation of taxable income. Following the standard approach in the empirical bunching literature, we fit a flexible polynomial model to a simulated income distribution, excluding data in a range around a prespecified kink. First, we propose to implement methods for the estimation of structural breaks to determine a bunching regime around the kink. A second procedure is based on Cook’s distances aiming to identify outlier observations. Finally, we apply the iterative counterfactual procedure proposed by Bosch, Dekker, and Strohmaier which evaluates polynomial counterfactual models for all possible bunching windows. While our simulation results show that all three procedures are fairly accurate, the iterative counterfactual procedure is the preferred method to detect the bunching window when no prior information about the true size of the bunching window is available.

Collaborative Computation Offloading and Resource Allocation in Multi-UAV Assisted IoT Networks: A Deep Reinforcement Learning Approach

2021 ◽  
pp. 1-1
Author(s):  
Abegaz Mohammed Seid ◽  
Gordon Owusu Boateng ◽  
Stephen Anokye ◽  
Thomas Kwantwi ◽  
Guolin Sun ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Computation Offloading ◽  
Learning Approach ◽  
Multi Uav

Collaborative Scheduling of Algorithms for Path Planning of Unmanned Systems

2021 ◽  
Vol 01 ◽  
Author(s):  
Ying Li ◽  
Chubing Guo ◽  
Jianshe Wu ◽  
Xin Zhang ◽  
Jian Gao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Unmanned Systems ◽  
Ant Colony Optimization Algorithm ◽  
A Algorithm ◽  
Collaborative Scheduling ◽  
Simulation Results ◽  
Planning Problems ◽  
Effective Path

Background: Unmanned systems have been widely used in multiple fields. Many algorithms have been proposed to solve path planning problems. Each algorithm has its advantages and defects and cannot adapt to all kinds of requirements. An appropriate path planning method is needed for various applications. Objective: To select an appropriate algorithm fastly in a given application. This could be helpful for improving the efficiency of path planning for Unmanned systems. Methods: This paper proposes to represent and quantify the features of algorithms based on the physical indicators of results. At the same time, an algorithmic collaborative scheme is developed to search the appropriate algorithm according to the requirement of the application. As an illustration of the scheme, four algorithms, including the A-star (A*) algorithm, reinforcement learning, genetic algorithm, and ant colony optimization algorithm, are implemented in the representation of their features. Results: In different simulations, the algorithmic collaborative scheme can select an appropriate algorithm in a given application based on the representation of algorithms. And the algorithm could plan a feasible and effective path. Conclusion: An algorithmic collaborative scheme is proposed, which is based on the representation of algorithms and requirement of the application. The simulation results prove the feasibility of the scheme and the representation of algorithms.

Multi-UAV-enabled AoI-aware WPCN: A Multi-agent Reinforcement Learning Strategy

2021 ◽  
Author(s):  
Omar Sami Oubbati ◽  
Mohammed Atiquzzaman ◽  
Abderrahmane Lakas ◽  
Abdullah Baz ◽  
Hosam Alhakami ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Strategy ◽  
Multi Agent ◽  
Multi Uav

Multi-UAV Cooperative Task Planning for Border Patrol based on Hierarchical Optimization

2021 ◽  
Author(s):  
Hai-shi Liu ◽  
Yu-xuan Sun ◽  
Nan Pan ◽  
Qi-yong Chen ◽  
Xiao-jue Guo ◽  
...  
Keyword(s):  
Chaos Theory ◽  
Hierarchical Optimization ◽  
Border Patrol ◽  
Complex Environments ◽  
Environmental Model ◽  
Other Information ◽  
Aerial Vehicle ◽  
Scene Information ◽  
Simulation Results ◽  
Multi Uav

In order to improve the patrol efficiency of border patrol drones, based on unmanned aerial vehicle (UAV) border patrol missions in multiple complex environments, this article proposes a whale algorithm based on chaos theory to plan patrol missions for multiple drones. First, according to the terrain the corresponding environmental model is established for the topography and then solved in layers to obtain the number of drones and other information that each base needs to send to the patrol area. Further, the use of drones with cameras and other detection equipment to patrol the scene information and images extract and transfer to the terminal in real time, and further detect suspicious persons and vehicles on the screen. The final simulation results show that the proposed scheme can be effectively applied to the planning of multi-UAV coordinated missions for border patrol.

scholarly journals Research on the Multiagent Joint Proximal Policy Optimization Algorithm Controlling Cooperative Fixed-Wing UAV Obstacle Avoidance

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4546
Author(s):  
Weiwei Zhao ◽  
Hairong Chu ◽  
Xikui Miao ◽  
Lihong Guo ◽  
Honghai Shen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Attitude Control ◽  
Cooperative Control ◽  
Learning Algorithm ◽  
State Equations ◽  
Learning Agent ◽  
Environmental Adaptability ◽  
Decentralized Execution ◽  
Policy Optimization ◽  
Multi Uav

Multiple unmanned aerial vehicle (UAV) collaboration has great potential. To increase the intelligence and environmental adaptability of multi-UAV control, we study the application of deep reinforcement learning algorithms in the field of multi-UAV cooperative control. Aiming at the problem of a non-stationary environment caused by the change of learning agent strategy in reinforcement learning in a multi-agent environment, the paper presents an improved multiagent reinforcement learning algorithm—the multiagent joint proximal policy optimization (MAJPPO) algorithm with the centralized learning and decentralized execution. This algorithm uses the moving window averaging method to make each agent obtain a centralized state value function, so that the agents can achieve better collaboration. The improved algorithm enhances the collaboration and increases the sum of reward values obtained by the multiagent system. To evaluate the performance of the algorithm, we use the MAJPPO algorithm to complete the task of multi-UAV formation and the crossing of multiple-obstacle environments. To simplify the control complexity of the UAV, we use the six-degree of freedom and 12-state equations of the dynamics model of the UAV with an attitude control loop. The experimental results show that the MAJPPO algorithm has better performance and better environmental adaptability.

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