Filters








372 Hits in 3.7 sec

Reinforcement Learning-Based Collision Avoidance Guidance Algorithm for Fixed-Wing UAVs

Yu Zhao, Jifeng Guo, Chengchao Bai, Hongxing Zheng, Zhile Yang
2021 Complexity  
Additionally, a simulator suitable for multiagent high-density route scene is designed for validation, in which all UAVs run the proposed algorithm onboard.  ...  A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited  ...  Since the reward function and the dynamics function (transition function) have been determined, the goal of reinforcement learning is to find the optimal policy for the multiagent problem in this paper  ... 
doi:10.1155/2021/8818013 fatcat:xezl64j7zfeatkwik2lfy2j5le

Multiagent Adjustable Autonomy Framework (MAAF) for multi-robot, multi-human teams

Amos Freedy, Onur Sert, Elan Freedy, James McDonough, Gershon Weltman, Milind Tambe, Tapana Gupta, William Grayson, Pedro Cabrera
2008 2008 International Symposium on Collaborative Technologies and Systems  
of advances in goal-oriented, multiagent planning and coordination technology.  ...  Our response to this challenge is the creation of a new infrastructure that will facilitate cooperative and collaborative performance of human and robots as equal team partners through the application  ...  Dan Corbett, DARPA-IPTO Science and Technical Support, for his contributions to our successful completion of this effort.  ... 
doi:10.1109/cts.2008.4543970 dblp:conf/cts/FreedySFMWTGGC08 fatcat:fd7nim4xxrhizplg6epicn47yy

Method of agents' state estimation in multiresolution multiagent simulation

Dariusz Pierzchała, Przemysław Czuba
2019 Computer Science and Mathematical Modelling  
the multiagent systems, where agents converge to predefined geometric shapes.  ...  The presented approach shows that multiagent methods seem to be very promising in multiresolution simulation.  ...  UAV team adopts the specified formation according to its mission.  ... 
doi:10.5604/01.3001.0013.1460 fatcat:2v2e3ddxgbcj7kljrh7rtoigsy

UAV Swarm Confrontation Using Hierarchical Multiagent Reinforcement Learning

Baolai Wang, Shengang Li, Xianzhong Gao, Tao Xie, Xingling Shao
2021 International Journal of Aerospace Engineering  
With the development of unmanned aerial vehicle (UAV) technology, UAV swarm confrontation has attracted many researchers' attention.  ...  In this paper, a multiagent reinforcement learning method with macro action and human expertise is proposed for autonomous decision-making of UAVs.  ...  According to the state transition function T : S × A 1 × ⋯ × A N × S ⟶ ½0, 1, the environment transitions to the next state.  ... 
doi:10.1155/2021/3360116 fatcat:ftfcbaa2wrh3nfjkn4hhiwhhvy

Group health management of UAV teams with applications to persistent surveillance

Brett Bethke, Jonathan P. How, John Vian
2008 2008 American Control Conference  
The complex and distributed nature of these missions often requires teams of UAVs to work together.  ...  Unmanned aerial vehicles (UAVs) are well-suited to a wide range of mission scenarios, such as search and rescue, border patrol, and military surveillance.  ...  The authors would like to thank Jim McGrew, Brandon Luders, Josh Redding, and Spencer Ahrens at MIT for their assistance in this research.  ... 
doi:10.1109/acc.2008.4586976 dblp:conf/amcc/BethkeHV08 fatcat:goc7ysjeqnffbjoegrcpcxp27a

Fault-Tolerant Time-Varying Formation Tracking Control for Unmanned Aerial Vehicle Swarm Systems with Switching Topologies

Ran Zhen, Yating Jin, Xiaojing Wu, Xueli Wu, Xuan Lv, Xingling Shao
2021 Mathematical Problems in Engineering  
For UAV swarm systems with switching topologies and actuator faults, the formation tracking control protocol designed is adopted to enable the followers form the desired time-varying formation and track  ...  The control protocol does not depend on the information of the actuator fault boundary by using adaptive technology.  ...  swarm systems under Markovian switching topologies with partially unknown transition rates. e formation tracking control problem of high-order multiagent systems in the case of unknown leader input has  ... 
doi:10.1155/2021/5519243 fatcat:me6zc2w5ojcg7n7plkuvglgjhu

A Mechanism for Recognizing and Suppressing the Emergent Behavior of UAV Swarm

Qiang Liu, Ming He, Daqin Xu, Ning Ding, Yong Wang
2018 Mathematical Problems in Engineering  
Similar to social animals in nature, UAV swarm is also a complex system that can produce emergent behavior.  ...  The emergent behavior of UAV swarm in specific airspace is undoubtedly the act that the defense side does not expect to see; therefore, recognition and suppression of the emergent behavior of UAVs swarm  ...  As a new technology, UAV swarm is a double-edged sword.  ... 
doi:10.1155/2018/6734923 fatcat:s325dgdc7rcanoec26oly352du

Multiagent Reinforcement Learning for Task Offloading of Space/Aerial-Assisted Edge Computing

Yanlong Li, Lei Liang, Jielin Fu, Junyi Wang, Yuyu Yin
2022 Security and Communication Networks  
By formulating the problem as a Markov decision process (MDP), we propose a multiagent deep reinforcement learning (MADRL)-based scheme to obtain the optimal task offloading policies considering dynamic  ...  In this paper, we investigate a space/aerial-assisted edge computing network architecture considering whether to take advantage of edge server mounted on the unmanned aerial vehicle and satellite for task  ...  Introduction e current in-depth development of fifth generation (5G) and beyond 5G technology is envisioned to build an interconnected world opening up to everyone. e increasing number of various ultradense  ... 
doi:10.1155/2022/4193365 fatcat:62vfmqsemzccbknvsparzvuwda

Brain-Swarm Control Interfaces: The Transition from Controlling One Robot to a Swarm of Robots

Panagiotis Artemiadis
2017 Advances in Robotics & Automation  
The transition from controlling one robot to a swarm of them using brain-machine interfaces has just started.  ...  Advancing our understanding of swarm perception and control at the brain level offer a myriad of applications that involve human-in-the-loop multiagent systems, spanning from industrial and entertainment  ... 
doi:10.4172/2168-9695.1000e127 fatcat:bqyj2bmhpbcppgmvgg44e5cvlq

Towards a Multi-Agent based Network Intrusion Detection System for a Fleet of Drones

Said OUIAZZANE, Fatimazahra BARRAMOU, Malika ADDOU
2020 International Journal of Advanced Computer Science and Applications  
Given the real-time nature of the application traffic exchanged between the nodes of a fleet of UAVs, this could lead to significant latencies in exchanges between the nodes.  ...  been made by large technology companies, namely Amazon's Prime Air service [12] .  ...  In [29] , the authors proposed to use the Blockchain technology to transmit signals between the controller and the UAV.  ... 
doi:10.14569/ijacsa.2020.0111044 fatcat:i5gnv5a4bbgdnmsxkkzxpir6em

Joint Optimization of Multi-UAV Target Assignment and Path Planning based on Multi-Agent Reinforcement Learning

Han Qie, Dianxi Shi, Tianlong Shen, Xinhai Xu, Yuan Li, Liujing Wang
2019 IEEE Access  
Then, the MADDPG framework is used to train the system to solve target assignment and path planning simultaneously according to a corresponding reward structure.  ...  One of the major research topics in unmanned aerial vehicle (UAV) collaborative control systems is the problem of multi-UAV target assignment and path planning (MUTAPP).  ...  The flight action each UAV chooses is only relevant to the current state. Fig. 2 shows the state transition process from s t to s t+1 .  ... 
doi:10.1109/access.2019.2943253 fatcat:42ep4d27avab5lcl2vmleimaaa

Biologically Based Control of a Fleet of Unmanned Aerial Vehicles Facing Multiple Threats

Sami El-Ferik
2020 IEEE Access  
This paper addresses a set of multiagent, unmanned, aerial vehicles (UAVs) in a mission within a threat-prone environment.  ...  Each UAV is considered as a nonholonomic, nonlinear model moving in a two dimensional space. The system is composed of a fleet of UAVs, competing UAVs and a target.  ...  FIGURE 2 . 2 State transition diagram for UAV. FIGURE 3 . 3 Predator state transition diagram. FIGURE 4 . 4 Target state transition diagram. FIGURE 5 . 5 Control Schematic -Single UAV.  ... 
doi:10.1109/access.2020.3000774 fatcat:gyob5mby3jebhkensu2qfdcr5u

A hybrid approach based on multi-agent geosimulation and reinforcement learning to solve a UAV patrolling problem

Jimmy Perron, Jimmy Hogan, Bernard Moulin, Jean Berger, Micheline Belanger
2008 2008 Winter Simulation Conference  
We propose a hybrid approach combining multi-agent geosimulation and reinforcement learning enabling a group of agents to find near optimal solutions in realistic geo-referenced virtual environments.  ...  In this paper we address a dynamic distributed patrolling problem where a team of autonomous unmanned aerial vehicles (UAVs) patrolling moving targets over a large area must coordinate.  ...  Since the environment is represented as a graph where nodes correspond to targets and links to transition costs, the model of the environment is assumed to be known prior to creating the graph.  ... 
doi:10.1109/wsc.2008.4736198 dblp:conf/wsc/PerronHMBB08 fatcat:zmpopjkh7fautflh53mpwuoi4m

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

Sagir M. Yusuf, Chris Baber
2022 Frontiers in Robotics and AI  
A team of UAVs tasked to conduct a forest fire search was selected as the use case, although solutions are applicable to other domains.  ...  We also demonstrate the proposed algorithm's application on real UAVs.  ...  Thus, our version of multiagent search is a team of agents (e.g., UAVs) tasked to conduct a search activity under the outlined constraints.  ... 
doi:10.3389/frobt.2022.851846 pmid:35845255 pmcid:PMC9277356 fatcat:vhxmhwfu6bexjgd34f277oj7x4

Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence [article]

Peng Wei, Kun Guo, Ye Li, Jue Wang, Wei Feng, Shi Jin, Ning Ge, Ying-Chang Liang
2022 arXiv   pre-print
dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they can be solved by RL solutions in diverse mobile applications  ...  Finally, the open challenges are discussed to provide helpful guidance for future research in RL training and learning MEC.  ...  to the real MEC environment is estimated by DT technology.  ... 
arXiv:2201.11410v4 fatcat:24igkq4kbrb2pjzwf3mf3n7qtq
« Previous Showing results 1 — 15 out of 372 results