Flocking Control of Fixed-Wing UAVs With Cooperative Obstacle Avoidance Capability

Zhao, Weiwei; Chu, Hairong; Zhang, Mingyue; Sun, Tingting; Guo, Lihong

doi:10.1109/access.2019.2895643

Cited by 40 publications

(10 citation statements)

References 26 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When a minority group encounters an obstacle, it is easy to fall into a local minimum state, making the group state worse. The use of a cluster obstacle avoidance algorithm with local interaction of obstacle information can improve the disadvantage poor consensus of the local obstacle avoidance algorithm, and promote the consensus of the UAV group while overcoming the obstacles [97]. However, the existing collision avoidance methods still have some theoretical and practical problems.…”

Section: D) Collaborative Search and Tracking Technologymentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

View full text Add to dashboard Cite

The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

show abstract

Section: D) Collaborative Search and Tracking Technologymentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

View full text Add to dashboard Cite

show abstract

“…The UAV, as the research object of RL algorithms, has attracted the attention of many researchers [5,6,20]. Multi-UAV formation can accomplish many tasks that cannot be completed by a single UAV [21,22]. This paper proposes the Multiagent Joint Proximal Policy Optimization (MAJPPO) algorithm, which uses the moving window average of the state-value functions of different agents to get the centralized state-value function to solve the problem of multi-UAV cooperative control.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhao

Chu

Miao

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Examples of this phenomenon include such as fish schools, bird flocks, ant colonies, and bacteria swarms, etc. Due to its broad applications in fields such as multi-target tracking of mobile sensor networks [4]- [6], cooperative control of swarm robots [7]- [9], and coordinated motion of unmanned aerial vehicles [10]- [12], etc., the flocking of multi-agents has attracted a great deal of attention among researchers from different disciplines [13]- [24].…”

Section: Introductionmentioning

confidence: 99%

Flocking for Multiple Subgroups of Multi-Agents With Different Social Distancing

Hui

Chen

2020

IEEE Access

View full text Add to dashboard Cite

In this paper, considering the difference in social distancing among individuals, according to the extent of social distancing, a group composed of N mobile agents is divided into multiple different subgroups. Especially, from the perspective of differential game theory, the flocking problem of different subgroups can be regarded as collision avoidance between neighboring agents, or obstacle avoidance between agents and virtual static/dynamic obstacles. To explore the internal mechanism of this interesting problem, a novel flocking algorithm with multiple virtual leaders is designed. The proposed algorithm is a modified version of the traditional flocking and semi-flocking algorithms. Based on the Lyapunov stability theorem and LaSalle's invariance principle, the stability analysis of the proposed algorithm is then proven. Furthermore, considering the complex environment that swarm robots or unmanned aerial vehicles (UAVs) may face when performing military missions such as surveillance, reconnaissance, and rescue, etc., we also investigate the flocking problem of multi-agents in both virtual static and dynamic obstacles environment. Finally, three kinds of simulation results are provided to demonstrate the effectiveness of the proposed results.INDEX TERMS Flocking, multi-agent systems, collision avoidance, obstacle avoidance, social distancing.

show abstract

Flocking Control of Fixed-Wing UAVs With Cooperative Obstacle Avoidance Capability

Cited by 40 publications

References 26 publications

UAV Swarm Intelligence: Recent Advances and Future Trends

UAV Swarm Intelligence: Recent Advances and Future Trends

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

Flocking for Multiple Subgroups of Multi-Agents With Different Social Distancing

Contact Info

Product

Resources

About