Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles
Abstract
:1. Introduction
2. Model of Starling Behavior
2.1. The Behavioral Mechanism of Starlings
2.2. Bevioral Patterns Based on Bevioral Mechanisms of Starlings
2.2.1. Collective Pattern
2.2.2. Evasion Pattern
2.2.3. Local-Following Pattern
3. Starling-Behavior-Inspired Flocking Control for UAV Swarm
3.1. Model of UAV
3.2. Collision Prediction Mechanism
3.3. Mapping of the Intelligent Behavioral Patterns of Starlings
Algorithm 1. Starling-behavior-inspired flocking control algorithm for UAVs |
/*Initialization*/ Set initial parameters of the proposed algorithm and the model of fixed-wing UAV Generate the position of UAV i randomly /*Begin*/ for i = 1 to n for j = 1 to n Select neighbors according to Equation (1) if UAV j is the neighbor of UAV i UAV i interact with UAV j according to Equation (21) according to Equation (9) end if according to Equation (12) according to Equations (10) and (11) end for to Equation (16) Execute collision prediction according to Equation (17) Calculate the control signal of evasion pattern of UAV i according to Equation (22) end if if there exist a local leader li Set parameter β = 0 UAV i follows the local leader according to Equation (21) end if end if Follow the virtual leader according to Equation (23) /*Limitation*/ Set limitations according to Equation (15) Update the position and velocity according to Equations (24) and (25) end for |
3.4. Conversion of Patterns
4. Simulation Results and Analysis
4.1. Performance Metrics
- (1)
- Order parameter: it captures the coordination of the motion of the UAV swarm and represents the velocity alignment degree of all UAVs in the swarm.
- (2)
- Safety metrics: it measures the risk of collision between the UAV swarm and obstacles and assesses the ability of the UAV swarm to avoid collisions with the obstacles.
- (3)
- Tracking error: it evaluates the tracking performance of the UAV swarm. The tracking error of position is error between the position of UAV swarm and virtual leader, which can be described as follows:The tracking error of altitude is error between the altitude of UAV swarm and virtual leader, which can be described as follows:
4.2. Simulation in Obstacle-Dense Environment
4.3. Simulation in Dynamic Threat Environment
4.4. Simulation in Obstacle Environment with Static and Dynamic Obstacles
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Value |
---|---|
Sensing range, R | 15 m |
Desired distance between neighboring UAVs, d | 5 m |
Desired distance between UAV and obstacle, | 15 m |
Coefficient for the selection of local leader, | 2.5 |
Control gains of local-following pattern, | 0.5, 2 |
Step-size, | 0.025 s |
Control gains of collective pattern, | 1, 4 |
Control gains of evasion pattern, | 1 |
Control gains of virtual leader-follower, | 1, 2 |
Parameter | Value |
---|---|
Velocity time constant, | 5 |
Heading angle time constant, | 0.75 |
Altitude time constant, | 0.3, 1 |
Minimum and maximum velocity, | 5 m/s, 15 m/s |
Maximum lateral overload, | 5 g |
Maximum climbing and gliding velocity, | −5 m/s, 5 m/s |
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Wu, W.; Zhang, X.; Miao, Y. Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles. Biomimetics 2022, 7, 214. https://doi.org/10.3390/biomimetics7040214
Wu W, Zhang X, Miao Y. Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles. Biomimetics. 2022; 7(4):214. https://doi.org/10.3390/biomimetics7040214
Chicago/Turabian StyleWu, Weihuan, Xiangyin Zhang, and Yang Miao. 2022. "Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles" Biomimetics 7, no. 4: 214. https://doi.org/10.3390/biomimetics7040214