Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization
Abstract
:1. Introduction
1.1. Related Work
1.2. Main Contributions
2. System Model for UAV Path Planning
2.1. Problem Description
2.2. Operational Area Representation
2.3. Trajectory Planning
3. Mathematical Model for Optimal Trajectory Planning
3.1. Dynamic Fitness Function Design
3.2. Objective Function Design
3.2.1. Energy Consumption (EC)
3.2.2. Flight Risk Estimation (FRE)
- Environmental Risk
- High Altitude Risk
3.2.3. Surveillance Area Important (SAI)
3.3. Constraint Function Design
3.3.1. Aerial Constraint (AC)
3.3.2. Restricted Area Constraint (RAC)
3.3.3. Turning Angle Constraint (TAC)
3.3.4. Operational Area Constraint (OAC)
3.3.5. Coverage Range Constraints (CRC)
3.3.6. Collision Avoidance (CA)
4. Proposed Distributed Trajectory Planner Based on PSO and Bresenham Algorithm
4.1. Particle Swarm Optimization
Algorithm 1: Pseudocode for Dynamic Fitness Function using PSO. |
|
4.2. Bresenham Algorithm
- Either the one to its right (lower-bound for the line)
- On top, it is right and up (upper-bound for the line).
- Start from the two-line, starting point (x1, y1) and endpoint (xend, yend) and then calculate the constants where and .
- Calculate the first value of the decision parameter by using the equation:
- For each value of xi along the line, check the following condition, if pi < 0, the next point needs to be selected as (xi+1, yi) and:
- Otherwise, the next point to be selected is (xi+1, yi+1) and:
Algorithm 2: Pseudocode of Bresenham Algorithm. |
|
4.3. Distributed Path Planning for Multi-UAVs
Algorithm 3: Pseudocode for Distributed Path Planning |
|
5. Simulation Results and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
UAV | Unmanned Aerial Vehicle |
PSO | Particle Swarm Optimization |
mPSO | PSO with modified parameters |
GBS | Ground Base Station |
EC | Energy Consumption |
FRE | Flight Risk Estimation |
SAI | Surveillance Area Importance |
AC | Aerial Constraint |
RAC | Restricted Area Constraint |
OAC | Operational Area Constraint |
CRC | Coverage Range Constraint |
TAC | Turning Angle Constraint |
CA | Collision Avoidance |
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Objective Function | ||||||
Name | Energy Consumption | Flight Risk Estimation | Surveillance Area Importance | |||
Abbreviation | EC | FRE | SAI | |||
Equation | (5)–(10) | (11)–(14) | (15)–(17) | |||
Value/Range | [0, 1] | [0, 1] | [0, 1] | |||
Constraint Function | ||||||
Name | Aerial Constraint | Restricted Area Constraint | Turning Angle Constraint | Operational Area Constraint | Coverage Range Constraint | Collision Avoidance |
Abbreviation | AC | RAC | TAC | OAC | CRC | CA |
Equation | (19) | (20) | (21) | (22) | (23) | (24) |
Value/Range | 0 | 0 | 0 | 0 | 0 | 0 |
Parameters | Symbols | Values |
---|---|---|
Grid Side (2-D environment) | - | 2020 |
Operational Space (3-D environment) | - | |
Number of drones | ND | 4 |
Drone unit power | Pw | 20 |
Monitoring drone speed | v | 10 m/s |
Number of particles | Npar | 32, 64, 128, 150, 256, 512 |
Number of iterations | Niter | 100 |
Minimum safe distance | dmin | 0.2 m |
Initial SAI value | 4 to 10 | |
Initial environment risk | 1–5 |
Parameter Name | Conventional PSO | PSO with Modified Parameters (mPSO) |
---|---|---|
Inertia value | 0.7298 | 0.8 |
Personal Cognitive value | 1.4960 | 2.0 |
Social Cognitive value | 1.4960 | 2.0 |
Weight Value | Energy Consumption | Flight Risk Estimation | Surveillance Area Importance | Objective Value |
---|---|---|---|---|
1 | −0.3619 | −0.6356 | 0.4597 | −0.5978 |
2 | −0.3577 | −0.6186 | 0.9096 | −0.0567 |
3 | −0.3478 | −0.5939 | 1.6792 | 0.7178 |
4 | −0.3789 | −0.5705 | 2.2389 | 1.2775 |
5 | −0.3652 | −0.5937 | 2.7986 | 1.8297 |
6 | −0.3580 | −0.5655 | 3.3583 | 2.3848 |
No. of Waypoints | Altitude (Meter) | |||
---|---|---|---|---|
UAV1 | UAV2 | UAV3 | UAV4 | |
1 | 0.4827 | 0.4792 | 0.3403 | 0.5184 |
2 | 1.3508 | 0.3393 | 1.3211 | 0.8365 |
3 | 1.1563 | 0.9815 | 1.2727 | 0.4283 |
4 | 1.3724 | 0.2747 | 1.1685 | 0.9781 |
5 | 0.8549 | 0.8525 | 1.3761 | 0.0475 |
6 | 0.8316 | 0.1218 | 0.8848 | 0.1951 |
7 | 0.8528 | 0.0602 | 1.0310 | 0.5889 |
8 | 0.5366 | 0.5191 | 0.4815 | 0.5165 |
UAV Number | Distance Covered in 2-D (Meter) | Time Required for 2-D (Second) | Distance Covered in 3-D (Meter) | Time Required for 3-D (Second) |
---|---|---|---|---|
1 | 3,152 | 315.2 | 3,159 | 315.9 |
2 | 3,155 | 315.5 | 3,164 | 316.4 |
3 | 3,184 | 318.4 | 3,195 | 319.5 |
4 | 3,052 | 305.2 | 3,056 | 305.6 |
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Ahmed, N.; Pawase, C.J.; Chang, K. Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization. Appl. Sci. 2021, 11, 3417. https://doi.org/10.3390/app11083417
Ahmed N, Pawase CJ, Chang K. Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization. Applied Sciences. 2021; 11(8):3417. https://doi.org/10.3390/app11083417
Chicago/Turabian StyleAhmed, Nafis, Chaitali J. Pawase, and KyungHi Chang. 2021. "Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization" Applied Sciences 11, no. 8: 3417. https://doi.org/10.3390/app11083417
APA StyleAhmed, N., Pawase, C. J., & Chang, K. (2021). Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization. Applied Sciences, 11(8), 3417. https://doi.org/10.3390/app11083417