A Survey on Multi-UAV Path Planning: Classification, Algorithms, Open Research Problems, and Future Directions
Abstract
:1. Introduction
1.1. Research Challenges
1.2. Research Scope and Contribution
- We critically review more than 100 published papers on UAV path planning. To this end, we carefully selected relevant refereed articles from scholarly journals and conference proceedings.
- We classify the existing research on UAV path planning based on multi-UAV algorithms and features. To this end, we focus on a survey of various approaches, including metaheuristic, classical, heuristic, machine learning, and hybrid methods. This significant work contributes to the design and development of next-generation UAV systems.
- We identify and discuss open research problems, including multi-UAV path planning, focusing on machine learning and hybrid algorithms for practical application scenarios such as disaster rescue and recovery management.
1.3. Summary of Existing Algorithms
1.4. Paper Organization
2. Multi-UAV Path Planning Algorithms
2.1. Classification of Multi-UAV Path Planning Algorithms
2.2. Existing Path Planning Algorithms
3. Description of Multi-UAV Path Planning Approaches
3.1. Metaheuristic Approach
3.2. Classical Approach
3.3. Heuristic Approach
3.4. Machine Learning Approach
3.5. Hybrid Approach
- If, , then Equation (5) is applied.
- Otherwise, if , is selected according to WoA.
- Otherwise, is selected randomly.
3.6. Summary of Multi-UAV Path Planning Approaches
4. Open Research Problems
4.1. Obstacle Avoidance
4.2. Communication and Collaboration
4.3. Safety and Reliability
4.4. Dynamic Environments
4.5. Energy Efficiency
4.6. System Complexity and Computational Cost
4.7. Summary of Open Research Issues
5. Future Research Directions
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Acronym
Acronym | Explanation |
ABC | Artificial Bee Colony |
APF | Artificial Potential Field |
AQLPSO | Adaptive Q-Learning-based Particle Swarm Optimization |
ACO | Ant Colony Optimization |
BFO | Bacteria Foraging Optimization |
BINN-HHO | Bioinspired neural network and improved Harris hawk optimization |
BRKGA | Biased Random Key Genetic Algorithm |
CGWO | Chaotic Gray Wolf Optimization |
CL-DMSPSO | Comprehensive learning dynamic multi-swarm particle swarm optimization |
CNN | Convolution Neural Network |
CPP | Coverage Path Planning |
CRO | Coral Reef Optimization |
CS | Cuckoo Search |
Deep RL ESN | Deep Reinforcement Learning Echo State Network |
DDPG | Deep deterministic policy gradient algorithm |
DE | Differential Evolution |
Distributed formation algorithm 3 | |
DPIO | Discrete pigeon-inspired optimization |
DQN | Deep Q-network |
DDQN | Double deep Q-network |
D3QN | Dueling double deep Q-network |
DQN - RRT | Deep Q-network–Rapidly exploring Random Tree |
EB-A* | Elastic band–A-Star |
ePFC | Extended potential field controller |
FP-GPSO | Fermat point-based grouping particle swarm optimization |
GA | Genetic Algorithm |
GA-Homotopic | Genetic Algorithm and Homotopic Algorithm |
GCS | Ground control station |
GH | Greedy Heuristic |
GP | Gaussian Process |
HAS-DQN | Hexagonal Area Search Deep Q-Network |
HTS-VND | Hybrid tabu search-variable neighborhood descent |
Hybrid GH | Hybrid Greedy Heuristic |
Improved PSO and GPM | Improved particle swarm optimization and Gaussian pseudo-spectral method |
Improved EB and A-Star | Improved Elastic Band and A-Star Algorithm |
IVD | Improved Voronoi Diagram |
IABC | Improved Artificial Bee Colony |
IAPF | Improved Artificial Potential Field |
IPSO | Improved Practical Swarm Optimization |
LM-GWO | Levy flight-based multi-population grey wolf optimisation |
MADDPG | Multi-Agent Deep Deterministic Policy Gradient |
MCMOPSO-RL | Multi-objective particle swarm optimization with multi-mode collaboration based on reinforcement learning |
MHS | Modified Harmony Search |
MMACO | Maximum minimum ant colony optimization |
MODRL | multi-objective deep reinforcement learning |
MPC and PSO | Model predictive control and particle swarm optimization |
MSFOA | Multiple Swarm Fruit Fly Optimization Algorithm |
MTSP | Multiple Traveling Salesman Problem |
MVD | Modified Voronoi Diagram |
MVO | Multi-Verse Optimizer |
MUCS-BSAE | Multi-UAV collaborative search algorithm based on the binary search algorithm |
NSGA multi-objective EA | Non Dominated Sorting Genetic Algorithm multi-objective Evolutionary algorithms |
OPP | Optimal Path Planning |
ORPFOA | Optimal reference point Fruit Fly Optimization Algorithm |
PDE | Partial Differential Equation |
PIO | Pigeon Inspired Optimization |
POMDP | Partially Observable Markov Decision Process |
PPSwarm | PSO (Practical Swarm Optimization) + RRT (Rapid-exploring Random Trees) |
RBF-ANN | Radial Basis Functions Artificial Neural Network |
RGV | Reduced Visibility Graph |
RRT | Rapidly exploring Random Tree |
SFLA | Shuffled Frog Leaping Algorithm |
SIRIPPA | Secondary Immune Responses-based Immune Path Planning Algorithm |
SPVM and PSO | Spatial Refined Voting Mechanism and Particle Swarm Optimization |
TLBO | Teaching Learning Based Optimization |
UAV | unmanned aerial vehicle |
VD | Voronoi Diagram |
WoLFIGA | Win or learn fast policy with Infinitesimal gradient ascent |
WPL | Weighted Policy Learner |
WDQN | Whale inspired deep Q-network |
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Algorithm | Problem Addressed | Limitation | Environmental Type | Extensive Area Scalability | Scenarios | Reference |
---|---|---|---|---|---|---|
HAS-DQN | Overlapped coverage areas and low energy efficiency | Computational cost and system complexity are not considered | Dynamic | No | Data collection | [19] |
WDQN | :imitations of standard route planning algorithms when traversing across unexpected and complex locations | UAV height is not taken into consideration | Dynamic | No | Emergency rescue | [20] |
MODRL | Addresses the computational offloading problem (COP) by balancing energy usage and latency issues | Communication from the UAV to the ground control station is not taken into consideration | Dynamic | Yes | Mountainous and disaster areas | [21] |
The problem of overfitting difficult problems | Energy efficiency is lacking | Dynamic | No | 270 Unseen scenarios | [22] | |
DQN | Battery constraints and their impact on data collection and flight safety | The pace of convergence is not taken into account | Dynamic | No | Collecting data in restricted areas | [23] |
GA and Homotopic Algorithm | Large-scale aerial recovery and secure returning from forbidden areas | Height and energy efficiency are not addressed | Dynamic | Yes | Multi-UAV recovery using a controller UAV | [24] |
MCMOPSO-RL | Determining an optimal approach in a complicated environment due to the presence of various impediments | Resource requirements are absent | Dynamic | Yes | Managing several static barriers in a complex setting | [25] |
FP-GPSO | Establishment of optimal path planning in a long flight range with high flexibility | Resource requirements and communication between UAVs and the ground control station are not taken into consideration | Dynamic | No | A carrier UAV and several parasite UAVs work together to provide a long flight range | [26] |
Improved PSO and GPM | Addresses the flying-time issue using swarm intelligence and graph-based algorithms | UAV height and resource requirements are not considered | Dynamic | No | Multi-UAV flight times are shortened in environments with a lot of obstacles | [27] |
MPC and PSO | Search with moving targets | Energy efficiency and UAV height are not considered | Ddynamic | No | Multi-UAVs look for several moving targets | [28] |
SPVM and PSO | Delayed convergence and local optimality problems | Resource requirements and energy efficiency are not considered | Dynamic | Yes | 4D path planning to increase search precision and prevent threats and obstructions | [29] |
PPSwarm | Issues in tight passages (standard algorithms frequently struggle to quickly discover a viable path) | Height and energy efficiency are not addressed | Dynamic | Yes | Space with static barriers and small passageways | [30] |
BINN-HHO | Efficiency issues in alpine situations, such as limited stability, long planned paths, and poor dynamic obstacle avoidance | Moving obstacles are not considered | Dynamic | Yes | The path is more prolonged, less stable, and has poor moving obstacle avoidance capability in hilly terrain | [31] |
CL-DMSPSO | Obstacles during diverse task operations in complex situations | Communication between UAVs and ground control stations is not considered | Dynamic | Yes | Path length reduction and collision avoidance in complex environments | [32] |
NSGA multi-objective EA | Coverage time, connectivity between drones, and data transmission to the ground control station | Unknown environments with flying items are not evaluated | Dynamic | No | Data collection, communication with the GCS, monitoring, and surveillance in a dynamic context | [33] |
Algorithm Name | Low Time Consumption | Low Computational Cost | Low System Complexity | Fast Convergence Speed | Adaptability to Complex Environments | Smooth Path | Online Algorithm | Extensive Area Scalability | Obstacle Avoidance Capability | Reference |
---|---|---|---|---|---|---|---|---|---|---|
GA + MTSP | √ | × | × | √ | √ | × | × | √ | × | [40,41,42] |
DPIO | × | × | √ | × | × | × | × | √ | × | [43] |
CGWO | √ | √ | √ | √ | √ | × | × | √ | × | [44] |
MMACO | × | √ | × | √ | √ | × | × | √ | √ | [45] |
Improved PSO | × | × | × | √ | × | √ | × | × | √ | [46] |
DQN + RRT | × | × | × | √ | √ | √ | √ | √ | √ | [47] |
Deep Reinforcement Learning | √ | × | × | √ | √ | √ | √ | √ | √ | [12,48] |
Reinforcement Learning | × | √ | √ | √ | √ | × | × | × | √ | [49,50] |
Deep Reinforcement Learning | √ | × | × | √ | √ | × | √ | √- | × | [51,52,53] |
IABC | √ | √ | √ | √ | √ | × | √ | × | √ | [54] |
IAPF | × | × | × | √ | √ | × | × | √ | √ | [55] |
MADDPG | √ | √ | √ | √ | √ | × | × | × | × | [56] |
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Rahman, M.; Sarkar, N.I.; Lutui, R. A Survey on Multi-UAV Path Planning: Classification, Algorithms, Open Research Problems, and Future Directions. Drones 2025, 9, 263. https://doi.org/10.3390/drones9040263
Rahman M, Sarkar NI, Lutui R. A Survey on Multi-UAV Path Planning: Classification, Algorithms, Open Research Problems, and Future Directions. Drones. 2025; 9(4):263. https://doi.org/10.3390/drones9040263
Chicago/Turabian StyleRahman, Mamunur, Nurul I. Sarkar, and Raymond Lutui. 2025. "A Survey on Multi-UAV Path Planning: Classification, Algorithms, Open Research Problems, and Future Directions" Drones 9, no. 4: 263. https://doi.org/10.3390/drones9040263
APA StyleRahman, M., Sarkar, N. I., & Lutui, R. (2025). A Survey on Multi-UAV Path Planning: Classification, Algorithms, Open Research Problems, and Future Directions. Drones, 9(4), 263. https://doi.org/10.3390/drones9040263