# Adaptive Path Planning for UAV-Based Pollution Sampling
## 2 Related Work  
Path planning for UAVs involves determining an optimal collision-free route between two points while addressing various constraints, such as energy efficiency, terrain complexity, and real-time obstacle avoidance [22; 23]. This challenge is further amplified in pollution sampling scenarios, where UAVs must balance precise coverage of sampling points with constraints such as limited battery life and dynamic environmental conditions [24]. The field has evolved significantly, with path planning algorithms broadly categorized into three groups: classical, intelligent, and hybrid approaches.  
Classical algorithms, such as A* and Dijkstra, rely on deterministic graph-based methods to calculate optimal paths in static environments [22; 25]. These methods offer reliability and precision but often struggle in dynamic or high-dimensional scenarios due to their computational overhead. Variants like Theta* and multi-layer step-search strategies have been developed to improve path smoothness and efficiency [22; 23]. Sampling-based methods, such as Rapidly Exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM), excel in navigating sparse obstacle environments but may produce suboptimal paths [24].  
Intelligent algorithms leverage techniques such as genetic algorithms (GAs), particle swarm optimization (PSO), and reinforcement learning to adapt dynamically to environmental changes [23; 24]. These approaches excel in handling uncertainties and optimizing paths under dynamic constraints but often come at the cost of increased computational complexity [26]. For example, particle swarm optimization has been applied successfully to UAV path planning, enabling efficient obstacle avoidance in real time by dynamically adjusting path weights [23; 24]. Similarly, reinforcement-learning-based methods show promise in developing adaptive strategies for UAV navigation, especially in unknown or partially mapped environments [27].  
Hybrid methods combine the strengths of classical and intelligent approaches to address their individual limitations. For instance, APPATT (Tangent Intersection and Target Guidance), a hybrid algorithm, demonstrates the ability to generate smooth, energy-efficient paths by integrating heuristic rules and environmental modeling [25]. Another example is the dual-layer optimization method that combines an enhanced A* algorithm with the Dynamic Window Approach (DWA), achieving high efficiency and real-time adaptability in obstacle-rich environments [22].  
Pollution sampling introduces additional complexities to UAV path planning as it often requires covering dispersed sampling points under uncertain and dynamic conditions. These tasks necessitate advanced algorithms capable of integrating environmental data, adjusting to real-time sensor feedback, and minimizing energy consumption [23; 28]. For instance, path planners incorporating environmental models, such as obstacle coverage metrics and adaptive heuristics, have shown significant improvements in efficiency and adaptability [22; 25]. Such advancements are particularly relevant for applications like air quality monitoring, where UAVs must navigate dense urban areas while sampling pollutants at multiple altitudes [27].