## 2 Related Work  
The dynamic TOP [14] with obstacles, introduced in this paper, represents a significant advancement in rich and real-life logistics, integrating environmental dynamics and physical constraints into the classical TOP. This section reviews state-of-the-art methodologies, identifying key gaps and positioning the novelty of this study within the existing literature.  
Heuristic and metaheuristic approaches have proven effective in addressing the computational challenges of the TOP. Methods such as iterated local search (ILS) have significantly improved solution quality, particularly in dynamic and constrained environments [15; 16]. These methods optimize node selection and route sequencing, efficiently delivering near-optimal solutions. Simulated annealing (SA) has also been employed to tackle the TOP and its variants. Enhancements in SA-based heuristics have successfully optimized multi-objective formulations of the problem [17; 18], while effective in static scenarios, these techniques have limited adaptability to dynamic conditions.  
Stochastic extensions of the TOP, such as the stochastic team orienteering problem (STOP), introduce uncertainty in travel times and rewards. Methods like simheuristics, which combine Monte Carlo simulation with traditional heuristics, have been used to manage variability effectively [6]. These techniques enable UAV route optimization under uncertainty, making them useful in applications such as surveillance and emergency response. Further advancements have addressed dynamic environments by integrating weather conditions and real-time changes in rewards. Works focusing on weather-adaptive routing highlight the influence of environmental factors on UAV performance [19; 20]. However, these studies often lack explicit modeling physical obstacles, limiting their applicability in complex operational scenarios.  
The team assignment orienteering problem (TAOP) extends the classical TOP by incorporating task-specific requirements and stochastic constraints. Studies on task allocation for UAV fleets have developed strategies to optimize both task assignment and route planning [21; 22]. These approaches use coordination mechanisms to ensure efficient coverage and reward maximization. Simheuristics and learnheuristics [23; 24] have also been applied to manage stochasticity and dynamism in task assignment and routing, particularly in applications involving heterogeneous fleets [25]. However, these methods rarely address dynamic obstacles or integrate predictive modeling for travel times.  
Recent studies emphasize the need to account for environmental and physical constraints in TOP solutions. Research addressing UAV battery limitations and weather impacts has demonstrated significant efficiency improvements [26; 27], while valuable, these advancements often simplify obstacle representation, limiting their utility in real-world scenarios.  
Despite progress in TOP research, several critical gaps remain: (i) few approaches explicitly incorporate obstacles, a critical component in real-world UAV routing [19; 20]; (ii) limited work has utilized machine learning for travel time predictions in dynamic scenarios; and (iii) balancing reward maximization with dynamic environmental factors remains underexplored. Our work addresses these gaps through modeling of physical obstacles using the A* algorithm for safe and efficient routing and a feedforward neural network predicts travel times, incorporating factors like wind speed, UAV load, and obstacle density.