# Route Optimization for UGVs: A Systematic Analysis of Applications, Algorithms and Challenges
## 3 Background and Related Works  
Unmanned vehicles, including surface vehicles (USVs), aerial vehicles (UAVs) and ground vehicles (UGVs), have advanced and are being used at an exponential rate. The main areas that have experienced this growth are industrial automation, agriculture, surveillance, disaster relief and environmental monitoring. According to [17], these vehicles' autonomy facilitates route planning and execution without requiring human involvement. Energy capacity is crucial for efficient route organization, especially in complex contexts. Ref. [18] considers route planning as an essential technology for utility vehicle automation. Figure 3 summarizes the main application areas of real-time route planning based on a literature review of 56 articles. In precision agriculture, autonomous systems optimize routes to maximize crop efficiency, reducing costs and input use [19; 20]. For autonomous navigation, algorithms such as RRT improve dynamic planning in complex environments [21; 22]. Optimal controller design has advanced with techniques such as Model Predictive Control (MPC) to smooth mobile robot trajectories [23]. In telecommunications, QoS protocols prioritize routes in IoT networks using latency and bandwidth metrics [17]. These advances demonstrate the relevance of route optimization in critical applications, although challenges remain in scalability and adaptability [24]. Figure 3: Current trends in route optimization for autonomous systems. Source: authors, 2025. Figure 2: Top 5 applications of service robots. Source: retrieved from [16]. Figure 4 shows the main difficulties identified in the literature review, highlighting that 30% of the studies face critical challenges when integrating spatiotemporal and curvature constraints in dynamic environments, such as urban navigation [25; 26]. Twenty-five percent of the papers point out the complexity of controller design and tuning, especially when combining deep reinforcement learning (DRL) techniques with kinematic models [27; 28]. Simultaneous minimization of distance and time (20% of the cases) requires hybrid algorithms, such as modified multi-objective-weighted A-weighting, while 15% address stability by calculating pitch/roll angles in non-holonomic vehicles. These issues underscore the need for adaptive frameworks, such as those proposed by [29] for agricultural environments, where safety constraints are a priority. However, scalability gaps persist for large-scale applications. Conventional planning methods may not be appropriate due to the challenges these vehicles face, such as inertia and environmental factors. According to [PERSON], these procedures generally require human involvement and lack intelligent decision-making. In the field of precision agriculture (PA), their restrictions have promoted the exploration of more efficient options. Among these, genetic and heuristic algorithms (GAs) stand out, based on sampling and evolutionary algorithms, created to optimize routes and enhance operational performance. These strategies make it possible to reduce travel time and increase the efficiency of the assigned tasks [18; 19; 30]. Figure 5 shows a series of optimization algorithms aimed at solving route optimization problems, highlighting the common use of heuristic techniques such as the Humpback Whale Optimization Algorithm (WOA), which has proven its effectiveness in different areas [31]. Furthermore, [PERSON]. [32; 33] mention the application of swarm-intelligence-based algorithms like the Firefly Search Algorithm (FA) and Particle Swarm Optimization (PSO), which are still widely used due to their capacity to locate optimal solutions in intricate search spaces. Also, reference is made to evolutionary algorithms, such as the Elite Adaptive Genetic Algorithm, which are relevant for multi-objective optimization problems [34]. Finally, the inclusion of more recent algorithms such as the Aquila Optimizer reflects the continuing evolution in the field of optimization [35]. Figure 4: Challenges in planning autonomous trajectories.