# Route Optimization for UGVs: A Systematic Analysis of Applications, Algorithms and Challenges
## 4 Discussion  
The observed prevalence of evolutionary computation techniques (e.g., PSO, ACO and the GA) in robotic trajectory optimization, particularly within dynamic contexts such as autonomous surveillance [55] and precision agriculture [56], underscores a field-wide preference for algorithms capable of adapting to environmental complexities. While this aligns with the broader trend identified in our analysis, it reveals a significant limitation: the dominant (78%) reliance on idealized hardware simulations. This overestimation of real-world performance challenges the direct applicability of deep learning (DL)-based methods, often touted for their potential in robotic control [57]. Our findings suggest that DL's readiness for field deployment may be overstated due to the absence of real-world constraints within these simulations. Theoretically, this highlights a need for robust validation methodologies; practically, it emphasizes the importance of hardware-in-the-loop (HIL) testing to bridge the simulation-reality gap and inform policy decisions regarding the deployment of autonomous systems in safety-critical applications. Future research should prioritize real-world validation, focusing on the impact of sensor noise, computational limitations and environmental uncertainties (e.g., weather and terrain). The inconsistent reporting of evaluation metrics across studies further complicates comparative benchmarking [58]. The computational cost associated with its implementation remains poorly quantified. This gap echoes the concerns raised by [30] regarding the lack of standardized evaluation protocols in robotics research, hindering objective comparisons between different approaches. Future research must prioritize the development and adoption of standardized evaluation protocols, including metrics for energy consumption, computational cost and robustness to real-world disturbances (e.g., those outlined by the IEEE RAS Technical Committee on Performance Evaluation). Furthermore, future studies should place greater emphasis on real-world testing to validate simulation-based results and assess the true potential of different trajectory optimization algorithms. The potential for PSO-optimized controllers to reduce energy consumption in AGVs by 15-20%, as indicated by [40], presents a compelling case for their adoption, albeit with the caveat that GPU acceleration may be essential for achieving real-time performance. The demonstrated effectiveness of ANFIS in navigating unstructured terrains by [27] suggests a viable pathway for retrofitting existing legacy systems with improved navigational capabilities. However, the inherent limitations of the current hardware, such as regarding processing speed, can create a bottleneck for computationally intensive algorithms like RRT*[21]. These observations reinforce the need for a co-design approach, simultaneously considering both algorithmic advancements and the constraints of embedded systems, a concept further emphasized by the growing trend towards integrating edge-computing solutions (e.g., the work of [29]). This holistic perspective ensures that theoretical improvements translate into tangible gains in real-world applications. Future work should address the development of hardware-aware algorithms that account for limited resources. The demonstrated preference for bio-inspired algorithms, such as the EOBA and DA, over classical MPC in nonlinear control [59] signifies a notable shift towards heuristic approaches capable of navigating complex real-world challenges. This trend is further substantiated by the superior convergence rate of FSA-SCA hybrids (92%) compared to SA (78%) in multi-objective tasks, as reported by [52]. While such results may challenge the established tenets of traditional optimization, they are consistent with the conclusions drawn by [44], who highlighted the efficiency of stochastic search methods in high-dimensional problem spaces.