# Route Optimization for UGVs: A Systematic Analysis of Applications, Algorithms and Challenges
## 6 Future Trends in Path Planning Optimization  
The incorporation of perception systems is established as a key trend in route planning, enabling robots to interact more effectively with dynamic environments. The use of multi-modal sensors, such as cameras, LiDAR and radar, provides a more comprehensive view of the surroundings, improving the capacity to recognize obstructions and forecast the actions of other components. Sensor fusion algorithms and deep learning play a crucial role in interpreting sensory information and accurately mapping the environment. This improved perception simplifies decision-making and the creation of safe and ideal paths. Simulation plays a key role in shaping perception systems [64].  
The implementation of robust and intelligent control systems is essential to ensure the stability and accuracy of robots in autonomous navigation. Reinforcement learning and predictive model-based controllers (MPCs) provide the capacity to adjust to environmental changes and the dynamic properties of the robot [65]. Designing fault-tolerant controllers and incorporating risk mitigation strategies are crucial to ensure safety in critical applications. Combining classical control with artificial intelligence techniques allows the creation of more flexible and adaptive systems. The use of distributed control techniques is also gaining popularity.  
The use of adaptive, genetic, hybrid and metaheuristic algorithms emerges as a key trend for path planning optimization, allowing for addressing complex problems with multiple constraints. These algorithms offer the ability to explore the solution space efficiently, finding optimal trajectories that minimize distance, time and energy consumption. Combining different algorithms in hybrid approaches overcomes the individual limitations of each. The use of adaptive algorithms allows the algorithm parameters to be adjusted in real time, improving the efficiency and robustness of the system. Evolutionary computation also offers interesting tools [66].  
Finally, in embedded systems and robotics, the comparative analysis of platforms such as Raspberry Pi, Jetson Nano and Arduino Portenta highlights the rapid evolution of hardware capabilities. Based on the information presented, Raspberry Pi 4 is appropriate for a variety of applications since it provides a balance between processing power and connection. Jetson Nano stands out for its advanced GPU and AI capabilities, which are increasingly relevant for machine learning and computer vision tasks in robotics. Meanwhile, Arduino Portenta is designed for low-power applications and high peripheral integration, supporting diverse industrial and IoT solutions [67]. This technological diversity suggests that future developments will focus on specialized hardware tailored to the growing demands of artificial intelligence, connectivity and energy efficiency in robotics.  
Conceptualization, D.F.Y.-P. and W.M.; methodology, D.F.Y.-P. and X.A.G.-G.; validation, W.M. and M.D.E.-C.; investigation, D.F.Y.-P., X.A.G.-G. and M.D.E.-C.; writing--original draft preparation, D.F.Y.-P. and M.D.E.-C.; writing--review and editing, W.M. and X.A.G.-G.; visualization, W.M.; supervision, D.F.Y.-P. and W.M.; funding acquisition, W.M. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Universidad Politecnica Salesiana (UPS).  
Not applicable.  
Not new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** During the preparation of this manuscript, the authors used Draw.io for graph development, SciSpice for SLR and Perplexity for support during writing. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
**Conflicts of Interest:** The authors declare no conflicts of interest.