# Route Optimization for UGVs: A Systematic Analysis of Applications, Algorithms and Challenges
## 1 Introduction  
The escalating demand for intelligent systems across various sectors necessitates robust and adaptable embedded platforms. According to a recent report by the World Economic Forum, the global expenditure on digital transformation is projected to reach USD 6.8 trillion between 2023 and 2026, highlighting the critical role of advanced computing solutions at the edge [1]. These platforms are essential for facilitating automation, real-time data processing and decision-making in a variety of applications, from smart agriculture to driverless cars. The deployment of efficient and powerful embedded systems is pivotal for realizing the potential of Industry 4.0 and ensuring sustainable technological advancements [2]. As the complexity of these systems increases, so does the need for versatile hardware capable of handling diverse computational tasks [3]. Currently, the landscape of embedded systems is dominated by a range of platforms, each offering a unique set of capabilities tailored to specific applications. These platforms, such as Raspberry Pi, Jetson Nano and Arduino Portenta, find utility in sectors rangingfrom industrial automation to environmental monitoring. In healthcare, they enable real-time patient monitoring and diagnostics, improving the efficiency of clinical workflows [4]. In agriculture, they facilitate precision farming through automated irrigation and crop management, enhancing resource utilization and yield [5]. Their affordability and ease of integration have made them indispensable tools for innovation. Moreover, embedded systems contribute significantly to achieving the Sustainable Development Goals (SDGs), particularly in areas such as affordable and clean energy (SDG 7) and industry, innovation and infrastructure (SDG 9) [6]; their role in data-driven decision-making is vital for sustainable development [7]. While there are multiple applications for these platforms, in this paper, we will focus exclusively on their use and relevance for unmanned ground vehicles (UGVs). Recent studies have explored the performance and applicability of various embedded platforms in different contexts. For instance, research by [8] focuses on Raspberry Pi's versatility in IoT applications, while [9] highlights Jetson Nano's capabilities in AI-driven robotics. Ref. [10] investigates Arduino Portenta's efficiency in low-power industrial sensing. These studies collectively demonstrate the potential of each platform for specialized tasks [11]. Despite these advancements, a comprehensive comparison of these platforms' hardware capabilities and performance benchmarks across diverse tasks is still needed [12]. The prior research often focuses on specific use cases, neglecting a holistic analysis of their potential across broader applications. This limitation underscores the need for a comparative study that elucidates the strengths and weaknesses of each platform in a standardized manner, particularly given the evolving demands of edge computing [13]. This paper addresses the existing gap by providing a detailed comparative analysis of path planning optimization based on their hardware specifications. Our research aims to evaluate the suitability of each platform for different application scenarios, providing valuable insights for developers and researchers in selecting the optimal solution for their specific needs. By benchmarking these platforms across a range of tasks, including processing power and memory capacity, we aim to offer a comprehensive overview of their capabilities. The objective is to identify the strengths and limitations of path planning optimization, enabling informed decision-making and fostering innovation in embedded system design. The novelty of our study lies in its systematic approach to comparing and standardizing benchmarks in real-world application scenarios.