Autonomous evolutionary learning for unmanned mobile platforms: Research progress and prospects

<p indent="0mm">The unmanned mobile platform stands as a pivotal technological innovation with transformative implications for various aspects of human society, ranging from production methodologies to lifestyle choices and even the dynamics of warfare. At the heart of this transformation lies the integration of artificial intelligence (AI) technologies, aimed at augmenting the autonomy and intelligence of these platforms. However, the current landscape of AI models is marked by inherent limitations, such as fixed parameters, rigid structures, and constrained inference chains. When faced with the intricacies of complex and ever-changing objects, diverse environments, multifaceted tasks, and hardware limitations, the unmanned mobile platforms encounter severe constraints in their generalization and extrapolation capabilities. These limitations pose a significant challenge, hindering their ability to meet the diverse and dynamic demands of practical applications. In response to this challenge, this research draws inspiration from the evolutionary mechanisms observed in living organisms. A groundbreaking solution is proposed in the form of an autonomous evolutionary learning approach, meticulously tailored to address the unique requirements of unmanned mobile platforms. This innovative approach reimagines the evolution process of intelligent models across three distinct levels: immediate evolution, long-term evolution, and inference chain Autonomous evolution. By embracing different evolutionary patterns at each level, this methodology seeks to enhance the adaptability and intelligence of unmanned mobile platforms, enabling them to navigate the complexities of their operational contexts with finesse. This paper meticulously explores the technical pathways and nuances associated with evolutionary methods deployed at each level. In-depth analyses are conducted to assess the advantages and disadvantages inherent in these approaches. By dissecting the intricacies of immediate evolution, which focuses on rapid adaptability to immediate changes; long-term evolution, emphasizing sustained learning and adaptation over extended periods; and inference chain evolution, concentrating on the refinement of reasoning processes, this research illuminates the multifaceted strategies employed to bolster the intelligence of unmanned mobile platforms. Furthermore, this paper provides a comprehensive prospective analysis of the practical applications of autonomous evolutionary learning techniques within the realm of unmanned mobile platforms. It delves into the myriad ways these techniques can revolutionize sectors such as autonomous transportation, disaster response, surveillance, and military operations. Additionally, this research critically examines the existing challenges faced by autonomous evolutionary learning methods, including ethical considerations, data privacy concerns, and computational complexities. In conclusion, this extensive exploration underscores the transformative potential of autonomous evolutionary learning techniques in reshaping the landscape of unmanned mobile platforms. By addressing the limitations of current AI models and harnessing the power of evolutionary principles, these platforms can transcend their existing constraints and evolve into highly intelligent, adaptable, and context-aware entities.