Jiawen Pan

As a core component of automated logistics systems, delivery robots hold significant application value in the field of unmanned delivery. This research addresses the robot path planning problem, aiming to enhance delivery efficiency and reduce operational costs through systematic improvements to the RIME optimization algorithm. Through in-depth analysis, we identified several major drawbacks in the standard RIME algorithm for path planning: insufficient global exploration capability in the initial stages, a lack of diversity in the hard RIME search mechanism, and oscillatory phenomena in soft RIME step size adjustment. These issues often lead to undesirable phenomena in path planning, such as local optima traps, path redundancy, or unsmooth trajectories. To address these limitations, this study proposes the Multi-Strategy Controlled Rime Algorithm (MSRIME), whose innovation primarily manifests in three aspects: first, it constructs a multi-strategy collaborative optimization framework, utilizing an infinite folding Fuch chaotic map for intelligent population initialization to significantly enhance the diversity of solutions; second, it designs a cooperative mechanism between a controlled elite strategy and an adaptive search strategy that, through a dynamic control factor, autonomously adjusts the strategy activation probability and adaptation rate, expanding the search space while ensuring algorithmic convergence efficiency; and finally, it introduces a cosine annealing strategy to improve the step size adjustment mechanism, reducing parameter sensitivity and effectively preventing path distortions caused by abrupt step size changes. During the algorithm validation phase, comparative tests were conducted between two groups of algorithms, demonstrating their significant advantages in optimization capability, convergence speed, and stability. Further experimental analysis confirmed that the algorithm’s multi-strategy framework effectively suppresses the impact of coordinate and dimensional differences on path quality during iteration, making it more suitable for delivery robot path planning scenarios. Ultimately, path planning experimental results across various Building Coverage Rate (BCR) maps and diverse application scenarios show that MSRIME exhibits superior performance in key indicators such as path length, running time, and smoothness, providing novel technical insights and practical solutions for the interdisciplinary research between intelligent logistics and computer science.

Leveraging extensive trajectory data to analyze the operation modes of agricultural machinery for gathering precise spatial information is an important fundamental task for subsequent agricultural machinery trajectory research. However, complex algorithm models hinder nonspecialized researchers from further processing agricultural machinery trajectory data. In the present application note, ChatGPT is taken as an example and a complete prompt guide for large language models (LLMs) is provided for autonomously identifying the operation mode of agricultural machinery. This guide provides low-cost workflows for processing agricultural machinery trajectory data when computer science or data science expertise is lacking. It even possesses the capability to utilize newly learned algorithms such as the random forest model, which has not been previously explored in the literature for operation mode identification, to accomplish the task. To the best of our knowledge, this is the first attempt to apply LLMs to identifying agricultural machinery operation mode based on trajectory data. The complete prompt guide is publicly available at https://github.com/kakushuu/prompt-guide/.