Robot path planning is a fundamental problem in robotics enabling autonomous robots to navigate safely, efficiently and naturally from a start position to a target position. In real robotic systems, path planning is not only about finding a collision-free path, but also about generating motions that satisfy the robot's physical, sensory, and task constraints. In this essay, the main robot path planning methods, including graph search, artificial potential field methods, sampling-based planning, local obstacle avoidance, and trajectory optimization are reviewed. Their respective strengths, limitations, and applicable scenarios are examined, with particular attention to how these approaches address issues such as computational complexity, environmental structure, and real-time responsiveness. It also addresses present day issues such as uncertainty, dynamic environments, computational efficiency and physical feasibility, emphasizing that no single method is sufficient for all robotic applications. Practical systems increasingly rely on hierarchical integration to balance global navigation with local reactivity. Finally, the essay argues that the future of robot path planning will be dominated by hybrid systems that combine global planning, local replanning, optimization, and learning-based prediction, enabling robots to operate more safely, intelligently, and adaptively in complex real-world environments.

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This paper aims to investigate and solve maze-solving problems by implementing the Breadth-First Search (BFS) algorithm and the Left-Hand Wall-Following algorithm, providing references for maze navigation of small embedded robots. In this study, the OCaml programming language is adopted. This paper implements both the BFS and Left-Hand Wall-Following algorithms to solve three mazes of different sizes (30×28, 30×17, 30×16). The total moving steps and the shortest valid paths are recorded. The time and space complexity of the two algorithms are calculated, and comparative tables are generated for analysis. Experiments show that the Left-Hand Wall-Following algorithm has a constant space complexity of O(1) with stable memory consumption. However, its generated path length can reach 2.5 to 10 times the theoretical shortest path, and it is only applicable to simple mazes. In contrast, BFS can stably output globally optimal paths and adapt to all types of complex mazes, yet its memory overhead grows linearly with maze size. The two algorithms fit distinct application scenarios and can be selected flexibly according to equipment memory constraints and requirements for optimal paths.

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Colorectal cancer causes persistently high global morbidity and mortality rates worldwide. Conventional chemo-targeted therapies carry severe toxic side effects and frequent drug resistance, while integrated Chinese and Western medicine provides low-toxic adjuvant treatment ideas for this malignant disease. This paper systematically summarizes the anti-tumor effects and molecular regulatory rules of two core active monomers, ursolic acid and quercetin, extracted from the medicinal herb Hedyotis diffusa. The two components jointly inhibit IL-6 secretion and block STAT3 phosphorylation to cut off inflammatory oncogenic cascades, suppress colorectal cancer cell proliferation and EMT-mediated metastasis, and produce synergistic sensitization effects when combined with routine chemotherapy. Current preclinical cell and animal data fully verify its multi-target pharmacological advantages, but large-sample multi-center clinical evidence and optimized targeted delivery systems are still insufficient. This paper sorts the pathological inflammatory mechanism of colorectal cancer and the intervention characteristics of Hedyotis diffusa, offering valuable theoretical reference for the development of natural monomer anti-colorectal adjuvant drugs.

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Angelica sinensis (Oliv.) Diels is one of the most widely used traditional Chinese medicinal materials in clinical practice in China. Its components include lipophilic volatile oils and phthalides, water-soluble phenolic acids and polysaccharides, as well as insoluble cellulose and inorganic salts. Owing to significant differences in solubility among these constituents, separate quality control technologies must be systematically established according to solubility. This paper categorizes and summarizes quality control techniques for these three types of constituents based on their solubility: lipophilic constituents, represented by the thermally unstable Z-ligustilide, are primarily analyzed using GC-MS and QAMS; water-soluble constituents, represented by ferulic acid and polysaccharides, are analyzed using well-established methods such as HPLC and the phenol-sulfuric acid method; insoluble constituents remain a weak link, with NIR and ICP-MS offering new avenues for analysis. Furthermore, the future direction of quality marker (Q-Marker)-guided screening is discussed, aiming to provide a reference for refining the quality standards of Angelica sinensis .

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