Abstract: To address the inefficiency and safety risks of manual patrols in large facilities and complex venues, this study aims to balance global coverage and the prioritization of high-risk areas while improving the efficiency and robustness of path planning. We propose a risk-aware Intelligent Patrol Strategy (IPS): (i) model patrol as a combination of comprehensive and single patrols; (ii) build static/dynamic risk heat map via a Gaussian Mixture Model (GMM); and (iii) design a tanh-based target-point updating method to suppress clustering and balance risk and spatial distribution. For path generation, we develop a Multi-Target Rapidly-exploring Random Tree (MT-RRT) algorithm comprising Multi-Target Feasible Path Planning (MTFPP) and Information Subset Optimization (ISO). MTFPP estimates feasible inter-point costs with an improved RRT-Connect and determines the visiting order using Ant Colony Optimization (ACO), yielding a single feasible path through all targets. ISO samples within an ellipse-shaped informed subset and applies RRT*-style rewiring to iteratively refine that path into a shorter and smoother one. Simulations show that, compared with Euclidean-distance baselines, our method significantly reduces final path length and improves success rate and convergence under limited iterations; it achieves full-area coverage while assigning higher patrol frequency to high-risk regions, making it suitable for industrial plants, hazardous-material warehouses, and large public buildings.

摘要： 针对大型设施与复杂场所安全监控中人工巡逻效率低、存在安全风险等问题，本文旨在在保证全域覆盖的同时优先关注高风险区域，并提升路径规划的效率与稳健性。为此提出风险导向的智能巡逻策略：将巡逻任务建模为“全面巡逻+单次巡逻”的组合；基于高斯混合模型构建静态/动态风险热图以评估优先级；设计基于双曲正切权重的目标点更新方法，抑制目标过度聚集，实现风险与空间分布的均衡。在路径生成方面，提出多目标点快速扩展随机树算法，包括多目标点可行路径规划与信息子集优化：多目标点可行路径规划估计任意两点间可行代价，并结合蚁群优化确定访问顺序，拼接得到贯穿所有目标点的单条可行路径；信息子集优化在以起终点为焦点、由当前最优代价确定主轴的椭圆信息子集内采样，并结合改进的快速扩展随机树重连对该路径进行迭代精炼，输出更短、更光滑的优化路径。仿真结果表明，相较于以欧氏距离为成本的基线方法，所提方法显著缩短最终路径长度，在迭代受限条件下具有更高的求解成功率与更快的收敛速度；系统能够实现全区域覆盖，并对高风险区域分配更高的巡逻频率，适用于工业厂房、危险品仓储与大型公共建筑等场景。