该数据集总数据量约1.92MB，包含24个txt文本文件。基于东海、北太平洋真实水下地形与水文环境构建，包含三维地形、海山、悬浮障碍物等仿真数据。Seamount（海山数据）：记录的是水下海山的高度。Suspension（悬浮物数据）：每行记录一个悬浮物，包含四个字段，依次为中心位置的 X、Y、Z 坐标和该悬浮物的尺寸大小。Current（海流数据）：记录海流在特定方向上的叠加力。例如current_x_25.txt表示X轴方向的海流数据。可为动态环境下大规模多智能体多维可信评估提供标准仿真环境支撑，同时用于课题任务书中规定的动态环境多智能体博弈协同、多维度效能指标量化、多路径并行规划优化及动态可信决策验证工作，满足动态复杂水下环境中大规模多智能体路径质量、能耗、风险、差异性等多维指标的高效可信评估需求。数据集可用于验证路径长度、航行能耗、航行风险、路径差异性、综合适应度五大维度评估指标，支撑多模态蚁群优化算法、小生境协同进化模型的测试迭代与效果验证；在课题成果中，该数据集为动态水下环境自适应优化、多维效能可信评估提供核心测试依据，相关算法采用C语言实现运行求解，并通过MATLAB完成结果绘图与可视化分析，有效保障了多维可信评估方法的科学性、有效性与可复现性。
", "ds_source": "MTrajPlanner自主水下航行器多轨迹规划数据集：总数据量约1.92MB。该数据集为国际公开开源标准仿真数据，来源于GitHub公开项目（https://github.com/GnauhGnit/MTrajPlanner/tree/main），无实地采集环节与特定采集时间，基于东海、北太平洋真实水下地形与水文环境构建，包含三维地形、海山、悬浮障碍物等仿真数据。
", "ds_process_way": "海底山数据采用规则栅格表示海床高度；悬浮障碍物采用中心坐标与尺寸参数描述；海流场采用离散三维网格方式存储各方向流场强度。数据经标准化文本格式组织，用于路径规划算法输入。
", "ds_quality": "数据来自公开发表论文配套数据集，结构完整，格式统一，可直接用于复现实验结果。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "中国，太平洋", "ds_acq_lon_east": 78.13333333333334, "ds_acq_lat_south": 85.51666666666667, "ds_acq_lon_west": 99.16666666666667, "ds_acq_lat_north": 65.73333333333333, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 2075746, "ds_files_count": 0, "ds_format": "*txt", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "a30ee5cf-db55-43bb-a75e-1a105ea44590.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "适用于AUV路径规划、轨迹优化、多目标优化和算法性能评测研究。", "ds_from_station": "", "organization_id": "9ecaaa78-39e9-411e-9f24-274e12aa643f", "ds_serv_man": "杨强", "ds_serv_phone": "13570466708", "ds_serv_mail": "mmmyq@126.com", "doi_value": "", "subject_codes": [ "170.60", "410" ], "quality_level": 0, "publish_time": "2026-07-09 10:58:08", "last_updated": "2026-07-09 10:58:08", "protected": false, "protected_to": "2028-06-30 00:00:00", "lang": "zh", "cstr": "", "i18n": { "en": { "title": "MTrajPlanner Multiple-Trajectory Planning Dataset for AUVs", "ds_format": "*txt", "ds_source": "MTrajPlanner Autonomous Underwater Vehicle Multi-Trajectory Planning Dataset: The total data size is about 1.92MB. This dataset is an internationally open-source standard simulation dataset from a public GitHub project (https://github.com/GnauhGnit/MTrajPlanner/tree/main), with no field collection or specific collection time. It is based on real underwater terrain and hydrological environments of the East China Sea and the North Pacific, and includes simulation data like 3D terrain, seamounts, and floating obstacles.", "ds_quality": "The dataset is complete, consistently formatted, and suitable for reproducing published experiments.", "ds_ref_way": "", "ds_abstract": "The dataset has a total size of approximately 1.92 MB and consists of 24 plain text (.txt) files. It is built upon real underwater terrain and hydrological environments from the East China Sea and the North Pacific Ocean, containing simulated data of three‑dimensional terrain, seamounts, and suspended obstacles. Specifically, the seamount data record the heights of underwater seamounts; the suspension data store each suspended obstacle as one line with four fields—the X, Y, Z coordinates of its center and its size; and the current data record the superimposed force of ocean currents in a given direction, for example current_x_25.txt denotes the current data along the X‑axis. This dataset can serve as a standard simulation environment for multi‑dimensional trustworthy evaluation of large‑scale multi‑agent systems in dynamic environments, and it is also used for the tasks specified in the project proposal, including dynamic environment multi‑agent game collaboration, quantification of multi‑dimensional performance indicators, optimization of multi‑path parallel planning, and verification of dynamic trustworthy decision‑making. It meets the requirements for efficient and trustworthy assessment of multiple indicators—such as path quality, energy consumption, risk, and diversity—for large‑scale multi‑agent systems in complex dynamic underwater environments. The dataset can be used to validate five major evaluation metrics: path length, navigation energy consumption, navigation risk, path diversity, and comprehensive fitness, thereby supporting the testing, iteration, and effectiveness verification of multimodal ant colony optimization algorithms and niche co‑evolutionary models. In the project outcomes, this dataset provides the core test basis for adaptive optimization in dynamic underwater environments and multi‑dimensional trustworthy performance evaluation. The related algorithms are implemented in C for solving and use MATLAB for result plotting and visualization analysis, effectively ensuring the scientific validity, effectiveness, and reproducibility of the multi‑dimensional trustworthy evaluation methods.", "ds_time_res": "", "ds_acq_place": "China, Pacific", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Terrain, obstacle and current information are discretized and organized in text files for trajectory-planning experiments.", "ds_ref_instruction": "Suitable for AUV trajectory planning and optimization research." } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "AUV；多轨迹规划；海底地形；海流；障碍物" ], "ds_subject_tags": [ "海洋科学", "工程与技术科学基础学科" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [], "ds_contributors": [ { "true_name": "杨强", "email": "mmmyq@126.com", "work_for": "南京信息工程大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "杨强", "email": "mmmyq@126.com", "work_for": "南京信息工程大学", "country": "中国" } ], "ds_managers": [ { "true_name": "杨强", "email": "mmmyq@126.com", "work_for": "南京信息工程大学", "country": "中国" } ], "category": "其他" }