Avoidance of Single Gamma Source in Simulation

Simulation results from a Jackal UGV commanded to navigate over a point gamma radiation source. The UGV and gamma source were in an open simulated environment, therefore able to take any diversion the path planner generated.<br>The robot has three possible states of knowledge of the radiation field: no information, known knowledge, or unknown knowledge. These three states are labelled as "no_avoidance", "known" and "unknown" respectively.<br>In the no avoidance scenario, the robot is not made aware of any radiation information. In the known environment, the robot is driven manually around the environment to build up a map of the radiation field, before being asked to perform navigation tasks. In the unknown case, the robot has no prior knowledge, but can react to new radiation information as it completes a navigation task.<br>By adjusting the upper thresholds of cost associated with radiation risk, the robot is more or less cautious (there is lower or higher cost associated with navigating through regions of increased radiation dose rate).<br>When the robot is aware of the radiation field, its accummulated dose is reduced, at the expense of greater time required to complete the task. Reduction in dose is readily an order of magnitude compared to the no_avoidance case.<br>Data is in comma separated format, with header. Header format is: time (s), x (m), y(m), z (m), value (arb units). Value represents radiation intensity.<br>

本数据集为Jackal型无人地面车辆（Unmanned Ground Vehicle, UGV）在被指令沿点状伽马辐射源区域导航时生成的仿真结果。实验场景为开放式仿真环境，机器人可遵循路径规划器生成的任意绕行路径。<br>机器人对辐射场的认知状态共分为三类：无信息、已知辐射场信息与未知辐射场信息，这三种状态分别标记为"no_avoidance"、"known"与"unknown"。<br>在无避障场景中，机器人未被赋予任何辐射相关信息；在已知辐射场场景中，需先由人工操控机器人遍历环境以构建辐射场地图，随后再令其执行导航任务；在未知辐射场场景中，机器人无先验辐射知识，但可在完成导航任务的过程中对新获取的辐射信息做出实时响应。<br>通过调整与辐射风险相关的代价上限，可调节机器人的谨慎程度：辐射剂量率升高区域的通行代价越高，机器人的规避行为越保守；反之则越激进。<br>当机器人已掌握辐射场信息时，其累计辐射剂量会降低，但任务完成耗时会增加。相较于无避障场景，累计辐射剂量的降幅可达一个数量级。<br>数据集采用带表头的逗号分隔值（Comma-Separated Values, CSV）格式，表头字段依次为：时间（s）、x轴坐标（m）、y轴坐标（m）、z轴坐标（m）、辐射强度（任意单位）。