动态调整风险权重，让机器人更安全高效地探索未知地形。
Multi-Objective Risk Assessment Framework for Exploration Planning Using Terrain and Traversability Analysis
- 根据地形与电池等条件，动态调节风险容忍度
- 实验显示无致命错误，计算开销极低
- 适合搜救、洞穴探测等高风险任务
在搜救、洞穴探测和行星任务等未知非结构化环境中，探索面临巨大挑战，其不可预测性可能导致路径规划效率低下甚至任务失败。本文提出一种多目标风险评估方法，用于此类环境中的探索规划。该方法通过动态调整各类风险因素的权重，避免机器人在任务初期过早执行高危动作；随着任务推进逐步提升允许的风险水平，从而实现更高效的探索。风险评估基于地形属性（如高程、坡度、粗糙度、可通行性），并考虑电池寿命、任务时长和行程距离等因素。我们在多种地下模拟洞穴环境中进行了实验验证，结果表明该方法能确保一致性的探索行为且不发生致命错误，同时对规划过程引入的计算开销极小。
原文摘要 · Abstract (English)
Exploration of unknown, unstructured environments, such as in search and rescue, cave exploration, and planetary missions,presents significant challenges due to their unpredictable nature. This unpredictability can lead to inefficient path planning and potential mission failures. We propose a multi-objective risk assessment method for exploration planning in such unconstrained environments. Our approach dynamically adjusts the weight of various risk factors to prevent the robot from undertaking lethal actions too early in the mission. By gradually increasing the allowable risk as the mission progresses, our method enables more efficient exploration. We evaluate risk based on environmental terrain properties, including elevation, slope, roughness, and traversability, and account for factors like battery life, mission duration, and travel distance. Our method is validated through experiments in various subterranean simulated cave environments. The results demonstrate that our approach ensures consistent exploration without incurring lethal actions, while introducing minimal computational overhead to the planning process.
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