为机器人团队在危险环境中的任务规划，提出兼顾收益与生存的双目标路径优化方法。
Bi-objective trail-planning for a robot team orienteering in a hazardous environment
- 采用双目标蚁群算法，结合信息素与问题启发式搜索最优路径组合。
- 在博物馆信息采集任务中，算法在收益与存活率间实现帕累托最优平衡。
- 适用于需权衡任务收益与机器人安全的搜救、巡检等高风险场景。
移动机器人团队在资源配送、巡逻、信息采集、农业、森林火灾扑救、化学烟雾源定位与测绘、搜救等任务中具有广泛应用。当机器人穿越危险环境（如崎岖地形、强风、敌对威胁）时，需规划并协调路径，以降低机器人被损毁、破坏或俘获的风险。具体而言，机器人应选择最安全路径，协同完成团队目标，并在访问地点带来的收益与机器人损失风险之间取得平衡。本文研究在危险环境中，机器人团队定向寻访的双目标路径规划问题。将危险环境抽象为有向图，其中每条边代表机器人穿越时的已知存活概率，每个节点若被机器人访问则提供团队奖励（如物资投递或图像采集）。目标是寻找帕累托最优的机器人团队路径方案，以最大化两个冲突目标：(i) 团队期望总收益，(ii) 任务结束后存活的机器人数量。人类决策者可据此根据自身偏好选择折衷方案。本文采用受启发式引导的蚁群优化算法搜索帕累托最优解集。案例研究展示其在艺术馆信息采集任务中的应用。结果表明，该方法在性能上优于或等同于模拟退火基线。
原文摘要 · Abstract (English)
Teams of mobile [aerial, ground, or aquatic] robots have applications in resource delivery, patrolling, information-gathering, agriculture, forest fire fighting, chemical plume source localization and mapping, and search-and-rescue. Robot teams traversing hazardous environments -- with e.g. rough terrain or seas, strong winds, or adversaries capable of attacking or capturing robots -- should plan and coordinate their trails in consideration of risks of disablement, destruction, or capture. Specifically, the robots should take the safest trails, coordinate their trails to cooperatively achieve the team-level objective with robustness to robot failures, and balance the reward from visiting locations against risks of robot losses. Herein, we consider bi-objective trail-planning for a mobile team of robots orienteering in a hazardous environment. The hazardous environment is abstracted as a directed graph whose arcs, when traversed by a robot, present known probabilities of survival. Each node of the graph offers a reward to the team if visited by a robot (which e.g. delivers a good to or images the node). We wish to search for the Pareto-optimal robot-team trail plans that maximize two [conflicting] team objectives: the expected (i) team reward and (ii) number of robots that survive the mission. A human decision-maker can then select trail plans that balance, according to their values, reward and robot survival. We implement ant colony optimization, guided by heuristics, to search for the Pareto-optimal set of robot team trail plans. As a case study, we illustrate with an information-gathering mission in an art museum.
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