鲁棒多路径定向问题：抵御对抗攻击
Robust Multiple-Path Orienteering Problem: Securing Against Adversarial Attacks
摘要：
多路径定向问题要求为一队机器人的路径，最大化收集的总奖励，同时满足预算约束的路径长度。该问题模拟了许多多机器人路由任务，如探索未知环境和环境监测信息收集。在本文中，我们专注于如何使机器人团队在对抗性环境中运行时对故障具有鲁棒性。我们介绍了强大的多路径定向问题（RMOP），我们寻求最坏情况下的保证，对对手是能够攻击最多$α$机器人。我们考虑这个问题的两个版本：RMOP离线和RMOP在线。在离线版本中，没有通信或重新规划时，机器人执行他们的计划，我们的主要贡献是一个一般的近似方案与有界近似保证，依赖于$α$和近似因子为单个机器人定向。特别是，我们表明，该算法产生一个（i）常数因子近似时的成本函数是模块化的;（ii）$\log$因子近似时的成本函数是次模块化的;和（iii）常数因子近似时的成本函数是次模块化的，但机器人被允许超过他们的路径预算的有界量。在在线版本中，RMOP被建模为一个两个玩家的顺序游戏，并解决了自适应的滚动时域时尚的基础上蒙特卡罗树搜索（MCTS）。除了理论分析外，我们还对海洋监测和隧道信息收集应用进行了模拟研究，以证明我们方法的有效性。
英文摘要：
The multiple-path orienteering problem asks for paths for a team of robots that maximize the total reward collected while satisfying budget constraints on the path length. This problem models many multi-robot routing tasks such as exploring unknown environments and information gathering for environmental monitoring. In this paper, we focus on how to make the robot team robust to failures when operating in adversarial environments. We introduce the Robust Multiple-path Orienteering Problem (RMOP) where we seek worst-case guarantees against an adversary that is capable of attacking at most $α$ robots. We consider two versions of this problem: RMOP offline and RMOP online. In the offline version, there is no communication or replanning when robots execute their plans and our main contribution is a general approximation scheme with a bounded approximation guarantee that depends on $α$ and the approximation factor for single robot orienteering. In particular, we show that the algorithm yields a (i) constant-factor approximation when the cost function is modular; (ii) $\log$ factor approximation when the cost function is submodular; and (iii) constant-factor approximation when the cost function is submodular but the robots are allowed to exceed their path budgets by a bounded amount. In the online version, RMOP is modeled as a two-player sequential game and solved adaptively in a receding horizon fashion based on Monte Carlo Tree Search (MCTS). In addition to theoretical analysis, we perform simulation studies for ocean monitoring and tunnel information-gathering applications to demonstrate the efficacy of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
