摘要翻译：
自主行星车，也被称为漫游者，是一种小型自主车辆，配备了各种传感器，用于在行星表面进行探索和实验。漫游车工作在部分未知的环境中，具有狭窄的能量/时间/运动约束，通常计算资源小，限制了在线规划和调度的复杂性，因此它们是自动驾驶汽车领域的一个巨大挑战。事实上，此类车辆的正式模型通常涉及具有非线性动力学的混合系统，这是目前大多数规划算法和工具难以处理的。因此，当需要离线规划车辆活动时，例如，对于在没有连续地球监督的情况下运行的漫游者，这种规划通常是在不完全现实的简化模型上进行的。本文介绍了基于UPMurphi模型检验的规划工具如何直接对自主行星车辆的混合动力模型进行优化，并考虑了几个安全约束条件，从而获得了非常精确的结果。
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英文标题：
《Resource-Optimal Planning For An Autonomous Planetary Vehicle》
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作者：
Giuseppe Della Penna (1), Benedetto Intrigila (2), Daniele Magazzeni
  (3) and Fabio Mercorio (1) ((1) University of L'Aquila, Italy, (2) University
  of Rome, Italy and (3) University of Chieti, Italy)
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Autonomous planetary vehicles, also known as rovers, are small autonomous vehicles equipped with a variety of sensors used to perform exploration and experiments on a planet's surface. Rovers work in a partially unknown environment, with narrow energy/time/movement constraints and, typically, small computational resources that limit the complexity of on-line planning and scheduling, thus they represent a great challenge in the field of autonomous vehicles. Indeed, formal models for such vehicles usually involve hybrid systems with nonlinear dynamics, which are difficult to handle by most of the current planning algorithms and tools. Therefore, when offline planning of the vehicle activities is required, for example for rovers that operate without a continuous Earth supervision, such planning is often performed on simplified models that are not completely realistic. In this paper we show how the UPMurphi model checking based planning tool can be used to generate resource-optimal plans to control the engine of an autonomous planetary vehicle, working directly on its hybrid model and taking into account several safety constraints, thus achieving very accurate results.
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PDF链接：
https://arxiv.org/pdf/1007.5130