Learning-based methods are increasingly popular for search algorithms in single-criterion optimization problems. In contrast, for multiple-criteria optimization there are significantly fewer approaches despite the existence of numerous applications. Constrained path-planning for Autonomous Ground Vehicles (AGV) is one such application, where an AGV is typically deployed in disaster relief or search and rescue applications in off-road environments. The agent can be faced with the following dilemma : optimize a source-destination path according to a known criterion and an uncertain criterion under operational constraints. The known criterion is associated to the cost of the path, representing the distance. The uncertain criterion represents the feasibility of driving through the path without requiring human intervention. It depends on various external parameters such as the physics of the vehicle, the state of the explored terrains or weather conditions. In this work, we leverage knowledge acquired through offline simulations by training a neural network model to predict the uncertain criterion. We integrate this model inside a path-planner which can solve problems online. Finally, we conduct experiments on realistic AGV scenarios which illustrate that the proposed framework requires human intervention less frequently, trading for a limited increase in the path distance.

中文翻译：

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约束多标准路径规划的基于学习的偏好预测

基于学习的方法在单标准优化问题中的搜索算法中越来越受欢迎。相比之下，尽管存在众多应用程序，但对于多标准优化，方法却少得多。自主地面车辆 (AGV) 的受限路径规划就是这样一种应用，其中 AGV 通常部署在越野环境中的救灾或搜救应用中。代理可能面临以下困境：在操作约束下根据已知准则和不确定准则优化源-目的地路径。已知标准与表示距离的路径成本相关联。不确定标准代表无需人工干预即可通过路径行驶的可行性。它取决于各种外部参数，例如车辆的物理特性、探索的地形状态或天气条件。在这项工作中，我们通过训练神经网络模型来利用通过离线模拟获得的知识来预测不确定标准。我们将此模型集成到可以在线解决问题的路径规划器中。最后，我们对真实的 AGV 场景进行了实验，这表明所提出的框架需要较少的人工干预，以有限的路径距离增加为代价。我们将此模型集成到可以在线解决问题的路径规划器中。最后，我们对真实的 AGV 场景进行了实验，这表明所提出的框架需要较少的人工干预，以有限的路径距离增加为代价。我们将此模型集成到可以在线解决问题的路径规划器中。最后，我们对真实的 AGV 场景进行了实验，这表明所提出的框架需要较少的人工干预，以有限的路径距离增加为代价。