Laconte Johann, Kasmi Abderrahim, Pomerleau François, Chapuis Roland, Malaterre Laurent, Debain Christophe, Aufrère Romuald
Institut Pascal, CNRS, Clermont Auvergne INP, Université Clermont Auvergne, F-63000 Clermont-Ferrand, France.
Sherpa Engineering, R&D Department, 333 Avenue Georges Clemenceau, 92000 Nanterre, France.
Sensors (Basel). 2021 Nov 14;21(22):7562. doi: 10.3390/s21227562.
In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited for computing risk assessments for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called the Lambda Field, which is specially designed for risk assessment. We first propose a way to compute such a map and the expectation of a generic risk over a path. Then, we demonstrate the benefits of our generic formulation with a use case defining the risk as the expected collision force over a path. Using this risk definition and the Lambda Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda Field gives a natural way to deal with unstructured environments, such as tall grass. Where standard environment representations would always generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken.
在自主机器人的背景下，最重要的任务之一是在导航过程中防止机器人受到潜在损坏。为此，通常假设必须处理已知的概率性障碍物，然后计算与每个障碍物碰撞的概率。然而，在复杂场景或非结构化环境中，可能很难检测到此类障碍物。在这些情况下，会使用一种度量地图，其中每个位置存储占用信息。最常见的度量地图类型是贝叶斯占用地图。然而，由于其离散性质，这种类型的地图不太适合计算连续路径的风险评估。因此，我们引入了一种名为拉姆达场（Lambda Field）的新型地图，它是专门为风险评估设计的。我们首先提出一种计算这种地图以及路径上一般风险期望的方法。然后，我们通过一个将风险定义为路径上预期碰撞力的用例来展示我们一般公式的优势。使用这个风险定义和拉姆达场，我们表明我们的框架能够在具有基于物理的度量的同时进行经典路径规划。此外，拉姆达场提供了一种处理非结构化环境（如高草）的自然方法。在标准环境表示总是会生成绕过此类障碍物的轨迹的情况下，我们的框架允许机器人在意识到所承担风险的同时穿过草地。
