论文标题
具有多传感器融合的机器人导航的主动异常检测
Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion
论文作者
论文摘要
尽管导航算法迅速发展，但移动机器人通常会产生异常行为，从而导致导航故障。检测这种异常行为的能力是现代机器人实现高级自治的关键组成部分。反应性异常检测方法基于当前的机器人状态确定异常任务执行，因此缺乏在发生实际故障之前提醒机器人的能力。由于机器人和周围物体的潜在损坏，这种警报延迟是不可取的。我们为在非结构化和不确定环境中的机器人导航提供了一个主动的异常检测网络（PAAD）。 PAAD基于预测控制器的计划动作以及感知模块的当前观察结果预测未来失败的概率。如在现场环境中所见，在存在传感器闭塞的情况下，有效地融合了多传感器信号，以提供可靠的异常检测。我们在现场机器人数据上的实验比以前的方法证明了出色的故障识别性能，并且我们的模型可以实时捕获异常行为，同时保持杂乱无章的虚假检测率较低。代码，数据集和视频可在https://github.com/tianchenji/paad上找到
Despite the rapid advancement of navigation algorithms, mobile robots often produce anomalous behaviors that can lead to navigation failures. The ability to detect such anomalous behaviors is a key component in modern robots to achieve high-levels of autonomy. Reactive anomaly detection methods identify anomalous task executions based on the current robot state and thus lack the ability to alert the robot before an actual failure occurs. Such an alert delay is undesirable due to the potential damage to both the robot and the surrounding objects. We propose a proactive anomaly detection network (PAAD) for robot navigation in unstructured and uncertain environments. PAAD predicts the probability of future failure based on the planned motions from the predictive controller and the current observation from the perception module. Multi-sensor signals are fused effectively to provide robust anomaly detection in the presence of sensor occlusion as seen in field environments. Our experiments on field robot data demonstrates superior failure identification performance than previous methods, and that our model can capture anomalous behaviors in real-time while maintaining a low false detection rate in cluttered fields. Code, dataset, and video are available at https://github.com/tianchenji/PAAD
