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
this https URL

提出了一种利用多传感器融合和预测控制器的主动异常检测网络，可以在未知的、不受限制的环境中预测机器人导航的失败，并及时提醒，实验表明此方法优于以往反应式方法的异常检测表现

机器人导航的多传感器融合主动异常检测

Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion

In this work a general framework is proposed to support the development of
software systems that are able to adapt their behaviour according to the
operating environment changes. The proposed approach, named REPTILE, works in a
complete proactive manner and relies on Deep Reinforcement Learning-based
agents to react to events, referred as novelties, that can affect the expected
behaviour of the system. In our framework, two types of novelties are taken
into account: those related to the context/environment and those related to the
physical architecture itself. The framework, predicting those novelties before
their occurrence, extracts time-changing models of the environment and uses a
suitable Markov Decision Process to deal with the real-time setting. Moreover,
the architecture of our RL agent evolves based on the possible actions that can
be taken.

该论文提出了一种名为 REPTILE 的自适应软件系统的通用框架，它完全采用主动方式，并依赖于基于深度强化学习的代理程序来响应事件，称为新奇性，这些新奇性可能影响系统的预期行为。该框架考虑两种类型的新奇性：与环境相关的和与物理架构本身相关的，预测出这些新奇性，提取出环境的时间变化模型，并使用适当的马尔可夫决策过程来处理实时设置，此外，我们的 RL 代理的架构会根据可能采取的行动进化。