This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach.

This article presents a novel Auto-encoder-enabled fault resilient multi-sensor fusion architecture while incorporating an extended Kalman filter framework. The auto-encoder facilitate reconstruction of the faulty measurements from multiple onboard sensors, while the centralized extended Kalman filter enables an accurate fusion architecture. Moreover, the process is capable of successfully eliminating the additive noise appearing from the raw sensor data. The proposed method provides a robust reconstruction mechanism in the presence of time-dependent anomalies and faulty sensor measurement. The efficacy of the proposed scheme is extensively evaluated in the context of pose estimation for a micro aerial vehicle equipped with multiple onboard sensors. In addition, the evaluation process incorporates various realistic failure scenarios with artificially introduced inaccurate measurements. The superiority of the proposed Auto-encoder enabled centralized Kalman filter (AEKF) fusion is demonstrated through an extensive comparison with a recently developed Fault Resilient Optimal Information Filter (FROIF) method.

This article proposes a novel Auto-encoder based fault detection and isolation framework approach for supporting the operation of a novel multi-sensorial distributed pose estimations scheme. The proposed work detects weak and strong timedependent anomalies in a decentralised fusion approach from the initial estimation layer. As it will be presented, at the end of the learning phase, the neural network-based auto-encoders provide synthetic actual position and orientation of the robotic system, based on the statistics of the learning data. As a result, the square error between the output and input signal of the auto-encoder can yield the actual outlier with reasonable success. On the other hand, an Extended Kalman Filter (EKF) based fault detection method has been introduced in this article, which consists of a set of judiciously designed EKF acts as filter assembly. Based on innovation obtained from each of the EKF an innovative detection logic is proposed to identify the outlier in sensor measurement autonomously at the appropriate time samples. Based on the degree of accuracy of detecting the anomaly, the estimated signal is accepted or rejected for each time sample in the second layer of the fusion architecture. Moreover, we will introduce two outlier detection methods for the demonstration purposes and outline a comparative study using experimental data from a micro aerial vehicle. An extensive analysis with supporting results demonstrate these two methods' effectiveness and accuracy.

Purpose of Review: The article provides an extensive overview on the resilient autonomy advances made across various missions, orbital or deep-space, that captures the current research approaches while investigating the possible future direction of resiliency in space autonomy. Recent Findings: In recent years, the need for several automated operations in space applications has been rising, that ranges from the following: spacecraft proximity operations, navigation and some station keeping applications, entry, decent and landing, planetary surface exploration, etc. Also, with the rise of miniaturization concepts in spacecraft, advanced missions with multiple spacecraft platforms introduce more complex behaviours and interactions within the agents, which drives the need for higher levels of autonomy and accommodating collaborative behaviour coupled with robustness to counter unforeseen uncertainties. This collective behaviour is now referred to as resiliency in autonomy. As space missions are getting more and more complex, for example applications where a platform physically interacts with non-cooperative space objects (debris) or planetary bodies coupled with hostile, unpredictable, and extreme environments, there is a rising need for resilient autonomy solutions. Summary Resilience with its key attributes of robustness, redundancy and resourcefulness will lead toward new and enhanced mission paradigms of space missions.

No abstract