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.

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

A decentralized multi-sensor fusion-based resilient pose estimation architecture for autonomous navigation of satellites around an asteroid is presented in this article. Navigation around an asteroid is challenging due to dynamic illumination conditions, which restricts the vision-based localization and is partially ineffective for a longer duration of the operation. Moreover, drift in sensor measurement and temporal sensor failure is often encountered in long-duration sustainable space missions. This is more so around a debris-prone region, where momentary obstruction leads to inaccurate sensor measurement for a temporary period of operation. In order to establish a resilient localization mechanism for satellites around an asteroid, the proposed framework embeds a unique automatic fault detection and isolation approach in a decentralized fusion formalism. Furthermore, a unified framework can operate autonomously during temporary and long-range inoperative periods. In the first stage, innovative fault detection is proposed, which operates based on the residual of a judiciously designed filter assembly. Secondly, a novel fault-resilient isolation fusion called the fault-resilient optimal information filter fusion (FR-OIF) technique is presented, enabling self-resiliency by embedding an inbuilt fault isolation mechanism. The proposed resilient asteroid navigation approach is demonstrated with a simulation study considering a satellite equipped with multiple onboard sensors such as an inertial measurement unit, star tracker, camera and 3D-Lidar in the proximity of the asteroid Ryugu. At the same time, its superiority is also demonstrated through a comparison with the centralized multi-sensorial fusion approach.

Reliable radar inertial odometry (RIO) demands precise correction of inertial measurement unit (IMU) acceleration drift during the synchronization of heterogeneous sensors. This bias drift, most evident in acceleration measurements, worsens in subterranean environments due to extreme cold or gravitational components acting along the robot’s feed-forward axis. Left uncorrected, such drifts substantially degrade sensor fusion performance, leading to dead-reckoning, particularly when employing cost-effective platforms such as the Pixhawk IMU in combination with multiple mmWave radars. In addition, radar point clouds differ fundamentally from LiDAR data, being inherently sparse, noisy, and prone to flickering effects, which further complicates the challenge of achieving stable and reliable odometry. To address these challenges, this article presents a novel two-stage multi-radar inertial odometry (MRIO) framework for resilient localization and mapping in GPS-denied subterranean environments. In the first stage, radar ego-velocity estimation, formulated through a least-squares approach, is incorporated into an Extended Kalman Filter (EKF) for IMU bias correction. The resulting drift-free accelerations are then fused in the second stage with measurements from multiple radars and the IMU to refine odometry performance. Beyond odometry, the framework also supports radar-based mapping by leveraging the generated robot’s translational and angular displacement by the proposed framework. Starting with a range-based outlier filter, least-squares ego-velocity reconstruction, the MRIO framework enables robust ego-velocity estimation, localisation, and mapping using only radar and IMU measurements. The proposed framework is extensively validated across multiple experimental scenarios, including real-time deployment in smoke-filled subterranean environments. Comparative evaluations involving multiple FMCW radar setups, LiDAR inertial odometry (LIO), and prior EKF-RIO methods further validate the framework’s robustness and its consistent capability to achieve precise localization and mapping in challenging perceptual environments.

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 time-dependent 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.

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.

The article introduces an adaptive threshold detection mechanism aimed at enhancing a decentralized radar inertial odometry (RIO) framework, enabling resilient localization in challenging hostile environments. Considering that frequency-modulated continuous-wave (FMCW) radars possess characteristics enabling measurements in challenging indoor and outdoor environments, the fusion of multiple radars in an ensemble configuration, along with an inertial measurement unit (IMU), holds promise in surpassing individual sensor limitations. This approach thereby enhances robust perception. The proposed adaptive autonomous residual threshold mechanism employs a real-time residual analysis to dynamically adjust the sensor fusion process by comparing the variance between two extended Kalman filters. This adaptive approach addresses irregularities in data samples from multiple sensors, thereby enhancing the decentralized smoothing estimator’s precision in providing localization while navigating through hostile environments marked by limited visibility, extreme weather, or high interference. Consequently, it contributes to the resilience and adaptability of autonomous systems in real-world scenarios. The proposed framework effectively showcases precise localization through decentralized radar inertial odometry(RIO).