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.

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.