Architecting Autonomy for Safe Microgravity Free-Flyer Inspection
Authors: Keenan Albee, David C. Sternberg, Alexander Hansson, David Schwartz, Ritwik Majumdar, Oliver Jia-Richards
Organizations: Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, USA · Department of Mechanical Engineering ETH Zürich Zürich, Switzerland · Department of Aerospace Engineering University of Michigan Ann Arbor, MI, USA
Small free-flying spacecraft can provide vital extravehicular activity (EVA) services like inspection and repair for future orbital outposts like the Lunar Gateway. Operating adjacent to delicate space station and microgravity targets, these spacecraft require formalization to describe the autonomy that a free-flyer inspection mission must provide. This work explores the transformation of general mission requirements for this class of free-flyer into a set of concrete decisions for the planning and control autonomy architectures that will power such missions. Flowing down from operator commands for inspection of important regions and mission time-criticality, a motion planning problem emerges that provides the basis for developing autonomy solutions. Unique constraints are considered such as velocity limitations, pointing, and keep-in/keep-out zones, with mission fallback techniques for providing hierarchical safety guarantees under model uncertainties and failure. Planning considerations such as cost function design and path vs. trajectory control are discussed. The typical inputs and outputs of the planning and control autonomy stack of such a mission are also provided. Notional system requirements such as solve times and propellant use are documented to inform planning and control design. The entire proposed autonomy framework for free-flyer inspection is realized in the SmallSatSim simulation environment, providing a reference example of free-flyer inspection autonomy. The proposed autonomy architecture serves as a blueprint for future implementations of small satellite autonomous inspection in proximity to mission-critical hardware, going beyond the existing literature in terms of both (1) providing realistic system requirements for an autonomous inspection mission and (2) translating these requirements into autonomy design decisions for inspection planning and control.
Figures & tables
Fig. 1: Free-flyer inspection can provide agile, efficient operations in proximity to orbital outposts but will likely require autonomy for maximum safety and optimality. Here, an autonomous inspection framework is depicted in the SmallSatSim simulation environment; red circles indicate waypoints interpolated from manually-specified inspection points, xins within a purple safety corridor of radius rins .
Fig. 2: Xout is efficiently constructed by taking convex hulls of major station geometry.
Fig. 3: The layout of the proposed autonomy framework for free-flyer inspection, consisting of inspection points that specify a desired free-flyer pose xins,i , which define keep-in corridors Xin and keep-out zones Xout . The instantaneous radius rins is adjusted based on the free-flyer radius r and desired safe geometry clearance.
Fig. 4: The proposed autonomy framework for free-flyer inspection planning and control. Note that additional layers of autonomy, like automated trajectory generation and online adaptation, are noted as optional modules. Localization and perception are assumed to be provided by an onboard or offboard global localization system.
Fig. 5: Flyby trajectory tracking in the simulation environment of Figure 1 ; the red circled portion at right is shown. xins are user-specified to provide a simple, intuitive inspection interface that can be widely integrated into inspection planning approaches. Note excellent tracking performance even with a single failure (green), and poor performance with severe model degradation (red). Note that linger mode has similar performance to flyby mode and is not shown.
Fig. 6: Degradation of the system model via wk can lead to dangerous behaviors without sufficient safety margin or online model learning. Here, a collision with the station geometry is shown after introducing multiple thruster failures causing the free-flyer to veer off course.
Average Lateral Dev. [m]
Total Translational Impulse [N-s]
Inspect Time [s]
Flyby Mode, Nominal
0.025
10.29
165.60
Linger Mode, Nominal
0.10
53.98
192.00
Flyby Mode, 1 Failure
0.065
18.10
171.50
Flyby Mode, 4 Failure
0.55
—
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TABLE I: Comparison of Inspection Performance under Nominal and Degraded Conditions.
This paper introduces inspection through GLASS, a Geometric Look-Angle Shaping Strategy for enclosed regions using unmanned aerial vehicles. In doing so, the vehicles guidance command is constructed through a bounded, geometry-consistent shaping of the look angle relative to a desired standoff path. By embedding a smooth, hyperbolic-tangent-type shaping function within a polar geometric framework, GLASS ensures global existence of the guidance dynamics. It avoids the far-field limitations inherent to conventional formulations. Lyapunov stability analysis establishes asymptotic convergence to a prescribed inspection standoff under explicit curvature feasibility conditions, along with analytical settling-time characteristics. The proposed strategy incorporates maximum turn-rate constraints without inducing singularities throughout the workspace. High-fidelity six-degree-of-freedom quadrotor simulations demonstrate the effectiveness of GLASS in representative enclosed inspection scenarios, highlighting a practically viable guidance framework for autonomous enclosed inspection missions.
Amit Shivam, Manuel C. R. M. Fernandes, Sergio Vinha +1
SYSTEC-ISR ARISE, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
We propose a safety-critical framework for the cooperative transportation of passive targets in microgravity, where a team of chaser robots acts through unilateral pushing contacts to track a human-provided desired twist while ensuring safe target motion. The pushing-only nature of the interaction introduces sparse, configuration-dependent actuation constraints requiring chasers to physically relocate on the target body when the desired pushing allocation changes. To address these challenges, we formulate a delay-aware feedback control architecture leveraging Control Lyapunov Function (CLF) and Control Barrier Function (CBF) constraints within a mixed-integer thrust allocation program to enforce stability and safety of the target, respectively. The proposed framework enables reference tracking while guaranteeing obstacle avoidance with a circular obstacle despite intermittent control authority, providing a foundation for human-supervised cooperative transportation of free-flyers in space environments. The proposed framework is validated through Gazebo simulations.
Gregorio Marchesini, Nicola De Carli, Sihyun Cho +5
KTH Royal Institute of Technology, Stockholm, Sweden · Seoul National University, Seoul, Korea
Free-flying robots rely on multiple thrusters to maneuver in space. If one or more of these thrusters fail, the robot may lose control authority and risk mission failure. At the same time, their free-flying nature implies that, even in the absence of actuation, they continue along (locally) straight-line trajectories. In this work we present a probabilistic, proactive, motion planning framework that explicitly accounts for actuator failures in space. We model actuator failure modes as a Markov chain and propagate the probability of successfully reaching the goal along the planning horizon. Precomputed reachable sets evaluate the robot's capabilities of reaching waypoints under potential failures and an RRT∗-based planner concatenates these waypoints. The resulting algorithm maximizes the overall target-reaching probability, providing maximally resilient motion plans utilizing free-flying properties. We validate our approach experimentally on a physical free-flyer platform with injected actuator failures.
Nicolas de Maddalena, Joris Verhagen, Jana Tumova
Institute for Dynamic Systems and Control, ETH Zürich, 8092 Zürich, ZH, Switzerland · Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden, and also affiliated with Digital Futures.