Research Topic

Visual GNC for Orbital and Planetary Robotics

About this Research Topic

Spacecraft Guidance, Navigation and Control (GNC) is among the key enabling technologies for future robotic applications in space environments, such as on-orbit servicing and assembly, space debris removal, and planetary surface exploration. Spacecraft requires low-latency and resource-efficient computation ...

Spacecraft Guidance, Navigation and Control (GNC) is among the key enabling technologies for future robotic applications in space environments, such as on-orbit servicing and assembly, space debris removal, and planetary surface exploration. Spacecraft requires low-latency and resource-efficient computation to observe their immediate environment and perform tasks within it. The GNC algorithms have traditionally put a premium on positioning and pose estimation quality at a cost to runtime performance, particularly when the GNC relies on vision-based sensing and perception. Therefore, visual GNC has remained an open research question for achieving effective and efficient space robotic and autonomous systems.

Space sensing and perception are crucial for providing rapid, autonomous navigation, control, rendezvous, and docking for future orbital and planetary missions; hence they are directly linked to GNC. The potential of visual perception in space robotics is largely untapped and is yet to be fully realized. Stereo-vision based depth perception using an optical camera is the de facto standard. In comparison, space-rated LIDAR is more power hungry and bulky, though it brings advantages such as range information and the sensing robustness needed by future missions. For example, state-of-the-art fast Simultaneous Localization And Mapping (SLAM) methods, have not been widely adopted on-board spacecraft GNC solutions. SLAM approaches introduce many parameters that need to be tuned to allow effective use in a given scenario. These include thresholds that control feature-matching, random sample consensus (RANSAC) parameters, and criteria to decide when to introduce new map elements or trigger a search for loop closure matches etc, depending on the SLAM method.

This Research Topic aims at presenting the latest, original research and results for achieving reliable and robust GNC for space robots despite limited sensing, energy, and on-board computing hardware, as well as safeguarding GNC against spacecraft hardware malfunction and failure.

Research papers can include, but are not limited to, the following topics:
• Navigation sensors and visual sensing techniques
• Robust multi-modal perception techniques
• Algorithms for space perception, state estimation, and data fusion
• Vision-based navigation algorithms for space orbital or planetary environments
• Path or motion planning techniques for orbital or planetary robots
• Robust dynamical control techniques for orbital or planetary robots
• Reconfigurability, verifiability and security techniques for spacecraft GNC


Keywords: Visual Guidance, Navigation, Control, Space Robotics, Autonomous Systems, Orbital Operations, Planetary Exploration


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

08 December 2020 Manuscript
31 March 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

08 December 2020 Manuscript
31 March 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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