[visionlist] CfP: Visual GNC for Orbital and Planetary Robotics, Research Topic

[Rui Araujo]

Call for Papers

Visual GNC for Orbital and Planetary Robotics, Research Topic

https://www.frontiersin.org/research-topics/14706/visual-gnc-for-orbital-and-planetary-robotics

Frontiers in Robotics and AI Journal
https://www.frontiersin.org/journals/robotics-and-ai

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

Topic Editors
- Yang Gao, University of Surrey, United Kingdom
- Ross Andrew Dungavell, Commonwealth Scientific and Industrial Research 
Organisation (CSIRO), Australia
- Rui Matos Araújo, University of Coimbra, Portugal