Aerobots and Hopping Vehicles for Exploration of Planetary Extreme Environments

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Astronautics & Space Science".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 23022

Special Issue Editor


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Guest Editor
Space and Terrestrial Robotic Exploration (SpaceTREx) Laboratory, Department of Aerospace and Mechanical Engineering, University of Arizona, Tucson, AZ 85721, USA
Interests: space robotics; spacecraft swarms; extreme environment exploration; neural networks; evolutionary computation; fuel cell power supplies; planetary rovers; spacecraft trajectory design; on-orbit servicing; docking; In-Situ Resource Utilization (ISRU)

Special Issue Information

Dear colleagues, 

Planetary exploration has advanced from the use of flyby spacecraft to orbiters to landers and to rovers. Lander and rovers have enabled in situ investigation, providing detailed pictures and enabling advanced experiments on the local rocks and regolith. However, due to limitations with current robotic landing systems, these landers and rovers can only land on relatively flat, benign terrain. Rovers, in turn, have advanced in size and mobility, enabling them to traverse kilometers over an entire mission. However, all these platforms are limited from exploring extreme and rugged environments such as crater walls, cliffs, canyons, and caves. Now, next-generation aerobot platforms are being proposed and built to fly off-world. These include a miniature helicopter supporting Perseverance Mars rover and quadcopter called Dragonfly to explore Titan. Others have proposed lighter-than-air vehicles, particularly balloons and airships that will float in the atmosphere for weeks or months and travel over and into rugged environments and complex atmospheres of Venus, Jupiter, Saturn, Uranus, and Neptune. Another breed of aerobot platforms includes sailplanes. Sailplanes exploit local wind and thermals in the atmosphere to gain kinetic energy. Together with aerobot platforms are increasingly sophisticated hopping vehicles that are being proposed for achieving mobility in low-gravity environments such as asteroids, comets, and moons. In these environments, gravity is too low for conventional wheeled vehicles to gain traction, and hence, hopping is a viable technique to achieve higher speeds and greater range. Together, these new generations of aerobots and hopping platforms promise to extend the reach and capabilities of future planetary missions.

Prof. Dr. Jekanthan Thangavelautham
Guest Editor

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Published Papers (3 papers)

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30 pages, 7891 KiB  
Article
Integrated Power and Propulsion System Optimization for a Planetary-Hopping Robot
by Himangshu Kalita, Alvaro Diaz-Flores and Jekan Thangavelautham
Aerospace 2022, 9(8), 457; https://doi.org/10.3390/aerospace9080457 - 19 Aug 2022
Cited by 3 | Viewed by 1588
Abstract
Missions targeting the extreme and rugged environments on the moon and Mars have rich potential for a high science return, although several risks exist in performing these exploration missions. The current generation of robots is unable to access these high-priority targets. We propose [...] Read more.
Missions targeting the extreme and rugged environments on the moon and Mars have rich potential for a high science return, although several risks exist in performing these exploration missions. The current generation of robots is unable to access these high-priority targets. We propose using teams of small hopping and rolling robots called SphereX that are several kilograms in mass and can be carried by a large rover or lander and tactically deployed for exploring these extreme environments. Considering that the importance of minimizing the mass and volume of these robot platforms translates into significant mission-cost savings, we focus on the optimization of an integrated power and propulsion system for SphereX. Hydrogen is used as fuel for its high energy, and it is stored in the form of lithium hydride and oxygen in the form of lithium perchlorate. The system design undergoes optimization using Genetic Algorithms integrated with gradient-based search techniques to find optimal solutions for a mission. Our power and propulsion system, as we show in this paper, is enabling, because the robots can travel long distances to perform science exploration by accessing targets not possible with conventional systems. Our work includes finding the optimal mass and volume of SphereX, such that it can meet end-to-end mission requirements. Full article
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21 pages, 7893 KiB  
Article
Mars Exploration Using Sailplanes
by Adrien Bouskela, Alexandre Kling, Tristan Schuler, Sergey Shkarayev, Himangshu Kalita and Jekan Thangavelautham
Aerospace 2022, 9(6), 306; https://doi.org/10.3390/aerospace9060306 - 03 Jun 2022
Cited by 3 | Viewed by 13877
Abstract
We present the preliminary design of sailplanes, used for Mars exploration. The sailplanes mitigate the weight and energy storage limitations traditionally associated with powered flight by instead exploiting atmospheric wind gradients for dynamic soaring, and slope/thermal updrafts for static soaring. Equations of motion [...] Read more.
We present the preliminary design of sailplanes, used for Mars exploration. The sailplanes mitigate the weight and energy storage limitations traditionally associated with powered flight by instead exploiting atmospheric wind gradients for dynamic soaring, and slope/thermal updrafts for static soaring. Equations of motion for the sailplanes were combined with wind profiles from the Mars Regional Atmospheric Modeling System (MRAMS) for two representative sites: Jezero crater, Perseverance’s landing site, and over a section of the Valles Marineris canyon. Optimal flight trajectories were obtained from the constrained optimization problem, using the lift coefficient and the roll angle as control parameters. Numerical results for complete dynamic soaring cycles demonstrated that the total sailplane energy at the end of a soaring cycle increases by 6.8–11%. The absence of a propulsion system, allowing for a compact form factor, means the sailplanes can be packaged into CubeSats and deployed as secondary payloads at a relatively low cost; providing scientific data over locations inaccessible by current landers and rovers. Various sailplane deployment methods are considered, including rapid deployment during Entry, Descent, and Landing (EDL) of a Mars Science Laboratory-class (MSL) vehicle and slow deployment using a blimp. Full article
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27 pages, 11385 KiB  
Article
An Efficient Sampling-Based Path Planning for the Lunar Rover with Autonomous Target Seeking
by Gang Chen, Hong You, Zeyuan Huang, Junting Fei, Yifan Wang and Chuankai Liu
Aerospace 2022, 9(3), 148; https://doi.org/10.3390/aerospace9030148 - 08 Mar 2022
Cited by 6 | Viewed by 2693
Abstract
This paper presents an efficient path planning method for the lunar rover to improve the autonomy and exploration ability in the complex and unstructured lunar surface environment. Firstly, the safe zone for the rover’s motion is defined, based on which a detecting point [...] Read more.
This paper presents an efficient path planning method for the lunar rover to improve the autonomy and exploration ability in the complex and unstructured lunar surface environment. Firstly, the safe zone for the rover’s motion is defined, based on which a detecting point selection strategy is proposed to choose target positions that meet the rover’s constraints. Secondly, an improved sampling-based path planning method is proposed to get a safe path for the rover efficiently. Thirdly, a map extension strategy for the unstructured and continually varying environment is included to update the roadmap, which takes advantage of the historical planning information. Finally, the proposed method is tested in a complex lunar surface environment. Numerical results show that the appropriate detecting positions can be selected autonomously, while a safe path to the selected detecting position can be obtained with high efficiency and quality compared with the Probabilistic Roadmap (PRM) and A* search algorithm. Full article
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