Ingenuity Mars Helicopter: From Technology Demonstration to Extraterrestrial Scout

Tzanetos, Theodore; Aung, M.; Balaram, J.; Grip, Håvard F.; Karras, Jaakko T.; Canham, Timothy; Kubiak, Gerik; Anderson, Joshua C.; Merewether, Gene; Starch, Michael; Pauken, Michael; Cappucci, Stefano; Chase, Matthew; Golombek, M. P.; Toupet, Olivier; Smart, Marshall C.; Dawson, Scott T. M.; Ramirez, Erick Blandon; Lam, Johnny; Stern, Ryan; Ravich, Joshua; Hogg, Robert; Pipenberg, Benjamin T.; Keennon, Matthew; Williford, Kenneth H.

doi:10.1109/aero53065.2022.9843428

Cited by 29 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the data from the rover's perspective have not been adequately enhanced in rendering, resulting in operators making decisions In recent years, in situ exploration missions to Mars have also been advancing rapidly. NASA's Mars 2020 program's Perseverance rover and Ingenuity helicopter deployed a drone on the surface of a celestial body other than Earth for the first time, pioneering a new approach to deep space exploration [51,52]. China's Zhurong rover landed in the southern region of Utopia Planitia on Mars in 2021, marking China's achievement of a soft landing on the Martian surface [53].…”

Section: Sensors On Planetary Roversmentioning

confidence: 99%

Enhanced Interactive Rendering for Rovers of Lunar Polar Region and Martian Surface

Bi,

Jin,

Chen

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Appropriate environmental sensing methods and visualization representations are crucial foundations for the in situ exploration of planets. In this paper, we developed specialized visualization methods to facilitate the rover’s interaction and decision-making processes, as well as to address the path-planning and obstacle-avoidance requirements for lunar polar region exploration and Mars exploration. To achieve this goal, we utilize simulated lunar polar regions and Martian environments. Among them, the lunar rover operating in the permanently shadowed region (PSR) of the simulated crater primarily utilizes light detection and ranging (LiDAR) for environmental sensing; then, we reconstruct a mesh using the Poisson surface reconstruction method. After that, the lunar rover’s traveling environment is represented as a red-green-blue (RGB) image, a slope coloration image, and a theoretical water content coloration image, based on different interaction needs and scientific objectives. For the rocky environment where the Mars rover is traveling, this paper enhances the display of the rocks on the Martian surface. It does so by utilizing depth information of the rock instances to highlight their significance for the rover’s path-planning and obstacle-avoidance decisions. Such an environmental sensing and enhanced visualization approach facilitates rover path-planning and remote–interactive operations, thereby enabling further exploration activities in the lunar PSR and Mars, in addition to facilitating the study and communication of specific planetary science objectives, and the production and display of basemaps and thematic maps.

show abstract

Section: Sensors On Planetary Roversmentioning

confidence: 99%

Enhanced Interactive Rendering for Rovers of Lunar Polar Region and Martian Surface

Bi,

Jin,

Chen

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The second optical property we specified for the OS container endcaps was for individual and localized surface features to have a BRDF, BRDFf, that differs from the surrounding surface BRDF over the 400-900 nm wavelength range by at least 25% as described by Equation (2).…”

Section: Vision System Overviewmentioning

confidence: 99%

“…In the event that the Perseverance rover cannot deliver its samples to the SRL, the SRL also will deliver to the Martian surface two helicopters that can be used to retrieve the samples that Perseverance has cached on the surface. These will be similar in design to the Mars helicopter that successfully flew on Mars for the first time on 19 April 2021 [2]. After the OS separates from the MAV and is inserted into Mars orbit, the MSR ERO spacecraft will rendezvous with the OS and capture it using part of its payload called the Capture, Containment and Return System (CCRS) (see Figure 3).…”

Section: Introductionmentioning

confidence: 99%

Vision System for the Mars Sample Return Capture, Containment and Return System (CCRS)

Bos,

Donovan,

Capone

et al. 2024

Preprint

View full text Add to dashboard Cite

The successful 2020 launch and 2021 landing of the National Aeronautics and Space Administration’s (NASA) Perseverance Mars rover initiated the first phase of the NASA and European Space Agency (ESA) Mars Sample Return (MSR) campaign. The goal of the MSR campaign is to collect scientifically interesting samples from the Martian surface and return them to Earth for further study in terrestrial laboratories. The MSR campaign consists of three major spacecraft components to accomplish this objective: the Perseverance Mars rover, the Sample Retrieval Lander (SRL) and the Earth Return Orbiter (ERO). Onboard the ERO spacecraft is the Capture, Containment and Return System (CCRS). CCRS will capture, process and return to Earth the samples that have been collected after they are launched into Mars orbit by the Mars Ascent Vehicle (MAV), which is delivered to Mars onboard the SRL. To facilitate the processing of the orbiting sample (OS) by CCRS we have designed and developed a vision system to determine the OS capture orientation. The vision system is comprised of two cameras sensitive to the visible portion of the electromagnetic spectrum and two illumination modules constructed from broadband light emitting diodes (LED). Vision system laboratory tests and physics-based optical simulations predict CCRS ground processing will be able to correctly identify the OS post-capture orientation using only a single vision system image that is transmitted to Earth from Mars orbit.

show abstract

“…The design of control systems for agile mobile robots is one of the central challenges in robotics. Control systems are at the core of every real-world robot and are deployed in an ever-increasing number of applications, such as self-driving cars (1,2), search and rescue (3), flying cars (4), autonomous vehicle racing (5,6), navigation of stratospheric balloons (7), and extraterrestrial exploration (8,9). Two schools of thought exist for the development of such systems: model-based optimal control (OC) and learningcentric reinforcement learning (RL); both have achieved impressive results.…”

Section: Introductionmentioning

confidence: 99%

Reaching the limit in autonomous racing: Optimal control versus reinforcement learning

Song,

Romero,

Müller

et al. 2023

Sci. Robot.

View full text Add to dashboard Cite

A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its objective better but that it optimizes a better objective. OC decomposes the problem into planning and control with an explicit intermediate representation, such as a trajectory, that serves as an interface. This decomposition limits the range of behaviors that can be expressed by the controller, leading to inferior control performance when facing unmodeled effects. In contrast, RL can directly optimize a task-level objective and can leverage domain randomization to cope with model uncertainty, allowing the discovery of more robust control responses. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour. Our policy achieved superhuman control within minutes of training on a standard workstation. This work presents a milestone in agile robotics and sheds light on the role of RL and OC in robot control.

show abstract

Ingenuity Mars Helicopter: From Technology Demonstration to Extraterrestrial Scout

Cited by 29 publications

References 9 publications

Enhanced Interactive Rendering for Rovers of Lunar Polar Region and Martian Surface

Enhanced Interactive Rendering for Rovers of Lunar Polar Region and Martian Surface

Vision System for the Mars Sample Return Capture, Containment and Return System (CCRS)

Reaching the limit in autonomous racing: Optimal control versus reinforcement learning

Contact Info

Product

Resources

About