Mars Helicopter Technology Demonstrator

Balaram, J.; Canham, Timothy; Duncan, Courtney; Golombek, M. P.; Grip, Håvard Fjær; Maki, J. N.; Quon, Amelia; Stern, Ryan; Zhu, David Q.

doi:10.2514/6.2018-0023

Cited by 149 publications

(80 citation statements)

References 5 publications

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“…Such lightweight flying robots are envisioned to be able to travel distances of up to 5 km (Thangavelautham et al, 2014), which is enough to gain an overview and aid the navigation on accessible terrain of slower ground rovers that can carry heavier payloads and manipulators. The Jet Propulsion Laboratory plans on sending a small coaxial copter to Mars in the Mars2020 mission (Balaram et al, 2018) for scouting. Also the Global Exploration Roadmap (ISECG, 2018) states that the exploration of Martian lava tubes is of high scientific interest.…”

Section: Related Workmentioning

confidence: 99%

ARDEA—An MAV with skills for future planetary missions

Lutz

Müller

Maier

et al. 2020

Journal of Field Robotics

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We introduce a prototype flying platform for planetary exploration: autonomous robot design for extraterrestrial applications (ARDEA). Communication with unmanned missions beyond Earth orbit suffers from time delay, thus a key criterion for robotic exploration is a robot's ability to perform tasks without human intervention. For autonomous operation, all computations should be done on-board and GlobalNavigation Satellite System (GNSS) should not be relied on for navigation purposes.Given these objectives ARDEA is equipped with two pairs of wide-angle stereo cameras and an inertial measurement unit (IMU) for robust visual-inertial navigation and time-efficient, omni-directional 3D mapping. The four cameras cover a 240 ∘ vertical field of view, enabling the system to operate in confined environments such as caves formed by lava tubes. The captured images are split into several pinhole cameras, which are used for simultaneously running visual odometries. The stereo output is used for simultaneous localization and mapping, 3D map generation and collision-free motion planning. To operate the vehicle efficiently for a variety of missions, ARDEA's capabilities have been modularized into skills which can be assembled to fulfill a mission's objectives. These skills are defined generically so that they are independent of the robot configuration, making the approach suitable for different heterogeneous robotic teams. The diverse skill set also makes the micro aerial vehicle (MAV) useful for any task where autonomous exploration is needed.For example terrestrial search and rescue missions where visual navigation in GNSSdenied indoor environments is crucial, such as partially collapsed man-made structures like buildings or tunnels. We have demonstrated the robustness of our system in indoor and outdoor field tests. K E Y W O R D S aerial robotics, computer vision, exploration, GPS-denied operation, planetary robotics ---This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%

ARDEA—An MAV with skills for future planetary missions

Lutz

Müller

Maier

et al. 2020

Journal of Field Robotics

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“…In Rover Navigation Problem (RNP) introduced here, the objective is to navigate a Mars rover from a starting point to a goal location while avoiding risk regions such as steep slopes, large rocks, etc. The rover is provided a map of the environment which is created by a Mars orbiter satellite [15] and a Mars helicopter [16] flying ahead of the rover. This global map contains the location of landmarks, which serve as information sources that the rover can use to localize itself on the global map.…”

Section: A Rover Navigation Problemmentioning

confidence: 99%

Bi-Directional Value Learning for Risk-Aware Planning Under Uncertainty

Kim

Thakker

Agha–mohammadi

2019

IEEE Robot. Autom. Lett.

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Decision-making under uncertainty is a crucial ability for autonomous systems. In its most general form, this problem can be formulated as a Partially Observable Markov Decision Process (POMDP). The solution policy of a POMDP can be implicitly encoded as a value function. In partially observable settings, the value function is typically learned via forward simulation of the system evolution. Focusing on accurate and long-range risk assessment, we propose a novel method, where the value function is learned in different phases via a bi-directional search in belief space. A backward value learning process provides a long-range and risk-aware base policy. A forward value learning process ensures local optimality and updates the policy via forward simulations. We consider a class of scalable and continuous-space rover navigation problems (RNP) to assess the safety, scalability, and optimality of the proposed algorithm. The results demonstrate the capabilities of the proposed algorithm in evaluating long-range risk/safety of the planner while addressing continuous problems with long planning horizons.Proof. We prove this by backward induction.Consider the terminal beliefs first. Trivially, from Eq. (4) and Eq. (8), ρ(b g ; π) = lim J F →∞ J(bg;π) J F = 0 for ∀b g ∈ B goal , and ρ(b f ; π) = lim J F →∞

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“…In 2014, NASA JPL and AeroVironment published an initial paper on what would ultimately become the conceptual foundation for the current Mars Helicopter effort [17]. The current design, described in a recent paper [18], has evolved significantly but retains many of the original core features.…”

Section: Previous Work On Mars Helicopter Technologymentioning

confidence: 99%

Guidance and Control for a Mars Helicopter

Grip

Scharf

Malpica

et al. 2018

2018 AIAA Guidance, Navigation, and Control Conference

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As part of a future mission to Mars, NASA is considering including a small helicopter capable of operating independently in the Martian environment. The Martian atmosphere is extremely thin, with a density of only 1-2% of Earth's atmospheric density at sea level; this significantly alters the flight dynamics of the vehicle and has implications for vehicle design and control. In this paper we focus on guidance and control for a Mars Helicopter, and in particular on the challenges that are unique to operating in the Mars environment. In 2016, the first-ever controlled flight of a helicopter in Martian atmospheric conditions was performed in the 25-ft Space Simulator at NASA's Jet Propulsion Laboratory. We provide details of the effort leading to this flight demonstration, including

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Mars Helicopter Technology Demonstrator

Cited by 149 publications

References 5 publications

ARDEA—An MAV with skills for future planetary missions

ARDEA—An MAV with skills for future planetary missions

Bi-Directional Value Learning for Risk-Aware Planning Under Uncertainty

Guidance and Control for a Mars Helicopter

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