Towards Energy Efficient Autonomous Exploration of Mars Lava Tube with a Martian Coaxial Quadrotor
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Abstract (arXiv)
Mapping and exploration of a Martian terrain with an aerial vehicle has become an emerging research direction, since the successful flight demonstration of the Mars helicopter Ingenuity. Although the autonomy and navigation capability of the state of the art Mars helicopter has proven to be efficient in an open environment, the next area of interest for exploration on Mars are caves or ancient lava tube like environments, especially towards the never-ending search of life on other planets. This article presents an autonomous exploration mission based on a modified frontier approach along with a risk aware planning and integrated collision avoidance scheme with a special focus on energy aspects of a custom designed Mars Coaxial Quadrotor (MCQ) in a Martian simulated lava tube. One of the biggest novelties of the article stems from addressing the exploration capability, while rapidly exploring in local areas and intelligently global re-positioning of the MCQ when reaching dead ends in order to to efficiently use the battery based consumed energy, while increasing the volume of the exploration. The proposed three layer cost based global re-position point selection assists in rapidly redirecting the MCQ to previously partially seen areas that could lead to more unexplored part of the lava tube. The Martian fully simulated mission presented in this article takes into consideration the fidelity of physics of Mars condition in terms of thin atmosphere, low surface pressure and low gravity of the planet, while proves the efficiency of the proposed scheme in exploring an area that is particularly challenging due to the subterranean-like environment. The proposed exploration-planning framework is also validated in simulation by comparing it against the graph based exploration planner.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Nov 2022.
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Citing papers per year: 2024: 2; 2025: 1.
3 papers.
- 2024Pathfinder for Low-altitude Aircraft with Binary Neural Networkcited by 4
- 2024STAGE: Scalable and Traversability-Aware Graph based Exploration Planner for Dynamically Varying Environmentscited by 3
- 2025Olympus: A Jumping Quadruped for Planetary Exploration Utilizing Reinforcement Learning for In-Flight Attitude Controlcited by 0
- 2025Olympus: A Jumping Quadruped for Planetary Exploration Utilizing Reinforcement Learning for In-Flight Attitude Control5 Mar 2025
- 2024Pathfinder for Low-altitude Aircraft with Binary Neural Network13 Sep 2024
- 2024STAGE: Scalable and Traversability-Aware Graph based Exploration Planner for Dynamically Varying Environments4 Feb 2024