AB
A.J. Becoy
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3 records found
1
Journal article
(2026)
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A.J. Hidding, H.H. Bier, L. Peternel, A.J. Becoy, F.A.P. Romio, G. Calabrese
As part of TU Delft Rhizome 2.0 and Moonshot projects, focusing on the development of extraterrestrial habitats in lava tubes, the robotic mapping of an analogue lava tube in Sicily has been studied with the future goal of assessing its suitability for building construction. The main objective of the research was to survey a lava tube and acquire a novel dataset for future research, while also analyzing the collected data to evaluate possible future lava tube exploration scenarios for the Moon and Mars. This paper presents the robotic mapping conducted in Grotta di Monte Intraleo, detailing the implementation and outlining challenges and insights, leading to the new dataset. Various mapping and data collection methods, including robotic mapping using LiDAR, was explored. The overall strategy relies on multi-technique scanning to enhance redundancy and ensure comprehensive data acquisition, that is providing a robust foundation for future robotic exploration and habitat planning.
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As part of TU Delft Rhizome 2.0 and Moonshot projects, focusing on the development of extraterrestrial habitats in lava tubes, the robotic mapping of an analogue lava tube in Sicily has been studied with the future goal of assessing its suitability for building construction. The main objective of the research was to survey a lava tube and acquire a novel dataset for future research, while also analyzing the collected data to evaluate possible future lava tube exploration scenarios for the Moon and Mars. This paper presents the robotic mapping conducted in Grotta di Monte Intraleo, detailing the implementation and outlining challenges and insights, leading to the new dataset. Various mapping and data collection methods, including robotic mapping using LiDAR, was explored. The overall strategy relies on multi-technique scanning to enhance redundancy and ensure comprehensive data acquisition, that is providing a robust foundation for future robotic exploration and habitat planning.
This article proposes a novel method of coverage path planning for the purpose of scanning an unstructured environment autonomously. The method uses the morphological skeleton of a prior 2D navigation map via SLAM to generate a sequence of points of interest (POIs). This sequence is then ordered to create an optimal path based on the robot’s current position. To control the high-level operation, a finite state machine (FSM) is used to switch between two modes: navigating toward a POI using Nav2 and scanning the local surroundings. We validate the method in a leveled, indoor, obstacle-free, non-convex environment, evaluating time efficiency and reachability over five trials. The map reader and path planner can quickly process maps of widths and heights ranging between [196,225] pixels and [185,231] pixels in 2.52ms and 1.7ms, respectively. Their computation time increases with 22.0ns/pixel and 8.17 μs/pixel, respectively. The robot managed to reach 86.5% of all waypoints across the five runs. The proposed method suffers from drift occurring in the 2D navigation map.
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This article proposes a novel method of coverage path planning for the purpose of scanning an unstructured environment autonomously. The method uses the morphological skeleton of a prior 2D navigation map via SLAM to generate a sequence of points of interest (POIs). This sequence is then ordered to create an optimal path based on the robot’s current position. To control the high-level operation, a finite state machine (FSM) is used to switch between two modes: navigating toward a POI using Nav2 and scanning the local surroundings. We validate the method in a leveled, indoor, obstacle-free, non-convex environment, evaluating time efficiency and reachability over five trials. The map reader and path planner can quickly process maps of widths and heights ranging between [196,225] pixels and [185,231] pixels in 2.52ms and 1.7ms, respectively. Their computation time increases with 22.0ns/pixel and 8.17 μs/pixel, respectively. The robot managed to reach 86.5% of all waypoints across the five runs. The proposed method suffers from drift occurring in the 2D navigation map.