Mapping planetary caves with an autonomous, heterogeneous robot team

Abstract

Abstract-d Caves on other planetary bodies offer sheltered habitat for future human explorers and numerous clues to a planet's past for scientists. While recent orbital imagery provides exciting new details about cave entrances on the Moon and Mars, the interiors of these caves are still unknown and not observable from orbit. Multi-robot teams offer unique solutions for exploration and modeling subsurface voids during precursor missions. Robot teams that are diverse in terms of size, mobility, sensing, and capability can provide great advantages, but this diversity, coupled with inherently distinct low-level behavior architectures, makes coordination a challenge. This paper presents a framework that consists of an autonomous frontier and capability-based task generator, a distributed market-based strategy for coordinating and allocating tasks to the different team members, and a communication paradigm for seamless interaction between the different robots in the system. Robots have different sensors, (in the representative robot team used for testing: 2D mapping sensors, 3D modeling sensors, or no exteroceptive sensors), and varying levels of mobility. Tasks are generated to explore, model, and take science samples. Based on an individual robot's capability and associated cost for executing a generated task, a robot is autonomously selected for task execution. The robots create coarse online maps and store collected data for high resolution offline modeling. The coordination approach has been field tested at a mock cave site with highly-unstructured natural terrain, as well as an outdoor patio area. Initial results are promising for applicability of the proposed multi-robot framework to exploration and modeling of planetary caves.

Heterogeneous Map Merging: State of the Art

“…One more direction in the heterogeneous metric map merging is the matching of maps from different sensors for the localization [93][94][95] or map building purposes [96]. Some solutions also exist for fundamentally different map merging, but there are restrictions-either the relative transformation of maps must be known [79] or other information is required (WiFi signal strength map in [83]). Some solutions exist, that may support the metric grid vs feature map merging [17,18,98] and metric grid vs topological map merging [99][100][101][102], but those are developed for other purposes and their use for heterogeneous map merging purposes remains unknown.…”

“…A case of metric grid vs feature map fusion is presented in Husain et al [79], where the merging of 2D occupancy grid maps and 3D point clouds to create a more complete environment representation is addressed. The robot relative positions are known, and the authors note the need for postprocessing due to limited bandwidth and processing capabilities of individual robots, and the robots only use coarse 2D maps during the mapping.…”

“…3D PC/Grid -Known Hybrid Different LIDAR [79] The method merges 2D occupancy grid with 3D point cloud in postprocessing.…”

“…Memory limitations are especially important when considering 3D maps, and 3D octree representation is mainly motivated by memory, bandwidth and processing capability limitations of the robots [38,39,45]. In some cases the available memory allows us to store more data than the robots can process or transfer, and it is possible to acquire higher quality maps after the end of mapping [79].…”

The ARCHES Space-Analogue Demonstration Mission: Towards Heterogeneous Teams of Autonomous Robots for Collaborative Scientific Sampling in Planetary Exploration

“…In contrast to these approaches and to [4], our robotic team will primarily operate in full autonomy governed by a complex mission control framework. The authors of [15] present a multi-robot coordination framework to map planetary caves. However, they employ rather simple wheeled/tracked robots evaluated in generic artificial environments.…”