Detailed surface images of the Moon and Mars reveal hundreds of cave-like openings. These cave-like openings are theorized to be remnants of lava-tubes and their interior maybe in pristine conditions. These locations may have well preserved geological records of the Moon and Mars, including evidence of past water flow and habitability. Exploration of these caves using wheeled rovers remains a daunting challenge. These caves are likely to have entrances with caved-in ceilings much like the lava-tubes of Arizona and New Mexico. Thus, the entrances are nearly impossible to traverse even for experienced human hikers. Our approach is to utilize the SphereX robot, a 3 kg, 30 cm diameter robot with computer hardware and sensors of a smartphone attached to rocket thrusters. Each SphereX robot can hop, roll or fly short distances in low gravity, airless or lowpressure environments. Several SphereX robots maybe deployed to minimize single-point failure and exploit cooperative behaviors to traverse the cave. There are some important challenges for navigation and path planning in these cave environments. Localization systems such as GPS are not available nor are they easy to install due to the signal blockage from the rocks. These caves are too dark and too large for conventional sensor such as cameras and miniature laser sensors to perform detailed mapping and navigation. In this paper, we identify new techniques to map these caves by performing localized, cooperative mapping and navigation. In our approach, a team of SphereX robots much like a team of cave explorer will adopt specialized roles to perform navigation. For a minimal science mission, these robots need to obtain camera images and basic maps of the cave interior to be transmitted back to a lander or rover situated outside the cave. The teams of SphereX robots form a bucket brigade and partition the currently accessible volume of the cave. Then the teams of robots attempt to expand their reach deeper into the cave and sense their progress. Imaging the cave interior is expensive and require use of high-power strobe lights. The images would be compiled into a 3D point cloud and meshed by the lander or transmitted to ground. Using this conservative approach, we ensure the robots are always within communication reach of a lander/rover outside the cave. Once large segments of the cave are mapped, the rovers may lay down a network of mirrors to beam sunlight and laser light from a base station at the cave entrance to the far reaches of the cave. These mirrors also help the robots identify a pathway back to the cave entrance. Efforts are underway to perform field experiments to validate the feasibility our proposed approach to cave exploration.

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We present NavACL, a method of automatic curriculum learning tailored to the navigation task. NavACL is simple to train and efficiently selects relevant tasks using geometric features. In our experiments, deep reinforcement learning agents trained using NavACL in collision-free environments significantly outperform state-of-the-art agents trained with uniform sampling -the current standard. Furthermore, our agents are able to navigate through unknown cluttered indoor environments to semantically-specified targets using only RGB images. Collision avoidance policies and frozen feature networks support transfer to unseen real-world environments, without any modification or retraining requirements. We evaluate our policies in simulation, and in the real world on a ground robot and a quadrotor drone. Videos of real-world results are available in the supplementary material. 1

Advances in planetary robotics have led to wheeled robots that have beamed back invaluable science data from the surface of the Moon and Mars. However, these large wheeled robots are unable to access rugged environments such as cliffs, canyons and crater walls that contain exposed rock-faces and are geological time-capsules into the early Moon and Mars. We have proposed the SphereX robot with a mass of 3 kg, 30 cm diameter that can hop, roll and fly short distances. A single robot may slip and fall, however, a multirobot system can work cooperatively by being interlinked using spring-tethers and work much like a team of mountaineers to systematically climb a slope. We consider a team of four or more robots that are interlinked with tethers in an "x" configuration. Each robot secures itself to a slope using spiny gripping actuators, and one by one each robot moves upwards by crawling, rolling or hopping up the slope. Apart from climbing, path planning, and navigation is another critical challenge that needs to be solved to make the whole approach feasible. For climbing navigation, a multirobot system needs to have up to date info of its location, together with a macroscopic map of the climbing surface and a detailed map ahead. This system with limited sensor range needs to discern and identify feasible pathways to make the next climbing step much like a human mountaineer. These climbing pathways consist of a series of anchor points for the robot to grip onto next. Identifying one or more feasible pathways is a critical challenge as the terrain ahead needs to be acquired, followed by identification and ranking of anchor points to grip. The climbing task resembles a maze with wrong pathways leading to dead-end. The multirobot systems need to autonomously explore climbing pathways and know when to give up. In this paper, we present a human devised autonomous climbing algorithm and evaluate it using a high-fidelity dynamics simulator. The climbing surfaces contain impassable obstacles and some loosely held rocks that can dislodge. Under these conditions, the robots need to autonomously map, plan and navigate up or down these steep environments. Autonomous mapping and navigation capability is evaluated using simulated lasers, vision sensors. The human devised planning algorithm uses a new algorithm called bounded-leg A*. Our early simulation results show much promise in these techniques and our future plans include demonstration on real robots in a controlled laboratory environment and outdoors in the canyons of Arizona.

Pits on the Moon and Mars are intriguing geological formations that have yet to be explored. These geological formations can provide protection from harsh diurnal temperature variations, ionizing radiation, and meteorite impacts. Some have proposed that these underground formations are wellsuited as human outposts. Some theorize that the Martian pits may harbor remnants of past life. Unfortunately, these geological formations have been off-limits to conventional wheeled rovers and lander systems due to their collapsed ceiling or "skylight" entrances. In this paper, a new low-cost method to explore these pits is presented using the Spring Propelled Extreme Environment Robot (SPEER). The SPEER consists of a launch system that flings disposable spherical microbots through skylights into the pits. The microbots are low-cost and composed of aluminium Al-6061 disposable spheres with an array of adapted COTS sensors and a solid rocket motor for soft landing. By moving most control authority to the launcher, the microbots become very simple, lightweight, and low-cost. We present a preliminary design of the microbots that can be built today using commercial components for under 500 USD. The microbots have a total mass of 1 kg, with more than 750 g available for a science instrument. In this paper, we present the design, dynamics and control, and operation of these microbots. This is followed by initial feasibility studies of the SPEER system by simulating exploration of a known Lunar pit in Mare Tranquillitatis.

Glacial science could benefit tremendously from autonomous robots, but previous glacial robots have had perception issues in these colorless and featureless environments, specifically with visual feature extraction. Glaciologists use near-infrared imagery to reveal the underlying heterogeneous spatial structure of snow and ice, and we theorize that this hidden near-infrared structure could produce more and higher quality features than available in visible light. We took a custom camera rig to Igloo Cave at Mt. St. Helens to test our theory. The camera rig contains two identical machine vision cameras, one which was outfitted with multiple filters to see only near-infrared light. We extracted features from short video clips taken inside Igloo Cave at Mt. St. Helens, using three popular feature extractors (FAST, SIFT, and SURF). We quantified the number of features and their quality for visual navigation using feature correspondence and the epipolar constraint. Our results indicate that near-infrared imagery produces more features that tend to be of higher quality than that of visible light imagery.

The discovery of ice deposits in the permanently shadowed craters of the lunar North and South Pole Moon presents an important opportunity for In-Situ Resource Utilization. These ice deposits maybe the source for sustaining a lunar base or for enabling an interplanetary refueling station. These ice deposits also preserve a unique record of the geology and environment of their hosts, both in terms of impact history and the supply of volatile compounds, and so are of immense scientific interest. To date, these ice deposits have been studied indirectly and by remote active radar, but they need to be analyzed in-situ by robotic systems that can study the depths of the deposits, their purity and composition. However, these shadowed craters never see sunlight and are one of the coldest places in the solar system. NASA JPL proposed use of solar reflectors mounted on crater rims to project sunlight into the crater depths for use by ground robots. The solar reflectors would heat the crater base and vehicles positioned at the base sufficiently to survive the cold-temperatures. Our approach analyzes part of the logistics of the approach, with teams of robots climbing up and down to the crater to access the ice deposits. The mission will require robots to climb down extreme environments and carry large structures, including instruments and communication devices.

Graph Neural Networks (GNNs) are a paradigmshifting neural architecture to facilitate the learning of complex multi-agent behaviors. Recent work has demonstrated remarkable performance in tasks such as flocking, multi-agent path planning and cooperative coverage. However, the policies derived through GNN-based learning schemes have not yet been deployed to the real-world on physical multi-robot systems. In this work, we present the design of a system that allows for fully decentralized execution of GNN-based policies. We create a framework based on ROS2 and elaborate its details in this paper. We demonstrate our framework on a case-study that requires tight coordination between robots, and present firstof-a-kind results that show successful real-world deployment of GNN-based policies on a decentralized multi-robot system relying on Adhoc communication. A video demonstration of this case-study can be found online 1 .