This paper presents a state-of-the-art tethered unmanned aerial vehicle (TUAV) for structural integrity assessment of underground stone mine pillars. The TUAV, powered by its tether, works in tandem with an unmanned ground vehicle (UGV) that hosts the TUAV batteries, a self-leveled landing platform, and the tether management system. The UGV and the TUAV were named Rhino and Oxpecker, respectively, given that the TUAV stays landed on the UGV while the ensemble moves inside a mine. The mission of Oxpecker is to create, using a LiDAR sensor, 3D maps of the mine pillars to support time-lapse hazard mapping and time-dependent pillar degradation analysis. Given the height of the pillars ( 7 – 12m), this task cannot be executed by Rhino alone. This paper describes the drone’s hardware and software. The hardware includes the tether management system, designed to control the tension of the tether, and the tether perception system, which provides information that can be used for localization and landing in global navigation satellite systems (GNSS)-denied environments. The vehicle’s software is based on a state machine that controls the several phases of a mission (i.e., takeoff, inspection, and landing) by coordinating drone motion with the tethering system. The paper also describes and evaluates our approach for tether-based landing and autonomous 3D mapping of pillars. We show experiments that illustrate and validate our system in laboratories and underground mines.

This paper presents a cooperative, multi-robot solution for searching, excavating, and transporting mineral resources on the Moon. Our work was developed in the context of the Space Robotics Challenge Phase 2 (SRCP2), which was part of the NASA Centennial Challenges and was motivated by the current NASA Artemis program, a flagship initiative that intends to establish a long-term human presence on the Moon. In the SRCP2 a group of simulated mobile robots was tasked with reporting volatile locations within a realistic lunar simulation environment, and excavating and transporting these resources to target locations in such an environment. In this paper, we describe our solution to the SRCP2 competition that includes our strategies for rover mobility hazard estimation (e.g. slippage level, stuck status), immobility recovery, rover-to-rover, and rover-to-infrastructure docking, rover coordination and cooperation, and cooperative task planning and autonomy. Our solution was able to successfully complete all tasks required by the challenge, granting our team sixth place among all participants of the challenge. Our results demonstrate the potential of using autonomous robots for autonomous in-situ resource utilization (ISRU) on the Moon. Our results also highlight the effectiveness of realistic simulation environments for testing and validating robot autonomy and coordination algorithms. The successful completion of the SRCP2 challenge using our solution demonstrates the potential of cooperative, multi-robot systems for resource utilization on the Moon.

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This paper presents a parallel motion planner for mobile robots and autonomous vehicles based on lattices created in the sensor space of planar range finders. The planner is able to compute paths in a few milliseconds, thus allowing obstacle avoidance in real time. The proposed sensor-space lattice (SSLAT) motion planner uses a lattice to tessellate the area covered by the sensor and to rapidly compute collision-free paths in the robot surroundings by optimizing a cost function. The cost function guides the vehicle to follow a vector field, which encodes the desired vehicle path. We evaluated our method in challenging cluttered static environments, such as warehouses and forests, and in the presence of moving obstacles, both in simulations and real experiments. In these experiments, we show that our algorithm performs collision checking and path planning faster than baseline methods. Since the method can have sequential or parallel implementations, we also compare the two versions of SSLAT and show that the run time for its parallel implementation, which is independent of the number and shape of the obstacles found in the environment, provides a speedup greater than 25.