To support sustainable infrastructure on the Moon, NASA must leverage robots to extract lunar resources for in-situ processing and construction. As part of this effort, NASA is launching the in-situ resource utilizati...
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ISBN:
(纸本)9781665491907
To support sustainable infrastructure on the Moon, NASA must leverage robots to extract lunar resources for in-situ processing and construction. As part of this effort, NASA is launching the in-situ resource utilization (ISRU) Pilot Excavator later this decade to validate a robotic regolith excavator based on the Regolith Advanced Surface Systems Operations Robot (RASSOR). RASSOR is designed to extract and transport regolith to meet the needs of ISRU architectures. During its mission, Pilot Excavator will be tasked with driving in test patterns to demonstrate the operational concept. One possible test pattern is a circular trajectory around the lander while avoiding surface hazards such as lunar rocks. To this end, we utilize dynamic movement primitives to represent navigation sequences as primitive trajectories. We introduce a novel obstacle avoidance parameter, which is configured to avoid rocks throughout testing exercises. We demonstrate the effectiveness our method in a newly developed simulation tool called the Simulated Excavation Environment for Lunar Operations (SEELO) using models based on the NASA RASSOR 2.0 excavator. Our results show that the robot is able to safety and robustly navigate the lunar surface with densely populated rock obstacles while retaining the desired circle pattern behavior.
We present spaceHopper, a three-legged, small-scale robot designed for future mobile exploration of asteroids and moons. The robot weighs 5.2kg and has a body size of 245mm while using space-qualifiable components. Fu...
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ISBN:
(纸本)9798350384581;9798350384574
We present spaceHopper, a three-legged, small-scale robot designed for future mobile exploration of asteroids and moons. The robot weighs 5.2kg and has a body size of 245mm while using space-qualifiable components. Furthermore, spaceHopper's design and controls make it well-adapted for investigating dynamic locomotion modes with extended flight-phases. Instead of gyroscopes or fly-wheels, the system uses its three legs to reorient the body during flight in preparation for landing. We control the leg motion for reorientation using Deep Reinforcement Learning policies. In a simulation of Ceres' gravity (0.029 g), the robot can reliably jump to commanded positions up to 6m away. Our real-world experiments show that spaceHopper can successfully reorient to a safe landing orientation within 9.7deg inside a rotational gimbal and jump in a counterweight setup in Earth's gravity. Overall, we consider spaceHopper an important step towards controlled jumping locomotion in low-gravity environments.
ReachBot is a new concept for planetary exploration, consisting of a small body and long, lightweight extending arms loaded primarily in tension. The arms are equipped with spined grippers for anchoring on rock surfac...
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ISBN:
(纸本)9781728196817
ReachBot is a new concept for planetary exploration, consisting of a small body and long, lightweight extending arms loaded primarily in tension. The arms are equipped with spined grippers for anchoring on rock surfaces. The design and testing of a planar prototype is presented here. Experiments with rock grasping and coordinated locomotion illustrate the advantages of low inertia passive grippers, triggered by impact and using stored mechanical energy for the internal force. Gripper design involves a trade-off among the range of possible grasp angles, maximum grasp force, required triggering force, and required reset force. The current prototype can pull with up to 8N when gripping volcanic rock, limited only by the strength of the 3D printed components. Calculations predict a maximum pull of 26N for the same spines and stronger materials.
In this paper, we propose an open-source lunar rover simulator integrated with a reinforcement learning framework. We incorporate lunar environmental effects in our simulator to enhance the rover locomotion realism wh...
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ISBN:
(纸本)9798350350319;9798350350302
In this paper, we propose an open-source lunar rover simulator integrated with a reinforcement learning framework. We incorporate lunar environmental effects in our simulator to enhance the rover locomotion realism while employing PyBullet to simulate the dynamics of a multi-degree-of-freedom planetary rover. We also extend our lunar rover simulator to interface with a reinforcement learning framework using OpenAI Gym environment and a compatible training workflow. We demonstrate the success of this integration through an autonomous rover navigation task on the lunar environment in our simulator using two reinforcement learning algorithms, DDPG and TD3. The lunar rover simulator is available at https://***/assawayut/LunarRoverSim and an accompanying video can be accessed at https://***/watch?v=8HMqAkDbpqI.
This article extends recent work in magnetic manipulation of conductive, nonmagnetic objects using rotating magnetic dipole fields. Eddy-current-based manipulation provides a contact-free way to manipulate metallic ob...
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This article extends recent work in magnetic manipulation of conductive, nonmagnetic objects using rotating magnetic dipole fields. Eddy-current-based manipulation provides a contact-free way to manipulate metallic objects. We are particularly motivated by the large amount of aluminum in space debris. We previously demonstrated dexterous manipulation of solid spheres with all object parameters known a priori. This work expands the previous model, which contained three discrete modes, to a continuous model that covers all possible relative positions of the manipulated spherical object with respect to the magnetic field source. We further leverage this new model to examine manipulation of spherical objects with unknown physical parameters by applying techniques from the online-optimization and adaptive-control literature. Our experimental results validate our new dynamics model, showing that we get improved performance compared to the previously proposed model, while also solving a simpler optimization problem for control. We further demonstrate the first physical magnetic manipulation of aluminum spheres, as previous controllers were only physically validated on copper spheres. We show that our adaptive control framework can quickly acquire useful object parameters when weakly initialized. Finally, we demonstrate that the spherical-object model can be used as an approximate model for adaptive control of nonspherical objects by performing magnetic manipulation of a variety of objects for which a spherical model is not an obvious approximation.
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