Limits to ground control in autonomous spacecraft

Wan, Alfred D. M.; Braspenning, Peter J.; Vreeswijk, Gerrard A. W.

doi:10.1016/0736-5853(95)00020-8

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Initially, the construction of this station is facilitated by advanced lunar rovers outfitted with robotic arms. Traditional planning methods are mainly based on ground control by creating and verifying all human instructions [2]. However, this approach is inadequate given the dynamic lunar environment, particularly the partially observable lunar surface.…”

Section: Introductionmentioning

confidence: 99%

Lunar Rover Collaborated Path Planning with Artificial Potential Field-Based Heuristic on Deep Reinforcement Learning

Lu,

Xu,

et al. 2024

Aerospace

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The International Lunar Research Station, to be established around 2030, will equip lunar rovers with robotic arms as constructors. Construction requires lunar soil and lunar rovers, for which rovers must go toward different waypoints without encountering obstacles in a limited time due to the short day, especially near the south pole. Traditional planning methods, such as uploading instructions from the ground, can hardly handle many rovers moving on the moon simultaneously with high efficiency. Therefore, we propose a new collaborative path-planning method based on deep reinforcement learning, where the heuristics are demonstrated by both the target and the obstacles in the artificial potential field. Environments have been randomly generated where small and large obstacles and different waypoints are created to collect resources, train the deep reinforcement learning agent to propose actions, and lead the rovers to move without obstacles, finish rovers’ tasks, and reach different targets. The artificial potential field created by obstacles and other rovers in every step affects the action choice of the rover. Information from the artificial potential field would be transformed into rewards in deep reinforcement learning that helps keep distance and safety. Experiments demonstrate that our method can guide rovers moving more safely without turning into nearby large obstacles or collision with other rovers as well as consuming less energy compared with the multi-agent A-Star path-planning algorithm with improved obstacle avoidance method.

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Section: Introductionmentioning

confidence: 99%

Lunar Rover Collaborated Path Planning with Artificial Potential Field-Based Heuristic on Deep Reinforcement Learning

Lu,

Xu,

et al. 2024

Aerospace

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“…In the challenging unknown and distant environments of other planets, too much autonomy could result in complete failure and loss of systems either because of compromised safety of individual agents or collections of agents, or because agents make bad decisions with irreversible consequences. These mission robustness concerns are being addressed by researchers, many in this volume [17,18,19,20], and it will be critical for mission managers to have confidence regarding these challenges before launching what will likely be a challenging mission profile.…”

mentioning

confidence: 99%

Cooperative Agents and the Search for Extraterrestrial Life

Lupisella

2003

Innovative Concepts for Agent-Based Systems

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Abstract. The primary purpose of this paper is to suggest how a cooperative agents exploratory approach could be useful in searching for extraterrestrial life.Certain features of cooperative agent approaches such as distribution of varied functions in space and time, and increased reliability and robustness through redundancy, map well to the requirements of searching for extraterrestrial life. This paper will outline the advantages of cooperative agent approaches and, more specifically, scenarios and architectures that might be used to leverage a cooperative agents approach to enable the effective and efficient search for extraterrestrial life.

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Experimental Swarm Design

Wan

2003

Innovative Concepts for Agent-Based Systems

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Limits to ground control in autonomous spacecraft

Cited by 4 publications

References 13 publications

Lunar Rover Collaborated Path Planning with Artificial Potential Field-Based Heuristic on Deep Reinforcement Learning

Lunar Rover Collaborated Path Planning with Artificial Potential Field-Based Heuristic on Deep Reinforcement Learning

Cooperative Agents and the Search for Extraterrestrial Life

Experimental Swarm Design

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