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Journal ArticleDOI

The Right Path: Comprehensive Path Planning for Lunar Exploration Rovers

TLDR
This path-planning method was found to have a large impact on the amount of power generated in the morning/evening and at high-latitude regions relative to in the daytime and at low-latitudes regions on the moon.
Abstract
This article presents a comprehensive path-planning method for lunar and planetary exploration rovers. In this method, two new elements are introduced as evaluation indices for path planning: 1) determined by the rover design and 2) derived from a target environment. These are defined as the rover's internal and external elements, respectively. In this article, the rover's locomotion mechanism and insolation (i.e., shadow) conditions were considered to be the two elements that ensure the rover's safety and energy, and the influences of these elements on path planning were described. To examine the influence of the locomotion mechanism on path planning, experiments were performed using track and wheel mechanisms, and the motion behaviors were modeled. The planned paths of the tracked and wheeled rovers were then simulated based on their motion behaviors. The influence of the insolation condition was considered through path plan simulations conducted using various lunar latitudes and times. The simulation results showed that the internal element can be used as an evaluation index to plan a safe path that corresponds to the traveling performance of the rover's locomotion mechanism. The path derived for the tracked rover was found to be straighter than that derived for the wheeled rover. The simulation results also showed that path planning using the external element as an additional index enhances the power generated by solar panels under various insolation conditions. This path-planning method was found to have a large impact on the amount of power generated in the morning/evening and at high-latitude regions relative to in the daytime and at low-latitude regions on the moon. These simulation results suggest the effectiveness of the proposed pathplanning method.

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Citations
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Journal ArticleDOI

A novel learning-based global path planning algorithm for planetary rovers

TL;DR: In this paper, a deep convolutional neural network with dual branches (DB-CNN) is designed and trained, which can plan path directly from orbital images of planetary surfaces without implementing environment mapping.
Journal ArticleDOI

Shortest Path Planning for Energy-Constrained Mobile Platforms Navigating on Uneven Terrains

TL;DR: Test results obtained using real terrain data verify the applicability of the proposed heuristic search algorithm called constraints satisfying A* in shortest path planning for energy-constrained mobile platforms on uneven terrains.
Proceedings ArticleDOI

Energy efficient slope traversability planning for mobile robot in loose soil

TL;DR: An energy-efficient trajectory planning method for a wheeled robot in slope ascending scenario using a bezier curve to provide a smooth trajectory designed with an appropriate arrival time as well as the control points for the curve.
Journal ArticleDOI

Learning-Based End-to-End Path Planning for Lunar Rovers with Safety Constraints.

TL;DR: In this paper, an end-to-end path planning algorithm based on deep reinforcement learning method is designed, including state space, action space, network structure, reward function considering slip behavior, and training method based on proximal policy optimization.
Journal ArticleDOI

Design and analysis of novel Ka band NOMA uplink relay system for Lunar farside exploration

TL;DR: This paper proposes a novel Ka band non-orthogonal multiple access (NOMA) uplink relay system for Lunar farside exploration, where a satellite relay node with the help of NOMA scheme, amplifies and forwards the signal from the Earth Base Station to a Lunar rover and a lander.
References
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MonographDOI

Planning Algorithms: Introductory Material

TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Book

Theory of Ground Vehicles

J.Y. Wong
TL;DR: In this article, the authors present an approach to the prediction of normal pressure distribution under a track and a simplified method for analysis of tracked vehicle performance, based on the Cone Index.
Proceedings Article

The focussed D* algorithm for real-time replanning

TL;DR: An extension to D* that focusses the repairs to significantly reduce the total time required for the initial path calculation and subsequent replanning operations for dynamic environments where arc costs can change during the traverse of the solution path.
Journal ArticleDOI

Ancillary data services of NASA's Navigation and Ancillary Information Facility

TL;DR: The SPICE system is described, current and future SPICE applications are identified, and customer support offered by NAIF is summarized.
Journal ArticleDOI

Using interpolation to improve path planning: The Field D* algorithm

TL;DR: An interpolation‐based planning and replanning algorithm for generating low‐cost paths through uniform and nonuniform resolution grids that addresses two of the most significant shortcomings of grid‐based path planning: the quality of the paths produced and the memory and computational requirements of planning over grids.
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