Efficient Coverage Path Planning for Lunar Micro-Rovers Leveraging Global and Local Environmental Data

Conceitos Básicos

This paper presents a novel 3D myopic coverage path planning algorithm for lunar micro-rovers that can efficiently explore unknown environments with limited sensing and computational capabilities by leveraging both global and local environmental data.

Resumo

The paper presents a novel 3D myopic coverage path planning algorithm for lunar micro-rovers that can explore unknown environments with limited sensing and computational capabilities. The algorithm expands upon traditional non-graph path planning methods to accommodate the complexities of lunar terrain, utilizing global data (such as digital elevation models from lunar orbiters) and local topographic features into motion cost calculations.
The algorithm also integrates localization and mapping to update the rover's pose and map the environment. The resulting environment map's accuracy is evaluated and tested in a 3D simulator. Outdoor field tests were conducted to validate the algorithm's efficacy in sim-to-real scenarios.
The results showed that the algorithm could achieve high coverage with low energy consumption and computational cost, while incrementally exploring the terrain and avoiding obstacles. This study contributes to the advancement of path planning methodologies for space exploration, paving the way for efficient, scalable and autonomous exploration of lunar environments by small rovers.

Estatísticas

The path length ratio, defined as the ratio of total path length to total number of cells, was minimized to improve exploration efficiency.
The energy consumption was formulated based on the work required for longitudinal motion and spot turns, considering the slope gradient.
The maximum pitch angle of the rover was monitored to assess the traversability risk.

Citações

"The goal is that each rover explores the assigned area thoroughly, thus increasing the area of coverage efficiently."
"To take terrain geometry into account, another cost based on DEM is added to the cost function. This cost can be calculated in advance while myopic cost is calculated in real-time based on the rover's sensor data. Thus it is regarded as hybrid path planning taking into account both global and local information."
"The efficiency of the path planning algorithm can be evaluated by the path length required to accomplish a specific percentage of coverage. As a reasonable compromise, the coverage is set to 95% completeness for this simulation."

Principais Insights Extraídos De

Risk-Aware Coverage Path Planning for Lunar Micro-Rovers Leveraging Global and Local Environmental Data

by Shreya Santr... às arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18721.pdf

Perguntas Mais Profundas

How can the proposed algorithm be extended to handle dynamic environments with moving obstacles on the lunar surface?

In order to adapt the proposed algorithm to dynamic environments with moving obstacles on the lunar surface, several enhancements can be implemented. One approach is to integrate real-time obstacle detection and tracking mechanisms using additional sensors such as cameras or radar systems. These sensors can provide continuous updates on the positions and movements of obstacles, allowing the rover to dynamically adjust its path planning to avoid collisions.
Furthermore, the algorithm can incorporate predictive modeling techniques to anticipate the future positions of moving obstacles based on their current trajectories. By predicting the potential paths of dynamic obstacles, the rover can proactively plan its movements to navigate around them effectively. This predictive capability can be crucial in ensuring the safety and efficiency of the rover's exploration missions in dynamic environments.
Additionally, the algorithm can leverage communication and coordination strategies between multiple rovers to share information about detected obstacles and collaboratively plan paths to avoid collisions. By establishing a networked system where rovers can exchange real-time data and coordinate their movements, the algorithm can enable efficient exploration of dynamic lunar environments with moving obstacles.

What are the potential limitations of the DEM-based cost function, and how can it be further improved to better capture the complexities of lunar terrain?

One potential limitation of the DEM-based cost function is its sensitivity to inaccuracies in the elevation data, which can lead to suboptimal path planning decisions. Inaccuracies in the DEM, such as noise or missing data, can result in incorrect slope gradient calculations and, consequently, inaccurate cost assignments for terrain traversal. To address this limitation, the DEM-based cost function can be enhanced by incorporating data fusion techniques that combine DEM data with real-time sensor measurements to improve the accuracy of terrain representation.
Furthermore, the DEM-based cost function may oversimplify the terrain complexity by considering only the absolute height differences between cells. To better capture the intricacies of lunar terrain, the cost function can be refined to account for additional factors such as surface roughness, soil composition, and terrain features like rocks or boulders. By integrating more comprehensive terrain analysis metrics into the cost function, the algorithm can make more informed decisions about path planning in diverse lunar landscapes.
Moreover, the DEM-based cost function may not adequately address dynamic changes in terrain conditions, such as erosion or shifting soil patterns. To enhance the function's adaptability to evolving terrain dynamics, real-time updates from onboard sensors can be integrated to continuously adjust the cost calculations based on the rover's immediate surroundings. This dynamic adjustment mechanism can improve the algorithm's responsiveness to changing terrain conditions and enhance its ability to navigate complex lunar landscapes effectively.

What other sensor modalities, in addition to LiDAR, could be integrated to enhance the robustness and reliability of the localization and mapping capabilities of the micro-rovers?

In addition to LiDAR, several other sensor modalities can be integrated to enhance the robustness and reliability of the localization and mapping capabilities of micro-rovers in lunar exploration missions. One key sensor modality is stereo vision cameras, which can provide depth perception and 3D mapping of the environment. By combining LiDAR data with stereo vision inputs, the rover can improve its localization accuracy and generate more detailed environmental maps.
Another valuable sensor modality is inertial measurement units (IMUs), which can provide precise information about the rover's orientation, acceleration, and angular velocity. By fusing IMU data with LiDAR and vision sensor data, the rover can enhance its localization accuracy, especially in challenging terrain conditions where GPS signals may be unreliable or unavailable.
Furthermore, thermal sensors can be integrated to detect temperature variations in the lunar environment, which can help identify potential hazards such as overheating components or thermal anomalies. By incorporating thermal data into the mapping and localization algorithms, the rover can adapt its navigation strategies based on thermal signatures in the environment.
Additionally, radar sensors can be utilized to penetrate subsurface layers of the lunar terrain and detect buried obstacles or resources. By integrating radar data with other sensor inputs, the rover can enhance its mapping capabilities and uncover hidden features beneath the lunar surface, expanding the scope of exploration and discovery during lunar missions.