Learning Rock Pushability on Rough Planetary Terrain

2025-05-14

AI Summary

To address inefficient repetitive path navigation and persistent reliance on reactive obstacle avoidance in unstructured planetary environments (e.g., lunar or Martian surfaces), this paper proposes an active terrain modification paradigm: mobile robots equipped with robotic manipulators proactively displace rock obstacles to construct reusable, high-efficiency traversable paths. The core contribution is the first introduction of “push affordance” modeling into planetary navigation—integrating visual priors with closed-loop force feedback for perception–manipulation–locomotion co-control. We further propose a multimodal (vision + force) interactive learning model for push affordance estimation. Experimental results demonstrate that our approach significantly reduces long-term navigation time over repeated traversals, enhances path stability, and improves system autonomy. This work establishes a novel paradigm for autonomous deployment of robotic infrastructure in extraterrestrial environments.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In the context of mobile navigation in unstructured environments, the predominant approach entails the avoidance of obstacles. The prevailing path planning algorithms are contingent upon deviating from the intended path for an indefinite duration and returning to the closest point on the route after the obstacle is left behind spatially. However, avoiding an obstacle on a path that will be used repeatedly by multiple agents can hinder long-term efficiency and lead to a lasting reliance on an active path planning system. In this study, we propose an alternative approach to mobile navigation in unstructured environments by leveraging the manipulation capabilities of a robotic manipulator mounted on top of a mobile robot. Our proposed framework integrates exteroceptive and proprioceptive feedback to assess the push affordance of obstacles, facilitating their repositioning rather than avoidance. While our preliminary visual estimation takes into account the characteristics of both the obstacle and the surface it relies on, the push affordance estimation module exploits the force feedback obtained by interacting with the obstacle via a robotic manipulator as the guidance signal. The objective of our navigation approach is to enhance the efficiency of routes utilized by multiple agents over extended periods by reducing the overall time spent by a fleet in environments where autonomous infrastructure development is imperative, such as lunar or Martian surfaces.

Problem

• Enhance mobile navigation by pushing obstacles instead of avoiding them
• Improve long-term efficiency for multi-agent path planning on rough terrain
• Develop autonomous infrastructure on planetary surfaces using robotic manipulation

Innovation

• Robotic manipulator enables obstacle repositioning
• Exteroceptive and proprioceptive feedback for push affordance
• Force feedback guides obstacle interaction