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This paper presents a novel active exploration framework for redundant robotic arms that combines a voxel-based information gain scoring method with Virtual Model Control to navigate confined spaces safely. By enabling real-time obstacle avoidance and whole-body reactive motion without path replanning, the approach achieves over 90% mapping coverage in complex environments within 120 seconds. The work advances the field of robotics by solving critical challenges in manipulating unknown geometries under tight geometric constraints.
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What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Reactive Exploration of Unknown Environments for Redundant Robots using Virtual Model Control

arXiv2 min read

arXiv:2610.08110v1 Announce Type: new Abstract: The exploration of confined, occluded, and partially known spaces poses significant challenges in robotic manipulation. The overall pose of the robotic arm must be carefully controlled to respect tight geometric constraints while avoiding newly discovered obstacles. We address this problem by proposing an active exploration approach for redundant robotic arms with an eye-in-hand camera configuration. Our approach navigates and acquires information in real-time based on a novel scoring method that directly selects a target voxel from the unexplored space using the robot's current state and expected information gain. To move the robot safely toward the target voxel, we utilize Virtual Model Control, which guarantees compliance and enables whole-body reactive obstacle avoidance without the need for path replanning. Simulated and real-robot experiments in both confined and open environments demonstrate the effectiveness of our approach, achieving over $90\%$ mapping coverage across all tested environments in under $120$ seconds without colliding with obstacles.

Abstract:The exploration of confined, occluded, and partially known spaces poses significant challenges in robotic manipulation. The overall pose of the robotic arm must be carefully controlled to respect tight geometric constraints while avoiding newly discovered obstacles. We address this problem by proposing an active exploration approach for redundant robotic arms with an eye-in-hand camera configuration. Our approach navigates and acquires information in real-time based on a novel scoring method that directly selects a target voxel from the unexplored space using the robot's current state and expected information gain. To move the robot safely toward the target voxel, we utilize Virtual Model Control, which guarantees compliance and enables whole-body reactive obstacle avoidance without the need for path replanning. Simulated and real-robot experiments in both confined and open environments demonstrate the effectiveness of our approach, achieving over $90\%$ mapping coverage across all tested environments in under $120$ seconds without colliding with obstacles.

8 pages, 10 figures, This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Robotics (cs.RO)

arXiv:2610.08110 [cs.RO]

(or arXiv:2610.08110v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.08110 arXiv-issued DOI via DataCite (pending registration)

Full text under the publisher's open licence · 197 words

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