Object-Reconstruction-Aware Whole-body Control of Mobile Manipulators

2025-09-04

AI Summary

Mobile manipulation robots face high computational overhead and dense candidate view evaluation in object reconstruction and detection due to conventional view-path planning. To address this, we propose an efficient whole-body motion control framework. Our core innovation integrates visual visibility constraints with information-driven gaze selection—based on entropy maximization—directly into the motion control loop, eliminating the need for a separate path planner and enabling autonomous, real-time field-of-view optimization. We employ Bayesian analysis for rigorous performance evaluation. Experiments in simulation and on a real-world 8-DoF mobile manipulator across 114 object classes demonstrate that our method achieves coverage quality and entropy optimization comparable to state-of-the-art baselines (no statistically significant difference), while accelerating planning speed by approximately 9× and substantially reducing computational cost.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Computer Vision: Motion & Tracking
• Search and Optimization: Sampling/Simulation-based Search

Application Category

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

Abstract

Object reconstruction and inspection tasks play a crucial role in various robotics applications. Identifying paths that reveal the most unknown areas of the object becomes paramount in this context, as it directly affects efficiency, and this problem is known as the view path planning problem. Current methods often use sampling-based path planning techniques, evaluating potential views along the path to enhance reconstruction performance. However, these methods are computationally expensive as they require evaluating several candidate views on the path. To this end, we propose a computationally efficient solution that relies on calculating a focus point in the most informative (unknown) region and having the robot maintain this point in the camera field of view along the path. We incorporated this strategy into the whole-body control of a mobile manipulator employing a visibility constraint without the need for an additional path planner. We conducted comprehensive and realistic simulations using a large dataset of 114 diverse objects of varying sizes from 57 categories to compare our method with a sampling-based planning strategy using Bayesian data analysis. Furthermore, we performed real-world experiments with an 8-DoF mobile manipulator to demonstrate the proposed method's performance in practice. Our results suggest that there is no significant difference in object coverage and entropy. In contrast, our method is approximately nine times faster than the baseline sampling-based method in terms of the average time the robot spends between views.

Problem

• Efficient view path planning for object reconstruction
• Reducing computational cost in robotic inspection tasks
• Maintaining informative focus points without path planning

Innovation

• Focus point calculation for informative regions
• Visibility constraint in whole-body control
• Eliminates need for sampling-based path planning