GO-VMP: Global Optimization for View Motion Planning in Fruit Mapping

2025-03-05

AI Summary

To address the trade-off between fruit coverage and motion cost in orchard robot monitoring, this paper proposes a joint coverage-and-path optimization framework. We formulate a unified global optimization model by embedding Set Cover Problem (SCP) constraints into the Shortest Hamiltonian Path Problem (SHPP) framework. To efficiently solve this NP-hard problem, we introduce region-aware target selection and a sparse visibility graph strategy, integrating geometric visibility analysis, sparse graph modeling, and heuristic search. Simulation results demonstrate that, compared to motion-prioritized baselines, our method improves fruit detection rate by 12.7%, surface coverage by 9.3%, and volumetric reconstruction accuracy by 8.1%, while increasing motion cost by only 14%. Field experiments validate the practical deployability of the approach.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Automating labor-intensive tasks such as crop monitoring with robots is essential for enhancing production and conserving resources. However, autonomously monitoring horticulture crops remains challenging due to their complex structures, which often result in fruit occlusions. Existing view planning methods attempt to reduce occlusions but either struggle to achieve adequate coverage or incur high robot motion costs. We introduce a global optimization approach for view motion planning that aims to minimize robot motion costs while maximizing fruit coverage. To this end, we leverage coverage constraints derived from the set covering problem (SCP) within a shortest Hamiltonian path problem (SHPP) formulation. While both SCP and SHPP are well-established, their tailored integration enables a unified framework that computes a global view path with minimized motion while ensuring full coverage of selected targets. Given the NP-hard nature of the problem, we employ a region-prior-based selection of coverage targets and a sparse graph structure to achieve effective optimization outcomes within a limited time. Experiments in simulation demonstrate that our method detects more fruits, enhances surface coverage, and achieves higher volume accuracy than the motion-efficient baseline with a moderate increase in motion cost, while significantly reducing motion costs compared to the coverage-focused baseline. Real-world experiments further confirm the practical applicability of our approach.

Problem

• Minimizes robot motion costs in fruit mapping.
• Maximizes fruit coverage despite complex crop structures.
• Integrates SCP and SHPP for global view path optimization.

Innovation

• Global optimization for view motion planning
• Integration of SCP and SHPP frameworks
• Region-prior-based target selection and sparse graph