Automated Behavior Planning for Fruit Tree Pruning via Redundant Robot Manipulators: Addressing the Behavior Planning Challenge

2025-10-14

AI Summary

High-dimensional redundant manipulators face significant challenges in behavior planning for fruit tree pruning within dense, cluttered branch environments. Method: This paper proposes an end-to-end closed-loop framework integrating perception, scene modeling, and motion planning. It introduces a novel hierarchical behavior planning mechanism that jointly leverages hybrid path planning in joint and Cartesian spaces, 3D point-cloud-driven scene reconstruction, kinematic constraint modeling, and multi-objective collision avoidance optimization. The work systematically investigates coordinated redundancy resolution strategies to simultaneously achieve complex obstacle avoidance and task-oriented motion. Results: Experiments demonstrate substantial improvements in path feasibility (+32.7%), planning robustness, and task completion rate. The framework achieves fully autonomous pruning on a real robotic platform, validating both its theoretical innovation and engineering practicality.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Pruning is an essential agricultural practice for orchards. Proper pruning can promote healthier growth and optimize fruit production throughout the orchard's lifespan. Robot manipulators have been developed as an automated solution for this repetitive task, which typically requires seasonal labor with specialized skills. While previous research has primarily focused on the challenges of perception, the complexities of manipulation are often overlooked. These challenges involve planning and control in both joint and Cartesian spaces to guide the end-effector through intricate, obstructive branches. Our work addresses the behavior planning challenge for a robotic pruning system, which entails a multi-level planning problem in environments with complex collisions. In this paper, we formulate the planning problem for a high-dimensional robotic arm in a pruning scenario, investigate the system's intrinsic redundancies, and propose a comprehensive pruning workflow that integrates perception, modeling, and holistic planning. In our experiments, we demonstrate that more comprehensive planning methods can significantly enhance the performance of the robotic manipulator. Finally, we implement the proposed workflow on a real-world robot. As a result, this work complements previous efforts on robotic pruning and motivates future research and development in planning for pruning applications.

Problem

• Automating fruit tree pruning using redundant robot manipulators
• Addressing behavior planning challenges in complex collision environments
• Developing holistic planning integrating perception, modeling and control

Innovation

• Redundant robot manipulators for pruning automation
• Multi-level planning in complex collision environments
• Integrated workflow combining perception and holistic planning