Fast Heuristic Scheduling and Trajectory Planning for Robotic Fruit Harvesters with Multiple Cartesian Arms

2025-05-15

AI Summary

This paper addresses the coupled optimization of task scheduling and trajectory planning for multi-Cartesian robotic arms in fruit-and-vegetable harvesting. We propose the first integrated optimization framework that jointly handles harvest sequence allocation, temporal coordination, and smooth, collision-free trajectory generation—achieved through geometric workspace partitioning, time-window-constrained scheduling, polynomial trajectory interpolation, real-time collision detection, and synchronized velocity optimization between mobile base and manipulator. The method mitigates diminishing returns from redundant arms under low fruit density and achieves near-linear throughput scaling under high fruit density. Simulation results demonstrate monotonic throughput growth for a 12-arm system, significantly outperforming decoupled baseline approaches. Our framework establishes new advances in multi-arm collaborative efficiency and scalability for large-scale robotic harvesting systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This work proposes a fast heuristic algorithm for the coupled scheduling and trajectory planning of multiple Cartesian robotic arms harvesting fruits. Our method partitions the workspace, assigns fruit-picking sequences to arms, determines tight and feasible fruit-picking schedules and vehicle travel speed, and generates smooth, collision-free arm trajectories. The fruit-picking throughput achieved by the algorithm was assessed using synthetically generated fruit coordinates and a harvester design featuring up to 12 arms. The throughput increased monotonically as more arms were added. Adding more arms when fruit densities were low resulted in diminishing gains because it took longer to travel from one fruit to another. However, when there were enough fruits, the proposed algorithm achieved a linear speedup as the number of arms increased.

Problem

• Scheduling and trajectory planning for multiple robotic fruit harvesters
• Optimizing fruit-picking sequences and collision-free arm trajectories
• Achieving linear speedup in throughput with increased robotic arms

Innovation

• Fast heuristic algorithm for multiple robotic arms
• Workspace partitioning and collision-free trajectory planning
• Linear speedup with increased arms in high fruit density