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

TL;DR

This paper introduces a fast heuristic algorithm for scheduling and trajectory planning in multi-arm robotic fruit harvesters, improving throughput and efficiency by optimizing arm coordination and movement.

Contribution

The work presents a novel heuristic method that efficiently schedules and plans trajectories for multiple robotic arms, enabling scalable and collision-free fruit harvesting.

Findings

Throughput increases with more arms, showing linear speedup with sufficient fruit density.

Adding more arms yields diminishing returns at low fruit densities due to increased travel time.

The algorithm effectively balances scheduling and trajectory planning for multiple robotic arms.

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.