Determination of robotic melon harvesting efficiency: A probabilistic approach

Moshe Mann, Boaz Zion, Itzhak Shmulevich, Dror Rubinstein

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

To automate the harvesting of melons, a mobile Cartesian robot is developed that traverses at a constant velocity over a row of precut melons whose global coordinates are known. The motion planner is programmed to have the robot harvest as many melons as possible. Numerous simulations of the robot over a field with different sets of randomly distributed melons resulted in nearly identical percentages of melons harvested. This result holds true over a wide range of robot dimensions, motor capabilities, velocities and melon distributions. Using probabilistic methods, we derive these results by modelling the robotic harvesting procedure as a stochastic process. In this simplified model, a harvest ratio is predicted analytically using Poisson and geometric distributions. Further analysis demonstrates that this model of robotic harvesting is an example of an infinite length Markov chain. Applying the mathematical tools of Markov processes to our model yields a formula for the harvest percentage that is in strong agreement with the results of the simulation. The significance of the approach is demonstrated in two of its applications: to select the most efficient actuators for maximal melon harvesting and determine the set of optimal velocities along a row of melons of varying densities.

Original languageEnglish
Pages (from-to)3216-3228
Number of pages13
JournalInternational Journal of Production Research
Volume54
Issue number11
DOIs
StatePublished - 2 Jun 2016
Externally publishedYes

Keywords

  • Geometric distribution
  • Harvesting robot
  • Markov chain
  • Performance prediction
  • Probabilistic analysis

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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