TY - JOUR
T1 - Determination of robotic melon harvesting efficiency
T2 - A probabilistic approach
AU - Mann, Moshe
AU - Zion, Boaz
AU - Shmulevich, Itzhak
AU - Rubinstein, Dror
N1 - Publisher Copyright:
© 2015 Taylor & Francis.
PY - 2016/6/2
Y1 - 2016/6/2
N2 - 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.
AB - 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.
KW - Geometric distribution
KW - Harvesting robot
KW - Markov chain
KW - Performance prediction
KW - Probabilistic analysis
UR - http://www.scopus.com/inward/record.url?scp=84941254434&partnerID=8YFLogxK
U2 - 10.1080/00207543.2015.1081428
DO - 10.1080/00207543.2015.1081428
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AN - SCOPUS:84941254434
SN - 0020-7543
VL - 54
SP - 3216
EP - 3228
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 11
ER -