soft robotic manipulation of onions & artichokes in food industry
TRANSCRIPT
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8/11/2019 Soft Robotic Manipulation of Onions & Artichokes in Food Industry
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Soft RoboticManipulation of Onions
and Artichokes in
the Food Industry
Pa
1R
R Morales, F J Badesa, N Garcia-Aracil, J M Sabater, and L Zollo
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Goal of the Paper
To present a robotic solution which deals withmanipulation of onion or artichokes during
feeding of cleaning and cutting machines.
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Introduction
Food industryLargest industry in terms of profit & employment
Onions & artichokes are used in picklings, sauces, chips and in manycanned foods.
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Ideal Requirements of Robots
High speed activation
Adaptation to a variety of shapes
Minimal pressure
Low maintenance
High reliability
Low weight
Maximum adherence
Low energy consumption
Non toxic non corrosive gr
Positional precision
Ease of cleaning
Easy and fast ejection of p
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Design of Robotic Solution
Evaluation
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Design of Robotic Solution
Proposed Robotic Solution
Delta structure
Horizontal workspace - 800mm
Cycle velocity
60 cycles per minute
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Design of Robotic Solution
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Design of Robotic Solution
Autodesk Inventor
Two platforms
Three closed kinematic chains
Each chain has 2 links and 4 spherical joints
Additional central arm to transmit 1 DOF
to the robot end-effector
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Kinematic Analysis
=
=
= = i=1,2,3
= = cos0 sin
+ cos
0sin
= is rotation matrix among and
First Constraint
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Kinematic Analysis
Second Constraint
2 + 2 = 2 =sin1
By combining the two constraints, a quadratic in x is
obtained. Solving which X coordinate is obtained. Similar
analysis is done for Y and Z coordinate
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Inverse Kinematic Analysis
Determines for given end effector pose From practical considerations
> 0, =sin1 else =-sin1
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Direct Kinematic Analysis
Determines end effector pose for a given
of the joints. Center of lower platform is intersection of
three spheres
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Computer Vision System
Computation of , position of the onion and
estimation of its coordinate
Blob detection
, coordinates of centre of mass is computed
coordinate is computed using prior knowledge
learned from a training set
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Overall System
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Components Used
Multiaxis board DMC 2143 from Galil Motion Control
Three high performance brushless servomotors BLM N23 with a cotorque of 0.39Nm, gearbox with gear ratio 1 : 10
Amplifier board AMP 20540 from Galil Motion Control
Communication software from Galil
Computer vision software
24VDC power source for the actuators
Ethernet cable between the PC and the multiaxis board
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Experimental Result
40 cycles per minute
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Conclusion
Parallel robotic system
New vacuum suction cup
Computer vision system
Control system