Soft Robotic Manipulation of Onions & Artichokes in Food Industry

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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Proposed Robotic Solution

Delta structure

Horizontal workspace - 800mm

Cycle velocity

60 cycles per minute


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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

Documents

Soft Robotic Manipulation of Onions & Artichokes in Food Industry