Amir Shapiro and Alon Ohev-Zion Ben-Gurion University of the Negev. Adaptable Grippers for Selective Fruits Harvesting

Transcription

1 Amir Shapiro and Alon Ohev-Zion Ben-Gurion University of the Negev Adaptable Grippers for Selective Fruits Harvesting

2 In this Presentation Survey of agricultural grippers by Picking Methods Survey of Versatile Advanced Grippers Grasp robustness and quality Grasp design 2

3 Picking Methods Pull and Twist Cut-off the peduncle 3

4 Picking Methods Early Manual Tools to Cut-off the Stem All of these methods are base on an instrument that cut the fruit s stem. Some hold the fruit and others drain it to a container on the ground. 4

5 Picking Methods Early Manual Tools to Pull and Twist the fruit Instruments that hold the fruit, while the manipulator can pull it off the tree. Than the fruit draines into a container on the ground. 5

6 Picking Methods Automated Selective Picking Machines Early developments in selective fruits picking, were based on simple manipulators with variouse end effector. Most of the end-effectors use some kind of cuting mechanisem, for detaching the fruit off the plant. All of selective harvesting machinery remained experimental, mainly due to low performence in precision and capacity. 6

7 Picking Methods Automated Selective Picking Machines Fruit Picking Mechanism - Florida s citrus-picking robot [Sarig, 1992] 3 DOF (R-R-D) manipulator. End-effector with CCD camera. Ultrasonic distance mesurement. Operated by shovel the fruit and cut its stem. 7

8 Picking Methods Automated Selective Picking Machines Argribot [R. Ceres et. a.l 1998] Citrus Picking Robot- Kubota [Hayashi et.al. 1991] laser range finder. computes picking sequences and controls. Fixes fruit with vacuum pad Pull it into the gripper Cut the stem 8

9 Picking Methods Automated Selective Picking Machines Citrus harvesting hand, Koyoto Uni. [Fujiara et.al. 1989] Three rubber fingers. Scissors. Pneumatic actuated. Developed for Summer Orange due to their relatively thick peal. Cucumber end-effector of autonomous harvesting robot [E.J. Van Henten et. Al. 2002] Autonomous vehicle. Two computer vision systems for detection and 3D imaging of the fruit and the environment. 7 DOF manipulator. Soft fruit grasping. Thermal cutting device, to prevent spreading of viruses through the greenhouse. 9

10 Picking Methods Automated Selective Picking Machines Watermelon harvester gripper [Sakai et.al. 2004] For harvesting heavy vegetables such as water-melons, pumpkins, cabbage, etc. Maximum end point force of 150[N]. 1 DOF gripper, using passive force closure, activated by gravitational force and friction with the object. Fuji Apples robotic harvaster end-effector [Bulanon 2010.] Machine vision combined with laser ranging sensor End-effector pinches the fruit s peduncle and the wrist rotates it to detach the fruit by breaking the peduncle. 10

11 Picking Methods Automated Selective Picking Machines Dent Seed Corn grasping instrument [Jia et.al. 1990] Used to measure the force required to remove the seed corn. Mobile Fruit Grading Robot: end-effector [Qiao et.al ] Two long and thin fingers to grasp pepper peduncle. Spring is utilized to generate a force to close fingers and pinches the fruit s peduncle 11

12 Picking Methods Automated Selective Picking Machines Strawberries Selective Harvester [institute of Agriculture Machinery,Japan] Stereoscopic cameras to locate the fruit. Image processing to detect ripeness. Cut and grasp the fruit by its peduncle. Harvester rides on a rails in the greenhouse. From recognition to collection, about 9 sec per fruit. 12

13 Picking Methods Automated Selective Picking Machines Tomato Automatic Gardner [MIT - Computer Science and Artificial Intelligence Laboratory] 1 DOF gripper. Servo actuated. Soft foam layer to prevent tomato damage. Monoscopic vision. 13

14 Picking Methods Automated Selective Picking Machines Tomato harvester end-effector [P.P.Ling et.al. 2004] Include image processing algorithms to determine sizes and locations of mature tomatoes. 1 DOF end-effector include four light weight fingers vacuum/suction, grasping. 14

15 Picking Methods Automated Selective Picking Machines Eggplant harvester end-effector [ Hyashi et.al. 2002] vision algorithm combining a color segment and a vertical dividing operations. Fuzzy manipulator controller. Size-judging mechanism add to the fruitgrasping mechanism. Peduncle-cutting mechanism. 15

16 Versatile Advanced Grippers Barrett Hand 8 DOF. 3 fingers. Fingertip force up to 60 N. Pressure sensors on fingertip & palm. Fingertip torque sensor. Fast reaction, 1 sec from fully open to fully close. 2 pivoting fingers 16

17 Versatile Advanced Grippers SDH Universal gripper instrument 7 DOF. 3 fingers. Fingertip torque up to 2.1 Nm. 2 Pressure sensors on each finger. Fingertip torque sensor. 2 pivoting fingers 17

18 Versatile Advanced Grippers Robotiq, MARS (Main Articulé Robuste Sousactionnée, i.e. robust underactuated robotic hand) 12 DOF - 6 actuators. 3 fingers. Fingertip force up to 700 N. Pressure sensors on fingers Able of force control. 18

19 Versatile Advanced Grippers Robotiq, SARAH - (Self Adaptive Robotic Auxilary Hand) 10 DOF - 2 Actuators. 3 fingers. Fingertip force up to 700 N. Pressure sensors on fingers Capable of force control. 19

20 Versatile Advanced Grippers Robotiq, S-Model Hand 10 DOF - 2 Actuators. 3 fingers. Fingertip force up to 100 N. 20

21 Versatile Advanced Grippers Festo Fin Ray Effect 1 DOF. 2-4 fingers. Pneumatic actuated. Load up to 800 gr. Light weight. 21

22 Versatile Advanced Grippers Mimic the Human Hand The human hand has 27 DOF. Most robotic human like hand, are underactuated, as the joints are mechanically couppled. 22

23 Versatile Advanced Grippers Mimic the Human Hand DLR-HIT-Hand. 17 DOF, 4 fingers X 4 DOF each + 1 DOF of the palm. Fingertip force up to 30 N. Sensors in each finger: 3 joint torque. 3 joint position 3 motor position 1 force/torque 2 Temperature. 23

24 Versatile Advanced Grippers Mimic the Human Hand MEKA-Hand. 5 DOF. 4 compliant fingers. 24

25 Versatile Advanced Grippers Mimic the Human Hand Panasonic assistant robots. 8 DOF. 4 fingers. 3 Axis force sensors at each fingertip. ** In development. 25

26 Versatile Advanced Grippers Mimic the Human Hand Shadow. 24 DOF. 5 fingers. The thumb has 5 degrees of freedom and 5 joints Each finger has 3 degrees of freedom and 4 joints. Actuated by Pneumatic muscles. Torque and angle sensors for each joint. 26

27 Versatile Advanced Grippers Mimic the Human Hand Bionic Robots Evologics & Festo. 27

28 Versatile Advanced Grippers Mimic the Human Hand Inflatable Robot 28

29 Grasp Model Is the set of equations that describe the dynamic behavior of an object, grasped by K fingers. Using the model we can analyze the system properties such as Stability Robustness The damage to the object Desired grasp quality criteria. The model is based of Dynamic equation Contact Model k M q q C q, q q g( q) Gi q Fi q, q i 1

30 Compliant Contact Model Linear contact model F = K U U 0 + F 0 Non Linear contact model F n = F t = U t p = 1 2 8μ 3(1 ν) R 1 2U n 2 3 D.Elata μ 1 2 ν R1 2U n 2 (U t U p t ) U n U n U t U t p ; U n >0 30

31 Grasp Simulation Environment Provides dynamic simulation for the gripper and the grasped object. Early stages: Modular implementation of gripper configuration object geometry grasp controller Provides tools for robustness and stability analysis. 31

32 Grasp Robustness and Quality Robustness in the sense of external load Force Closure: A grasp is said to be Force Closure, if for the grasp map G: The columns of G positively spans. p The convex hull of contains the origin. w o f p=3 in case of planer. p=6 in case of spatial. p G i fc 1 wo G Gf f cm G i = c I Rr i 32

33 Grasp Quality Criterion Robustness in the sense of external load Convex Hull based quality criterion example: Q=

34 Grasp Quality for a Given Task Wrench Space Minimum ratio between the grasp s wrench capability and the task requirement can be interpreted as a safety measure of how strong the grasp is when compared to the most difficult task requirement. w 2,max t 2 w 1,max t 1 TWS t 4 w 4,max t 3 w 3,max wi,max Q min, i 1,, k i t i

35 Exact Grasp Robustness and Stability Stability force closure Linear compliant contact model enables the exact solution of the statically indeterminate equations of motion under the Equilibrium Stability constraints. Shapiro et.al. IJRR

36 Experiments of Grasp Robustness and Stability Measuring the external that can be applied to an object without losing stability Shapiro et.al. IJRR

37 Grasp Simulation Development of simulation environment that Simulate dynamic behavior of arbitrary object and gripper Is flexible in its gripper controller Is flexible in its implementation of a different contact models Presents a grasp null space 37

38 Grasp Experiments Setup Experimental system The system aim is to validate the grasp s stability and robustness analysis. A second experimental system is being built, that will measure and validate: Material properties: Contact model G, Coefficient of friction 38

39 Grasp Planning and Matching Problem Definition Given: A set of objects to be grasped. One or several tasks. Objective: Determine a common optimal grasp for all or a class of objects such that the grasp can resist the task s external forces.

40 Algorithm concept Input: 3D CAD's of m objects to be grasped.

41 Step 1 - Mesh Each object B i (i=1,..,m) is meshed with defined parameters and resolution. Every point within the mesh is defined with a point P j =(x,y,z) and the normal to surface vector at the point n j.

42 Step 2 Grasp regions Forbidden/allowed grasp regions are to be marked on each mesh. These zones are defined according to task or other restrictions.

43 Grasp representation A Grasp feature vector: An n - dimension parameterization of the grasp. It has no reference to any coordinate frame and can be included into the grasp space with no consideration to the transformation. For 3 fingers holding planer object: c 2 x d c 1 ej d ˆn ˆn 3 p ˆn 1 1 y 3 Grasp feature vector for n fingers holding 3D object: c 3 e d d j 1 n 1 1 n 2 1 n T 43

44 Step 3- Force Closure Grasp Space generation Sample possible grasp feature vectors for an object and compute a grasp space representing all combinations of n- points from this set that achieve force-closure. e j Force- Closure Check e,, j u1 u t 0 interior CH ( W ) and Q Q d

45 Grasp Matching Recognizing the same grasp configuration for different objects or object approaches: Every point in the vector set is in fact a FC grasp configuration which implies for the grasp planning 45

46 Grasp Matching Recognizing the same grasp configuration for different objects Intersection between sets of feature vectors for classification in order to find common grasps. 46

47 Step 5 Grasp design According to the common grasps found for a certain class, design of the grasp is acquired. Grasp design

48 Simulation Results Four objects Force Closure Grasp Space for rectangle object

49 Simulation Results Common Grasp Intersection of all possible grasps over all objects Result: 10 possible grasps

50 Simulation Results Common Grasp A common grasp example Quality criterion = 0.51

51 Simulation Results - Performance Polinomial complexity O(n 2 )

52 How to best grasp fruits? Two fingers? Multiple fingers? Fully - actuated? Under - actuated? Grasp should be Force Closure, robust, and stable 52

53 Thank you For more information: