- Email: [email protected]
Assembly by Nongrasping Manipulation
Assembly b y Nongrasping Manipulation T. Arai ( l ) ,J. Ota, Y. Aiyarna Received on January 6,1997
Abstract This paper deals with assembly and transportation of nmn,-grasping manipulations. especially releasing. In releasing, a robot makes a part slide away on a plane. It has both advantages and disadvantages. Learning control using visual feedback is introduced so as t o accommodate the uncertainty of the manipulation and environment. Experiments verifies that releasing is pctent t o construct a flexible transportation system. Tne research makes assembly more dexterous and flexible enough t o make a Holonic Manufacturing System.
Keywords : Assembly machine.
1
Manipulator.
Introduction
Flexible and dexterous assembly has been required strongly these years because a lot size of a product becomes smaller and investment in an assembly line turns less than before. Taking measures to meet the requirement, several autonomous manufacturing systems have been proposed in research such as Holonic Manufacturing System[6]. where each subsystem can perform autonomously and individually. In practical operations, however, each subsystem should be more flexible and more tolerant of various errors so as to achieve the concept. Non-grasping manipulations. which handle parts flexibly and dexterously, have been studied by various researchers these years. This is partly because human operators utilize these manipulations dexterously by simple fingers, and partly because computer control especially visual feedback accommodates errors generated by the manipulations. However, control of non-grasping manipulation has not been studied well. This paper deals with assembly and transportation by non-grasping manipulation. First, manipulations are classified from the viewpoint o f applied force onto a manipulated object in Chap. 2. As an example of dexterity of the manipulations, insertion of a part into a narrow hole is discussed. Next, releasing is studied as a useful manipulation for assembly. Releasing is discussed, and visual feedback is introduced in Chap. 3. Then, releasing is experimented with learning control in Chap. 4. The research makes assembly more dexterous and flexible.
2
Holonic system
Non-grasping Manipulation
Non-grasping manipulations are operations done by an effector without holding an object rigidly. They are different from a conventional pick-and-place operation, because they use its environment positively. In robotics. various researchers have studied on non-grasping manipulation. for example, pushing[9], tumbling, pivoting(21,
Annals
of the ClRP Vol. 46/1/1997
I
I
I -’
-/
Fig.1: Insertion task with collision hitting, throwing[7] and so forth. These manipulations bring us many merits: an object can move t o a narrow space; an object can change its orientation without fitting t o that of an end-effector. An Insertion task is shown as an example in Fig. 1. If a robot grasps a part hgidly. it cannot make the part insert into a hole because a side of a wall becomes an obstacle against a finger. To assemble the part. the robot needs to remove one finger from the object and makes the part slide down into the hole using an edge of the hole as drawn in the lower figures in Fig. 1. The orientation of the part may change even if that of the robot is fixed. This can be achieved easily by human operators but not by robots. Including these non-grasping manipulations. authors defined manipulation generally(31 and classified them into two large categories and seven small classes as shown in Table 1. The classification is made on force applied onto an object. The major two categories indicate whether a manipulation is made dynamically or not. The minor classification depends on sources of the force applied onto the object. Four sources are assumed: inertia force of an object, gravity force or force from some other potential fields. force applied by end-effectors, and force applied by its environment. A check mark in Table 1 indicates the degree of contribution.
15
Table 1: Classification of several transportation niethods as a manipulat,ion
3
Quasi - Static >lanipulation
pick and p,ace
r 1
l
:
conveyor, fork li@
mainly used
1
tumbling, pivm'ng
Definition
Releasing belongs t o a class of "dynamic manipulation".
It is defined as follows:
DEFINITION: R e l e a s i n g is a manipulation methodology that an effector provides initial velocity for an object on a plane and makes it slide freely till the object stops. The final position is determined in dynamics dominantly by the initial velocity and contact friction between the object and the plane. The greatest advantage of releasing is that the object
cun be positioned even outside of the workspace of a robot. This advantage also makes the coordinated control among robots easy. The object can be transferred t o another without risk o f collision among the robots[l2].
16
tumbling. pushrng
I
: used at times
I
Releasing
3.1
1
7 1
Various manipulations in assembly are listed up as examples in this table. Pick-and-place manipulation belongs t o a class where end-effector dominantly applies force onto the object. Neither conveyor or fork lift grasps an object but transfers it in stable. This is because the gravity force presses the object on the effector, i.e.. the top surface of the conveyor. Pushing, tumbling and pivoting belong t o non-grasping manipulations which are defined as manipulations using environment around[2]. When motion of a part obey dynamics, the motion can be easily influenced by small deviation of applied force. Lynch planned dynamic manipulations and demonstrated them in experiments; snatching, rolling, throwing and catching[7]. He used a direct drive manipulator with one DoF and demonstrated the manipulations according to an optimal action planned by theoretical analysis. Rizzi et.al.[lO] realized robot juggling as dynamically dexterous behaviors with a three DoF direct drive manipulator. Parts shooters are also classified into dynamic manipulation; they utilize the gravity force t o drop a part. Parts bowl feeders shake parts t o line them up. These two were analyzed in early stage of automatic assembly[4].
3
Dynamic hianipuiatian
'
pons
7 11
releasing (him'ng, throwing) ports
feeder
: seldom used
I
Flexible assenibly system with non-grasping manipulation
Fig.2:
Sliding of an object has been often utilized in industry. Boothroyd analyzed parts shooting[4]. Voyenli[ll] analyzed motions of ice hockey pucks, and Huang[5] simulated numerically motion of a circular object by hitting and called the process impulsive manipulation. However, the control of the target position has not been studied in accordance with the effect o f the environment. In assembly of complicated products such as robots themselves, "assembly cell system" is more popular t o use than ones with conveyors. For cell compsition, robots can be used both in assembling and in parts feeding.
3.2
Part T r a n s p o r t a t i o n by R e l e a s i n g
When two robots work together, co!lision between the two should be avoided. Releasing satisfies this requirement. This bring us an advantage of flexible assembly cell as illustrated in Fig. 2. The cell consists of several robots around a flat table. Several cells would be connected by conventional conveyors. When the cell needs t o be reconstructed, it is much easier in this flexible assembly cell than in conventional assembly lines, where conveyors often become a strong constraint of cell arrangement. The concept has been proposed as a Holonic Manufacturing System in IMS programs[6]. The cell seems similar t o an assembly center, but has more flexibility in part feeding and work transportation as discussed by Makino(81.
3.3
Visual Feedback
Table 2: Accommodaricm by three methods coefficients
Although releasing has various advantages as described above, it has also disadvantages; (1)uncertainty caused by environment, (2)uncertainty caused by hitting, and (3)shape of a part. Because the part is hit and slid. its side should be hard enough and has a good bottom to slide. Against the first and the second disadvantages. we can introduce visual feedback. A vision system measures the initial velocity and the final position of the part. It accumulates the operations and estimates the condition of the environment. Learning control using vision accommodates the change of the environmental conditions. Visual guidance will bring several merits not only for object position measurement but also for recognition of other objects in assembling job. Let us discuss learning process o f visual feedback in the next section.
4
(1) fixed model ( 2 ) refined model 13) Darameter tunincr
No Y?S Yes
environment Yes Yes
No
Learning for Releasing Fig.3: Experimental system
4.1
Learning for Error
The purpose of releasing is t o transport an object t o a desired position and orientation as precisely as possible. The model of releasing coisists of two phases[l2]. In the first phase. a robot gives an object an initial velocity. It is modeled as rigid bodies collide each other with a coefFicient of restitution e. In the second phase, the object slides on a flat table with constant coefficient of Coulomb's friction p. Since the model is given as simultaneous integral equations and cannot be solved algebraically, large numerical computation is required. Even if a precise model is prepared, there exist both parameter errors and unmodeled errors. The former needs parameter tuning in accordance with the distance and rotation angles. The latter, which cannot be modified with parameter tuning, is produced because of minute bumps of the table surface, errors of the hitting model and so on. Against these unmodeled errors we adopt learning control. We use the control method proposed by Aboaf et. al.[l] by which errors between an actual system and a model generate a virtual target t o a robot.
4.2
Learning Algorithm
Here we apply three methods;
(1)"fied model method' by which the parameters such as coefficient of friction and that of restitution are fixed. Unmodeied error is accommodated by means of changing the target . (2)"refined model method' by which the parameters are refined after each trial, and the accommodation by the change of the target is also introduced. (3)"parameter tuning method' by which just the parameters are refined but the target i s not accommodated. Table 2 indicates the accommodations. In (1). a robot tries t o manipulate the object t o a desired target position Pd but it might stop a t P t . Then next time, the robot re-tries t o manipulate it to a modified target position Pd+Ic(Pt- P d ) . After several trials, the robot can manipulate the object t o the desired position with certain accuracy. Note that only the target position is modified in the fixed model method. In (2), the robot
modifies the model parameters after each trial as well
as it modifies the target position:. so it modifies. all the coefficient of friction, that of restitution and the target position. The last (3) is to adjust only the two coefficients but not t o modify the target position This is chosen for comparison.
4.3
Experiment
The experimental system is illustrated in Fig. 3, which consists of two CCD cameras, a small manipulator with 6 DoF and a flat table covered with paper. An additional manipulator is installed for catching but not illustrated here. The manipulator has a flat square plate, which hits the object, an round plate like an ice hockey puck. The initial velocity of the object is estimated by means of measuring the position and orientation o f the object a t intervals of &[sec] by one of the CCD cameras. W i t h a desired position shown in Fig. 4 , ten trials are made. The results a t each trial are shown in Fig. 5 . which have (a)distance in translation, (b)angle in rotation, and (c)direction o f the motion. From these graphs we can generally find that the learning works very well. The distance (a) converges t o the desired value within several trials; the direction o f slide motion (c) converges more quickly than others except for the parameter tuning method (3). The (3) cannot make either rotation angle or motion direction converged. It means that these declinations, which might occur as unmodeled error, cannot be removed only by parameter tuning. Both of the former two proposed learning methods (1) and ( 2 ) almost converge t o the appropriate values with only 5 or 6 % uncertainty. 4.4
Experiment to a different goal
Although the two methods have no significant difference, we may safely say that the refined model method is more correct rather than the fixed model method. To ascertain the effect of the former, we supplement an experiment with a different goal after the above-mentioned
17
-
fixedmodel
....*... para met?r tuning
-9-
refined model I
i
- T i I ; I ( d e g ~- direction-
J
...... nrget(0.5 1 Om)
0.45
6 number of ma1
8
4
Fig.4: Target position and orientation for experiment experiment. The desired goal position and orientation is moved from the original set. 11.31 [deg] and 0.510 [m] to a new set. 9.46 [deg] and 0.608 [m]. All parameters obtained in the first experiment are used a t the second. Among results of the second experiment, just the distance is plotted in Fig. 6 . The distance converges a bit faster here than that in the first experiment, and the first trial indicates much good in the refined model method (2). This is because the coefficient of restitution has been estimated in advance. Relative accuracy seems t o be good enough t o utilize manipulators for transportation. The combination with releasing and visual feedback can provide a flexible transportation subsystem. It can be also used in assembly cell.
275 6
- --.- i
...... ..........-
................................
7ji
'3-
2
"I
4
tarnet( 180de-p)
8
6.
.......
1 10
target( 1 1.3 1 deg)
6. 4number of trial
Conclusion
In this paper, non-grasping manipulations are classified into several classes by means of the applied force. Releasing is discussed and experimented. learning control with vision is introduced, which accommodates both parameter tuning in a model and target changes for unmodeled error. - t h e experiments verified that the learning works well. releasing is potent in flexible and dexterous assembly. Assembly by non-grasping manipulation has a great possibility t o make a Holonic Manufacturing System. In recent years, distributed autonomous systems like Holonic Manufacturing System have been studied. They require universal handling which is independent of size of parts and its attributes. Non-grasping manipulations will be utilized for these new production systems.
.......
number of tnal
J
5
10
8
10
Fig.5: Result of learning performance experiments
-
-
fixedmodel
....*... parameter tuning
0.80
-9-
refined model
r-
I
-
Acknowledgements This research is supported in part by IMS program.
4 O I
.......
2
target(0.608m)
4 6 number of trial
Fig.6: Performance of re-learning
8
10
(distance)
[6] IMS Promotion Center. 1996, "Holonic Manufacturing Systems: System Components of Autonomous Modules and rheir Distributed Control." IMS Domestic Feasi-
bility Study h'o. 9520.
References [l] Ab0af.E.W. et al.. 1988, "Task-Levei Robot Learning," Proc. IEEE ICRA '88, 1309-1310. [2] Aiyama.Y. et a l , 1993. "Pivoting: A New Method of Graspless Manipulation of an Object by Robot Fingers." Proc. IEEE/RSJ IROS'93. 136-143. [3] Aiyarna.Y. and Arai,T.. 1996, "Dexterous Manipulation with General Manipulation Methodology." Proc. IEEE/RSJ IROS.96. 905-910 (41 6oothroyd.G. and Redf0rd.A.H.. 1968. "Mechanized Assembly." McGraw- Hill. [5] Huang W.H.. Krotkov E.P. and Mason M.T.. 1995, "lmpulsive Manipulation." Proc. IEEE ICRA '95.120-125.
18
[7] Lynch.K.M..1996. "Nonprehensile Robotic Manipulation: Controllability and Planning," CMU-RI-TR-96-05. [8] Makin0.H. and Arai.T.,1994,"New Developments in Assembly Systems." Annals of the CIRP, 4
Abstract This paper deals with assembly and transportation of nmn,-grasping manipulations. especially releasing. In releasing, a robot makes a part slide away on a plane. It has both advantages and disadvantages. Learning control using visual feedback is introduced so as t o accommodate the uncertainty of the manipulation and environment. Experiments verifies that releasing is pctent t o construct a flexible transportation system. Tne research makes assembly more dexterous and flexible enough t o make a Holonic Manufacturing System.
Keywords : Assembly machine.
1
Manipulator.
Introduction
Flexible and dexterous assembly has been required strongly these years because a lot size of a product becomes smaller and investment in an assembly line turns less than before. Taking measures to meet the requirement, several autonomous manufacturing systems have been proposed in research such as Holonic Manufacturing System[6]. where each subsystem can perform autonomously and individually. In practical operations, however, each subsystem should be more flexible and more tolerant of various errors so as to achieve the concept. Non-grasping manipulations. which handle parts flexibly and dexterously, have been studied by various researchers these years. This is partly because human operators utilize these manipulations dexterously by simple fingers, and partly because computer control especially visual feedback accommodates errors generated by the manipulations. However, control of non-grasping manipulation has not been studied well. This paper deals with assembly and transportation by non-grasping manipulation. First, manipulations are classified from the viewpoint o f applied force onto a manipulated object in Chap. 2. As an example of dexterity of the manipulations, insertion of a part into a narrow hole is discussed. Next, releasing is studied as a useful manipulation for assembly. Releasing is discussed, and visual feedback is introduced in Chap. 3. Then, releasing is experimented with learning control in Chap. 4. The research makes assembly more dexterous and flexible.
2
Holonic system
Non-grasping Manipulation
Non-grasping manipulations are operations done by an effector without holding an object rigidly. They are different from a conventional pick-and-place operation, because they use its environment positively. In robotics. various researchers have studied on non-grasping manipulation. for example, pushing[9], tumbling, pivoting(21,
Annals
of the ClRP Vol. 46/1/1997
I
I
I -’
-/
Fig.1: Insertion task with collision hitting, throwing[7] and so forth. These manipulations bring us many merits: an object can move t o a narrow space; an object can change its orientation without fitting t o that of an end-effector. An Insertion task is shown as an example in Fig. 1. If a robot grasps a part hgidly. it cannot make the part insert into a hole because a side of a wall becomes an obstacle against a finger. To assemble the part. the robot needs to remove one finger from the object and makes the part slide down into the hole using an edge of the hole as drawn in the lower figures in Fig. 1. The orientation of the part may change even if that of the robot is fixed. This can be achieved easily by human operators but not by robots. Including these non-grasping manipulations. authors defined manipulation generally(31 and classified them into two large categories and seven small classes as shown in Table 1. The classification is made on force applied onto an object. The major two categories indicate whether a manipulation is made dynamically or not. The minor classification depends on sources of the force applied onto the object. Four sources are assumed: inertia force of an object, gravity force or force from some other potential fields. force applied by end-effectors, and force applied by its environment. A check mark in Table 1 indicates the degree of contribution.
15
Table 1: Classification of several transportation niethods as a manipulat,ion
3
Quasi - Static >lanipulation
pick and p,ace
r 1
l
:
conveyor, fork li@
mainly used
1
tumbling, pivm'ng
Definition
Releasing belongs t o a class of "dynamic manipulation".
It is defined as follows:
DEFINITION: R e l e a s i n g is a manipulation methodology that an effector provides initial velocity for an object on a plane and makes it slide freely till the object stops. The final position is determined in dynamics dominantly by the initial velocity and contact friction between the object and the plane. The greatest advantage of releasing is that the object
cun be positioned even outside of the workspace of a robot. This advantage also makes the coordinated control among robots easy. The object can be transferred t o another without risk o f collision among the robots[l2].
16
tumbling. pushrng
I
: used at times
I
Releasing
3.1
1
7 1
Various manipulations in assembly are listed up as examples in this table. Pick-and-place manipulation belongs t o a class where end-effector dominantly applies force onto the object. Neither conveyor or fork lift grasps an object but transfers it in stable. This is because the gravity force presses the object on the effector, i.e.. the top surface of the conveyor. Pushing, tumbling and pivoting belong t o non-grasping manipulations which are defined as manipulations using environment around[2]. When motion of a part obey dynamics, the motion can be easily influenced by small deviation of applied force. Lynch planned dynamic manipulations and demonstrated them in experiments; snatching, rolling, throwing and catching[7]. He used a direct drive manipulator with one DoF and demonstrated the manipulations according to an optimal action planned by theoretical analysis. Rizzi et.al.[lO] realized robot juggling as dynamically dexterous behaviors with a three DoF direct drive manipulator. Parts shooters are also classified into dynamic manipulation; they utilize the gravity force t o drop a part. Parts bowl feeders shake parts t o line them up. These two were analyzed in early stage of automatic assembly[4].
3
Dynamic hianipuiatian
'
pons
7 11
releasing (him'ng, throwing) ports
feeder
: seldom used
I
Flexible assenibly system with non-grasping manipulation
Fig.2:
Sliding of an object has been often utilized in industry. Boothroyd analyzed parts shooting[4]. Voyenli[ll] analyzed motions of ice hockey pucks, and Huang[5] simulated numerically motion of a circular object by hitting and called the process impulsive manipulation. However, the control of the target position has not been studied in accordance with the effect o f the environment. In assembly of complicated products such as robots themselves, "assembly cell system" is more popular t o use than ones with conveyors. For cell compsition, robots can be used both in assembling and in parts feeding.
3.2
Part T r a n s p o r t a t i o n by R e l e a s i n g
When two robots work together, co!lision between the two should be avoided. Releasing satisfies this requirement. This bring us an advantage of flexible assembly cell as illustrated in Fig. 2. The cell consists of several robots around a flat table. Several cells would be connected by conventional conveyors. When the cell needs t o be reconstructed, it is much easier in this flexible assembly cell than in conventional assembly lines, where conveyors often become a strong constraint of cell arrangement. The concept has been proposed as a Holonic Manufacturing System in IMS programs[6]. The cell seems similar t o an assembly center, but has more flexibility in part feeding and work transportation as discussed by Makino(81.
3.3
Visual Feedback
Table 2: Accommodaricm by three methods coefficients
Although releasing has various advantages as described above, it has also disadvantages; (1)uncertainty caused by environment, (2)uncertainty caused by hitting, and (3)shape of a part. Because the part is hit and slid. its side should be hard enough and has a good bottom to slide. Against the first and the second disadvantages. we can introduce visual feedback. A vision system measures the initial velocity and the final position of the part. It accumulates the operations and estimates the condition of the environment. Learning control using vision accommodates the change of the environmental conditions. Visual guidance will bring several merits not only for object position measurement but also for recognition of other objects in assembling job. Let us discuss learning process o f visual feedback in the next section.
4
(1) fixed model ( 2 ) refined model 13) Darameter tunincr
No Y?S Yes
environment Yes Yes
No
Learning for Releasing Fig.3: Experimental system
4.1
Learning for Error
The purpose of releasing is t o transport an object t o a desired position and orientation as precisely as possible. The model of releasing coisists of two phases[l2]. In the first phase. a robot gives an object an initial velocity. It is modeled as rigid bodies collide each other with a coefFicient of restitution e. In the second phase, the object slides on a flat table with constant coefficient of Coulomb's friction p. Since the model is given as simultaneous integral equations and cannot be solved algebraically, large numerical computation is required. Even if a precise model is prepared, there exist both parameter errors and unmodeled errors. The former needs parameter tuning in accordance with the distance and rotation angles. The latter, which cannot be modified with parameter tuning, is produced because of minute bumps of the table surface, errors of the hitting model and so on. Against these unmodeled errors we adopt learning control. We use the control method proposed by Aboaf et. al.[l] by which errors between an actual system and a model generate a virtual target t o a robot.
4.2
Learning Algorithm
Here we apply three methods;
(1)"fied model method' by which the parameters such as coefficient of friction and that of restitution are fixed. Unmodeied error is accommodated by means of changing the target . (2)"refined model method' by which the parameters are refined after each trial, and the accommodation by the change of the target is also introduced. (3)"parameter tuning method' by which just the parameters are refined but the target i s not accommodated. Table 2 indicates the accommodations. In (1). a robot tries t o manipulate the object t o a desired target position Pd but it might stop a t P t . Then next time, the robot re-tries t o manipulate it to a modified target position Pd+Ic(Pt- P d ) . After several trials, the robot can manipulate the object t o the desired position with certain accuracy. Note that only the target position is modified in the fixed model method. In (2), the robot
modifies the model parameters after each trial as well
as it modifies the target position:. so it modifies. all the coefficient of friction, that of restitution and the target position. The last (3) is to adjust only the two coefficients but not t o modify the target position This is chosen for comparison.
4.3
Experiment
The experimental system is illustrated in Fig. 3, which consists of two CCD cameras, a small manipulator with 6 DoF and a flat table covered with paper. An additional manipulator is installed for catching but not illustrated here. The manipulator has a flat square plate, which hits the object, an round plate like an ice hockey puck. The initial velocity of the object is estimated by means of measuring the position and orientation o f the object a t intervals of &[sec] by one of the CCD cameras. W i t h a desired position shown in Fig. 4 , ten trials are made. The results a t each trial are shown in Fig. 5 . which have (a)distance in translation, (b)angle in rotation, and (c)direction o f the motion. From these graphs we can generally find that the learning works very well. The distance (a) converges t o the desired value within several trials; the direction o f slide motion (c) converges more quickly than others except for the parameter tuning method (3). The (3) cannot make either rotation angle or motion direction converged. It means that these declinations, which might occur as unmodeled error, cannot be removed only by parameter tuning. Both of the former two proposed learning methods (1) and ( 2 ) almost converge t o the appropriate values with only 5 or 6 % uncertainty. 4.4
Experiment to a different goal
Although the two methods have no significant difference, we may safely say that the refined model method is more correct rather than the fixed model method. To ascertain the effect of the former, we supplement an experiment with a different goal after the above-mentioned
17
-
fixedmodel
....*... para met?r tuning
-9-
refined model I
i
- T i I ; I ( d e g ~- direction-
J
...... nrget(0.5 1 Om)
0.45
6 number of ma1
8
4
Fig.4: Target position and orientation for experiment experiment. The desired goal position and orientation is moved from the original set. 11.31 [deg] and 0.510 [m] to a new set. 9.46 [deg] and 0.608 [m]. All parameters obtained in the first experiment are used a t the second. Among results of the second experiment, just the distance is plotted in Fig. 6 . The distance converges a bit faster here than that in the first experiment, and the first trial indicates much good in the refined model method (2). This is because the coefficient of restitution has been estimated in advance. Relative accuracy seems t o be good enough t o utilize manipulators for transportation. The combination with releasing and visual feedback can provide a flexible transportation subsystem. It can be also used in assembly cell.
275 6
- --.- i
...... ..........-
................................
7ji
'3-
2
"I
4
tarnet( 180de-p)
8
6.
.......
1 10
target( 1 1.3 1 deg)
6. 4number of trial
Conclusion
In this paper, non-grasping manipulations are classified into several classes by means of the applied force. Releasing is discussed and experimented. learning control with vision is introduced, which accommodates both parameter tuning in a model and target changes for unmodeled error. - t h e experiments verified that the learning works well. releasing is potent in flexible and dexterous assembly. Assembly by non-grasping manipulation has a great possibility t o make a Holonic Manufacturing System. In recent years, distributed autonomous systems like Holonic Manufacturing System have been studied. They require universal handling which is independent of size of parts and its attributes. Non-grasping manipulations will be utilized for these new production systems.
.......
number of tnal
J
5
10
8
10
Fig.5: Result of learning performance experiments
-
-
fixedmodel
....*... parameter tuning
0.80
-9-
refined model
r-
I
-
Acknowledgements This research is supported in part by IMS program.
4 O I
.......
2
target(0.608m)
4 6 number of trial
Fig.6: Performance of re-learning
8
10
(distance)
[6] IMS Promotion Center. 1996, "Holonic Manufacturing Systems: System Components of Autonomous Modules and rheir Distributed Control." IMS Domestic Feasi-
bility Study h'o. 9520.
References [l] Ab0af.E.W. et al.. 1988, "Task-Levei Robot Learning," Proc. IEEE ICRA '88, 1309-1310. [2] Aiyama.Y. et a l , 1993. "Pivoting: A New Method of Graspless Manipulation of an Object by Robot Fingers." Proc. IEEE/RSJ IROS'93. 136-143. [3] Aiyarna.Y. and Arai,T.. 1996, "Dexterous Manipulation with General Manipulation Methodology." Proc. IEEE/RSJ IROS.96. 905-910 (41 6oothroyd.G. and Redf0rd.A.H.. 1968. "Mechanized Assembly." McGraw- Hill. [5] Huang W.H.. Krotkov E.P. and Mason M.T.. 1995, "lmpulsive Manipulation." Proc. IEEE ICRA '95.120-125.
18
[7] Lynch.K.M..1996. "Nonprehensile Robotic Manipulation: Controllability and Planning," CMU-RI-TR-96-05. [8] Makin0.H. and Arai.T.,1994,"New Developments in Assembly Systems." Annals of the CIRP, 4