Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Construction or agricultural-type vehicle
Reexamination Certificate
2000-11-15
2004-02-10
Cuchlinski, Jr., William A. (Department: 3661)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Construction or agricultural-type vehicle
C701S027000, C701S044000, C037S414000, C172S002000
Reexamination Certificate
active
06691010
ABSTRACT:
TECHNICAL FIELD
This invention relates generally to excavating machines and, more particularly, the present invention relates to automated excavating machines and processes.
BACKGROUND ART
U.S. Pat. No. 5,065,326 discloses the process of autonomously controlling the linkage of an excavating machine during a full work cycle. Different methods on how to control the linkage of an excavating machine have also been disclosed in the literature. One approach is to generate the trajectory for the endpoint of the linkage and then regulate the linkage about this trajectory. The method of generating a three dimensional trajectory and computing the necessary control to follow a three dimensional spline is computationally intensive. This type of control algorithm also needs a reference on how to generate the three dimensional trajectory. Additionally this method does not try to minimize the amount of time or power required to execute a work cycle.
U.S. Pat. No. 5,065,326 also discloses an approach in the form of a finite state machine where each joint rotates until the next joint begins its motion. In this algorithm the state transitions are programmed a priori.
U.S. Pat. No. 5,908,458 discloses a variation of the above referenced finite state machine algorithm which creates parameterized scripts that vary the state transition points for each joint as work progresses. This control method generates a learning base to evaluate previous cycle executions and to predict future cycles. From the generated scores a trajectory is determined and appropriate parameters are set for the system. The score is generated by a knowledge base that is built as the machine operates. This method relies on parallel processing to execute multiple algorithms to generate future parameters for the system.
Other methods of generating a trajectory for the linkage to follow are teach and playback methods. This approach assumes the operators input was efficient and does not update the motion of the linkage as the system changes or the geometry of the dig and dump locations change.
The present invention is directed to overcoming one or more of the problems or disadvantages associated with the prior art.
DISCLOSURE OF THE INVENTION
The present invention is a system and method for controlling movement of a work implement from a specified dig location to a specified dump location. The work implement has at least four degrees of freedom defined by a swing joint angle &thgr;
swing
, a boom joint angle &thgr;
boom
, a stick joint angle &thgr;
stick
, and a bucket joint angle &thgr;
bucket
. In one embodiment, the control system comprises a central processing unit and a memory device. The control system further comprises an initialization module stored on the memory device and adapted to determine specified initial and final joint angles for an initial cycle of the work implement indicative of the specified dig and dump locations, respectively. The control system further comprises a hydraulic valve connected to the central processing unit and to the work implement. The control system further comprises a controller module stored on the storage device. The controller module comprises a fuzzy-logic swing valve control module, a fuzzy-logic boom valve control module and a fuzzy-logic stick valve control module each adapted to instruct the central processing unit in a finite state manner to output at least one control signal to the hydraulic valve to cause the work implement to smoothly move from the dig location to the dump location while minimizing energy requirements and computing power.
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“A First Course in Fuzzy Logic Second Edition”, by Hung T. Nguyen et al., Published 2000, pp., 177-179, 191-192.
Benni Todd
Broadhead Brian J.
Caterpillar Inc
Charlton Diana
Cuchlinski Jr. William A.
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