Data processing: generic control systems or specific application – Specific application – apparatus or process – Robot control
Reexamination Certificate
1998-02-20
2001-08-07
Grant, William (Department: 2121)
Data processing: generic control systems or specific application
Specific application, apparatus or process
Robot control
C700S248000, C318S568130, C901S003000
Reexamination Certificate
active
06272396
ABSTRACT:
FIELD AND BACKGROUND OF THE INVENTION
The present invention relates to robotics and, more particularly, to a method of applying knowledge from a human operator to a mobile slave expert machine via a master expert machine.
Robots are used for performing tasks in the factory at the production lines or a special purpose tasks in the laboratory or the like for full automation of the process. A traditional robotic system consists of:
a. The robot (for example a 6 degrees of freedom)
b. The end effector (gripper) and tooling equipment
c. Installation and the operator/programmer.
Installing a robotic system includes the developing of an end effector for the specific task and accessories needed for the automatic activity of the robot, in addition to the task programming of the robot. The robot's operator should be trained for several months, mostly at the robot's manufacturer place. Those facts cause a manual manufacturing of robots and massive integration & installation activity, leading to a very high cost of the robotic system, and explaining the missing mass production of robotic systems. For the same reasons, performing a professional task (only a skilled worker does) via the present equipment (traditional robotic systems) is a very complicated mission due to the complexity of the integration/controlling of the robot in such activity having clear economic consequences.
There exist known expensive robots of multi-tasking ability, with remarkable flexible reprogramming possibilities, for different tasks. Most types share common problems: high costs, operator training, specific coding (custom software), complicated final debug process at factory and high maintenance cost.
SUMMARY OF THE INVENTION
A robotic control method for implementing low cost robots for repetitive tasks is disclosed below.
The object of the new robotic method is a Low Cost Expert Machine, single tasked with limited flexibility in changing tasks, for operating in repetitive activity. Assuming that a robot can perform one task per one time unit at a given working area, then the relative advantage of the expensive robot is minimized or canceled during that period, relative to the low cost machine.
The expert machine is an autonomous system, working outside the production room and intended to replace the traditional formula which claims that approximately 55% of an overall robotic system's costs is for the robot, 30% is for additional tooling and about 15% is for installation. The main goal of this invention is to implement a low cost expert machine for a single task activity, and totally eliminate the additional tooling and installation elements, required in the traditional robotic system.
A professional single tasked activity, according to this invention, is implemented via a Master—Slave Robotic system and method. This system comprises substantially:
1. A Master Expert Machine (MEM) for learning, and recording a professional task from a skilled worker and for calculating and processing appropriate parameters for a Slave Expert Machine.
2. A Slave Expert Machine (SEM) for performing the single task whose parameters were obtained from the MEM. The SEM has a similar number of links as the MEM but less sensors & transmission means, less electronics and requires significantly less computing algorithms than the Master Expert Machine. Any number of SEMs can be located in the working areas without any physical or communication touch with the MEM. The Master—Slave concept opens a new robotic area for autonomous Expert Machines for a professional single tasked activity.
According to the present invention there is provided a method of applying knowledge from a human operator to a mobile slave expert machine via a master expert machine, the knowledge serving for computing an optimal control law being required for performing professionally preferably repetitive tasks consisting of a sequence of elementary moves to be performed by the slave expert machine instead of a human operator, the method including the following main sequence of steps: (a) teaching of the master expert machine to perform the required professional task so as to create within the master expert machine a sharable data base for computing a control law for the task, (b) computing the control law for performing the task and dividing the task into subtasks, associated with elementary move, (c) adapting of the sharable data base to the computed control law and to the slave expert machine, (d) transferring the adapted sharable data base and the control law from the master expert machine to the slave expert machine, whereby the slave expert machine can perform the task autonomously and independent of any form of connection to the master expert machine subsequent to the transfer, and, (e) providing the slave expert machine with a programmed data associated with a particular task to be performed.
According to further features in preferred embodiments of the invention described below, the teaching is performed by tracking activity of the master expert machine, the tracking activity is carried out by the human operator so as to move the master expert machine spatially along a predefined trajectory corresponding to the task and the tracking activity is accompanied by generating of plurality of signals in response to movements of the master expert machine, the signals are converted into digital form and stored in the sharable data base.
According to still further features in the described preferred embodiments, the plurality of signals defining movement of the master expert machine refers to at least one parameter chosen from the group comprising displacement, force, speed and acceleration.
According to still further features in the described preferred embodiments, tracking of the master expert machine is carried out along elementary trajectories corresponding to division of the task into the sequence of elementary moves.
According to still further features in the described preferred embodiments, the plurality of signals defining movement of the master expert machine refers to all four of the parameters: displacement, force, speed and acceleration.
According to still further features in the described preferred embodiments, the sharable adapted data base and said computed control law are transferred from the master expert machine to the slave expert machine via a communication link chosen from the group comprising a remote control line or a wire communication link.
According to still further features in the described preferred embodiments, the programmed data comprises spatial 3D representation of the task to be performed.
According to still further features in the described preferred embodiments, the slave expert machine performs a task via superposition and concatenation of a plurality of subtasks and elementary moves transmitted by the master expert machine.
Advantages of a slave expert machine in accordance with the present invention are:
1. Low cost—(50-80)% less than existing robots performing a similar task. The SEM's cost depends on various parameters as complexity of the repetitive task that it performs or on quantity and characteristics of its attached performance sensors which have a high valued contribution to the control complexity of the SEM. The cost varies between a minimum and maximum price: the minimum price includes limit switches and alarm sensory. The maximum price includes complete performance sensory in addition to limit switches and alarm sensory. A complete performance sensing is implemented via vision means (like TV camera), optical encoders, etc. A partial performance sensing may be implemented, for example, via a potentiometer instead of an optical encoder, sensed, for example, once per elementary move.
2. The expert's machine “learning” process eliminates the overhead usually required for specific coding (custom software) for a given trajectory. There is no need for additional software in order to perform the SEM's task.
3. There is no need of time for acclimating personnel to use the new machine, meaning the sl
Bahta Kidest
Friedman Mark M.
Grant William
Tairob Industrial Technology Ltd.
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