ROBOT AND METHOD OF CONTROLLING THEREOF

• US 20170106542A1
• Filed: 10/14/2016
• Published: 04/20/2017
• Est. Priority Date: 10/16/2015
• Status: Abandoned Application

• Current Assignee: Amit Wolf, Lior Wolf
• Original Assignee: Amit Wolf, Lior Wolf
• Inventors: Wolf, Amit, Wolf, Lior
• Application Number:
  US15/293,528
• Publication Number:
  US 20170106542A1
• Time in Patent Office: Days
• CPC Class Codes: B25J 13/02 Hand grip control means handles or pedals for crane control B66C13/56; for measuring the force applied to control members G01L5/22; hand-held casings for switching devices, e.g. joy-sticks H01H9/0214
• CPC Class Codes: B25J 13/085 Force or torque sensors B25J13/082, B25J13/084 take precedence
• CPC Class Codes: B25J 13/088 with position, velocity or acceleration sensors
• CPC Class Codes: B25J 9/0081 with master teach-in means
• CPC Class Codes: B25J 9/163 learning, adaptive, model based, rule based expert control
• CPC Class Codes: B25J 9/1633 compliant, force, torque control, e.g. combined with position control
• CPC Class Codes: G05B 2219/39312 Double neural network for tracking, slave microprocessor for servo control
• CPC Class Codes: G06N 3/045 Combinations of networks

Abstract

A robot having at least one member having at least one controllably actuable articulation, said articulation having at least one torque sensor for providing a torque signal indicative of a torque applied to the articulation, and one angle sensor for providing an angle signal indicative of an angle of actuation of the articulation; the robot further comprising:

• • a controller for controlling said at least one controllably actuable articulation;
  • a first neural network arranged for receiving the torque and angle signals and arranged for providing to the controller a force signal indicating that an external force is applied to said at least one member:
  • a second neural network arranged for receiving the torque and angle signals and arranged for providing to the controller a direction signal indicating the direction along which said external force is applied to said at least one member.

Claims

1. A robot having at least one member, said at least one member having at least one controllably actuable articulation, said articulation having at least one torque sensor for providing a torque signal indicative of a torque applied to the articulation, and one angle sensor for providing an angle signal indicative of an angle of actuation of the articulation;

the robot further comprising;

a controller for controlling said at least one controllably actuable articulation;

a first neural network arranged for receiving the torque and angle signals and arranged for providing to the controller a force signal indicating that an external force is applied to said at least one member;

a second neural network arranged for receiving the torque and angle signals and arranged for providing to the controller a direction signal indicating the direction along which said external force is applied to said at least one member.

2. The robot of claim 1, having a plurality of controllable articulations each controlled by the controller, each articulation comprising an angle sensor and a torque sensor, wherein the first and second neural networks are arranged for receiving the torque and angle signals from all the articulations, and for providing respectively said force signal indicating that an external force is applied to said at least one member and said direction signal indicating the direction along which said external force is applied to said at least one member.

3. The robot of claim 1, wherein said member comprises a distal end and a proximal end, and wherein the controller is capable of controlling the at least one controllably actuable articulation to move the distal end with respect to the proximal end along a programmable trajectory;

and

wherein the processor comprises a trajectory change module that processes the force and direction signals from the neural networks to change said programmable trajectory such that the member additionally moves in the direction indicated by the direction signal as long as the force signal is present.

4. The robot of claim 1, wherein said member comprises a distal end and a proximal end, and wherein the controller is capable of controlling the at least one controllably actuable articulation to move the distal end with respect to the proximal end;

and

wherein the processor processes the force and direction signals from the neural networks to move the distal end with respect to the proximal end in the direction indicated by the direction signal as long as the force signal is present.

5. The robot of claim 1, wherein the first neural network comprises a training input;

the training input being connected to the output of a force detector;

the force detector being attached to said at least one member and being provided for outputting a force detection signal upon detection that an external force is applied to said at least one member.

6. The robot of claim 5, wherein said force detection signal is Boolean.

7. The robot of claim 5, wherein the force detector is removably attached to said at least one member.

8. The robot of claim 5, wherein the second neural network comprises a training input;

the training input being connected to the output of a direction detector;

the direction detector being attached to said at least one member and being provided for outputting a direction detection signal upon detection of the direction along which said external force is applied to said at least one member.

9. The robot of claim 8, wherein said direction detection signal is one of a predetermined number of Boolean signals indicating each a predetermined direction of application of said external force relative to the member.

10. The robot of claim 8, wherein the direction detector is removably attached to said at least one member.

11. The robot of claim 8, wherein the direction detector comprises one push button associated to each of said predetermined directions, each push button associated to a predetermined direction facing said predetermined direction;

wherein the pressing of a push button associated to a predetermined direction produces a direction detection signal associated to said predetermined direction, and wherein the pressing of any push button produces a force detection signal.

12. The robot of claim 1, wherein said member comprises a distal end and a proximal end and wherein the first and second neural networks additionally use a signal indicating a position of the distal end with respect to the proximal end to generate respectively the force signal and the direction signal.

13. The robot of claim 1, wherein said member comprises a distal end and a proximal end and wherein the first and second neural networks additionally use a signal indicating a rotation of the distal end with respect to the proximal end to generate respectively the force signal and the direction signal.

14. The robot of claim 1, wherein said member comprises a distal end and a proximal end and wherein the first and second neural networks additionally use a signal indicating a speed of the distal end with respect to the proximal end to generate respectively the force signal and the direction signal.

27. A method of training a robot according to claim 3, the method comprising an operator moving the distal end of the robot to a sequence of predetermined positions by gently pushing the distal end of the robot until it moves to each predetermined position;

commanding the robot to learn each of these predetermined positions, and commanding the robot to move on its own along the sequence of predetermined positions.

15. A method of controlling a robot having at least one member, said at least one member having at least one controllably actuable articulation;

the method comprising;

periodically generating a torque signal indicative of a torque applied to the articulation, and an angle signal indicative of an angle of actuation of the articulation;

with a first neural network receiving the torque and angle signals, generating a force signal indicating that an external force is applied to said at least one member;

with a second neural network receiving the torque and angle signals, generating a direction signal indicating the direction along which said external force is applied to said at least one member; and

modifying a control of said at least one controllably actuable articulation with said force and direction signals.

16. The method of claim 15, wherein the robot comprises a plurality of controllable articulations each controlled by the controller:

wherein said periodically generating a torque signal indicative of a torque applied to the articulation, and an angle signal indicative of an angle of actuation of the articulation comprises periodically generating torque signals indicative of the torque applied to each articulation, and angle signals indicative of the angles of actuation of each articulation;

the method further comprising providing all of the torque and angle signals to the first and second neural networks.

17. The method of claim 15, wherein said member comprises a distal end and a proximal end, and wherein the method further comprises:

with the controller, controlling the at least one controllably actuable articulation to move the distal end with respect to the proximal end along a programmable trajectory; and

processing the force and direction signals from the neural networks to change said programmable trajectory such that the member additionally moves in the direction indicated by the direction signal.

18. The method of claim 15, wherein said member comprises a distal end and a proximal end, and wherein the method further comprises:

with the controller, controlling the at least one controllably actuable articulation to move the distal end with respect to the proximal end along a programmable trajectory; and

processing the force and direction signals from the neural networks to change said programmable trajectory such that the member ceases to move in a direction opposite the direction indicated by the direction signal.

19. The method of claim 15, wherein the first neural network comprises a training input and wherein the method further comprises, with a force detector attached to said at least one member, generating a force detection signal upon detection that an external force is applied to said at least one member;

and providing said force detection signal to said training input of the first neural network.

20. The method of claim 19, wherein said generating a force detection signal comprises generating a Boolean force detection signal.

21. The method of claim 19, wherein the second neural network comprises a training input and wherein the method further comprises, with a direction detector attached to said at least one member, generating a direction detection signal upon detection of the direction along which said external force is applied to said at least one member;

and providing said direction detection signal to said training input of the second neural network.

22. The method of claim 21, wherein said generating a direction detection signal comprises generating one of a predetermined number of Boolean signals indicating each a predetermined direction of application of said external force relative to the member.

23. The method of claim 21, wherein said generating a direction detection signal comprises pressing one of a plurality of push buttons associated each to a predetermined direction;

and wherein said generating a force detection signal comprises the pressing of any of said push buttons.

24. The method of claim 15, wherein said member comprises a distal end and a proximal end and wherein the method further comprises additionally providing to the first and second neural networks a signal indicating a position of the distal end with respect to the proximal end to generate respectively the force signal and the direction signal.

25. The method of claim 15, wherein said member comprises a distal end and a proximal end and wherein the method further comprises additionally providing to the first and second neural networks a signal indicating a rotation of the distal end with respect to the proximal end to generate respectively the force signal and the direction signal.

26. The method of claim 15, wherein said member comprises a distal end and a proximal end and wherein the method further comprises additionally providing to the first and second neural networks a signal indicating a speed of the distal end with respect to the proximal end to generate respectively the force signal and the direction signal.