This invention relates to force/torque sensor and more particularly to multi-axis force/torque sensor and the methods of use for directly teaching a task to a mechatronic manipulator. The force/torque sensor has a casing, an outer frame forming part of or connected to the casing, an inner frame forming part of or connected to the casing, a compliant member connecting the outer frame to the inner frame, and one or more measurement elements mounted in the casing for measuring compliance of the compliant member when a force or torque is applied between the outer frame and the inner frame.

A robot control device executes assist control for generating an assist force in a direction of an external force applied to a robot in a case where a position of the robot is located in a first area set in a work area of the robot when the external force is applied to the robot. The robot control device stops the execution of the assist control in a case where the position of the robot is located in a second area set outside the work area of the robot. The robot control device restricts the execution of the assist control in a case where the position of the robot is located in a third area set outside the first area and inside the second area.

An exemplary method and system are disclosed to flexibly and adaptably manufacture and assemble a workpiece by using recordings of a user in machine learning/artificial intelligence algorithms to train a robot for subsequent automated manufacture. Machine learning and artificial intelligence learning can generate libraries of generalized dynamic motion primitives that can be subsequently combined for any type of manufacturing or assembling activity. The exemplary method and system can flexibly generate a model of an existing workpiece as a template or primer workpiece that can then be used in conjunction with the DMP operations to fabricate subsequent workpieces.

There is provided a method and computer program product for programming a robot by manually operating it in gravity-compensation kinesthetic-guidance mode. More specifically there is provided method and computer program product that uses kinesthetic teaching as a demonstration input modality and does not require the installation or use of any external sensing or data-capturing modules. It requires a single user demonstration to extract a representation of the program, and presents the user with a series of easily-controllable parameters that allow them to modify or constrain the parameters of the extracted program representation of the task.

There is provided a method and computer program product for programming a robot by manually operating it in gravity-compensation kinesthetic-guidance mode. More specifically there is provided method and computer program product that uses kinesthetic teaching as a demonstration input modality and does not require the installation or use of any external sensing or data-capturing modules. It requires a single user demonstration to extract a representation of the program, and presents the user with a series of easily-controllable parameters that allow them to modify or constrain the parameters of the extracted program representation of the task.

There is provided a robot control apparatus that controls a vertical articulated robot and is suitable for direct teaching. In the apparatus, an axis setting section sets operation axes and control axes from among the axes subjected to angle control, when performing the direct teaching of changing a position of the arm tip, while retaining a posture thereof at a target posture. The operation axes can be dominant factors when determining the position of the arm tip and are allowed to freely move according to an external force, and the control axes can be dominant factors when determining the posture of the arm tip and are controlled by an angle control section. When performing the direct teaching, the angle control section receives an input of current angles of the operation axes and the target posture to calculate command angles of the respective control axes according to inverse kinematics calculation.

A method for teaching a robot to perform an operation based on human demonstration using force and vision sensors. The method includes a vision sensor to detect position and pose of both the human's hand and optionally a workpiece during teaching of an operation such as pick, move and place. The force sensor, located either beneath the workpiece or on a tool, is used to detect force information. Data from the vision and force sensors, along with other optional inputs, are used to teach both motions and state change logic for the operation being taught. Several techniques are disclosed for determining state change logic, such as the transition from approaching to grasping. Techniques for improving motion programming to remove extraneous motions by the hand are also disclosed. Robot programming commands are then generated from the hand position and orientation data, along with the state transitions.

A teaching method for detecting external force applied to a robot arm, driving the robot arm with force control based on the external force, and teaching a position and a posture of the robot arm, the teaching method including gradually relaxing, according to an elapsed time from when operation of the robot arm is started or a movement amount of the robot arm from when the operation of the robot arm is started, a restrictive condition for restricting the driving of the robot arm.

A teaching method for detecting external force applied to a robot arm, driving the robot arm with force control based on the external force, and teaching a position and a posture of the robot arm, the teaching method including gradually relaxing, according to an elapsed time from when operation of the robot arm is started or a movement amount of the robot arm from when the operation of the robot arm is started, a restrictive condition for restricting the driving of the robot arm.

Efficient and effective workspace condition analysis systems and methods are presented. In one embodiment, a method comprises: accessing information associated with an activity space, including information on a newly discovered previously unmodeled entity; analyzing the activity information, including activity information associated with the previously unmodeled entity; forwarding feedback on the results of the analysis, including analysis results for the updated modeled information; and utilizing the feedback in a coordinated path plan check process. In one exemplary implementation the coordinated path plan check process comprises: creating a solid/CAD model including updated modeled information; simulating an activity including the updated modeled information; generating a coordinated path plan for entities in the activity space; and testing the coordinated path plan. The coordinated path plan check process can be a success. The analyzing can include automatic identification of potential collision points for a first actor, including potential collision points with the newly discovered object. The newly discovered previously unmodeled entity interferes with an actor from performing an activity. The newly discovered object is a portion of a tool component of a product.