A characteristic estimation system, comprising circuitry configured to, cause a robot hand configured to grip an object to operate based on operation information defining an operation of the robot hand, acquire a physical quantity at a time when the robot hand grips the object, and estimate a characteristic of the object based on the physical quantity.

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.

A force detection apparatus includes first and second force sensors each including a force detection device having a force detection axis, a first inertial sensor disposed in the vicinity of the first force sensor and having an inertia detection axis extending along the force detection axis of the first force sensor, and a second inertial sensor disposed in the vicinity of the second force sensor and having an inertia detection axis extending along the force detection axis of the second force sensor.

A method for controlling a robot comprising an end effector includes: establishing at steady state between the end effector and a working surface through a preset impedance control mechanism, and adjusting a contact force between the end effector and the working surface according to a preset desired force; obtaining a contact torque generated by the contact force; controlling the end effector to rotate according to the contact torque until a pose of the end effector is consistent with a pose of the working surface; and controlling the end effector to move tangentially along the working surface.

Systems and methods relating to an end-of-arm-tool that can be used in connection with the automated handling of vehicles, such as unmanned aerial vehicles (UAV), are disclosed. The described systems and methods can include an end-of-arm-tool which may include a load cell coupled to an end effector, such that forces and torques exerted on the end effector are translated onto the load cell. The measurement of forces and torques exerted on the end effector can facilitate determining various information in connection with the aerial vehicle, such as inertial properties or parameters associated with the aerial vehicle, the quality of the engagement between the end effector and the aerial vehicle, as well as diagnostic information in connection with the aerial vehicle. Additionally, the use of a load cell to measure forces and torques exerted on the end effector can eliminate the need to utilize traditional contact sensors typically required on the contact surfaces of an end-of-arm tool.

A method for training a neural network to derive, from a force and a moment exerted on an object when pressed on a plane in which an insertion for inserting the object is located, a movement vector to insert an object into an insertion. The method includes, for a plurality of positions in which the object or the part of the object held by the robot touches a plane in which the insertion is located, controlling the robot to move to the position, controlling the robot to press the object onto the plane, measuring the force and moment experienced by the object, scaling the pair of force and moment by a number randomly chosen between zero and a predetermined positive maximum number and labelling the scaled pair by a movement vector between the position and the insertion, and training the neural network using the labelled pairs of force and moment.

A robot control system includes a robot having a servo control unit, a lower-level control apparatus transmitting a control command to the servo control unit and receiving robot status information representing a status of the robot from the servo control unit with respect to each preset control period, and an upper-level control apparatus transmitting command information for creation of the control command to the lower-level control apparatus. The lower-level control apparatus transmits the robot status information to the upper-level control apparatus in synchronization with the control period. The upper-level control apparatus transmits the command information to the lower-level control apparatus within a predetermined transmission time shorter than the control period from a time when receiving the robot status information from the lower-level control apparatus.

A bag-shaped actuator system includes: a bag-shaped actuator including an airtight bag member and flowable particulates filled in the bag member; a bag-member communication pipe configured to communicate with an inside of the bag member; a low-air-pressure-source communication pipe configured to communicate with a low air pressure source; a high-air-pressure-source communication pipe configured to communicate with a high air pressure source; a switching mechanism configured to perform switching between communication destinations of the bag member such that the inside of the bag member communicates with any of external air, the low-air-pressure-source communication pipe, and the high-air-pressure-source communication pipe via the bag-member communication pipe; and a switching controlling portion configured to control the switching between the communication destinations by the switching mechanism.

The present disclosure provides a redundant robotic arm control method, a redundant robotic arm, and a computer readable storage medium. The method includes: obtaining an external force acting on an end of the robotic arm and an external torque acting on each joint; calculating a first joint speed of each joint based on a degree of influence of the joint on the end in each motion dimension and the external force acting on the end; determining a zero space speed of each joint corresponding to a current position of the end based on a link torque of an external force acting on a link with respect to the joint; calculating a total joint speed based on the first joint speed and the zero space speed; and controlling the robotic arm to the move according to the total joint speed.