A method for determining placement of support-platform joints (8a, 9a, 10a, 11a, 12a, 13a) on a support-platform (17) of a parallel kinematic manipulator, PKM. The PKM comprises: the support-platform (17), a first support linkage (SL1), a second support linkage (SL2) and a third support linkage (SL3). The first support linkage (SL1), the second support linkage (SL2) and the third support linkage (SL3) together comprises at least five support-links (8, 9, 10, 11, 12, 13). The method comprises estimating (S1) parameters indicative of stiffness for the PKM, based on a kinematic model and an elastic model of the PKM and chosen defined forces and/or torques applied to a tool (22) during a processing sequence, and checking (S2) whether the estimated parameters indicative of stiffness of the PKM fulfill one or more stiffness criteria. Upon the estimated parameters indicative of stiffness fulfilling one or more stiffness criteria, the method comprises choosing (S3) the current placement configuration as an optimal placement configuration of the support-platform joints. The disclosure also relates to a system comprising a computer configured to perform the method and to output an optimal placement configuration, and a PKM with support-platform joints that are placed to the support-platform according to the optimal placement configuration outputted by the computer. The disclosure also relates to PKMs with support-platform joints that are placed to the support-platform to achieve high stiffness.

A method, apparatus and a system are disclosed for robotic programming. In at least one embodiment of a method for robotic programming, the method includes receiving, from a controller of a robot, movement parameters reflecting movement of the robot manipulated by a user; making a first data model of a robot move, according to the movement parameters; calculating, upon the first data model touching a second data model of a virtual object, parameters of a first force to be fed back to the user for feeling touch by the robot on a physical object corresponding to the virtual object; and sending the parameters of the first force to the controller of the robot, to drive the robot to feed back the first force to the user.

A method includes generating a workspace model having one or more digital robots, one or more digital sensors, and a digital transport system. The method includes simulating, for a task of the one or more digital robots, a sensor operation of the one or more digital sensors within the workspace model based on sensor characteristics of the one or more digital sensors. The method includes identifying, for the task of the one or more digital robots, an undetectable area within the workspace model based on the simulated sensor operation. The method includes selectively positioning, by a transport system, a set of sensors from among the one or more sensors based on the undetectable areas associated with the task.

A model generator implements a data-driven approach to generating a robot model that describes one or more physical properties of a robot. The model generator generates a set of basis functions that generically describes a range of physical properties of a wide range of systems. The model generator then generates a set of coefficients corresponding to the set of basis functions based on one or more commands issued to the robot, one or more corresponding end effector positions implemented by the robot, and a sparsity constraint. The model generator generates the robot model by combining the set of basis functions with the set of coefficients. In doing so, the model generator disables specific basis functions that do not describe physical properties associated with the robot. The robot model can subsequently be used within a robot controller to generate commands for controlling the robot.

A robot application development system and method includes a robot application unit that determines a robot application, which defines the industrial robot in a robot workspace. An input interface receives robot application information. An object data interface receives work piece information. A gripper finger design unit determines a gripper finger design. The robot application unit determines the robot application using the robot application information. The gripper finger design unit determines the gripper finger design using the work piece information and the robot application information.

According to other embodiments, a method planning of motions to lift heavy objects using a robot system comprising a robot and an end effector, includes identifying data comprising (a) rigid bodies included in the robot and the end effector, (b) joints connecting the rigid bodies, and (c) torque limits for each of the joints. The torque limit for a joint indicates a maximum supported torque by a drive operating the joint. A motion path searching algorithm is applied to the input data to identify feasible robot paths. The motion path searching algorithm determines torque of each of joint when evaluating points for inclusion in a feasible robot path. An evaluated point is only included in a feasible robot path if the torque of each of the joints do not exceed the torque limits. At least one of the feasible robot paths is transferred to a controller associated with the robot.

A method for designing a customized robot to perform a specified function begins by initially creating an electronic library of data about modules and accessories for a robot assembly. The library stores, for each module and accessory, at least firmware for operation and dimensions. Per robot, the method includes receiving a set of functions and requirements of the customized robot, generating design(s) for the customized robot by finding a set of modules or accessories which match at least one of its functions and requirements, based on information in the electronic library, performing a set of validation tests of each design against criteria, identifying the design(s) which passed the set of validation tests, and generating a list of modules and accessories of a selected design and a connection map for the selected design indicating how the modules and accessories are to be connected together to form the customized robot.

The objective of the present invention is to allow a user to recognize, at a glance, the degree to which deterioration is occurring to a specific torque sensor from among torque sensors provided for an articulated arm of a robot. In the control device for a robot provided with sensors each of which detects an external force torque about a joint, the objective is achieved by providing a display device which displays, together with 3D graphics of a robot body, a warning icon in color at a mounted location of a deteriorated torque sensor, and changes the color according to the degree of deterioration.

Techniques are described to implement a probe sensor that improves data capture and data analysis. A probe sensor can be emulated in a virtual environment. A robot simulation session is initialized. The session includes a virtual environment with several objects and a set of robots. Each robot has a virtual sensor. A separate client controls each robot. Data perceived by the virtual sensor is provided to the client for controlling the robot. To capture the data the virtual sensor emits a plurality of rays, each ray transmitted in a stochastically selected direction, and performs raytracing to determine an object(s) in the virtual environment on which each ray is incident. The stochastic data capture can also be performed by a sensor in a real world scenario. Further, in some cases, the data captured by a sensor is stochastically sampled to improve the computing.

Disclosed are a method and an apparatus for controlling a multi-legged robot, and a storage medium. The method includes: acquiring current state parameters of the multi-legged robot; when types and/or quantities of the current state parameters meet a first preset condition, acquiring a first motion control policy by inputting the current state parameters into a first model generated by training; and controlling the multi-legged robot based on the first motion control policy.