An alarm notification system configured to assist an operator so that the operator can effectively carry out a teaching operation, etc. The alarm notification system includes: a storing section configured to, with respect to a past alarm which occurred when a program generated by a teach pendant was executed, store alarm data including a name of the program and a number of a line of the program when the alarm occurred; a judging section configured to judge as to whether or not an alarm prediction condition using the alarm data stored in the storing section is satisfied, when the program is executed again; and an alarm predicting section configured to notify the operator who is carrying out teaching of the robot of alarm information relating to the alarm, when the alarm prediction condition is satisfied.

Robotic visualization systems and methods include running and analyzing perception algorithms and models for robotic visualization systems on multiple computing platforms to obtain a successful complete an object processing request.

Inspection robots with swappable drive modules are described. An example inspect robot may include a first removeable interface plate on the side of a robot chassis. The first removable interface plate may couple a first drive module to an electronic board, within the chassis, where the electronic board includes a drive module interface circuit communicatively coupled to the first drive module. The example inspect robot may also include a second removeable interface plate on a side of a robot chassis. The second removable interface plate may couple a second drive module to an electronic board, within the chassis, where the electronic board includes a drive module interface circuit communicatively coupled to the second drive module.

The present disclosure relates generally to the operation of autonomous machinery for performing various tasks at various industrial work sites, and more particularly to the volumetric estimation and dimensional estimation of a pile of material or other object, and the use of multiple sensors for the volumetric estimation and dimensional estimation of a pile of material or other object at such work sites. An application and a framework is disclosed for volumetric estimation and dimensional estimation of a pile of material or other object using at least one sensor, preferably a plurality of sensors, on an autonomous machine (e.g., robotic machines or autonomous vehicles) in various work-site environments applicable to various industries such as, construction, mining, manufacturing, warehousing, logistics, sorting, packaging, agriculture, etc.

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.

Systems and methods for reconfigurable, fixtureless manufacturing are provided. Material handling robots grasp and move parts within an assembly area to adjoin one another in a predetermined orientation. While the parts remain grasped and suspended within the assembly area, out of contact with any fixtures, work surfaces, jigs, and locators, a machine vision system performs an alignment scan to determine locations of datums on the parts which are transmitted to a controller for comparison against stored virtual datums for a subassembly comprising the joined parts. The location of the datums are transmitted to a joining robot which joins the parts to form the subassembly. The machine vision system performs an inspection scan of the datums on the parts after joining.

A control device 1B mainly includes a subgoal setting means 17B and an operation sequence generation means 18B. The subgoal setting means 17B is configured to set a subgoal “Sg” based on abstract states in which states in a workspace where a robot works are abstracted, the subgoal Sg indicating an intermediate goal for achieving a final goal or constraint conditions required to achieve the final goal. The operation sequence generation means 18B is configured to generate an operation sequence to be executed by the robot based on the subgoal.

The arithmetic device configured to perform a calculation for controlling a motion of a grasping apparatus that performs work involving a motion of sliding a grasped object includes: an acquisition unit configured to acquire a state variable indicating a state of the grasping apparatus during the work; a storage unit storing a learned neural network that has been learned by receiving a plurality of training data sets composed of a combination of the state variable acquired in advance and correct answer data corresponding to the state variable; an arithmetic unit configured to calculate a target value of each of various actuators related to the work of the grasping apparatus by inputting the state variable to the learned neural network read from the storage unit; and an output unit configured to output the target value of each of the various actuators to the grasping apparatus.

Provided are a robot that calculates a level of liquid contained in a liquid container and a method for calculating such liquid level. The robot includes a robot arm to which a tool is attached to an end of the robot arm, a torque sensor disposed on the robot arm and measuring a torque value of the robot arm, and a processor that controls the robot arm and receives the torque value from the torque sensor and calculates information related to the torque value, and calculates the level value of liquid contained in the liquid container based on the information related to the torque value.

Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).