A processing system having at least one processor operatively coupled to at least one memory. The processor receives sensor data from the at least one sensor indicating one or more characteristics of the asset. The processor generates, updates, or maintains a digital representation that models the one or more characteristics of the asset. The processor detects a defect of the asset based at least in part on the one or more characteristics. The processor generate an output signal encoding or conveying instructions to provide a recommendation to an operator, to control the at least one robot to address the defect on the asset, or both, based on the defect and the digital representation of the asset.

A method for obstacle avoidance in degraded environments of robots based on intrinsic plasticity of an SNN is disclosed. A decision network in a synaptic autonomous learning module takes lidar data, distance from a target point and velocity at a previous moment as state input, and outputs the velocity of left and right wheels of the robot through the autonomous adjustment of the dynamic energy-time threshold, so as to carry out autonomous perception and decision making. The method solves the difficulty of the lack of intrinsic plasticity in the SNN, which leads to the difficulty of adapting to degraded environments due to the homeostasis imbalance of the model, is successfully deployed in mobile robots to maintain a stable trigger rate for autonomous navigation and obstacle avoidance in degraded, disturbed and noisy environments, and has validity and applicability on different degraded scenes.

A system and a method using a system for planning a use includes at least one marking, a control and evaluation unit, a database, a display unit, at least one time of flight sensor for a spatial scanning of a real environment, and at least one camera for imaging the real environment. The real environment in a spatial model can be displayed as a virtual environment on the display unit. The marking in the real environment is arranged at a position and has an orientation. The position and the orientation of the marking can be detected at least by the time of flight sensor and the position and orientation of the marking are linked by a virtual sensor model. The virtual sensor model in the spatial model of the virtual environment can be displayed on the display unit at the position and having the orientation of the marking.

An end effector, coupleable to a robot, for delivering a material to a surface, comprises a material applicator assembly, comprising a central shaft and an applicator co-rotatably coupled to the central shaft and configured to apply the material to the surface. The end effector also comprises a material supply carrier, comprising a base and a supply of the material coupled to the base. The material from the supply is feedable to the applicator. The end effector further comprises an actuator that rotatably couples the material supply carrier with the material applicator assembly. The actuator is operable to rotate the material supply carrier relative to the material applicator assembly.

A disclosed system includes a physically plausible virtual runtime environment to simulate a real-life environment for a simulated robot and a test planning and testing component to define a robotic task and generate virtual test cases for the robotic task. The test planning and testing component is further operable to generate virtual test cases for the robotic task, determine a control strategy for executing the virtual test cases, and create the physics-based simulated environment. The system further includes a robot controller operable to execute the virtual test cases in parallel in the physics-based simulated environment, measure a success of the execution, and store training and validation data to a historical database to train a machine learning algorithm. The robot controller may continuously execute the virtual test cases and use the machine learning algorithm to adjust parameters of the control strategy until optimal test cases are determined.

A programming method for a robot arm includes setting and saving operational configurations of the robot arm, establishing an operation process of the robot arm, selecting the operational position icon for applying to the operation sub-process, displaying a selected operational position icon and an operational configuration sub-icon, modifying an operational configuration displayed on the operational configuration sub-icon for facilitating to execute a programming process of the robot arm.

This disclosure relates generally to a method and system that provides personalized neuro motor rehabilitation therapy using a musculoskeletal arm model. The arm model is personalized using anthropometric measures and further adapted to operate using an optimized set of muscle actuators considering associated redundancy. The method generates trajectories associated with reach motion profiles for each motion task utilizing joint kinematics and inverse dynamics by integrating forward dynamics and muscle synergy concepts to select the optimized set of muscle actuators. The generated trajectories are further ranked based on muscle synergy, minimum energy consumption and optimized trajectory to select rehabilitation therapy best suited for effective recovery. Conventional methods that work with neural dynamics in deriving muscle synergy are dependent on single tasks, leaving synergy variation with task variability unexplored. The present disclosure provides understanding of work space, task variability and synergy paradigm to derive conclusive control actions for aiding rehabilitation effectively.

A robotic torso sensing system and method includes: a robotic torso comprising a mobile torso, the robotic torso further comprising a fixed torso; a motor configured to move the mobile torso; a torso encoder configured to provide information to the motor; a master controller operably connected to the motor, the master controller configured to control the motor, the master controller operably connected to the torso encoder, the master controller further configured to control the mobile torso; and a sensor configured to measure a position of the mobile torso, the sensor further configured to transmit the measurement to the master controller.

The present invention relates to a computer-implemented method. The method includes steps of causing a visual programming panel including a timeline editor and a variety of action blocks configured to enable a variety of basic actions correspondingly for a target robot to perform to be displayed in a visualization interface provided by a robot simulator shown on a web browser; at the visual programming panel, operating by a user to group at least two action blocks representing at least two basic actions selected from the variety of basic actions to form an action collection; and generating a program capable of commanding an end effector equipped on the target robot in a work cell to perform according to the action collection in the robot simulator.