A method of evaluating safety of a robot includes a step of obtaining a three-dimensional image or three-dimensional model of a test robot comprising shape information of a real robot, a step of setting a movement time and movement path of the test robot by inputting profile information comprising movement time information and movement path information of the test robot, a step of calculating a collision pressure and collision force applied to a collision object in consideration of a shape, effective mass, movement speed, and direction of an injury-causing dangerous portion of the test robot, and a step of evaluating safety of the robot by determining whether magnitudes of the calculated collision pressure and collision force fall within magnitudes of a predetermined maximum collision pressure and predetermined maximum collision force.

The present invention provides a method for monitoring a balanced state of a humanoid robot, comprising: acquiring state data of the robot falling in different directions and being stable, forming a support vector machine (SVM) training data set and obtaining, by training, an initial SVM classifier; inputting the state data of the robot to the trained SVM classifier, so that the SVM classifier outputs a classification result; taking statistics on a proportion of cycles judged to have an impending fall in the total number of control cycles within a judgment buffer time after the SVM classifier outputs the classification result, and finally determining a monitoring result of the balanced state of the robot according to the proportion and finally extracting state data of misjudged cycles within the buffer time, adding the state data to the current training data set and updating the SVM classifier, eventually enabling the classifier to achieve the effects of matching motion capabilities of the robot and monitoring the balanced state.

A robot control system includes circuitry configured to: acquire an input command value indicating a manipulation of a robot by a subject user; acquire a current state of the robot and a target state associated with the manipulation of the robot; determine a state difference between the current state and the target state; acquire from a learned model, a degree of distribution associated with a motion of the robot, based on the state difference, wherein the learned model is generated based on a past robot manipulation; set a level of assistance to be given during the manipulation of the robot by the subject user, based on the degree of distribution acquired; and generate an output command value for operating the robot, based on the input command value and the level of assistance.

A design method of serial manipulator that comprises an end effector, a number of random-access links, and a number of motors, includes: obtaining a desired motion profile of the end effector; discretizing the desired motion profile into a plurality of points, wherein each of the points carries information of speed, acceleration, and force/torque of the end effector at the point; determining the number of degrees of freedom of the serial manipulator, and initializing the length of each of the links and the motor type of each of the motors; and at each of the points, optimizing the initialized lengths of the links and the motor types of the motors by calculating a dynamic manipulability ellipsoid at the end effector, to obtain desired lengths of the links and desired motor types of the motors, which allows the end effector to execute the desired motion profile under predetermined constraints.

The present invention relates to a movement-dependent stabilisation support system (100) for stabilising a moving body (200), which comprises a plurality of sensors (110), a plurality of actuators (120) and a control unit (130). The plurality of sensors (110) continuously detects movement parameters of the body (200), on which basis the control unit (130) determines whether there is an instability of the body (200). If it is determined that there is an instability, the control unit (130) selects a stabilisation strategy, according to which the actuators (120) are controlled. When controlled, the actuators (120) attached to the body (200) stiffen and limit the freedom of movement of the body (200), such that a movement in the direction of the imminent unstable state is prevented or suppressed. In this way, the body (200) is supported in its stabilisation and an imminent fall is prevented.

A dual-arm robot assembling system including a controlling unit, a GUI, a first robotic-arm, and a second robotic-arm is disclosed. The GUI provides a graphic program editing page, which provides multiple instruction blocks used for editing a graphical program executed by the assembling system. At least one of the first robotic arm and the second robotic arm is disposed with a point-teaching tool thereon. Before the controlling unit controls the two robotic arms to perform an assembling operation based on the graphical program, a manager may directly drag the two robotic arms through the point-teaching tool, so as to implement a point-teaching procedure for the two robotic arms. Therefore, the assembling system may accomplish the assembling operation through the two robotic arms with cooperative movement.

A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robotic system performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.

A system for controlling interactions between a plurality of real and virtual robots, includes one or more real robots present in the real environment, one or more virtual robots present in a virtual environment corresponding to the real environment, and a processing device operable to control interactions between one or more of the real robots and one or more of the virtual robots, where the interactions between the real and virtual robots are dependent upon at least the positions of the one or more real robots in the real environment and the positions of the one or more virtual robots in the virtual environment.

The present disclosure provides a robot pose determination method including: collecting laser frames; calculating a current pose of the robot in a map pointed by a first pointer based on the laser frames, and obtaining an amount of the laser frames having been inserted into the map pointed by the first pointer; inserting the laser frames into a map pointed by the first pointer, if less than a first threshold; inserting the laser frames into the map pointed by the first pointer and a map pointed by a second pointer, if greater than or equal to the first threshold and less than a second threshold; and pointing the first pointer to the map pointed by the second pointer, pointing the second pointer to a newly created empty map, and inserting the laser frames into the map pointed by the first pointer, if equal to the second threshold.

The invention proposes an axis-invariant multi-axis system dynamics modeling and solving principle, and realizes iterative explicit dynamic modeling of multi-axis systems with tree chains, closed chains, friction and viscous joints and moving pedestals. The established model has elegant chain symbol system with pseudo-code function, which realizes complete parameterization including “topology, coordinate system, polarity, structural parameters, mass inertia, etc.”. The principle can be set to circuit, code, directly or indirectly, partially or fully executed inside a multi-axis robot system. In addition, the present invention also includes analytical verification system constructed on these principles for designing and verifying a multi-axis robot system.