A safety device according to the present disclosure includes a sensor that is attached to a self-propellable travel device or a robot provided to the travel device, is set with a given detection area on the basis of a position of the sensor, and detects an object existing within the given detection area. The safety device further includes a motion suppressing device that suppresses motions of the travel device and the robot, when the existence of the object within the given detection area is detected by the sensor, and an area changing device that changes the given detection area according to operating states of the travel device and the robot.

An automatic program-correction device includes: a clearance detecting unit that detects an amount of clearance between a robot and a peripheral device in an operation program; a near-miss detecting unit that detects a near-miss section; a closest-point detecting unit that detects a pair of closest points, in the near-miss section; and a program updating unit that generates a new operation program having an intermediate teaching point to which the closest points have been moved, along a straight line passing through the detected pair of closest points, until the amount of clearance becomes greater than a minimum amount of clearance and equal to or less than the threshold. While gradually reducing, from the threshold, the amount of clearance at the intermediate teaching point, the program updating unit obtains an intermediate teaching point that provides a maximum amount of clearance at which a new near-miss section is not detected.

An automatic program-correction device includes: a clearance detecting unit that detects an amount of clearance between a robot and a peripheral device in an operation program; a near-miss detecting unit that detects a near-miss section; a closest-point detecting unit that detects a pair of closest points, in the near-miss section; and a program updating unit that generates a new operation program having an intermediate teaching point to which the closest points have been moved, along a straight line passing through the detected pair of closest points, until the amount of clearance becomes greater than a minimum amount of clearance and equal to or less than the threshold. While gradually reducing, from the threshold, the amount of clearance at the intermediate teaching point, the program updating unit obtains an intermediate teaching point that provides a maximum amount of clearance at which a new near-miss section is not detected.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for causing a transducer to transmit an acoustic input signal into a member of a device. Receiving a detection signal representing reverberations of the input signal traveling within the member from a receiver. Detecting a contact of the member with an object external to the member based on a change in the detection signal, where the change in the detection signal represents an alteration in the reverberations of the input signal caused by the contact of the member with the object. Determining a position along the member of a point of the contact of the member with the object based on the change in the detection signal.

In an embodiment, a method includes determining a given material to manipulate to achieve a goal state. The goal state can be one or more deformable or granular materials in a particular arrangement. The method further includes, for the given material, determining, a respective outcome for each of a plurality of candidate actions to manipulate the given material. The determining can be performed with a physics-based model, in one embodiment. The method further can include determining a given action of the candidate actions, where the outcome of the given action reaching the goal state is within at least one tolerance. The method further includes, based on a selected action of the given actions, generating a first motion plan for the selected action.

In an embodiment, a method includes determining a given material to manipulate to achieve a goal state. The goal state can be one or more deformable or granular materials in a particular arrangement. The method further includes, for the given material, determining, a respective outcome for each of a plurality of candidate actions to manipulate the given material. The determining can be performed with a physics-based model, in one embodiment. The method further can include determining a given action of the candidate actions, where the outcome of the given action reaching the goal state is within at least one tolerance. The method further includes, based on a selected action of the given actions, generating a first motion plan for the selected action.

A method and apparatus for determining a trajectory of a robot's end effector are disclosed. In an embodiment, the apparatus includes a force obtaining device to obtain a collision force of the end effector of the robot, caused by a collision of the end effector upon the collision being detected; and a trajectory determining device to determine a second trajectory of the end effector based on the collision force of the end effector obtained, and based on a recorded first trajectory of the end effector. The recorded first trajectory is a trajectory recorded before the collision, and the second trajectory is a trajectory determined after the collision. As such, an efficient protection for the robot and its working environment at the moment of collision may be achieved.

A machine learning method includes: a first learning step which is performed in a phase before a neural network is installed in a mobile robot and in which a stationary first obstacle is placed in a set space and the first obstacle is placed at different positions using simulation so that the neural network repeatedly learns a path from a starting point to the destination which avoids the first obstacle; and a second learning step which is performed in a phase after the neural network is installed in the mobile robot and in which, when the mobile robot recognizes a second obstacle that operates around the mobile robot in a space where the mobile robot moves, the neural network repeatedly learns a path to the destination which avoids the second obstacle every time the mobile robot recognizes the second obstacle.

Medical systems and methods for making and using medical systems are disclosed. Example medical systems may include an atherectomy system configured to engage and remove plaque from walls in vessels of a vascular system. The atherectomy system may include a drive shaft, a rotational member coupled to an end of the drive shaft, a motor coupled to the drive shaft to rotate the rotational tip, and a control unit configured to control a motor state of the motor. The motor may be an electric motor. The control unit may adjust the motor state to decelerate the motor in response to detecting a jam or a stall condition. The jam or stall condition may be detected when a speed of the motor or other motor state reaches or goes beyond a threshold value as prescribed by a reference schedule.

Medical systems and methods for making and using medical systems are disclosed. Example medical systems may include an atherectomy system configured to engage and remove plaque from walls in vessels of a vascular system. The atherectomy system may include a drive shaft, a rotational member coupled to an end of the drive shaft, a motor coupled to the drive shaft to rotate the rotational tip, and a control unit configured to control a motor state of the motor. The motor may be an electric motor. The control unit may adjust the motor state to decelerate the motor in response to detecting a jam or a stall condition. The jam or stall condition may be detected when a speed of the motor or other motor state reaches or goes beyond a threshold value as prescribed by a reference schedule.