A robot management system (RMS) includes a plurality of service robots deployed within an operations venue that includes a plurality of gaming devices, an operator terminal presenting a graphical user interface (GUI) to an operator, and a robot management system server (RMS server) configured in networked communication with the plurality of service robots. The RMS server is configured to: identify location data for the service robots; create an interactive overlay map of the operations venue that includes a static map of the operations venue, overlay data showing the location data of the plurality of service robots over the static map, and an interactive icon for each service robot of the plurality of service robots; display, via the GUI, the overlay map; receive a first input indicating a selection of a first interactive icon associated with a first service robot; and display current status information associated with the first service robot.

The present teaching relates to a method and system for path planning. A target is tracked via one or more sensors. Information of a desired pose of an end-effector with respect to the target and a current pose of the end-effector is obtained. Also, a minimum distance permitted between an arm including the end-effector and each of at least one obstacle identified between the current pose of the end-effector and the target is obtained. A weighting factor previously learned is retrieved and a cost based on a cost function is computed in accordance with a weighted smallest distance between the arm including the end-effector and the at least one obstacle, wherein the smallest distance is weighted by the weighting factor. A trajectory is computed from the current pose to the desired pose by minimizing the cost function.

Systems and methods disclosed herein include a robotic arm positioned outside of the vehicle. The robotic arm may include an end effector configured as a cleaning implement for cleaning a surface in the interior of the vehicle. The system may include a first camera configured to determine a position of the vehicle with respect to a reference point. The system may include a second camera configured to scan the interior of the vehicle. The second system may include a first controller configured to create and/or modify a tool path to execute a cleaning operation, based on the scan, and to send instructions to the robotic arm to execute the cleaning operation in accordance with the created and/or modified tool path.

Disclosed are a collision avoidance method for a moving device, a collision avoidance apparatus for a moving device, and a computer-readable storage medium. This application relates to the field of artificial intelligence technologies. According to the method, a parking direction of a moving device in an avoidance area is adjusted, so that a startup time used by the moving device after avoidance completes may be reduced. The method includes: determining a target path direction of a moving device; determining a first candidate parking direction and a second candidate parking direction; determining, based on the target path direction, a target parking direction of the moving device from the first candidate parking direction and the second candidate parking direction; and controlling, based on the target parking direction, the moving device to be parked in the avoidance area.

A picking apparatus in an embodiment includes: a gripper, an arm, a detector, and a control unit. The gripper picks and grips an object to be conveyed. The arm moves the gripper and causes the gripper to convey the object to be conveyed. The detector is attached to the arm and senses a force applied to the gripper. The control unit controls an operation of the gripper and the arm. The control unit includes a calculator and a subtractor. The calculator calculates a gravitational force and an inertial force applied to the gripper when the gripper grips and moves the object to be conveyed using an arithmetic expression including a coefficient determined in accordance with a mass of the object to be conveyed. The subtractor subtracts the gravitational force and the inertial force calculated by the calculator from a force applied to the gripper sensed by the detector.

An obstacle avoidance method for a robot arm is provided, including a modeling step, a collecting and evaluating coordinates step, an obtaining control parameter step, an establishing an occupation function step, and a finding an obstacle avoiding posture step. The present invention pre-stores the data obtained in performing the modeling step, the step of collecting and evaluating coordinates, the step of obtaining control parameter, and the step of establishing the occupation function into a database, thereby allowing the robot arm to quickly evaluate whether a collision behavior will occur in subsequent execution of a task. If a collision will occur, the robot arm executes the step of the finding the obstacle avoiding posture to dodge obstacles. The invention uses a non-contact approach for anti-collision design, which can improve the shortcomings faced by the existing contact type anti-collision design.

A system for automated guided vehicle safety may include an automated guided vehicle (AGV) having a propulsion system configured to move the AGV, and a processor configured to control the propulsion system, and a laser imaging system configured to deploy a virtual safety fence at least partially surrounding the AGV. The laser imaging system may include a plurality of laser imaging sensors including a front sensor and a rear sensor, and a movable boom, the front sensor being mounted to the movable boom and configured to extend in front of the housing of the AGV.

A method for controlling a legged robot includes: in response to detection of a collision event associated with a foot of a swing leg of the biped robot, terminating a trajectory component planning of the swing leg in a collision direction; calculating a position offset in the collision direction according to an external force that is received by the foot of the swing leg in the collision direction and obtained in real time, based on a foot dragging control mode, and determining a replanned trajectory component in the collision direction based on the position offset; and controlling the swing leg to move based on the replanned trajectory component in the collision direction and a desired trajectory component of the swing leg in a non-collision direction.

A system for generating a machine learning system to generate guidance information based on locations of objects is provided. The system accesses training data that includes training time-of-arrival (“TOA”) information of looks and guidance information for each look. The guidance information is based on a training collection of object locations. The TOA of a look represents, for each object location of a training collection of object locations, times between signals transmitted by transmitters and return signals received by receivers. The return signals represent signals reflected from an object at the object location. The system trains a machine learning system using the training data wherein the machine learning system inputs TOA information and outputs guidance information.

Robotic systems can be capable of collision detection and avoidance. A medical robotic system can include a first kinematic chain and one or more sensors positioned to detect one or more objects detected within a vicinity of the first kinematic chain. The medical robotic system can be configured to cause adjustment of a configuration of the first kinematic chain from a first configuration to a second configuration based on a constraint determined from the one or more objects detected by the one or more sensors within the vicinity of the first kinematic chain.