A controller controls a motion of an object performing a task for changing a state of the object from a start state to an end state while avoiding collision of the object with an obstacle according to an optimal trajectory determined by solving an optimization problem of the dynamics of the object producing an optimal trajectory for performing the task subject to constraints on a solution of first-order stationary conditions modeling a minimum distance between the convex hull of the object and the convex hull of the obstacle using complementarity constraints.

Automatically performing an operation on an object-with a tool-carried by a polyarticulated system-that can be moved in a working environment, the object-and the working environment being open-ended or insufficiently defined to carry out the operation. A method comprises: capturing a scatter plot image of the object-and the working environment-with a 3D sensor, merging this image with the CAD model of the system and the environment into a working image, and defining anti-collision parameters;— defining a path of the tool-on the portion of the working image representing the object and executing a simulation of the corresponding movement of the system-and the tool in the working image so as to ensure that the operation is feasible;— and if the operation is feasible executing the actual movement of the system carrying the tool-according to the path defined for performing the operation on the object.

An autonomous mobile robot includes a first arithmetic unit configured to calculate a course direction based on an own position, a moving-object position, and a moving-object velocity vector, the course direction being a direction in which the autonomous mobile robot should travel, a second arithmetic unit configured to input the own position, the moving-object position, the moving-object velocity vector, and the course direction into a trained model and thereby calculate an estimated position, the trained model being a model that has been trained, the estimated position being a position at which the autonomous mobile robot is estimated to arrive a predetermined time later without colliding with the moving object, a generating unit configured to generate a remaining route from the estimated position to a destination, and a movement control unit configured to control a movement to the destination based on the course direction and the remaining route.

Systems and methods for object guidance and collision avoidance are provided. One system includes a location sensor disposed on a movable crane. The system also includes a plurality of sensors disposed on a plurality of objects within a facility. The system further includes a controller having a receiver for monitoring signals transmitted from the location sensor disposed on a movable crane and the plurality of sensors disposed on a plurality of objects within the facility. The controller is configured to generate a travel path for the movable crane to move an object coupled with the movable crane based on the one or more intersection regions and generate an output signal to an alarm device to provide an alert, when at least one object of the plurality of objects is within a predetermined proximity of at least the object being moved by the crane.

Learning to effectively imitate human teleoperators, even in unseen, dynamic environments is a promising path to greater autonomy, enabling robots to steadily acquire complex skills from supervision. Various motion generation techniques are described herein that are rooted in contraction theory and sum-of-squares programming for learning a dynamical systems control policy in the form of a polynomial vector field from a given set of demonstrations. Notably, this vector field is provably optimal for the problem of minimizing imitation loss while providing certain continuous-time guarantees on the induced imitation behavior. Techniques herein generalize to new initial and goal poses of the robot and can adapt in real time to dynamic obstacles during execution, with convergence to teleoperator behavior within a well-defined safety tube.

A device for indicating appropriate social distancing includes the ability to project light (in various forms and in a various ways) onto the ground in a manner visible by the user and those around them. A boundary of the projected light can be set to an acceptable social distance and can include different colors to indicated varying social distances for varying levels of trusted individuals. Proximity sensors set to appropriate distancing requirements, along with the varying levels of trust can be implemented into multiple devices being used in a group setting.

The present disclosure relates to robot path planning. Depth information of a plurality of obstacles in an environment of a robot are obtained at a first time instance. A static distance map is generated based on the depth information. A path is computed for the robot based on the static distance map. At a second time instant, depth information of one or more obstacles is obtained. A dynamic distance map is generated based on the one or more obstacles, wherein for each obstacle that satisfies a condition: a vibration range of the obstacle is computed based on a position of the obstacle and the static distance map, and the obstacle is classified as a dynamic obstacle or a static obstacle based on a criterion associated with the vibration range. A repulsive speed of the robot is computed based on the dynamic distance map to avoid the dynamic obstacles.

According to the present invention, disclosed are a device and a method of generating an object image, recognizing an object, and learning an environment of a mobile robot which perform a deep learning algorithm which allows a robot to create a map and load environment information acquired during the autonomous movement while the autonomous mobile robot is being charged and may be used for an application which finds out a location by finally recognizing objects such as furniture using a method of checking a location of the recognized objects to mark the location on the map.

A mobile epidemic prevention and disinfection robot includes a body and a control system installed on the body. The body includes a mobile base, a dust collection module, a disinfection system, a temperature detection system, and a delivery system. The delivery system includes two robot arms and a storage box. The control system includes a control module, a path planning module, and an obstacle dodge module. The control module is used to control the movement of the mobile epidemic prevention and disinfection robot, and the two robot arms to perform corresponding actions. The path planning module plans the optimal movement information of the mobile epidemic prevention and disinfection robot from the current position to a destination according to a three-dimensional map. The obstacle dodge module controls the mobile epidemic prevention and disinfection robot to dodge obstacles.

A trajectory generating method includes a first generating process of generating a plurality of trajectories between a start teaching point and a target teaching point, an evaluation process of evaluating a motion of the robot arm on each trajectory to calculate an evaluation value of each trajectory, a selection process of selecting one of the plurality of trajectories based on calculated evaluation values, and an update process of updating the trajectory by repeating the processes of generating a plurality of new trajectories by changing a selected trajectory in the selection process, of calculating an evaluation value of a motion of the robot arm on each changed trajectory and of selecting a trajectory based on calculated evaluation values.