To effectively enhance the operation efficiency of an autonomous mobile robot, an autonomous mobile robot control system includes a processor and a plurality of environmental cameras. The processor estimates a moving route of each of a plurality of moving bodies on the basis of characteristics of each of the plurality of moving bodies and sets a subset of the plurality of moving bodies whose moving routes overlap among the detected moving bodies as avoidance processing target moving bodies. The processor generates an avoidance procedure for the avoidance processing target moving bodies so the motion of the avoidance processing target moving bodies does not interfere with the motion of other avoidance target moving bodies.

Low profile object detection can be performed on mowers or other vehicles that may be autonomous. An autonomy controller can be employed on a mower to receive and process sensor data for a detection area to determine whether an object may be present in a region of interest within the detection area. When the autonomy controller determines that an object may be present, it can cause the ground speed of the mower to be slowed and can commence buffering region of interest sensor data over a period of time. The autonomy controller can process the buffered region of interest sensor data to determine whether an object is present in the region of interest, and if so, can alter the path of the mower appropriately.

One embodiment of an unmanned protective vehicle (UPV) includes a chassis, wheels, an engine, a barrier fixed to the chassis, and an autonomous driving system. The UPV has an opening which enables a manned vehicle to enter a space that is protected by the barrier. And the autonomous driving system is configured to drive the UPV in cooperation with the manned vehicle, while the manned vehicle is located inside the space. Having the manned vehicle inside the space of the UPV improves the survivability of the manned vehicle following a collision, while the manned vehicle is located inside the space, compared to the survivability of the manned vehicle following a collision, while the manned vehicle is not located inside the space.

The invention relates to a method for preventing a collision between an autonomous vehicle (A) and a user (B) in a movement range of the autonomous vehicle comprising the steps: receiving a time-dependent, planned path (P1) of the autonomous vehicle (A) and a time-dependent, planned path (P2) of the user (B), determining a time-dependent path network by means of a path network unit (8), wherein the time-dependent path network describes the planned paths (P1, P2), determining collision information which describes an overlap of the planned paths (P1, P2) in the time-dependent path network, determining a safe zone (23) for the user on the basis of the collision information, wherein the safe zone (23) describes an area in the movement range which is safe for the user (B) in respect of a collision with the autonomous vehicle (A), and making available a display (20) for the user (B) by means of a display device (19), wherein the display (20) describes the safe zone (23).

An establishing method of semantic distance map for a moving device, includes capturing an image; obtaining a single-point distance measurement result of the image; performing recognition for the image to obtain a recognition result of each obstacle in the image; and determining a semantic distance map corresponding to the image according to the image, the single-point distance measurement result and the recognition result of each obstacle of in the image; wherein each pixel of the semantic distance map includes an obstacle information, which includes a distance between the moving device and an obstacle, a type of the obstacle, and a recognition probability of the obstacle.

Vehicles can be operated according to an artificial intelligence model contained in an on-board processor. The AI model can analyze sensor data, such as visible or infrared images of traffic, and determine when a collision is possible, whether it has become imminent, and whether the collision is avoidable or unavoidable using sequences of accelerations, braking, and steering. The AI model can also select the most appropriate sequence of actions from a large plurality of calculated sequences to avoid the collision if avoidable, and to minimize the harm if unavoidable. The AI model can also cause a processor to actuate linkages connected to the throttle (or electric power control), brakes (or regenerative braking), and steering to implement the selected sequence of actions. Thus the collision can be avoided or mitigated by an ADAS system or a fully autonomous vehicle.

A system having a picking device for medicaments are provided. The system including a picking device that includes a movement space, an optical detection device and a control device. The system also includes a memory and a processor configured to create an image of the movement space, compare predefined areas of the image of the movement space with corresponding areas of a reference image, determine that an obstacle is present in a detected portion of the movement space based on the image comparison and provide corresponding signals for responding to the obstacle. A machine-readable medium for operating picking devices for medicaments is also provided.

According to one aspect, methods to validate an autonomy system in conjunction with a teleoperations system are provided. A computing device obtains simulation data of an autonomous driving system of a vehicle. The simulation data includes first environment data representing an environment in which the vehicle is driven using the autonomous driving system. The computing device obtains teleoperations driving data of a teleoperations driving system by which the vehicle is remotely controlled with an aid of a human. The teleoperations driving data includes second environment data representing the environment in which the vehicle is driven using the teleoperations driving system. The computing device determines whether the autonomous driving system in conjunction with the teleoperations driving system meets a minimum deployment standard based on the simulation data and the teleoperations driving data.

Techniques for determining a prediction probability associated with a disengagement event are discussed herein. A first prediction probability can include a probability that a safety driver associated with a vehicle (such as an autonomous vehicle) may assume control over the vehicle. A second prediction probability can include a probability that an object in an environment is associated the disengagement event. Sensor data can be captured and represented as a top-down representation of the environment. The top-down representation can be input to a machine learned model trained to output prediction probabilities associated with a disengagement event. The vehicle can be controlled based the prediction probability and/or the interacting object probability.

Techniques for determining a prediction probability associated with a disengagement event are discussed herein. A first prediction probability can include a probability that a safety driver associated with a vehicle (such as an autonomous vehicle) may assume control over the vehicle. A second prediction probability can include a probability that an object in an environment is associated the disengagement event. Sensor data can be captured and represented as a top-down representation of the environment. The top-down representation can be input to a machine learned model trained to output prediction probabilities associated with a disengagement event. The vehicle can be controlled based the prediction probability and/or the interacting object probability.