A method in accordance with at least some embodiments of the present technology includes determining first hazard information about a human in an environment at a first time. The method further includes decelerating a mobile robot in the environment based at least partially on the first hazard information. The method further includes determining second hazard information about the human at a second time after the first time. The method further includes reconfiguring the mobile robot based at least partially on the second hazard information. Reconfiguring the mobile robot includes moving the mobile robot from a standing configuration to a non-standing configuration. The method further includes determining third hazard information about the human at a third time after the second time. Finally, the method includes causing a safe operating stop of the mobile robot based at least partially on the third hazard information.

A method for preventing a robot from colliding with another robot that is provided with an identifying image includes steps of: controlling the robot to move along a predetermined path; stopping the robot when it is determined that a first image captured by the robot while the robot is moving contains the identifying image; and controlling the robot to resume moving along the predetermined path when it is determined that a second image captured by the robot while the robot is not moving does not contain the identifying image.

A method for controlling a robot device includes (i) receiving, from each sensor of a plurality of sensors, a respective sensor data set from the sensor, (ii) determining, for each object of a set of objects containing at least one object, for each of a plurality of different combinations of the sensor data sets, a position prediction for the object by way of sensor data fusion of the sensor data sets according to the combination of the sensor data sets, (iii) determining, for each object of the set of objects, for each pair of a plurality of pairs of combinations, a distance between the position predictions determined for the object according to the combinations of the pair, (iv) feeding the determined distances to a neural network trained to determine confidence information for the position predictions from distances between position predictions for the pairs of combinations, and (v) controlling the robot device using one or a plurality of the position predictions taking into account the confidence information.

A method for controlling a robot device includes (i) receiving, from each sensor of a plurality of sensors, a respective sensor data set from the sensor, (ii) determining, for each object of a set of objects containing at least one object, for each of a plurality of different combinations of the sensor data sets, a position prediction for the object by way of sensor data fusion of the sensor data sets according to the combination of the sensor data sets, (iii) determining, for each object of the set of objects, for each pair of a plurality of pairs of combinations, a distance between the position predictions determined for the object according to the combinations of the pair, (iv) feeding the determined distances to a neural network trained to determine confidence information for the position predictions from distances between position predictions for the pairs of combinations, and (v) controlling the robot device using one or a plurality of the position predictions taking into account the confidence information.

A technique for detecting and avoiding obstacles by an unmanned aerial vehicle (UAV) includes: querying a knowledge graph having information related to a dynamic obstacle that may be in proximity to the UAV when traveling along a planned route; comparing the location of the dynamic obstacle to the UAV to detect conflicts; and in response to detecting a conflict, performing an action to avoid conflict with the dynamic obstacle. The knowledge graph can be updated by receiving a VHF radio signal containing the information related to the dynamic obstacle in the audible speech format; translating the audible speech format to a text format using speech recognition; analyzing the text format for relevant information related to the dynamic obstacle; comparing the relevant information related to the dynamic obstacle of the text format to the knowledge graph to detect changes; and updating the knowledge graph.

Provided is a robot and method of controlling same, where the robot includes: a sensor; a driver; a memory storing an instruction; and a processor configured to execute the instruction to: identify, through the sensor, a height difference between a first stair and a second stair of an escalator, identify whether the robot is adjacent to a disembarkment area of the escalator based on the identified height difference, based on identifying that the robot is adjacent to the disembarkment area, identify, through the sensor, whether an object is located within a first distance of the robot in a movement direction of the escalator, and based on identifying the object located within the first distance of the robot in the movement direction of the escalator, control the driver to cause the robot to move on the escalator in a direction opposite to the movement direction of the escalator.

Systems and methods of obstacle detection and AGV control comprise and/or utilize a thermal imaging sensor; and an automation processing system (APS) having a processor and a memory, the APS coupled with the thermal imaging sensor and being configured to: receive sensor data based on an output of the thermal imaging sensor, process the sensor data to determine at least one of a presence or a motion of a heat-emitting obstacle in a vicinity of the AGV, generate, based on the processed sensor data, an output comprising an indication of a control action for the AGV, and send the generated output to the VCS.

Systems and methods of obstacle detection and AGV control comprise and/or utilize a thermal imaging sensor; and an automation processing system (APS) having a processor and a memory, the APS coupled with the thermal imaging sensor and being configured to: receive sensor data based on an output of the thermal imaging sensor, process the sensor data to determine at least one of a presence or a motion of a heat-emitting obstacle in a vicinity of the AGV, generate, based on the processed sensor data, an output comprising an indication of a control action for the AGV, and send the generated output to the VCS.

A moving object control system that controls an operation of a moving object obtains information of a sensor configured to recognize a periphery of the moving object, generates a dynamic prediction map including information indicating a position of a static obstacle recognized based on the information of the sensor and information indicating a position of a dynamic obstacle that changes with time and is recognized based on the information of the sensor, and generates a target trajectory for controlling traveling of the moving object by using the generated dynamic prediction map.

The present invention provides a method, an apparatus, an electronic device, and a computer-readable storage medium for controlling an autonomous driving vehicle in a restricted area for a control center to adjust traveling of the autonomous driving vehicle. The method includes acquiring state information of a moving object in the restricted area, the moving object including a non-autonomous moving object and an autonomous driving vehicle, the non-autonomous moving object including a pedestrian and/or a non-autonomous driving vehicle, the state information including motion information and position information, the motion information including a motion direction and a motion speed; determining, based on state information of each of the moving objects, a temporary restricted passage space corresponding to the each moving object; and adjusting, based on state information of a current autonomous driving vehicle and a temporary restricted passage space corresponding to each of all other moving objects, traveling of the current autonomous driving vehicle. The method does not require installation of a dedicated vehicle passage area, can ensure safe operation of an autonomous driving vehicle in the entire restricted area, and is inexpensive.