A system for mitigating thermal runaway in a battery-powered electric vehicle (EV). The system includes a gas sensor configured to measure a level of at least one type of gas in a vicinity of a battery of the EV, a thermal event detector configured to determine, based on the measured level of the at least one type of gas, that the battery is experiencing out-gassing, and a communications interface configured to transmit an alert to a fleet management system regarding the out-gassing of the battery. The fleet management system alters an assignment of the EV in response to the out-gassing of the battery.

A control system, a control method, and a program capable of lowering difficulty of a user's work on an autonomous mobile robot having a placement part are provided. A control system for controlling an autonomous mobile robot including a placement part on which a load is placed includes a user recognition unit configured to recognize a user of the placement part, a feature information acquisition unit configured to acquire feature information of the recognized user, and an operation control unit configured to control a height of the placement part based on the feature information.

A method for automatically controlling a vehicle based on a driving specification received from an external infrastructure. In a normal mode the vehicle is controlled along a trajectory specified thereby and in a test mode the vehicle is controlled along a test trajectory that deviates from the trajectory specified by the driving specification, and/or is controlled using test parameters. This allows the reliable operation of the infrastructure to be verified.

A method for automatically controlling a vehicle based on a driving specification received from an external infrastructure. In a normal mode the vehicle is controlled along a trajectory specified thereby and in a test mode the vehicle is controlled along a test trajectory that deviates from the trajectory specified by the driving specification, and/or is controlled using test parameters. This allows the reliable operation of the infrastructure to be verified.

A system and method for a motorized mobile chair use a plurality of sensors having a plurality of sensor types to detect a plurality of objects and generate sensor data about the detected objects, each of the detected objects being a person, the sensor data about the objects comprising a plurality of range measurements to the people and a plurality of bearing measurements to the people. The system has at least one processor to receive the sensor data about the people, group the detected people into a plurality of zones, determine a closest person in each zone, and generate one or more control signals to cause the motorized mobile chair to match a speed and a direction of the closest person in the zone corresponding to a direction of travel of the motorized mobile chair while at least approximately maintaining a selected space to the closest person in the zone corresponding to the direction of travel of the motorized mobile chair.

A robot includes: a plurality of wheels; a plurality of motors; at least one sensor; a memory configured to store first information on a size of the robot; and a processor. The processor is configured to: acquire image data of an escalator from the at least one sensor, acquire second information on a size of a plurality of steps included in the escalator based on the image data, based on the first information and the second information, identify both a boarding position available for the robot to board the escalator among the plurality of steps, and a posture of the robot configured to allow the robot to board at the boarding position, acquire control information for controlling the robot to board at the boarding position in the posture when the boarding position and the posture have been identified, and control the plurality of motors based on the control information.

A robot includes: a plurality of wheels; a plurality of motors; at least one sensor; a memory configured to store first information on a size of the robot; and a processor. The processor is configured to: acquire image data of an escalator from the at least one sensor, acquire second information on a size of a plurality of steps included in the escalator based on the image data, based on the first information and the second information, identify both a boarding position available for the robot to board the escalator among the plurality of steps, and a posture of the robot configured to allow the robot to board at the boarding position, acquire control information for controlling the robot to board at the boarding position in the posture when the boarding position and the posture have been identified, and control the plurality of motors based on the control information.

The present disclosure relates to a self-propelled robotic work tool (1), e.g. an automatic robotic lawn mower, and a corresponding method. The robotic tool comprises an inertia measurement unit (IMU 15) which generally obtains (25) measured IMU parameters regarding the robotic working tool's movement. A prediction algorithm (17) predicts (27,36) required motor currents for driving the robotic work tool's wheels (5) based on the measured IMU parameters. The predicted motor current is compared (29,37) to the actual current used and the difference constitutes an error (19), which is used in a collision detection unit (21). If the collision detection unit (21) senses that the actually used motor current is much higher than the predicted current, a collision may be indicated (31). The prediction algorithm is repeatedly updated based on the error (19) by incrementing or decrementing an error category counter (error cat, 41,45) if the error (19) is above or bellow a first or second threshold (39,43), and increasing or decreasing a prediction algorithm setting (49,53), e.g. a motor current offset term i offsetif the error category counter (error cat, 41,45) is above or bellow a third or fourth threshold (47,51). This allows the prediction algorithm to adapt to circumstances where the robotic tool is used. For instance, if a lawn mower operates in thick grass, the prediction algorithm can be adapted not to detect false collisions due to increased motor current values.

In one embodiment, a method includes receiving a sequence of location data points associated with a vehicle from a first source and a sequence of motion data points associated with the vehicle from a second source. The method includes determining a first turn angle of the vehicle based on at least one location data point in the sequence of location data points associated with the first source. The method includes determining that an additional location data point in the sequence of location data points is inaccurate. The method includes determining a second turn angle of the vehicle by using at least one motion data point in the sequence of motion data points corresponding to the additional location data point that is inaccurate. The method includes determining a turn trajectory of the vehicle by using at least the first turn angle and the second turn angle.

In one embodiment, a method includes receiving a sequence of location data points associated with a vehicle from a first source and a sequence of motion data points associated with the vehicle from a second source. The method includes determining a first turn angle of the vehicle based on at least one location data point in the sequence of location data points associated with the first source. The method includes determining that an additional location data point in the sequence of location data points is inaccurate. The method includes determining a second turn angle of the vehicle by using at least one motion data point in the sequence of motion data points corresponding to the additional location data point that is inaccurate. The method includes determining a turn trajectory of the vehicle by using at least the first turn angle and the second turn angle.