A vehicle computing system may identify a scenario in an environment that violates an operating constraint. The vehicle computing system may request remote guidance from a guidance system of a service computing device. The vehicle computing system may receive input from the guidance system including one or more waypoints and/or associated orientations for the vehicle to navigate through the scenario. The vehicle computing system may be configured to validate the input. A validation may include processing the input to determine whether the waypoint(s) and/or orientation(s) associated therewith may cause the vehicle to violate a safety protocol. Based on a determination that the input will not cause the vehicle to violate the safety protocol, the vehicle computing system may control the vehicle according to the input, such as by causing a drive system to operate the vehicle to each waypoint at the associated orientation.

Provided is a control technique for achieving formation control which is applicable even when there is a non-connection location in a common portion between a set of initial positions of robot units and a set of target positions of the robot units. Each of a plurality of robots constituting a mobile robot includes a position distance calculation command transmission unit 1, a position distance calculation command transfer unit 2, a return position distance calculation command transmission unit 3, a direction storage unit 4, a return position distance calculation command transfer unit 5, a first head robot determination command transmission unit 6, a void generation order determination unit 7, a void generation command transfer unit 8, a push command transmission unit 9, a return push command transmission unit 10, a return push command transfer unit 11, a void operation command transmission unit 12, a first movement unit 13, a void operation command transfer unit 14, a movement command transmission unit 15, a return movement command transmission unit 16, a second movement unit 17, a pull command transmission unit 18, a return pull command transmission unit 19, a third movement unit 20, a control unit 21, a set updating unit 22, and a second head robot determination command transmission unit 23.

Systems and methods for optimizing sensory signal capturing by reconfiguring robotic device configurations. A method includes determining at least one predicted future sensor reading for a robotic device based on navigation path data of the robotic device, wherein the robotic device is deployed with at least one sensor, wherein each predicted future sensor reading is an expected value of a future sensory signal; determining an optimized sensor configuration based on the at least one predicted future sensor reading, wherein the optimized sensor configuration optimizes capturing of sensor signals by the at least one sensor; and reconfiguring the at least one sensor based on the optimized sensor configuration, wherein reconfiguring the at least one sensor further comprises modifying at least one sensor parameter of the at least one sensor based on the optimized sensor configuration.

A robot control system is a robot control system that controls a plurality of mobile robots, in which: each of the mobile robots includes right and left wheels, and a sensor that detects actions of the right and left wheels; and the control system calculates abrasion degrees of right and left components for the right and left wheels, depending on a detection result of the sensor, and manages traveling of the plurality of mobile robots, depending on the abrasion degrees.

A lawn mower robot may include a plurality of distance sensor units. Each distance sensor unit detects distance information between the lawn mower robot and a fence for partitioning a region. A controller calculates adjacent direction information using each detected distance information. In addition, the lawn mower robot includes a position sensor module. The position sensor module detects position information related to the lawn mower robot. The controller detects whether the lawn mower has deviated from a preset region by using the position information. When the lawn mower robot has deviated, the lawn mower robot can be moved in a direction according to the calculated adjacent direction information and return along the shortest path.

Systems, methods, and apparatus for tracking location of an inspection robot are disclosed. An example apparatus for tracking inspection data may include an inspection chassis having a plurality of inspection sensors configured to interrogate an inspection surface, a first drive module and a second drive module, both coupled to the inspection chassis. The first and second drive module may each include a passive encoder wheel and a non-contact sensor positioned in proximity to the passive encoder wheel, wherein the non-contact sensor provides a movement value corresponding to the first passive encoder wheel. An inspection position circuit may determine a relative position of the inspection chassis in response to the movement values from the first and second drive modules.

A materials handling vehicle includes a camera, odometry module, processor, and drive mechanism. The camera captures images of an identifier for a racking system aisle and a rack leg portion in the aisle. The processor uses the identifier to generate information indicative of an initial rack leg position and rack leg spacing in the aisle, generate an initial vehicle position using the initial rack leg position, generate a vehicle odometry-based position using odometry data and the initial vehicle position, detect a subsequent rack leg using a captured image, correlate the detected subsequent rack leg with an expected vehicle position using rack leg spacing, generate an odometry error signal based on a difference between the positions, and update the vehicle odometry-based position using the odometry error signal and/or generated mast sway compensation to use for end of aisle protection and/or in/out of aisle localization.

A method for controlling a robot is provided. The method includes the steps of: determining a first comparison axis with reference to a first target area specified by a camera module of a robot, and determining a second comparison axis with reference to a second target area specified by a scanner module of the robot and associated with the first target area; and correcting a reference coordinate system associated with the camera module with reference to a relationship between the first comparison axis and the second comparison axis.

An obstacle avoidance method for a robot, applied to a robot side, comprises: obtaining trap feature information in real time during travel of the robot, wherein the trap feature information comprises a sensor parameter and/or image information; determining whether a current location is located in a trap region when determining that the trap feature information meets a trap condition, wherein the trap region indicates a region in which the robot was trapped or is prone to be trapped; and giving up a current traveling route and traveling out of the trap region when determining that the current location is located in the trap region.

A method of localizing a robot includes receiving odometry information plotting locations of the robot and sensor data of the environment about the robot. The method also includes obtaining a series of odometry information members, each including a respective odometry measurement at a respective time. The method also includes obtaining a series of sensor data members, each including a respective sensor measurement at the respective time. The method also includes, for each sensor data member of the series of sensor data members, (i) determining a localization of the robot at the respective time based on the respective sensor data, and (ii) determining an offset of the localization relative to the odometry measurement at the respective time. The method also includes determining whether a variance of the offsets determined for the localizations exceeds a threshold variance. When the variance among the offsets exceeds the threshold variance, a signal is generated.