Abstract: Systems and methods for flexible conveyance in an assembly-line or manufacturing process are disclosed. A fleet of self-driving vehicles and a fleet-management system can be used to convey workpieces through a sequence of workstations at which operations are performed in order to produce a finished assembly. An assembly can be transported to a first workstation using a self-driving vehicle, where an operation is performed on the assembly. Subsequently, the assembly can be transported to a second workstation using the self-driving vehicle. The operation can be performed on the assembly while it is being conveyed by the self-driving vehicle.

Abstract: A system for controlling a fleet of unmanned vehicles includes a plurality of unmanned vehicles connected to a computing device. The computing device stores a dynamic attribute and a static attribute respective to each of the plurality of unmanned vehicles. The computing device is configured to: receive a task request including (i) an item identifier of an item, (ii) an action type defining an action to be performed respective to the item, and (iii) a location identifier of a location at which to perform the action; responsive to receiving the request, retrieve the stored dynamic attributes and static attributes; based on a comparison of the task request with the dynamic attributes and the static attributes, select one of the plurality of unmanned vehicles; and transmit, via the network, a command to the selected unmanned vehicle to perform the action respective to the item at the location.

Abstract: Systems and methods for autonomous provision replenishment are disclosed. Parts used in a manufacturing process are stored in an intermediate stock queue. When the parts are consumed by the manufacturing process and the number of parts in the queue falls below a threshold, a provision-replenishment signal is generated. One or more self-driving material-transport vehicles, a fleet-management system, and a provision-notification device.

Abstract: Methods and systems are provided for traction detection and control of a self-driving vehicle. The self-driving vehicle has drive motors that drive drive-wheels according to a drive-motor speed. Traction detection and control can be obtained by measuring the vehicle speed with a sensor such as a LiDAR or video camera, and measuring the wheel speed of the drive wheels with a sensor such as a rotary encoder. The difference between the measured vehicle speed and the measured wheel speeds can be used to determine if a loss of traction has occurred in any of the wheels. If a loss of traction is detected, then a recovery strategy can be selected from a list of recovery strategies in order to reduce the effects of the loss of traction.

Abstract: Systems and methods for utilizing fleets of robots are disclosed. The method comprises receiving a task with a computing device, and retrieving, from a memory, battery-charge attributes associated, respectively, with at least three robots. A determination is made that the first battery-charge attribute is greater than the second battery-charge attribute, which is greater than the third battery-charge attribute. The battery-charge attributes are evaluated against a criterion. After determining that the first and second battery-charge attributes satisfy the criterion, the first and second robots are added to a selection set. After determining that the third battery-charge attribute does not satisfy the criterion, the third robot is omitted from the selection set. The selection is then optimized based on the first and second battery-charge attributes, and then one of the first and second robots is selected from the selection set to perform an action associated with the task request.

Abstract: An augmentation module is described for an automated guided vehicle (AGV) deployed in a facility and including a control module for controlling a drive mechanism based on navigational data received from a navigation sensor. The module includes a inter-module communications interface connected to the control module; a memory; and a processor connected to the communications interface and the memory. The processor is configured to: obtain an operational command; generate control data to execute the operational command; convert the control data to simulated sensor data; and send the simulated sensor data to the control module.

Abstract: Systems and methods for obstacle avoidance with a self-driving vehicle are provided. The system comprises a processor connected to the self-driving vehicle and a sensor in communication with the processor. The sensor is configured to detect objects. The processor is configured to receive a measurement of the self-driving vehicle's speed, and define a sensor region based on the speed. The processor can determine that an object detected by the sensor is within the sensor region, and then initiate a fail-safe routine. The sensor region may be defined based on a range parameter. The sensor region may be defined based on the stopping distance of the vehicle. The sensor region may be redefined when the vehicle's speed changes.

Abstract: An autonomous vehicle is disclosed. The vehicle comprises a chassis, two or more drive wheels extending below the chassis, a drive motor housed within the chassis for driving the drive wheels, and a payload surface on top of the chassis for carrying a payload. An illumination system, for emitting light from at least one portion of the chassis, is mounted substantially around the entire perimeter of the chassis. The illumination system may be implemented using an array of light-emitting diodes (“LEDs”) that are arranged as segments. For example, there may be “headlight” segments on the front left and front right corners of the chassis.

Abstract: Systems and methods for electronically mapping a facility are presented. The method comprises obtaining a CAD file that includes graphical representations of a facility. An occupancy-map image is generated based on the CAD file. A sensor, such as a sensor on a self-driving vehicle, is used to detect a sensed feature within the facility. Based on the sensed feature, the occupancy-map image can be updated, since the sensed feature was not one of the known features in the CAD file prior to the sensed feature being detected by the sensor.

Abstract: Systems and methods for WiFi mapping an industrial facility are disclosed. The system comprises a self-driving vehicle having a WiFi transceiver. The self-driving vehicle communicates with a fleet-management using the WiFi transceiver, via a WiFi access point. The self-driving vehicle receives a mission from the fleet-management system, and moves to a destination location based on the mission, using autonomous navigation. While executing the mission, the self-driving vehicle simultaneously measures the received signal strength indication of the WiFi access point and other WiFi access points in the facility, and stores the received signal strength indication in association with the location at which the received signal strength indication was measured.

Abstract: A mobile platform for materials transport is provided. The platform includes a pair of suspension devices that in turn include a pair of rocker beams which can be rotated between two positions: a first position where central wheels attached thereto can be used to drive the platform; and a second position where the central wheels are retracted and the platform can be rolled on end wheels without the friction of the central wheels, and an associated drive system, impeding movement of the platform. Furthermore, data from sensors and/or load cells can be used to control movement of the platform; specifically shifts in load distribution and/or sensed forces at the suspension devices can indicate that a load (and/or materials) has shifted and/or is shifting and movement of the platform is adjusted accordingly, for example to prevent the platform and/or the load (and/or materials) from tipping.

Abstract: There is provided a driver-support system for use with a human-operated material-transport vehicle, and methods for using the same. The system has at least one sensor, a human-vehicle interface, and a transceiver for communicating with a fleet-management system. The system also has a processor that is configured to provide a mapping application and a localization application based on information received from the sensor. The mapping application and localization application may be provided in a single localization-and-mapping (“SLAM”) application, which may obtain input from the sensor, for example, when the sensor is an optical sensor such as a LiDAR or video camera.

Abstract: A system, method and apparatus for executing tasks with unmanned vehicles is provided. The system includes an unmanned vehicle comprising: a chassis; a propulsion system configured to move the chassis; sensor(s) configured to sense features around the chassis; a memory storing feature reference data; a communication interface; and a processor configured to: receive, using the interface, a command having task data and a location associated with a given feature; control the propulsion system to move the chassis to the location; while the chassis is moving to the location, determine, using the sensor(s), that the given feature is detected based on the feature reference data; and, responsive to the given feature being detected, control the propulsion system to execute a task based on the task data.

Abstract: Systems and methods for WiFi mapping an industrial facility are disclosed. The system comprises a self-driving vehicle having a WiFi transceiver. The self-driving vehicle communicates with a fleet-management using the WiFi transceiver, via a WiFi access point. The self-driving vehicle receives a mission from the fleet-management system, and moves to a destination location based on the mission, using autonomous navigation. While executing the mission, the self-driving vehicle simultaneously measures the received signal strength indication of the WiFi access point and other WiFi access points in the facility, and stores the received signal strength indication in association with the location at which the received signal strength indication was measured.

Abstract: An autonomous vehicle is disclosed. The vehicle comprises a chassis, two or more drive wheels extending below the chassis, a drive motor housed within the chassis for driving the drive wheels, and a payload surface on top of the chassis for carrying a payload. An illumination system, for emitting light from at least one portion of the chassis, is mounted substantially around the entire perimeter of the chassis. The illumination system may be implemented using an array of light-emitting diodes (“LEDs”) that are arranged as segments. For example, there may be “headlight” segments on the front left and front right corners of the chassis.

Abstract: Systems and methods for self-driving collision prevention are presented. The system comprises a self-driving vehicle safety system, having one or more sensors in communication with a control system. The control system is configured determine safety fields and instruct the sensors to scan a region corresponding to the safety fields. The control system determines exclusion regions, and omits the exclusion regions from the safety field. The safety system may also include capability reduction parameters that can be used to constrain the drive system of the vehicle, for example, by restricting turning radius and speed in accordance with the safety fields.

Abstract: An augmentation module is described for an automated guided vehicle (AGV) deployed in a facility and including a control module for controlling a drive mechanism based on navigational data received from a navigation sensor. The module includes a inter-module communications interface connected to the control module; a memory; and a processor connected to the communications interface and the memory. The processor is configured to: obtain an operational command; generate control data to execute the operational command; convert the control data to simulated sensor data; and send the simulated sensor data to the control module.

Abstract: Systems and methods for self-driving vehicle traffic management are disclosed. The system comprises one or more self-driving vehicles. One or more vehicles has a sensor system, a control system, and a drive system. The control system is configured to determine a current position and an intent position associated with itself, and to receive a current position and intent decision associated with a neighbor vehicle. The control system then determines a current spacing and relative spacing between itself and the neighbor vehicle. Based on the current and intent positions, the current spacing, and the relative spacing, the control system determines that the vehicle should yield to the neighbor vehicle, and sends a yield signal to the drive system in order to cause the vehicle to yield to the neighbor vehicle.

Abstract: A system for controlling a fleet of unmanned vehicles includes a plurality of unmanned vehicles connected to a computing device. The computing device stores a dynamic attribute and a static attribute respective to each of the plurality of unmanned vehicles. The computing device is configured to: receive a task request including (i) an item identifier of an item, (ii) an action type defining an action to be performed respective to the item, and (iii) a location identifier of a location at which to perform the action; responsive to receiving the request, retrieve the stored dynamic attributes and static attributes; based on a comparison of the task request with the dynamic attributes and the static attributes, select one of the plurality of unmanned vehicles; and transmit, via the network, a command to the selected unmanned vehicle to perform the action respective to the item at the location.

Abstract: Systems and methods for electronically mapping a facility are presented. The method comprises obtaining a CAD file that includes graphical representations of a facility. An occupancy-map image is generated based on the CAD file. A sensor, such as a sensor on a self-driving vehicle, is used to detect a sensed feature within the facility. Based on the sensed feature, the occupancy-map image can be updated, since the sensed feature was not one of the known features in the CAD file prior to the sensed feature being detected by the sensor.