Abstract: The method can include: sampling images of a conveyor region S110; determining a set of patches S120; estimating a displacement for each patch S130; estimating a conveyor motion parameter(s) based on the patch displacement S140; and optionally performing an action based on the conveyor motion parameter S150. However, the method S100 can additionally or alternatively include any other suitable elements. The method S100 functions to estimate a set of conveyor motion parameters (e.g., conveyor speed). Additionally or alternatively, the method can function to enable dynamic control of an independent robotic assembly system(s) to accommodate conveyor motion changes.

Abstract: The method S100 can include: providing bowls within the workspace based on the assembly context S105; sampling sensor data for the workspace S110; detecting bowls based on the sensor data S120; determining a labeled training dataset S130; and training a classifier for the assembly context S140. However, the method S100 can additionally or alternatively include any other suitable elements.

Abstract: Sun-aware routing and controls of an autonomous vehicle is described herein. A location and an orientation of a sensor system is identified within an environment of the autonomous vehicle to determine whether the sun causes a threshold level of perception degradation to the sensor system incident to generating a sensor signal indicative of a traffic light. The determination is based upon perception degradation data that can be precomputed for locations and orientations within the environment for dates and times of day. The perception degradation data is based upon the location of at least one traffic light and positions of the sun relative to the locations and the orientations within the environment. A mechanical system of the autonomous vehicle is controlled to execute a maneuver that reduces perception degradation to the sensor system when the perception degradation is determined to exceed the threshold level of perception degradation.

Abstract: A vehicle includes one or more cameras that capture a plurality of two-dimensional images of a three-dimensional object. A light detector and/or a semantic classifier search within those images for lights of the three-dimensional object. A vehicle signal detection module fuses information from the light detector and/or the semantic classifier to produce a semantic meaning for the lights. The vehicle can be controlled based on the semantic meaning. Further, the vehicle can include a depth sensor and an object projector. The object projector can determine regions of interest within the two-dimensional images, based on the depth sensor. The light detector and/or the semantic classifier can use these regions of interest to efficiently perform the search for the lights.

Abstract: An occlusion detection system for an autonomous vehicle is described herein, where a signal conversion system receives a three-dimensional sensor signal from a sensor system and projects the three-dimensional sensor signal into a two-dimensional range image having a plurality of pixel values that include distance information to objects captured in the range image. A localization system detects a first object in the range image, such as a traffic light, having first distance information and a second object in the range image, such as a foreground object, having second distance information. An occlusion polygon is defined around the second object and the range image is provided to an object perception system that excludes information within the occlusion polygon to determine a configuration of the first object. A directive is output by the object perception system to control the autonomous vehicle based upon occlusion detection.

Abstract: An autonomous vehicle incorporating a multimodal multi-technique signal fusion system is described herein. The signal fusion system is configured to receive at least one sensor signal that is output by at least one sensor system (multimodal), such as at least one image sensor signal from at least one camera. The at least one sensor signal is provided to a plurality of object detector modules of different types (multi-technique), such as an absolute detector module and a relative activation detector module, that generate independent directives based on the at least one sensor signal. The independent directives are fused by a signal fusion module to output a fused directive for controlling the autonomous vehicle.

Abstract: The system 100 can include a robotic assembly system 110 and a set of barriers 120. The robotic assembly system can include a robot arm, a frame, an optional utensil, an optional bin, and/or any other suitable components. However, the system 100 can additionally or alternatively include any other suitable set of components. The system 100 functions to facilitate robotic assembly of items/ingredients (e.g., in an assembly line environment). Additionally or alternatively, the system 100 can function to guard robotic assembly components during operation. Additionally or alternatively, the system 100 can function to mitigate the influence of a robotic assembly system on surrounding workers (e.g., in an assembly line environment).

Abstract: Systems, methods, and computer-readable media are provided for receiving traffic object data from a plurality of autonomous vehicles, the traffic object data including a geographic location of a traffic object, comparing the traffic object data of each of the plurality of autonomous vehicles with known traffic object data, determining a discrepancy between the traffic object data of each of the plurality of autonomous vehicles and the known traffic object data, grouping the traffic object data of each of the plurality of autonomous vehicles based on the determining of the discrepancy between the traffic object data of each of the plurality of autonomous vehicles and the known traffic object data, determining whether a group of traffic object data of the grouping of the traffic object data of each of the plurality of autonomous vehicles exceeds a threshold; and updating a traffic object map based on the traffic object data of the group that exceeds the threshold.

Abstract: The foodstuff assembly system can include: a robot arm, a frame, a set of foodstuff bins, a sensor suite, a set of food utensils, and a computing system. The system can optionally include: a container management system, a human machine interface (HMI). However, the foodstuff assembly system 100 can additionally or alternatively include any other suitable set of components. The system functions to enable picking of foodstuff from a set of foodstuff bins and placement into a container (such as a bowl, tray, or other foodstuff receptacle). Additionally or alternatively, the system can function to facilitate transferal of bulk material (e.g., bulk foodstuff) into containers, such as containers moving along a conveyor line.

Abstract: The method can include: receiving sensor measurements; maintaining an ingredient model for a foodstuff bin; determining a set of ingredient parameters; providing feedback based on the ingredient parameters; and/or any other suitable elements. The method can optionally include determining a refill event; and responding to a refill event. However, the method can additionally or alternatively include any other suitable elements. The method can function to facilitate automatic refill notifications at a human-machine interface (HMI) and/or can function to facilitate automatic control adjustments based on ingredient refill events. Additionally or alternatively, the method can function to facilitate provision of user feedback for key ingredient parameters, such as a remaining ingredient amount in a particular foodstuff bin, pick weight/volume statistics, via the HMI.

Abstract: A vehicle includes one or more cameras that capture a plurality of two-dimensional images of a three-dimensional object. A light detector and/or a semantic classifier search within those images for lights of the three-dimensional object. A vehicle signal detection module fuses information from the light detector and/or the semantic classifier to produce a semantic meaning for the lights. The vehicle can be controlled based on the semantic meaning. Further, the vehicle can include a depth sensor and an object projector. The object projector can determine regions of interest within the two-dimensional images, based on the depth sensor. The light detector and/or the semantic classifier can use these regions of interest to efficiently perform the search for the lights.

Abstract: The system can include: a base structure, a set of support elements, and/or any other suitable components. The system can optionally include a foodstuff bin and a force sensor. However, the system can additionally or alternatively include any other suitable set of components. The system functions to facilitate refilling of ingredients within foodstuff bins and/or replacement of foodstuff bins. Additionally or alternatively, the system can function to facilitate repeatable positioning of foodstuff bins within a workspace of the robotic arm and/or a robotic assembly module. Additionally or alternatively, the system can function to facilitate rapid calibration, servicing, and/or cleaning of foodstuff assembly modules.

Abstract: The foodstuff assembly system can include: a robot arm, a frame, a set of foodstuff bins, a sensor suite, a set of food utensils, and a computing system. The system can optionally include: a container management system, a human machine interface (HMI). However, the foodstuff assembly system 100 can additionally or alternatively include any other suitable set of components. The system functions to enable picking of foodstuff from a set of foodstuff bins and placement into a container (such as a bowl, tray, or other foodstuff receptacle). Additionally or alternatively, the system can function to facilitate transferal of bulk material (e.g., bulk foodstuff) into containers, such as containers moving along a conveyor line.

Abstract: A method can include: receiving imaging data; identifying containers using an object detector; scheduling insertion based on the identified containers; and optionally performing an action based on a scheduled insertion. However, the method can additionally or alternatively include any other suitable elements. The method functions to schedule insertion for a robotic system (e.g., ingredient insertion of a robotic foodstuff assembly module). Additionally or alternatively, the method can function to facilitate execution of a dynamic insertion strategy; and/or facilitate independent operation of a plurality of robotic assembly modules along a conveyor line.

Abstract: An occlusion detection system for an autonomous vehicle is described herein, where a signal conversion system receives a three-dimensional sensor signal from a sensor system and projects the three-dimensional sensor signal into a two-dimensional range image having a plurality of pixel values that include distance information to objects captured in the range image. A localization system detects a first object in the range image, such as a traffic light, having first distance information and a second object in the range image, such as a foreground object, having second distance information. An occlusion polygon is defined around the second object and the range image is provided to an object perception system that excludes information within the occlusion polygon to determine a configuration of the first object. A directive is output by the object perception system to control the autonomous vehicle based upon occlusion detection.

Abstract: An end effector system can include: an optional actuator; an optional utensil mating connector; and a utensil. The utensil can include: a set of scoops; a set of mechanical linkages; a set of ingressive elements; and/or any other suitable components. However, the end effector system can additionally or alternatively include any other suitable set of components. The end effector system functions to facilitate picking of ingredients (e.g., foodstuffs), ingredient transformation, and/or ingredient insertion (e.g., at a target foodstuff container and/or placement location) via a grasp cavity of the utensil. Additionally or alternatively, the end effector system can function to shape a profile of ingredients (e.g., upon insertion). Additionally or alternatively, the end effector system can function to evacuate ingredients from the grasp cavity (e.g., clearing out ingredients adhering to the utensil).

Abstract: The foodstuff assembly system can include: a robot arm, a frame, a set of foodstuff bins, a sensor suite, a set of food utensils, and a computing system. The system can optionally include: a container management system, a human machine interface (HMI). However, the foodstuff assembly system 100 can additionally or alternatively include any other suitable set of components. The system functions to enable picking of foodstuff from a set of foodstuff bins and placement into a container (such as a bowl, tray, or other foodstuff receptacle). Additionally or alternatively, the system can function to facilitate transferal of bulk material (e.g., bulk foodstuff) into containers, such as containers moving along a conveyor line.

Abstract: Systems, methods, and computer-readable media are provided for receiving traffic object data from a plurality of autonomous vehicles, comparing the traffic object data of each of the plurality of autonomous vehicles with known traffic object data, determining a discrepancy between the traffic object data of each of the plurality of autonomous vehicles and the known traffic object data, grouping the traffic object data of each of the plurality of autonomous vehicles based on the determining of the discrepancy between the traffic object data of each of the plurality of autonomous vehicles and the known traffic object data, determining whether a group of traffic object data of the grouping of the traffic object data of each of the plurality of autonomous vehicles exceeds a threshold, and updating a traffic object map based on the traffic object data of the group that exceeds the threshold.

Abstract: An occlusion detection system for an autonomous vehicle is described herein, where a signal conversion system receives a three-dimensional sensor signal from a sensor system and projects the three-dimensional sensor signal into a two-dimensional range image having a plurality of pixel values that include distance information to objects captured in the range image. A localization system detects a first object in the range image, such as a traffic light, having first distance information and a second object in the range image, such as a foreground object, having second distance information. An occlusion polygon is defined around the second object and the range image is provided to an object perception system that excludes information within the occlusion polygon to determine a configuration of the first object. A directive is output by the object perception system to control the autonomous vehicle based upon occlusion detection.

Abstract: The foodstuff assembly system can include: a robot arm, a frame, a set of foodstuff bins, a sensor suite, a set of food utensils, and a computing system. The system can optionally include: a container management system, a human machine interface (HMI). However, the foodstuff assembly system 100 can additionally or alternatively include any other suitable set of components. The system functions to enable picking of foodstuff from a set of foodstuff bins and placement into a container (such as a bowl, tray, or other foodstuff receptacle). Additionally or alternatively, the system can function to facilitate transferal of bulk material (e.g., bulk foodstuff) into containers, such as containers moving along a conveyor line.