A work site equipment grouping system includes a plurality of work machines including a first work machine and a second work machine. Each work machine is configured to wirelessly communicate with other work machines. The system further includes a local area network including a plurality of communicatively connected nodes, the nodes including the first work machine and the second work machine. The system further includes a third work machine configured to detect one of the first work machine or the second work machine within a signal range of the third work machine and, upon detecting one of the first work machine or the second work machine, automatically join the local area network.

A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.

Examples described may enable provision of remote assistance for an autonomous vehicle. An example method includes a computing system operating by default in a first mode and periodically transitioning from operation in the first mode to operation in a second mode. In the first mode, the system may receive environment data provided by the vehicle and representing object(s) having a detection confidence below a threshold, where the detection confidence is indicative of a likelihood of correct identification of the object(s), and responsive to the object(s) having a confidence below the threshold, provide remote assistance data comprising an instruction to control the vehicle and/or a correct identification of the object(s). In the second mode, the system may trigger user interface display of remote assistor alertness data based on pre-stored data related to an environment in which the pre-stored data was acquired, and receive a response relating to the alertness data.

A system for providing remote assistance to an autonomous vehicle is disclosed herein. The system includes at least one remote assistance button configured to be selectively activated to initiate remote assistance for the autonomous vehicle. Each remote assistance button corresponds to a dedicated remote assistance function for the autonomous vehicle. For example, the system can include remote assistance buttons for causing the autonomous vehicle to stop, decelerate, or pull over. The system includes a controller configured to detect activation of the remote assistance button(s) and to cause remote assistance to be provided to the autonomous vehicle in response to the activation. For example, the autonomous vehicle may perform an action corresponding to the activated button with input assistance from the controller but without the system taking over control of the autonomous vehicle.

The present invention relates to an autonomous driving mobile service robot warehousing and delivery system that is capable of systematically storing and delivering a plurality of robot bodies from line parts by means of a server and comparing the number of delivery times of the robot bodies by line part to allow the robot bodies to be delivered sequentially from the line part having the smallest number of delivery times, thereby distributing the usage rate of the robot bodies disposed by line part.

Methods and apparatus are provided for allocating tasks to be performed by one or more autonomous vehicles to achieve a mission objective. Generally, a task allocation system identifies a final task associated with a given mission objective, identifies predecessor tasks necessary to complete the final task, generates one or more candidate tasks sequences to accomplish the mission objective, generates a task allocation tree based on the candidate task sequences, and searches the task allocation tree to find a task allocation plan that meets a predetermined selection criteria (e.g., lowest cost). Based on the task allocation plan, the task allocation system determines a task execution plan and generates control data for controlling one or more autonomous vehicles to complete the task execution plan.

An autonomous lawn mower includes removable batteries, one or more electric motors, a cutting blade, one or more wheels, an angle sensor, and a controller. The cutting blade is driven by one of the electric motors. The wheels are driven by one of the electric motors. The controller is configured to allow the lawn mower to perform a grass cutting function at a jobsite without human interaction. The controller is configured to determine whether a roll angle is greater than a first predefined amount using data from the angle sensor. In response to determining the roll angle is greater than the first predefined amount, the controller is configured to stop operation of one of the one or more electric motors configured to provide rotational drive power to the cutting blade.

Dynamic allocation and coordination of auto-navigating vehicles uses robotic vehicles and centrally dispatched roaming order selectors to create a significantly more efficient, yet flexible, approach to picking goods within a warehouse. Robotic vehicles are configured to be loaded with goods from pick faces to fill orders. Each robotic vehicle follows a route that includes appropriate pick face locations. The robotic vehicles navigate from pick face to pick face where particular goods are located. Order selectors are dynamically and independently dispatched to meet the robotic vehicles at their pick face locations to load goods. Movement of the order selectors is orchestrated to increase efficiency in the order filling process within the warehouse.

Disclosed herein are embodiments for providing a trusted autonomy framework for unmanned aerial systems. One embodiment of a method includes receiving a request from an entity to participate in secure data sharing within the trusted autonomy framework for unmanned aerial systems, receiving a type of data that will be shared via the entity, and verifying an identity of the entity, a security infrastructure of the entity, and validating the data to be shared. In some embodiments, in response to verifying, accepting the entity into the trusted autonomy framework for unmanned aerial systems.

A system and method for autonomously controlling a set of unmanned aerial vehicles is provided. The autonomous ground control system may include a communications module and a fleet configuration module in communication with one or more user interface applications. The autonomous ground control system may receive one or more flight commands and generate fleet configuration instructions and safety information. The autonomous ground control system may provide the fleet configuration instructions to each unmanned aerial vehicle in the set in order to carry out the fleet configuration instructions in real time.