Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating paths for a robot based on optimizing multiple objectives. One of the methods includes: receiving, by a motion planner, request to generate a path for a robot between a start point and an end point in a workcell of the robot, wherein the workcell is associated with one or more soft margin values that define spaces in which the robot should avoid when transitioning between points in the workcell; classifying path segments within the workcell as being inside the soft margin or outside the soft margin; generating a respective cost for each of the plurality of path segments within the workcell; generating a plurality of alternative paths; evaluating the plurality of alternative paths according to the respective costs; and selecting an alternative path based on respective total costs of the plurality of alternative paths.

A method of generating a building assembly that includes spraying a coating material onto a plurality of pieces of substrate disposed on a first assembly face. The spraying includes spraying the coating material onto the plurality of pieces of substrate via a sprayer configured to apply the coating material to a target surface via a nozzle coupled with a mobile storage container storing the coating material, the coating material impregnating voids of the substrate. The method also includes allowing the coating material impregnating the voids to dry and harden and become rigid to generate the building assembly.

Techniques are disclosed to perform task allocation for autonomous systems by implementing machine-learning to perform task allocation to Autonomous Mobile Robots (AMRs) in an environment. The disclosed techniques also provide for enhanced path planning and the identification of AMR health and failure prediction to further improve upon task allocation and system efficiency.

A robotic arm assembly includes a robotic arm, a base, and a utility member, the robotic arm extending between a root end attached to the base and a distal end including the utility member. A method for controlling the robotic arm assembly includes: determining a position of the base, the root end, or both relative to the environment; determining a task position and orientation for the utility member within the environment; determining a three-dimensional constraint of the environment; and determining a path for the robotic arm through the environment based on each of the position of the base, the root end, or both relative to the environment, the task position and orientation for the utility member within the environment, and the three-dimensional constraint of the environment.

A programming assistance apparatus includes circuitry. The circuitry generates a first display data to be displayed in a first input area in which to input, for each of a plurality of task groups including a plurality of tasks, a first condition under which at least one robot executes the tasks. The circuitry generates a second display data to be displayed in a second input area in which to input a second condition for an execution order of the plurality of task groups. The circuitry sets the first condition based on an input into the first input area. The circuitry sets the second condition based on an input into the second input area. The circuitry generates, based on the first condition and the second condition, a motion program for causing the at least one robot to execute the plurality of task groups.

Robots are deployed for handling different tasks in various field of applications. For the robots to function, task planning is required to be done. During the task planning, goal setting is done, as well as actions to be executed for corresponding to each goal are decided. Traditionally, this is carried out first and then the robots start executing the task plan, thereby failing to capture any change in the environment the robots operate, post the task plan generation. Disclosed herein is a method and system for robotic task planning in which a task plan is generated and is executed. However if the task execution fails due to change in any of the parameters/factors, then the system dynamically invokes an adaptation and re-planning mechanism which either updates the already generated task plan (by capturing the change) or generates a new task plan, which the robot can execute to achieve the goal.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing robot planning using a process definition graph. The techniques can include receiving a process definition graph having a plurality of task nodes that represent respective tasks to be performed by a respective robot of a plurality of robots, wherein each task node is associated with a location at which the task will be performed; generating, from the process definition graph, an initial modified process definition graph that adds constraints for respective swept volumes occupied by each task represented by the plurality of task nodes; and generating, from the initial modified process definition graph, a refined process definition graph, wherein the refined process definition graph includes respective motion plans for robots moving between tasks, wherein the motion plans define transitions that avoid the swept volumes occupied by each task represented by the plurality of task nodes.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating paths for a robot based on optimizing multiple objectives. One of the methods includes: receiving, by a motion planner, request to generate a path for a robot between a start point and an end point in a workcell of the robot, wherein the workcell is associated with one or more soft margin values that define spaces in which the robot should avoid when transitioning between points in the workcell; classifying path segments within the workcell as being inside the soft margin or outside the soft margin; generating a respective cost for each of the plurality of path segments within the workcell; generating a plurality of alternative paths; evaluating the plurality of alternative paths according to the respective costs; and selecting an alternative path based on respective total costs of the plurality of alternative paths.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing a plan for one or more robots using a process definition graph. One of the methods includes receiving a process definition graph for a robot, the process definition graph having a plurality of action nodes, and the plurality of action nodes including a plurality of motion nodes that were previously split from a single motion node due to a conflict with a second motion node representing a second motion to be performed by another robot; determining that the conflict with the second motion node no longer exists; and in response to determining that the conflict with the second motion node no longer exists, modifying the process definition graph including combining the plurality of motion nodes into a new single motion node representing all of the motions of the plurality of motion nodes.

A device intelligence architecture configures and controls on-site devices and performs environment monitoring to facilitate effective device functionality. The architecture facilitates efficient use of devices (e.g., robotics) in an unstructured and dynamic environment, which will allow deployments in many more environments than is currently possible. The architecture stores and shares information between segregated devices to avoid the silo effect of vendor specific stacks. The architecture also models capabilities of devices and maps actions to intelligence packages to deploy a specific intelligence package to the device. The architecture also implements distributed processing. For instance, computationally intensive tasks may be offloaded to the back end processing, with action updates resulting from the processing pushed to the devices.