MOTION PLANNING AND CONTROL FOR ROBOTS IN SHARED WORKSPACE EMPLOYING STAGING POSES

    公开(公告)号:US20230286156A1

    公开(公告)日:2023-09-14

    申请号:US18119381

    申请日:2023-03-09

    IPC分类号: B25J9/16

    CPC分类号: B25J9/1666 B25J9/1682

    摘要: The structures and algorithms described herein employ staging poses to facilitate the operation robots operating in a shared workspace or workcell, preventing or at least reducing the risk of collision while efficiently moving robots to one or more goals to perform respective tasks. Motion planning can be performed during runtime, and includes identifying one or more staging poses for a robot to advantageously position or configure a robot whose path is blocked or is expected to be blocked by one or more other robots, monitoring the other robots and moving the robot toward a goal in response to the path becoming unblocked or cleared. The staging pose can be identified using various heuristics to efficiently position or configure the robot to complete its task one its path becomes unblocked or cleared.

    Configuration of robots in multi-robot operational environment

    公开(公告)号:US11623346B2

    公开(公告)日:2023-04-11

    申请号:US17153662

    申请日:2021-01-20

    IPC分类号: B25J9/16 G06N3/12 G06N3/126

    摘要: Solutions for multi-robot configurations are co-optimized, to at least some degree, across a set of non-homogenous parameters based on a given set of tasks to be performed by robots in a multi-robot operational environment. Non-homogenous parameters may include two or more of: the respective base position and orientation of the robots, an allocation of tasks to respective robots, respective target sequences and/or trajectories for the robots. Such may be executed pre-runtime. Output may include for each robot: workcell layout, an ordered list or vector of targets, optionally dwell time durations at respective targets, and paths or trajectories between each pair of consecutive targets. Output may provide a complete, executable, solution to the problem, which in the absence of variability in timing, can be used to control the robots without any modification. A genetic algorithm, e.g., Differential Evolution, may optionally be used in generating a population of candidate solutions.