Dynamic modulation of multi-task priority for controlling redundancy insufficient robots

Redundant robots are gaining popularity for their agility in service tasks, but they struggle with managing multiple tasks in dynamic and unstructured environments. Research is currently centered around adjusting task priorities to facilitate the robot’s adaptability to different situational demands. This paper addresses the challenge of automated task prioritization in multi-task handling and presents a solution for robots to effectively execute demanding tasks, even when faced with limited redundancy and multiple constraints. We introduce the concept of secondary merged tasks and formulate task merging as a matrix design problem. An iterative updating algorithm based on real-time task status is proposed to enable automatic prioritization and dynamic adjustment of tasks. This methodology ensures appropriate execution of all tasks at the right time. We analyze the convergence of weight transfer between redundancies and task dependencies, ensuring stable task execution. Simulation experiments and real-world experiments using 9-DOF mobile manipulator and 6-DOF fixed manipulator are conducted to validate the proposed method. This research provides a feasible approach for task prioritization in multi-task handling and holds potential applications.