Mechanical arm task planning system based on behavior tree and application method

Abstract

The invention discloses a mechanical arm task planning system based on a behavior tree and an application method. The mechanical arm task planning system comprises a track segmentation module, a target pose detection module, a behavior tree design module, a dynamic motion primitive generalization module, an upper computer system and a Kinova Jaco mechanical arm. On the one hand, a Kinova Jaco mechanical arm is controlled by teleoperation to perform multiple grabbing task demonstration, an upper computer system obtains action elements from collected demonstration data through a track segmentation module, and an action element library is constructed; on the other hand, according to the grabbing task execution logic, a grabbing task behavior tree is created through a behavior tree design module; the Kinect sensor captures an object depth image, after the object position and posture are estimated through the target posture detection module, generalization is carried out through the dynamic motion primitive generalization module in combination with corresponding motion primitives in the motion primitive library, the motion primitives obtained through generalization serve as action nodes of a behavior tree, the behavior tree transmits an execution instruction to the upper computer system, and the action primitives serve as action nodes of the dynamic motion primitive generalization module. And the Kinova Jaco mechanical arm is controlled to execute the grabbing task.

Description

technical field [0001] The invention relates to the field of robotic arm task planning, in particular to a behavior tree-based robotic arm task planning system and an application method. Background technique [0002] As all kinds of robots are gradually being used in households, medical services and other fields, with operation skills such as assisted eating, clothes sorting, and passing objects, people are increasingly expecting robots to have stronger autonomous operation capabilities. In the actual environment, it can help or even replace humans to complete more complex operational tasks. Conventional methods such as traditional complex programming and teleoperation can enable robots to have certain operating skills and enable them to work better in a structured work environment. However, with the continuous expansion of robot application fields, robots are often faced with unknown, complex and dynamic environments. Traditional task planning methods cannot make robots ad...

AI Technical Summary

Problems solved by technology

However, with the continuous expansion of robot application fields, robots often face unknown, complex and dynamic environments. Traditional task planning methods cannot make robots well adapt to unstructured working environments, and traditional robot task planning methods have a long cycle, Many problems such as low efficiency, heavy workload and inability to dynamically adapt to the environment

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