Seven-degree-of-freedom redundancy mechanical arm task constraint path planning method under Descartes space

Abstract

The invention provides a seven-degree-of-freedom redundancy mechanical arm task constraint path planning method under a Descartes space, and relates to a redundancy mechanical arm task constraint pathplanning technology. A collision-free path is planned for a seven-degree-of-freedom mechanical arm end executor in the Descartes space for achieving task constraint, and the problem that an existingplanning method is mostly and only suitable for joint space planning, but can not ensure optimization of the end track is solved. According to the seven-degree-of-freedom redundancy mechanical arm task constraint path planning method under the Descartes space, an improved FMT* planning algorithm is adopted in the Descartes space, and the end collision-free path meeting the task constraint is planned out for a mechanical arm, so that the optimization of the end track is achieved, and the movement reasonability of the mechanical arm is ensured. The method comprises the steps that a description method of a mechanical arm end constraint position space based on a task function under the Descartes space is provided, Gaussian sampling is used for replacing a sampling scheme of an original algorithm, distance measurement based on the previous constraint position space is put forward, and the effectiveness of a sampling point and the effectiveness of local connection are judged with the adoption of a mode based on arm configuration description.

Description

technical field [0001] The invention relates to a redundant manipulator task constraint path planning technology. Background technique [0002] Currently, planning methods based on random sampling of joint space are widely used in the path planning of redundant manipulators under task constraints due to the difficulty of fully describing the available arm configurations and the lack of effective inverse kinematics algorithms. However, task constraints are usually imposed on the end-cartesian space. Random sampling planning in joint space usually maps task constraints directly to joint space. Random sampling is performed in joint space. Many constraints (for example, the execution of The constrained manifold defined by the pose constraint on the controller) occupies an infinitesimal volume in the joint space, and the probability of sampling points in the constrained manifold is almost zero. It is extremely unlikely to find configurations located on such a manifold by random s...

AI Technical Summary

Problems solved by technology

[0004] The present invention aims to plan a collision-free path for the end effector of a seven-degree-of-freedom mechanical arm in Cartesian space, and solve the problem that the existing planning method is only applicable to

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