CN-121973206-A - Self-adaptive collaborative trajectory planning method and system for multi-end effector

CN121973206ACN 121973206 ACN121973206 ACN 121973206ACN-121973206-A

Abstract

The invention relates to the technical field of machine control, in particular to a self-adaptive collaborative trajectory planning method and system for a multi-end effector. The method comprises the steps of establishing a decoupling control model of internal force and external force, defining independent force tracking targets pointing to a main end effector for each auxiliary end effector by carrying out mathematical transformation on an expected internal force group meeting force closed conditions, constructing a main-slave cooperative control framework, modeling the force tracking targets as compression forces of virtual springs between the main end effector and the auxiliary end effector, designing an adaptive estimation law based on a Liapunov theory for online joint estimation aiming at the uncertainty of object rigidity and contact point movement, and generating a reference movement track of the auxiliary end effector based on estimation parameters and an impedance model to realize robust force tracking. The invention converts the complex multi-point internal force control problem into the structured 'master-slave' motion tracking problem, and can realize the stable collaborative operation of the multi-end effector under the unstructured environment with unknown rigidity and unknown geometric shape.

Inventors

WANG ZHALA
FAN WENLIANG
SONG FEI
ZHANG YUNFEI

Assignees

鄂尔多斯应用技术学院

Dates

Publication Date: 20260505
Application Date: 20260130

Claims (8)

1. A method for adaptive collaborative trajectory planning for a multi-end effector, the method comprising the steps of: step S1, establishing a decoupling control model of internal force-external force of the multi-end effector; step S2, based on the decoupling control model, performing mathematical transformation on the cooperative internal force control targets meeting the force closed condition, and defining an independent force tracking target pointing to the main end effector for each slave end effector except the main end effector in the system; S3, based on the independent force tracking target, constructing an absolute-relative motion master-slave cooperative control framework taking the master end effector as a reference; Step S4, modeling the independent force tracking target of each slave end effector as the compression force of a virtual spring between the slave end effector and the master end effector, wherein the axial direction of the virtual spring is consistent with the connecting line direction of the slave end effector and the master end effector; s5, designing a combined self-adaptive estimation law for the rigidity of an object and the uncertainty of the movement of a contact point based on the Liapunov theory so as to estimate the equivalent rigidity of the virtual spring and the natural length parameter of the equivalent rigidity; And S6, generating a reference motion track from the end effector based on the impedance control model and the equivalent stiffness and natural length parameters of the virtual spring of the combined self-adaptive estimation law, and realizing the robust tracking of the independent force tracking target.
2. The method for adaptive collaborative trajectory planning for a multi-end effector according to claim 1, wherein in step S1, the establishment of the decoupling control model includes: introducing a virtual link connecting the end effector contact point with the object reference point; constructing a linear mapping matrix G for describing the relation between the force/moment of each contact point and the external force/moment of the object; Calculating a zero-space basis matrix V of the linear mapping matrix G, and establishing a generalized force vector applied by an end effector A mathematical model decomposed into an external force component and an internal force component, the model expressed as: ; Wherein the method comprises the steps of Is the generalized inverse of G and, In order to apply the resultant force vector to the object, Is any vector corresponding to the internal force subspace.
3. The method according to claim 1, wherein in step S2, the mathematical transformation of the collaborative internal force control objective that satisfies a force blocking condition comprises: Constructing a first transformation matrix, and mapping a generalized force vector applied by an end effector into an expected internal force vector conforming to a force sealing condition; Constructing a second transformation matrix, and mapping the generalized force vector into the independent force tracking target vector; and establishing an equivalent mapping relation between the expected internal force vector and the independent force tracking target vector by calculating the generalized inverse of the first transformation matrix.
4. The method according to claim 1, wherein in step S5, the joint adaptive estimation law is: Wherein, the For the estimated equivalent stiffness of the virtual spring, For the estimated natural length parameter of the virtual spring, For the internal force estimate calculated based on the current estimation parameters, For the value of the internal force actually measured, To determine the current position of the end effector along the link from the end effector, t is time, And The gain is positive and the gain is fixed, Indicating the slave end effector, i indicates the number of polynomial coefficients.
5. The method according to claim 4, wherein in step S6, the reference motion trajectory is calculated by the following formula: Wherein, the For the generated reference motion trail position, M, B, K are impedance control parameters, Based on estimated parameters The reconstructed virtual spring natural length end point estimates the position, Target values are tracked for independent forces derived by the mathematical transformation.
6. The method of claim 5, further comprising smoothing the trajectory with a first order low pass filter after generating the reference motion trajectory, the smoothing formula being Wherein For a smoothing coefficient, k is a time step.
7. The method of claim 4, wherein the actual measured internal force values are measured in real time by force sensors mounted on the end effector.
8. A multi-end effector adaptive collaborative trajectory planning system, the system comprising: at least one controller configured to perform the multi-end effector adaptive collaborative trajectory planning method of any one of claims 1-7; Each robot unit at least comprises a mechanical arm and an end effector arranged at the tail end of the mechanical arm; And the force sensing module is installed on the end effector and is used for measuring the internal force contacted with the object.

Description

Self-adaptive collaborative trajectory planning method and system for multi-end effector Technical Field The invention relates to the technical field of machine control, in particular to a self-adaptive collaborative trajectory planning method and system for a multi-end effector. Background Along with the evolution of intelligent manufacturing to the flexible and collaborative directions, the collaborative operation of multiple robots has become a key technology for breaking through the bottleneck of complex operation tasks. However, existing multi-robot systems face multiple challenges in dealing with collaborative operations in unstructured environments. First, at the force control level, when multiple End Effectors (EEs) co-operate with an object of unknown parameters (e.g., stiffness, mass, geometry), there is a dual coupling uncertainty in the force and pose of the contact point. The traditional control method based on the model is severely dependent on accurate object dynamic parameters, on-line parameter identification is slow, real-time requirements of dynamic operation are difficult to meet, and system instability is easy to cause when parameters are suddenly changed. The existing self-adaptive control research focuses on single parameter estimation, ignores the coupling effect among parameters, and leads to the control performance reduction in complex cooperative tasks. More critical is that the internal force is the core for determining the operation stability, but the existing method mostly uses the internal force as a secondary constraint treatment, lacks a systematic internal force optimization and control framework, and is difficult to realize parameter robustness on the premise of ensuring force closure and slip prevention. Secondly, links such as task allocation, motion planning, internal force control and the like are often designed by cutting, and a unified optimization framework is lacked in a system level. The separation results in suboptimal overall performance of the system, and the system-level targets such as load balancing, energy consumption optimization and the like are difficult to realize while the kinematic and dynamic constraints are met. Therefore, a need exists for a multi-end effector cooperative control method that can handle multiple parameter uncertainties simultaneously, achieve robust internal force tracking, and can be deeply integrated with upper layer mission planning. Disclosure of Invention In view of the above, the present invention aims to provide a multi-end effector adaptive collaborative trajectory planning system, so as to solve the problem that the overall performance of the proposed system is suboptimal, and it is difficult to achieve system-level targets such as load balancing and energy consumption optimization while satisfying kinematic and dynamic constraints. Based on the above object, the present invention provides a method for adaptive collaborative trajectory planning of a multi-end effector, comprising the steps of: step S1, establishing a decoupling control model of internal force-external force of the multi-end effector; step S2, based on the decoupling control model, performing mathematical transformation on the cooperative internal force control targets meeting the force closed condition, and defining an independent force tracking target pointing to the main end effector for each slave end effector except the main end effector in the system; S3, based on the independent force tracking target, constructing an absolute-relative motion master-slave cooperative control framework taking the master end effector as a reference; Step S4, modeling the independent force tracking target of each slave end effector as the compression force of a virtual spring between the slave end effector and the master end effector, wherein the axial direction of the virtual spring is consistent with the connecting line direction of the slave end effector and the master end effector; s5, designing a combined self-adaptive estimation law for the rigidity of an object and the uncertainty of the movement of a contact point based on the Liapunov theory so as to estimate the equivalent rigidity of the virtual spring and the natural length parameter of the equivalent rigidity; And S6, generating a reference motion track from the end effector based on the impedance control model and the equivalent stiffness and natural length parameters of the virtual spring of the combined self-adaptive estimation law, and realizing the robust tracking of the independent force tracking target. Preferably, in step S1, the establishing of the decoupling control model includes: introducing a virtual link connecting the end effector contact point with the object reference point; constructing a linear mapping matrix G for describing the relation between the force/moment of each contact point and the external force/moment of the object; Calculating a zero-space basis matrix V of the linear mapping