Dexterity enhancement of continuum robot for natural orifice transluminal endoscopic surgery in the dual-manipulator collaborative space

Background: Suturing and knotting in Natural Orifice Transluminal Endoscopic Surgery (NOTES) requires the robot not only to be able to work with multiple manipulators but also to have a high degree of dexterity. However, little attention has been paid to the design and enhancement of dexterity in multi-manipulated robots. Methods: In this paper, the dexterity of a new dual-manipulator collaborative continuum robot in collaborative space is analyzed and enhanced. A kinematic model of the continuum robot was developed. The dexterity function of the robot is evaluated based on the concepts of the low-Degree-of-Freedom Jacobian matrix. Then an Adaptive Parameter Gray Wolf Coupled Cuckoo Optimization Algorithm with faster convergence and higher accuracy is innovatively proposed to optimize the objective function. Finally, experiments demonstrate that the dexterity of the optimized continuum robot is enhanced. Results: The optimization results show that the optimized dexterity is 24.91% better than the initial state. Conclusion: Through the work of this paper, the robot for NOTES can perform suturing and knot more dexterously, which has significant implications for the treatment of digestive tract diseases.