Model-Based Motion Planning for Robotic Assembly of Non-Cylindrical Parts

Purpose The purpose of this paper is to propose a new method based on three-dimensional (3D) vision technologies and human skill integrated deep learning to solve assembly positioning task such as peg-in-hole. Design/methodology/approach Hybrid camera configuration was used to provide the global and local views. Eye-in-hand mode guided the peg to be in contact with the hole plate using 3D vision in global view. When the peg was in contact with the workpiece surface, eye-to-hand mode provided the local view to accomplish peg-hole positioning based on trained CNN. Findings The results of assembly positioning experiments proved that the proposed method successfully distinguished the target hole from the other same size holes according to the CNN. The robot planned the motion according to the depth images and human skill guide line. The final positioning precision was good enough for the robot to carry out force controlled assembly. Practical implications The developed framework can have an important impact on robotic assembly positioning process, which combine with the existing force-guidance assembly technology as to build a whole set of autonomous assembly technology. Originality/value This paper proposed a new approach to the robotic assembly positioning based on 3D visual technologies and human skill integrated deep learning. Dual cameras swapping mode was used to provide visual feedback for the entire assembly motion planning process. The proposed workpiece positioning method provided an effective disturbance rejection, autonomous motion planning and increased overall performance with depth images feedback. The proposed peg-hole positioning method with human skill integrated provided the capability of target perceptual aliasing avoiding and successive motion decision for the robotic assembly manipulation.

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Motion planning of a fish-like piezoelectric actuated robot using model-based predictive control

Journal of Vibration and Control ◽

10.1177/10775463211048255 ◽

2021 ◽

pp. 107754632110482

Author(s):

Arthur S Barbosa ◽

Lucas Z Tahara ◽

Maíra M da Silva

Keyword(s):

Motion Planning ◽

Predictive Control ◽

Fiber Composite ◽

Beam Theory ◽

Mode Shapes ◽

Design Strategies ◽

Mechanical Coupling ◽

Model Based ◽

Input Signals ◽

Bounded Input

This work proposes a novel methodology for planning the motion of fish-like soft robots actuated by macro-fiber composite (MFC) pairs. These structures should mimic oscillatory and undulation movements, which can be accomplished if the amplitude of the tail motion is larger than that of the head motion. Design strategies, such as the use of concentrated and distributed masses, are addressed to mimic fish-like motion since they guarantee suitable mode shapes for the structure. The motion planning proposal explores a model-based predictive control (MPC) strategy for deriving the input signals for the MFC actuators. This model-based control strategy requires the use of reasonably small-sized models. This is accomplished by extracting modal state-space models based on the free–free Euler–Bernoulli beam theory considering the electro-mechanical coupling of the MFC actuator pairs. Numerical results demonstrate the capability of the proposal for deriving bounded input signals that generate oscillatory and undulation movements even in the presence of disturbances. This general approach can be further extended for other applications.

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