Learning peg-in-hole assembly using Cartesian DMPs with feedback mechanism

Liu, Nailong; Zhou, Xiaodong; Liu, Zhaoming; Wang, Hongwei; Cui, Long

doi:10.1108/aa-04-2020-0053

Cited by 19 publications

(8 citation statements)

References 14 publications

(10 reference statements)

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“…Two experiments are taken based on Omni Phantom joystick to certify the effectiveness of the proposed framework and methods. The future work is expected to apply the proposed method to industrial and surgical scenes such as autonomous assembly (Liu et al , 2020) and application with robotic tool use for the surgical operation. After using tool dynamics identification, NNs-based manipulator control (Su et al , 2020a, 2020b) and patient’s abdominal cavity recognition, an autonomic trajectory will be planned to guide the operational route.…”

Section: Discussionmentioning

confidence: 99%

Dynamic movement primitives based cloud robotic skill learning for point and non-point obstacle avoidance

Wang

2021

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Purpose Dynamic movement primitives (DMPs) is a general robotic skill learning from demonstration method, but it is usually used for single robotic manipulation. For cloud-based robotic skill learning, the authors consider trajectories/skills changed by the environment, rebuild the DMPs model and propose a new DMPs-based skill learning framework removing the influence of the changing environment. Design/methodology/approach The authors proposed methods for two obstacle avoidance scenes: point obstacle and non-point obstacle. For the case with point obstacles, an accelerating term is added to the original DMPs function. The unknown parameters in this term are estimated by interactive identification and fitting step of the forcing function. Then a pure skill despising the influence of obstacles is achieved. Using identified parameters, the skill can be applied to new tasks with obstacles. For the non-point obstacle case, a space matching method is proposed by building a matching function from the universal space without obstacle to the space condensed by obstacles. Then the original trajectory will change along with transformation of the space to get a general trajectory for the new environment. Findings The proposed two methods are certified by two experiments, one of which is taken based on Omni joystick to record operator’s manipulation motions. Results show that the learned skills allow robots to execute tasks such as autonomous assembling in a new environment. Originality/value This is a new innovation for DMPs-based cloud robotic skill learning from multi-scene tasks and generalizing new skills following the changes of the environment.

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Section: Discussionmentioning

confidence: 99%

Dynamic movement primitives based cloud robotic skill learning for point and non-point obstacle avoidance

Wang

2021

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“…In this article, the DMP model is used to generate the motion trajectory (Liu N. et al, 2020 ). The principle and main calculation process of the DMP model are as follows (Ude et al, 2014 ).…”

Section: Methodsmentioning

confidence: 99%

A Compliant Force Control Scheme for Industrial Robot Interactive Operation

et al. 2022

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To meet the enormous demand for smart manufacturing, industrial robots are playing an increasingly important role. For industrial operations such as grinding 3C products, numerous demands are placed on the compliant interaction ability of industrial robots to interact in a compliant manner. In this article, an adaptive compliant control framework for robot interaction is proposed. The reference trajectory is obtained by single-point demonstration and DMP generalization. The adaptive feedforward and impedance force controller is derived in terms of position errors, and they are input into an admittance controller to obtain the updated amount of position deviation. The compliant interaction effect is achieved, which is shown that the grinding head fits on the curved surface of a computer mouse, and the interaction force is within a certain expected range in the grinding experiment based on the performance an Elite robot. A comparative experiment was conducted to demonstrate the effectiveness of the proposed framework in a more intuitive way.

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“…In the aspect of motion trajectory planning and collision detection, many motion-oriented studies have come into play. Traditionally, dynamic movement primitives (DMPs) [15] are represented low-level motion primitives which can be stored in a library. And then DMPs are called by high-level planner.…”

Section: A Motion-oriented Programming By Demonstrationmentioning

confidence: 99%

A Skill Programming Method Based on Assembly Motion Primitive for Modular Assembly System

et al. 2021

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To improve the programming efficiency of automatic assembly system, a novel skill programming framework based on task learning is proposed for modular assembly system in this paper. In this framework, the motion sequence of assembly skills can be modeled by demonstration data. And the assembly task is represented hierarchically. A complete assembly process of a part is divided into several skills, and each skill is divided into several sequential assembly motion primitives (AMP) of multiple modules. Then, a learning method of assembly motion sequence based on Hidden Markov Model is proposed, and the maximum probability method is used to generate the optimal sequential AMP. Each AMP is input to the assembly system in the form of instruction to complete the assembly. Aiming at the problem of accurate positioning and trajectory planning, visual guidance and direct teaching method are used to settle this problem. To evaluate the viability of the proposed framework, a customized modular assembly system is used to acquire the demonstration data, and a graphical user interface (GUI) software is designed. Five assembly skills are learned. Experimental are conducted to validate the effectiveness of the proposed method.

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Learning peg-in-hole assembly using Cartesian DMPs with feedback mechanism

Cited by 19 publications

References 14 publications

Dynamic movement primitives based cloud robotic skill learning for point and non-point obstacle avoidance

Dynamic movement primitives based cloud robotic skill learning for point and non-point obstacle avoidance

A Compliant Force Control Scheme for Industrial Robot Interactive Operation

A Skill Programming Method Based on Assembly Motion Primitive for Modular Assembly System

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