Sensing and Control for Simultaneous Precision Peg-in-Hole Assembly of Multiple Objects

The problem of simultaneous precision assembly of multiple objects is quite practical one to form compact physical structures and functionalities in mechatronics and advanced robotics. The core research aspects facing the problem are the contact status perception between each two object and the motion planning of each separate object. These two aspects mutually affect each other and cannot be discussed separately. In this paper, we first strategically discuss the possible approaches to solve the simultaneous assembly problem and analyze their advantages and drawbacks. Then, a probabilistic control method is developed based on the incomplete perceived information of the assembly process, which can achieve the highest assembly efficiency from the strategic perspective. Specifically, by fully utilizing the mechanical properties of materials in micrometer scale, the interaction between objects is first characterized as stochastic state-transition process. Second, adopting the simultaneous feeding strategy instead of serial feeding, the current contact status between each two object is determined based on the state-transition equation as a probability distribution along a hyperline. Finally, the motion planning technique is designed taking all possible radial forces on every contact surface into consideration. The experimental results demonstrate the effectiveness of the proposed method. Note to Practitioners-This paper is motivated to develop an automatic control method to realize the 3-D simultaneous assembly of multiple objects under simultaneous feeding scheme, which can strategically achieve the highest efficiency compared with the serial feeding scheme. The peg-hole fitting is modeled as a stochastic state-transition process, which needs to be pretrained with experience offline. In practical task, the multiple objects are first aligned in 3-D space in six degrees of freedom (DOFs) based on the microscopic vision. Then, the contact states on the multiple contacting surfaces during the assembly process are described as multivariate Gaussian probabilistic distribution. Finally, the motions of each object are planned based on the state-transition confidence and will be evaluated once taken. Motions may be withdrawn if the confidence is unacceptable, while action withdrawing rate is a critical assessment factor of the proposed method.