An Adaptive Control Approach to Robotic Assembly with Uncertainties in Vision and Dynamics

Abstract

The objective of this paper is to propose an adaptive impedance control framework to cope with uncertainties in vision and dynamics in robotic assembly tasks. The framework is composed of an adaptive controller, a vision system, and an interaction planner, which are all supervised by a finite state machine. In this framework, the target assembly object's pose is detected through the vision module, which is then used for the planning of the robot trajectories. The adaptive impedance control module copes with the uncertainties of the vision and the interaction planner modules in alignment of the assembly parts (a peg and a hole in this work). Unlike the classical impedance controllers, the online adaptation rule regulates the level of robot compliance in constrained directions, acting on and responding to the external forces. This enables the implementation of a flexible and adaptive Remote Center of Compliance (RCC) system, using active control. We first evaluate the performance of the proposed adaptive controller in comparison to classical impedance control. Next, the overall performance of the integrated system is evaluated in a pegin-hole setup, with different clearances and orientation mismatches.

A Contact-Adaptive Control Framework for Co-Manipulation Tasks with Application to Collaborative Screwing

“…and K f 4 . The first two are set high to guarantee a good tracking capability, as in [14]. When it comes to K f 3 and K f 4 , they are selected equal to each other and denoted as K f from now on.…”

“…To confront these challenges, in the framework presented in this paper, an adaptive control technique is employed [14]. In this controller, the robot stiffness can be modulated in constrained directions depending on the assigned compliance gains and the measured force at the end-effector.…”

“…In more details, a power tool is fixed at the end-effector of the robot and a force sensor is attached on it to measure the interaction forces, while the end-effector forces are estimated through the torque sensors located at the robot joints. The former force data is used to change the robot stiffness depending on the constrained directions (similar to [14]), whereas the latter force information is utilized to adapt the robot trajectories to comply with the operators' needs.…”

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“…D ESPITE the significant progress achieved in robotics domain to automate and increase the flexibility of industrial tasks [1], [2], manual operations such as packaging [3] and assembly [4] still account for a large proportion of tasks in this area. To increase the productivity, ergonomics, and physical performance of the workers, in manual operations, wearable assistive devices have been developed for assisted walking [5], material handling [6], and sit-to-stand [7] applications in the last decade.…”

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“…Therefore, it is necessary to tune the impedance profiles of the robots based on the actual task, human, and environmental states. In such a way, robots can respond to modeling/environment uncertainties or unexpected changes [14], [15].…”