Trust-Based Optimal Subtask Allocation and Model Predictive Control for Human-Robot Collaborative Assembly in Manufacturing

Abstract

We develop a one human-one robot hybrid cell for collaborative assembly in manufacturing. The selected task is to assemble a few LEGO parts into a final assembled product following specified instructions and sequence in collaboration between the human and the robot. We develop a two-level feedforward optimization strategy that determines the optimal subtask allocation between the human and the robot for the selected assembly before the assembly starts. We derive dynamics models for human’s trust in the robot and the robot’s trust in the human for the assembly and estimate the trusts. The aim is to maintain satisfactory trust levels between the human and the robot through the application of the optimal subtask allocation. Again, subtask re-allocation is proposed to regain trusts if the trusts reduce to below the specified levels. Furthermore, it is hypothesized that fluctuations in human’s trust in the robot may cause fluctuations in human’s speeds and the human may appreciate if the robot adjusts its speeds with changes in human speeds. Hence, trust-based Model Predictive Control (MPC) is proposed to minimize the variations between human and robot speeds and to maximize the trusts. Experiment results prove the effectiveness of the hybrid cell, the feedforward optimal subtask allocation and of the trust-based MPC. The results also show that the overall assembly performance can be enhanced and the performance status can be monitored through a single dynamic parameter, i.e. the trust.

Calibrating intuitive and natural human–robot interaction and performance for power-assisted heavy object manipulation using cognition-based intelligent admittance control schemes

“…Similar optimization approaches were used for other applications such as optimizing subtask allocation between human worker and collaborative robot in human–robot collaborative assembly in manufacturing. 35 In addition, a plethora of well-established classical optimization techniques are available in the state-of-the-art literatures. 34 However, many of these standard methods may not provide the most appropriate solution for the situation of HRI due to their complexity and lack of practicality, which motivated us to use the proposed search-based local optimization method.…”

“…We formulated an MPC taking theoretical and conceptual inspiration from Rawlings and Mayne 36 and Kouvaritakis and Cannon 37 and practical inspiration from Rahman and Ikeura 29 and Rahman et al 35 for the PARS, as shown Figure 10. Mean (n ¼ 10) workload rating scores (out of 100) with standard deviations for the six dimensions of the NASA TLX for the FAC with lightweight and heavy object manipulation.…”

“…We formulated an MPC taking theoretical and conceptual inspiration from Rawlings and Mayne 36 and Kouvaritakis and Cannon 37 and practical inspiration from Rahman and Ikeura 29 and Rahman et al 35 for the PARS, as shown in Figure 12. In Figure 12, a ( r u n ) is the predicted optimal output (acceleration) in a run (lifting trial), a r is the reference acceleration, and u ( r u n ) is the predicted optimal input to produce a ( r u n ) in the run.…”

Allowing a Real Collaboration Between Humans and Robots

“…Reference [4] describes a genetic algorithm for a collaborative assembly station which minimises the assembly time and costs. In [14], a trust-based dynamic subtask allocation strategy for manufacturing assembly processes has been presented. The method, which relies on a Model Predictive Control (MPC) scheme, accounts for human and robot performance levels, as well as far their bilateral trust dynamics.…”

Building Trust in Robots in Robotics-Focused STEM Education under TPACK Framework in Middle Schools

“…In this paper, by trust of teachers and students in robotics, we mean their willingness to believe in, understand, and accept the solutions provided by robots and to rely on the contributions of robots in STEM teaching and learning. 24 Many factors of robots may affect the trust of teachers and students towards robots. 25 However, two critical factors, viz., the robot's overall performance and its incomprehensible or erroneous response while working with its human counterparts (teachers and students) usually affect a human's trust in the robots.…”