No abstract

No abstract

In human and robot collaborative hybrid assembly cell as we proposed, it is important to develop automatic subtask allocation strategy for human and robot in usage of their advantages. We introduce a folk-joint task model that describes the sequential and parallel features and logic restriction of human and robot collaboration appropriately. To preserve a cost-effectiveness level of task allocation, we develop a logic mathematic method to quantitatively describe this discrete-event system by considering the system tradeoff between the assembly time cost and payment cost. A genetic based revolutionary algorithm is developed for real-time and reliable subtask allocation to meet the required cost-effectiveness. This task allocation strategy is built for a human worker and collaborates with various robot co-workers to meet the small production situation in future. The performance of proposed algorithm is experimentally studied, and the cost-effectiveness is analyzed comparatively on an electronic assembly case.Note to Practitioners-This paper was motivated by the critical demand within the manufacturing industry to meet the High-Mix, Low-Volume requirements for the changing consumer market demands. A fully robotic manufacturing process cannot obtain sufficient flexibility with a highly variable product line. Therefore, it must aim towards a complimentary cost-effectiveness to improve productivity from other ways. By taking advantage of a human's adaptability and flexibility, we can exploit the concept of a hybrid assembly system for medium sized manufacturing processes. Hybrid assembly creates a modern assembly mode where the robot works as co-worker to collaborate with the human and share the same working space and time. Hybrid assembly cell emphasizes two challenging issues to somehow improve the manufacturing productivity: (1) the way of describing and modeling human and robot collaboration and coordination, and (2) the effective task scheduling and allocation strategy for human and robot. This research is focused on the subtask allocation method while considering the features of human and robot collaboration. The original contribution of this work is the design of an offline and online resource constraint project scheduling problem (RCPSP) algorithm for hybrid assembly systems. The resource is not only limited to the recycle resource, but also extend to the features of human and robot, such as human fatigue issues, and robot assembly failure issues. This RCPSP for hybrid assembly is to realize both sequential and parallel task scheduling between human and several robots while minimizing the assembly time and payment Manuscript cost. This algorithm is fast in reaching the semi-optimal solution, therefore it can be used for both offline and online situations. The simulation results demonstrates the effectiveness of this task scheduling algorithms. We believe this study is helpful to improve the productivity for hybrid assembly system. Index Terms-Genetic algorithm, human and robot collaboration, hybrid assembly sys...

No abstract

This paper proposes a 3-D biped dynamic walking algorithm based on passive dynamic autonomous control (PDAC). The robot dynamics is modeled as an autonomous system of a 3-D inverted pendulum by applying the PDAC concept that is based on the assumption of point contact of the robot foot and the virtual constraint as to robot joints. Due to autonomy, there are two conservative quantities named "PDAC constant," which determine the velocity and direction of the biped walking. We also propose the convergence algorithm to make PDAC constants converge to arbitrary values, so that walking velocity and direction are controllable. Finally, experimental results validate the performance and the energy efficiency of the proposed algorithm.

Purpose -The purpose of this paper is to propose a model of assembly strategy generation and selection for human and robot coordinated (HRC) cell assembly. High-Mix, Low-Volume production in small production manufacturing industry, tends to employ more flexible assembly cells. The authors propose innovative human and robot coordinated assembly cells to solve the problem of persistent growing cost for human resources and occasional changes in programs and configurations for robots. The first issue is to find out an optimal way to allocate the assembly subtasks to both humans and robots. Design/methodology/approach -A dual Generalized Stochastic Petri Net (GSPN) model is theoretically studied and then off line built based on a practical assembly task for human and robot coordination. Based on GSPN, Monte Carlo method is carried out to study the time cost and payment cost or possible strategies, and Multiple-Objective Optimization (MOOP) method related Cost-effectiveness analysis is adopted to select the optimal ones. Findings -It is discovered that human and robot coordinated assembly can reduce the assembly time and meanwhile reduce the assembly cost. The authors demonstrate the effectiveness of this approach by comparing the simulation and experimental results. Originality/value -The novelty with this work is that the human and robot coordinated flexible assembly cell, as the authors proved, is the main stream in small production in future due to the higher human source pressure from society and cost pressure upon the company. Based on this innovative work, the authors proposed a dual GSPN model to model the assembly task allocation process for human and robot, the model of which is also effective in modeling the possible robot and human behaviors.

No abstract

No abstract