Aljaž Kramberger, Rok Piltaver, Bojan Nemec, Matjaž Gams and Aleš Ude
In this paper, the authors aim to propose a method for learning robotic assembly sequences, where precedence constraints and object relative size and location constraints can be…
Abstract
Purpose
In this paper, the authors aim to propose a method for learning robotic assembly sequences, where precedence constraints and object relative size and location constraints can be learned by demonstration and autonomous robot exploration.
Design/methodology/approach
To successfully plan the operations involved in assembly tasks, the planner needs to know the constraints of the desired task. In this paper, the authors propose a methodology for learning such constraints by demonstration and autonomous exploration. The learning of precedence constraints and object relative size and location constraints, which are needed to construct a planner for automated assembly, were investigated. In the developed system, the learning of symbolic constraints is integrated with low-level control algorithms, which is essential to enable active robot learning.
Findings
The authors demonstrated that the proposed reasoning algorithms can be used to learn previously unknown assembly constraints that are needed to implement a planner for automated assembly. Cranfield benchmark, which is a standardized benchmark for testing algorithms for robot assembly, was used to evaluate the proposed approaches. The authors evaluated the learning performance both in simulation and on a real robot.
Practical implications
The authors' approach reduces the amount of programming that is needed to set up new assembly cells and consequently the overall set up time when new products are introduced into the workcell.
Originality/value
In this paper, the authors propose a new approach for learning assembly constraints based on programming by demonstration and active robot exploration to reduce the computational complexity of the underlying search problems. The authors developed algorithms for success/failure detection of assembly operations based on the comparison of expected signals (forces and torques, positions and orientations of the assembly parts) with the actual signals sensed by a robot. In this manner, all precedence and object size and location constraints can be learned, thereby providing the necessary input for the optimal planning of the entire assembly process.
Details
Keywords
Fares J. Abu-Dakka, Bojan Nemec, Aljaž Kramberger, Anders Glent Buch, Norbert Krüger and Ales Ude
– The purpose of this paper is to propose a new algorithm based on programming by demonstration and exception strategies to solve assembly tasks such as peg-in-hole.
Abstract
Purpose
The purpose of this paper is to propose a new algorithm based on programming by demonstration and exception strategies to solve assembly tasks such as peg-in-hole.
Design/methodology/approach
Data describing the demonstrated tasks are obtained by kinesthetic guiding. The demonstrated trajectories are transferred to new robot workspaces using three-dimensional (3D) vision. Noise introduced by vision when transferring the task to a new configuration could cause the execution to fail, but such problems are resolved through exception strategies.
Findings
This paper demonstrated that the proposed approach combined with exception strategies outperforms traditional approaches for robot-based assembly. Experimental evaluation was carried out on Cranfield Benchmark, which constitutes a standardized assembly task in robotics. This paper also performed statistical evaluation based on experiments carried out on two different robotic platforms.
Practical implications
The developed framework can have an important impact for robot assembly processes, which are among the most important applications of industrial robots. Our future plans involve implementation of our framework in a commercially available robot controller.
Originality/value
This paper proposes a new approach to the robot assembly based on the Learning by Demonstration (LbD) paradigm. The proposed framework enables to quickly program new assembly tasks without the need for detailed analysis of the geometric and dynamic characteristics of workpieces involved in the assembly task. The algorithm provides an effective disturbance rejection, improved stability and increased overall performance. The proposed exception strategies increase the success rate of the algorithm when the task is transferred to new areas of the workspace, where it is necessary to deal with vision noise and altered dynamic characteristics of the task.
Details
Keywords
Zoltan Dobra and Krishna S. Dhir
Recent years have seen a technological change, Industry 4.0, in the manufacturing industry. Human–robot cooperation, a new application, is increasing and facilitating…
Abstract
Purpose
Recent years have seen a technological change, Industry 4.0, in the manufacturing industry. Human–robot cooperation, a new application, is increasing and facilitating collaboration without fences, cages or any kind of separation. The purpose of the paper is to review mainstream academic publications to evaluate the current status of human–robot cooperation and identify potential areas of further research.
Design/methodology/approach
A systematic literature review is offered that searches, appraises, synthetizes and analyses relevant works.
Findings
The authors report the prevailing status of human–robot collaboration, human factors, complexity/ programming, safety, collision avoidance, instructing the robot system and other aspects of human–robot collaboration.
Practical implications
This paper identifies new directions and potential research in practice of human–robot collaboration, such as measuring the degree of collaboration, integrating human–robot cooperation into teamwork theories, effective functional relocation of the robot and product design for human robot collaboration.
Originality/value
This paper will be useful for three cohorts of readers, namely, the manufacturers who require a baseline for development and deployment of robots; users of robots-seeking manufacturing advantage and researchers looking for new directions for further exploration of human–machine collaboration.