Path planning self-learning algorithm for a dynamic chaning environment

The relevance of Human Robot Interaction to complement human skills in a manufacturing environment with industrial robots increases the concerns over safety of human and the robot. It is necessary to identify collision risks and avoid them otherwise production stops may cost a huge amount to the industry. A robot working at manufacturing facility should be able to predict potential collisions and must be able to prevent i.e. react automatically for safe detour around obstacle/human. Currently, industrial robots are able to detect collisions after a real contact but the existing proposals for avoiding collisions are either computationally expensive or not very well adapted to human safety. The objective of this paper is to provide intelligence to the industrial robot to predict collision risks and react automatically without stopping the production in a static environment. The proposed approach using Time of Flight (TOF) camera, provides decision regarding trajectory correction and improvement by shifting robot to a secure position. The application presented in this paper is for safe KUKA robot trajectory generation in peg-in-hole assembly process in the laboratory context

Real-time visual detection and correction of automatic screw operations in dimpled light-gauge steel framing with pre-drilled pilot holes

Modular and panelized construction have been promoted and recognized globally as advanced construction techniques for residential and commercial industries alike. Light-Gauge Steel (LGS) panels have become more popular for commercial buildings and high-rise residential buildings in the last decades. When constructing such panels, for ease of manufacturing and assembling, a common practice in the construction industry is the use of dimples and pre-drilled pilot holes. Current automatic LGS machinery, however, is not designed to operate with such constraints. In this study, a real-time vision-based approach is proposed to enable current machinery to use dimpled studs with pre-drilled pilot holes. An algorithm designed for hole detection inside the dimples on LGS steel studs, based on edge detection and ellipse fitting is proposed. Finally, an adaptive approach is proposed to adjust the screw driving manipulators to ensure that the drilling operation occurs accurately, avoiding any possible damage to the LGS studs or failure of the screwing operation. The proposed algorithm is validated on a real steel assembly and a comparison is provided with other well-known algorithms for ellipse detection to demonstrate the effectiveness of the proposed method. This real-time algorithm gives real-time results for pilot hole detection and screwing location estimation within 3 mm tolerance. When compared with other well-known approaches in the literature, the proposed approach is 59% more accurate than the fastest available algorithm

New computer vision based Snakes and Ladders algorithm for the safe trajectory of two axis CNC machines

International audienceMulti-axis machine production process optimization, automation and intelligence are the key codes of today's scientific community. Rapid decision and intelligence are becoming more important for precise and safe virtual and real production. Multi-axis CNC production is a high speed machining process that demands less human intervention and high intelligence, to deal with any uncomfortable situation regarding collisions. Current CAM softwares as well as CNC machines are able to detect collisions but are unable to avoid these collisions automatically. This paper aims to make the CAD-CAM/CNC multi-axis safe trajectory generation process optimal, intelligent and automatic, using vision based image processing by the Snakes and Ladders game analogy. Applying the Snakes and Ladders analogy on machine virtual scene (trajectory preparation) and real scene (during production) images gives promising methodology for safe and efficient trajectory generation while avoiding collisions named Snakes and Ladders Analogy for Production Trajectory (SLAPT). Our Rectangular Enveloped Safe and Efficient Trajectory (RESET) algorithm, based on the same principle of the SLAPT methodology is also discussed in this paper. Results include some applications of algorithms on virtual and real machine scene images for the safe and optimized trajectory of tools. This paper focuses on intelligence and optimization of 2D non-functional transversal trajectories of 2-axis machines for production and preparation processes as an initial effort towards the complex safe trajectory generation process (mill-turn)

Craft-oriented hybrid analogue/digital practices; their values and our future relations with technology

This paper focuses on a hybrid digital/analogue making project that sought to investigate the aesthetic opportunities that digital design and production technologies holds for the craftsperson. It is presented as a demonstration of how a disruptive craft-based approach to engaging with digital making tools can act as a stimulus to reconsider the relationship between hand and machine, and our wider relationship with technologies and how we assess their role and value. Through challenging some assumptions about what digital technologies are ‘good’ for, it proposes a digital craft ethos that aspires to: fidelity not accuracy, sensitive making not efficient manufacturing, affective not effective technologies, to augment existing practices not replace established ways of working, uniqueness not infinite replicability, and continual ‘hands-on’ interaction with tools not full automation. Taking this digital craft ethos beyond the boundaries of the sector, the paper will conclude with an argument that our relationship with making technologies needs to evolve. If we continue to only use an established industrially focused myopic lens to view and assess the value of all technologies, (i.e. their productive efficiency, their speed, and their ability to accurately achieve predetermined goals), then as automation and machine learning have an increasing impact on labour markets and work, questions arise such as; what is the future of making? and what can, and do we want, our roles to be