Vacuum mechatronics

The discipline of vacuum mechatronics is defined as the design and development of vacuum-compatible computer-controlled mechanisms for manipulating, sensing and testing in a vacuum environment. The importance of vacuum mechatronics is growing with an increased application of vacuum in space studies and in manufacturing for material processing, medicine, microelectronics, emission studies, lyophylisation, freeze drying and packaging. The quickly developing field of vacuum mechatronics will also be the driving force for the realization of an advanced era of totally enclosed clean manufacturing cells. High technology manufacturing has increasingly demanding requirements for precision manipulation, in situ process monitoring and contamination-free environments. To remove the contamination problems associated with human workers, the tendency in many manufacturing processes is to move towards total automation. This will become a requirement in the near future for e.g., microelectronics manufacturing. Automation in ultra-clean manufacturing environments is evolving into the concept of self-contained and fully enclosed manufacturing. A Self Contained Automated Robotic Factory (SCARF) is being developed as a flexible research facility for totally enclosed manufacturing. The construction and successful operation of a SCARF will provide a novel, flexible, self-contained, clean, vacuum manufacturing environment. SCARF also requires very high reliability and intelligent control. The trends in vacuum mechatronics and some of the key research issues are reviewed

Eigenstates of excitons near a surface

Journal ArticleThe exact eigenstates and energies of an electron and a hole of equal effective masses, with an attractive δ-function interaction and hard-wall repulsion at the surfaces of a solid, are classified and obtained explicitly for a solid of arbitrary thickness. Both bound and scattering states of the exciton are significantly quantized for thin films

Towards a distributed planning of decision making under uncertainty for a fleet of robots

International audienceCoordination is required in order to solve a multi robot navigation problem and allow an efficient and fast search of a solution while avoiding any possible collisions. Planning with a fleet of robots can rely on Multi-agent Markovian Decision Processes (MMDPs) model This assumes that it is possible to share the local perceptions of robots every time. However the computation of a distributed policy is not necessarily distributable between robots as with multiple path planning, where the movement of one robot depends on all the other's paths. The global search space would be of exponential size (in the number of robots) in most of multi-robot scenarios. Distributed planning over the robots would allow each robot to plan its own policy while taking advantage of parallel computing. In this paper, this problem is addressed by presenting an approach consisting in starting from a simplified model which can be distributed, then by adding robots interactions constraints while maintaining the model distributable. The results of the experimentations with different configurations highlight some of the strength and limitations of the current approach

Teaching Object-Oriented Programming in Secondary Schools Using Swarm Robotics

The recent inclusion of computer science in the British secondary school education system has resulted in existing teaching staff who are not able to effectively deliver the curriculum. Existing environments—such as Greenfoot—help substantially but anecdotal evidence suggests that many pupils still struggle with some aspects of the computer science curriculum. This paper presents a workshop for teaching pupils about method calls in object-oriented programming, using swarm robotics and the firefly synchronisation algorithm as inspiration

Genetic programming for job shop scheduling

© 2019, Springer International Publishing AG, part of Springer Nature. Designing effective scheduling rules or heuristics for a manufacturing system such as job shops is not a trivial task. In the early stage, scheduling experts rely on their experiences to develop dispatching rules and further improve them through trials-and-errors, sometimes with the help of computer simulations. In recent years, automated design approaches have been applied to develop effective dispatching rules for job shop scheduling (JSS). Genetic programming (GP) is currently the most popular approach for this task. The goal of this chapter is to summarise existing studies in this field to provide an overall picture to interested researchers. Then, we demonstrate some recent ideas to enhance the effectiveness of GP for JSS and discuss interesting research topics for future studies