Guillaume J. Laurent

Distributed control architecture for smart surfaces

This paper presents a distributed control architecture to perform part recognition and closed-loop control of a distributed manipulation device. This architecture is based on decentralized cells able to communicate with their four neighbors thanks to peer-to-peer links. Various original algorithms are proposed to reconstruct, recognize and convey the object levitating on a new contactless distributed manipulation device. Experimental results show that each algorithm does a good job for itself and that all the algorithms together succeed in sorting and conveying the objects to their final destination. In the future, this architecture may be used to control MEMS-arrayed manipulation surfaces in order to develop Smart Surfaces, for conveying, fine positioning and sorting of very small parts for micro-systems assembly lines.

Designing Decentralized Controllers for Distributed-Air-Jet MEMS-Based Micromanipulators by Reinforcement Learning

Distributed-air-jet MEMS-based systems have been proposed to manipulate small parts with high velocities and without any friction problems. The control of such distributed systems is very challenging and usual approaches for contact arrayed system don’t produce satisfactory results. In this paper, we investigate reinforcement learning control approaches in order to position and convey an object. Reinforcement learning is a popular approach to find controllers that are tailored exactly to the system without any prior model. We show how to apply reinforcement learning in a decentralized perspective and in order to address the global-local trade-off. The simulation results demonstrate that the reinforcement learning method is a promising way to design control laws for such distributed systems.

2-DOF contactless distributed manipulation using superposition of induced air flows

Many industries require contactless transport and positioning of delicate or clean objects such as silicon wafers, glass sheets, solar cell or flat foodstuffs. The authors have presented a new form of contactless distributed manipulation using induced air flow. Previous works concerned the evaluation of the maximal velocity of transported objects and one degree-of-freedom position control of objects. This paper introduces an analytic model of the velocity field of the induced air flow according to the spatial configuration of vertical air jets. Then two degrees-of-freedom position control is investigated by exploiting the linearity property of the model. Finally the model is validated under closed-loop control and the performances of the position control are evaluated.

A new contactless conveyor system for handling clean and delicate products using induced air flows

In this paper, a new contactless conveyor system based on an original aerodynamic traction principle is described and experimented. This device is able to convey without any contact flat objects like silicon wafer, glass sheets or foodstufff thanks to an air cushion and induced air flows. A model of the system is established and the identification of the parameters is carried out. A closed-loop control is proposed for one dimension position control and position tracking. The PID-controller gives good performances for different reference signals. Its robustness to object change and perturbation rejection are also tested.

Twin-scale vernier micro-pattern for visual measurement of 1D in-plane absolute displacements with increased range-to-resolution ratio

This paper presents a visual method for 1D in-plane displacement measurement which combines a resolution of a few nanometers with an unambiguous excursion range of 168μm. Furthermore, position retrieval is only based on elementary phase computations and thus might become compatible with high-rate processing by implementing the processing algorithm on high speed computing architectures like a DSP or a FPGA device. The method is based on a twin scale Vernier micro-pattern fixed on the moving target of interest. The two periodic grids have slightly different periods in order to encode the period order within the phase difference observed between the two sub-patterns. As a result, an unambiguous range of 168μm is obtained from grid periods of 8μm and 8.4μm. The resolution is evaluated to be of 11.7nm despite remaining mechanical disturbances. Differential measurements demonstrated indeed a measurement accuracy better than 5nm.

Vision-Based Microforce Measurement With a Large Range-to-Resolution Ratio Using a Twin-Scale Pattern

Force sensors are often required in order to work at the microscale, but existing ones rarely meet all expectations, particularly in terms of resolution, range, accuracy, or integration potential. This paper presents a novel microforce measurement method by vision, based on a twin-scale pattern fixed on a compliant structure. This approach enabled subpixelic measurement of position by the use of a micromachined pattern based on the Vernier principle. This method also presents flexibility, insensitivity to electronic noise, fast operating time, and ease of calibration. The major contribution consists in the large range-to-resolution ratio of the measurement system. With an experimental range of 50 mN and a resolution below 50 nN, a range-to-resolution ratio of 106 is obtained. A repeatability under 7.8 μN and a trueness under 15 μN have been experimentally measured. Finally, the method can be applied to other specifications and applications in terms of range.

3-DOF potential air flow manipulation by inverse modeling control

Potential air flows can be used to perform non-prehensile contactless manipulations of objects gliding on air-hockey table. In this paper, we introduce a general method able to perform 3-DOF position control of an object with potential air flow manipulators. This approach is based on an inverse modeling control scheme to perform closed-loop position servoing. We propose to use a linear programming algorithm to determine which sinks have to be activated in order to produce the suitable potential air flow to obtain the desired object motion. This approach is then validated on an experimental manipulator.

Compressive Sensing-Based Metrology for Micropositioning Stages Characterization

High accuracy is a necessary condition for reliable performance of micropositioning stages (MPSs). However, there are various sources of errors that affect their precision. Characterization is a prior step to calibration for compensating systematic errors so as to improve the positioning accuracy. In this letter, the compressive sensing (CS) theory is applied to characterize system errors of MPSs. This method could be flexibly collaborated with any sensors and applicable to widespread microsystems where the motions and errors are required to be measured. CS 1) improves the data acquisition and processing in terms of time and 2) could be employed as an interpolating strategy to efficiently replace the lookup tables. As a case study, the CS-based method is applied to characterize the position-dependent errors of an XY serial MPS. Experimental results show that the method is able to retrieve the microscale positions with largely shortened time and high precision. The spent time for data acquisition and processing is shortened by more than 84% for X stage and 82% for Y stage. These results are especially promising for microscale purposes where the system behavior is varying and difficult to characterize.

Design, modeling and control of a modular contactless wafer handling system

In the photovoltaic solar cell industry as in the semiconductor industry, efforts to reduce the thickness of silicon wafers are in progress. Wafer damage and breakage during handling can lead to unacceptable yields and alternative solutions have to be proposed. This paper presents a modular contact-free wafer handling system that responds to the industrial requirements in terms of throughput and flexibility. The system is based on simple unidirectional modules that can be assembled together to form the desired trajectory. A complete and accurate physical model of the modular system describing the motion of the wafer transported by directed air-jets is proposed. A decentralized control at the block level is realized to damp the object motion. The experimental results show a great reduction of the response time compared to free motion and a standard deviation of the servo error below the millimeter. In addition, simulations show that a 150 mm wafer could reach a speed up to 2.9 m/s on large conveyors.

Design of semi-decentralized control laws for distributed-air-jet micromanipulators by reinforcement learning

Recently, a great deal of interest has been developed in learning in multi-agent systems to achieve decentralized control. Machine learning is a popular approach to find controllers that are tailored exactly to the system without any prior model. In this paper, we propose a semi-decentralized reinforcement learning control approach in order to position and convey an object on a contact-free MEMS-based distributed-manipulation system. The experimental results validate the semi-decentralized reinforcement learning method as a way to design control laws for such distributed systems.