Reza Haghighi

Multi-group coordination control for robot swarms

One of the most challenging goals in multi-robot systems is the cooperative control of swarms of robots to establish a complex formation. The large size of the community and the complexity of the task are the main obstructions that hinder the attainment of the goal. In this paper, we overcome these difficulties by introducing a multi-group coordination control methodology for robot swarms. Decomposition of robot swarms to multiple groups increases versatility of the whole community in operating in intricate environment. We formulate the problem into two parts: inter-group formation and intra-group formation. In the inter-group formation, coordination of groups in the community is considered, and in the intra-group formation, the formation of individuals in the groups is studied. Moreover, the concept of adaptive interactive force is proposed to cope with the inter-group interactions. Theoretical analysis of the stability of the multi-group control system is presented. Simulation results are presented to illustrate the performance of the proposed method.

MEMS sensors for assessing flow-related control of an underwater biomimetic robotic stingray.

A major difference between manmade underwater robotic vehicles (URVs) and undersea animals is the dense arrays of sensors on the body of the latter which enable them to execute extreme control of their limbs and demonstrate super-maneuverability. There is a high demand for miniaturized, low-powered, lightweight and robust sensors that can perform sensing on URVs to improve their control and maneuverability. In this paper, we present the design, fabrication and experimental testing of two types of microelectromechanical systems (MEMS) sensors that benefit the situational awareness and control of a robotic stingray. The first one is a piezoresistive liquid crystal polymer haircell flow sensor which is employed to determine the velocity of propagation of the stingray. The second one is Pb(Zr(0.52)Ti(0.48))O3 piezoelectric micro-diaphragm pressure sensor which measures various flapping parameters of the stingrays fins that are key parameters to control the robot locomotion. The polymer flow sensors determine that by increasing the flapping frequency of the fins from 0.5 to 3 Hz the average velocity of the stingray increases from 0.05 to 0.4 BL s(-1), respectively. The role of these sensors in detecting errors in control and functioning of the actuators in performing tasks like flapping at a desired amplitude and frequency, swimming at a desired velocity and direction are quantified. The proposed sensors are also used to provide inputs for a model predictive control which allows the robot to track a desired trajectory. Although a robotic stingray is used as a platform to emphasize the role of the MEMS sensors, the applications can be extended to most URVs.

Optical micromanipulation of multiple groups of cells

Microbiology is mainly concerned with the study of coexistence, cooperation and interaction among groups of microorganisms. In order to study the biological systems consist of interacting groups of microorganisms, optical manipulation can be utilized as a useful tool. In fact, the ability to manipulate several groups of microorganisms is an essential step towards studying how biological microorganisms communicate and cooperate to perform a wide range of multicellular behaviours. In addition, decomposition of a group of cells into smaller groups gives capability and flexibility in manipulation tasks and remarkably increases environmental adjustability. In this paper, we aim to provide a primary result in micromanipulation of multiple groups of cells. We develop a control methodology for dynamic coordination of multiple groups of cells. A rigorous mathematical formulation is provided and the stability analysis is presented. Using the proposed method, we are able to manipulate multiple groups of microparticles to construct time-varying micro-formations. The proposed method is also useful in examination of the interactions between several groups of living organisms in the desired inter-cellular distance. Experimental results are presented to illustrate the performance of the proposed method.

Optical Manipulation of Multiple Groups of Microobjects Using Robotic Tweezers

Micromanipulation has received increasing attention from robotics researchers due to its wide applications in the manipulation of microobjects like biological cells and Bio-MEMS components. The demand for accurate and precise manipulation of microobjects opens up new challenges in automation of micromanipulation tasks. In this paper, we present a concurrent framework for optical manipulation of multiple groups of microobjects using robotic tweezers. The proposed framework is based on laser-stage coordination control and consists of two concurrent subschemes: 1) local coordination achieved by asynchronous manipulation of multiple groups of microobjects using laser beams and 2) global coordination achieved by manipulation of whole groups using a motorized stage. Unlike existing methods that are limited to the manipulation of a single microobject or a single group of microobjects, the proposed method considers concurrent laser-stage coordination of multiple groups of microobjects, which enhances the capability and flexibility in micromanipulation tasks. In addition, we introduce a unified social interaction function to achieve various cellular behaviors. A mathematical formulation is provided and stability analysis is presented. Using the proposed method, we are able to manipulate multiple groups of microobjects to construct time-varying microformations. Experimental results are presented to illustrate the performance of the proposed method.

Robotic manipulation of a biological cell using multiple optical traps

Existing control techniques for optical tweezers utilize a single focused laser beam to directly trap and manipulate a target cell. However, a typical force generated by an optical trap is extremely small (few pico-newtons) and therefore it is not sufficient to manipulate a larger cell or object. The optical trap is also sensitive to the shape of the biological cell and the refractive index. Therefore, current automatic control techniques for optical tweezers cannot be used to manipulate a large cell or cell with irregular shape. In addition, excessive irradiation of the laser beam to the cell may also cause photodamage of even lead to death of the cell. In this paper, we propose a robotic control technique for optical tweezers to achieve automated manipulation of cell, which is beyond the capability of a single optical trap. First, multiple laser beams are generated, and each laser beam is used to trap one micro-particle to create a formation around the target cell to hold it. Then the target cell is manipulated to a desired position by controlling the motorized stage. The proposed control technique is particularly suitable for automated manipulation of sensitive biological cells, cells with large size or cells of irregular shape. Rigorous mathematical formulations have been developed to analyze the control system for automated cell manipulation. Experimental results are presented to illustrate the performance of the proposed controller.

Grasping and manipulation of a micro-particle using multiple optical traps

In existing control techniques for optical tweezers, a target particle is directly trapped and manipulated by a single laser beam. However, a typical force generated by an optical trap is extremely small (on the order of piconewtons) and thus it is not sufficient to manipulate a large cell or object. Besides, the feasibility of optical manipulation also depends on the physical properties of the specimen. An opaque object or object with the same refractive index as the fluid media may not be trapped directly by the laser beam. Therefore, current control techniques for optical tweezers cannot be utilized to manipulate various types of cells or objects, including untrappable or large ones. In this paper, robotic control techniques are developed for optical tweezers to achieve grasping and manipulation of a microscopic particle, which is beyond the capability of a single optical trap. First, multiple laser beams are generated, and each laser beam is utilized to trap and drive one grasping particle to form a desired shape around the target particle. A grasping formation of trapped particles is thus generated to hold the target particle. Then the target particle is manipulated to a desired position by controlling the motorized stage. The proposed control strategy is particularly suitable for manipulation of large particles, or even untrappable cells or objects. Rigorous mathematical formulations have been developed to analyze the control system for grasping and manipulation of the microscopic particle. Experimental results are presented to illustrate the performance of the proposed grasping and manipulation techniques.

Algorithm for Identifying Minimum Driver Nodes Based on Structural Controllability

Existing methods on structural controllability of networked systems are based on critical assumptions such as nodal dynamics with infinite time constants and availability of input signals to all nodes. In this paper, we relax these assumptions and examine the structural controllability for practical model of networked systems. We explore the relationship between structural controllability and graph reachability. Consequently, a simple graph-based algorithm is presented to obtain the minimum driver nodes. Finally, simulation results are presented to illustrate the performance of the proposed algorithm in dealing with large-scale networked systems.

Multi-cell formation following in a concurrent control framework

Existing methods on automated multiple cells manipulation are limited to manipulation of cells within the Field of View (FoV). However, in many clinical studies of biological processes it is required to manipulate a group of cells in which the area of interest is larger than the FoV. In this paper, a concurrent control framework is proposed to control the multiple optical traps and motorized stage simultaneously. The proposed method allows us to bring the individual cells into a desired formation using optical traps and also to manipulate the whole group of cells collectively using the motorized stage. In fact, this work opens up challenges in introducing dual controllers to achieve concurrent trap-stage manipulation. Moreover, a distance based allocation algorithm is designed to assign laser beams to the cells. Experimental results are presented to illustrate the performance of the proposed methodology.

Self-aggregation in multi-agent shape control

This paper presents a new interactive force in shape control to deal with group fragmentation during movement toward a desired shape. The proposed interactive force can maintain minimum distance between agents as well as group unity. Unlike other collective potential functions which have only one local minima, the proposed one has a local minima area which increase the flexibility of group during movement in obstacle based environment. In fact this potential function divides the area around each agent into four distinct areas: separation area, neutral area, attractive area and inactive area. Simulation results show the performance of the proposed interactive force during maneuvering of agents in multi-obstacle environment.

Distributed shape formation of multi-agent systems

In region based shape control of multi-agent systems, the agents move together as a group inside a desired region while maintaining minimum distance among themselves. The desired formation is specified as the desired region for the whole group instead of desired trajectories for individual agents. One limitation of region based shape formation method is the necessity of access to the desired reference of the region i.e. desired movement of the entire group. This paper presents a distributed shape formation control method for multi-agent systems. We develop a state estimator for each agent to construct the desired reference based on local information. A Lyapunov-like function is proposed to examine the stability of the overall system. Simulation results are presented to illustrate the performance of the proposed method in distributed shape formation.