Devendra P. Garg

Optimization techniques applied to multiple manipulators for path planning and torque minimization

Abstract This paper presents the formulation and application of a strategy for the determination of an optimal trajectory for a multiple robotic configuration. Genetic Algorithm (GA) and Simulated Annealing (SA) have been used as the optimization techniques and results obtained from them compared. First, the motivation for multiple robot control and the current state-of-art in the field of cooperating robots are briefly given. This is followed by a discussion of energy minimization techniques in the context of robotics, and finally, the principles of using genetic algorithms and simulated annealing as an optimization tool are included. The initial and final positions of the end effector are specified. Two cases, one of a single manipulator, and the other of two cooperating manipulators carrying a common payload illustrate the proposed approach. The GA and SA techniques identify the optimal trajectory based on minimum joint torque requirements. The simulations performed for both the cases show that although both the methods converge to the global minimum, the SA converges to solution faster than the GA.

Segregation of Heterogeneous Units in a Swarm of Robotic Agents

There are several examples in natural systems that exhibit the self-organizing behavior of segregation when different types of units interact with each other. One of the best examples is a system of biological cells of heterogeneous types that has the ability to self-organize into specific formations, form different types of organs and, ultimately, develop into a living organism. Previous research in this area has indicated that such segregations in biological cells and tissues are made possible because of the differences in adhesivity between various types of cells or tissues. Inspired by this differential adhesivity model, this technical note presents a decentralized approach utilizing differential artificial potential to achieve the segregation behavior in a swarm of heterogeneous robotic agents. The method is based on the proposition that agents experience different magnitudes of potential while interacting with agents of different types. Stability analysis of the system with the proposed approach in the Lyapunov sense is carried out in this technical note. Extensive simulations and analytical investigations suggest that the proposed method would lead a population of two types of agents to a segregated configuration.

Vertical Mode Human Body Vibration Transmissibility

Frequency response of standing humans subjected to sinusoidal vibration is presented. The vibratory input was a vertical displacement to the feet, and the output was the corresponding vertical response of the head. Twelve subjects (eight male and four female) were tested in the frequency range of 1-50 Hertz (Hz) with small input amplitudes (0.003 to 0.02 in). The twelve experimentally obtained frequency response plots were averaged and a sixteen-mass linear lumped-parameter model was developed to match the average response in both magnitude and phase angle. This proposed matching model is analogous to human anatomy. Parametric values of mass distribution and joint stiffnesses available in the literature were incorporated in the model. Damping parameters for various joints in the human body were indirectly determined from this study.

Spill Detection and Perimeter Surveillance via Distributed Swarming Agents

The problem of perimeter detection and monitoring has a variety of applications. In this paper, a hybrid system of finite states is proposed for multiple autonomous robotic agents with the purpose of hazardous spill perimeter detection and tracking. In the system, each robotic agent is assumed to be in one of three states: searching, pursuing, and tracking. The agents are prioritized based on their states, and a potential field is constructed for agents in each state. For an agent in the tracking state, the agents location and velocity as well as those of its closest leading and trailing agents are utilized to control its movement. The convergence of the tracking algorithm is analyzed for multiple spills under certain conditions. Simulation and experiment results show that with the proposed method, the agents can successfully detect and track the spills of various shapes, sizes, and movements.

Artificial neural network based robot control: An overview

The current thrust of research in robotics is to build robots which can operate in dynamic and/or partially known environments. The ability of learning endows the robot with a form of autonomous intelligence to handle such situations. This paper focuses on the intersection of the fields of robot control and learning methods as represented by artificial neural networks. An in-depth overview of the application of neural networks to the problem of robot control is presented. Some typical neural network architectures are discussed first. The important issues involved in the study of robotics are then highlighted. This paper concentrates on the neural network applications to the motion control of robots involved in both non-contact and contact tasks. The current state of research in this area is surveyed and the strengths and weakness of the present approaches are emphasized. The paper concludes by indentifying areas which need future research work.

A generalized approach for inconsistency detection in data fusion from multiple sensors

This paper presents a sensor fusion strategy based on Bayesian method that can identify the inconsistency in sensor data so that spurious data can be eliminated from the sensor fusion process. The proposed method adds a term to the commonly used Bayesian technique that represents the probabilistic estimate corresponding to the event that the data is not spurious conditioned upon the data and the true state. This term has the effect of increasing the variance of the posterior distribution when data from one of the sensors is inconsistent with respect to the other. The proposed strategy was verified with the help of extensive simulations. The simulations showed that the proposed method was able to identify inconsistency in sensor data and also confirmed that the identification of inconsistency led to a better estimate of desired state variable

A numerical optimization approach for tuning fuzzy logic controllers

This paper develops a method to tune fuzzy controllers using numerical optimization. The main attribute of this approach is that it allows fuzzy logic controllers to be tuned to achieve global performance requirements. Furthermore, this approach allows design constraints to be implemented during the tuning process. The method tunes the controller by parameterizing the membership functions for error, change-in-error, and control output. The resulting parameters form a design vector which is iteratively changed to minimize an objective function. The minimal objective function results in an optimal performance of the system. A spacecraft mounted science payload line-of-sight pointing control is used to demonstrate results.

Sensor-Based Estimation and Control of Forces and Moments in Multiple Cooperative Robots

Control of multiple robots presents numerous challenges, some of which include synchronization in terms of position, motion, force, load sharing and internal force minimization. This paper presents formulation and application of a fuzzy logic based strategy for control of two 6 degree-of-freedom robots carrying an object in a cooperative mode. The paper focuses on control of internal forces that get generated when two or more robots carry an object in coordination. Force/torque (F/T) sensors mounted on wrist of each robot provide the force and torque data in six dimensions. A fuzzy logic controller has been designed to use these force/torque (F/T) data to achieve a cooperating movement in which one robot acts as leader and the other robot follows. The paper also deals with estimation of external forces acting on end effector with the use of data provided by F/T sensors. These external forces and moments are not directly measured by F/T sensor since the quantities measured by F/T sensor are corrupted by the dynamics of the end effector and manipulator (a F/T sensor is usually mounted between wrist and end effector of the robot). This paper investigates the use of Kalman filtering technique to extract the external forces acting on robot end effector utilizing the underlying dynamics of the end effector. Matlabs Fuzzy logic, Simulink, and State Flow toolboxes are used for achieving real-time, autonomous and intelligent behavior of the two robots. Simulation results from two separate experiments show that the above strategy was able to constrain the internal forces and provide a smooth movement of the manipulators.

Research in active composite materials and structures: an overview

During the past several years, the Materials Science Division and the Mechanical and Environmental Sciences Division of the Army Research Office have been supporting projects focusing on basic resaserch in the area of smart materials and structures. The major emphasis of the ARO Structures and Dynamics Program has been on the theoretical, computational, and experimental analysis of smart structures and structural dynamics, damping, active control, and health monitoring as applied to rotor craft, electromagnetic antenna structures, missiles, land vehicles, and weapon systems. The research projects supported by the program have been primarily directed towards improving the ability to predict, control, and optimize the dynamic response of complex, multi-body deformable structures. The projects in the field of smart materials and structures have included multi-disciplinary research conducted by teams of several faculty members as well as research performed by individual investigators.

Self-sorting in a swarm of heterogeneous agents

Sorting of heterogeneous units is a self-organized behavior which is seen in many biological systems. One of the best examples of such systems is a system of biological cells of heterogeneous types that has the ability to self-organize into specific formations, form different types of organs and, ultimately, develop into a living organism. Earlier research in this area has indicated that such self-sorting behaviors in biological cells and tissues are made possible because of difference in the adhesivity between different types of cells or tissues. Inspired by this differential adhesivity model, this paper presents a decentralized approach based on differential artificial potential to achieve the self sorting behavior in a swarm of heterogeneous robotic agents. The method is based on the proposition that agents of different types experience different magnitude of potential while they are interacting with agents of different types. An analysis of the system with the proposed approach in Lyapunov sense is carried out for stability. Extensive simulation studies and numerical analysis suggest that the proposed method would always lead a population of heterogeneous agents closer to the sorted or segregated configuration.