Describes how decentralized control theory can be used to analyze the control of multiple cooperative robotic vehicles. Models of cooperation are discussed and related to the input/output reachability, structural observability, and controllability of the entire system. Whereas decentralized control research in the past has concentrated on using decentralized controllers to partition complex physically interconnected systems, this work uses decentralized methods to connect otherwise independent nontouching robotic vehicles so that they behave in a stable, coordinated fashion. A vector Liapunov method is used to prove stability of two examples: the controlled motion of multiple vehicles along a line and the controlled motion of multiple vehicles in formation. Also presented are three applications of this theory: controlling a formation, guarding a perimeter, and surrounding a facility.

This paper discusses how phase plane analysis can be used to describe the overall behaviour of single and multiple autonomous robotic vehicles with finite state machine rules. The importance of this result is that we can begin to design provably asymptotically stable group behaviours from a set of simple control laws and appropriate switching points with decentralized variable structure control. The ability to prove asymptotically stable group behaviour is especially important for applications such as locating military targets or land mines.

This paper describes current research and development on a robotic visual servoing system for assembly of LIGA (lithography galvanoforming abforming) parts. The workcell consists of an AMTI robot, precision stage, long working distance microscope, and LIGA fabricated tweezers for picking up the parts. Fourier optics methods are used to generate synthetic microscope images from CAD drawings. These synthetic images are used off-line to test image processing routines under varying magnifications and depths of field. They also provide reference image features which are used to visually servo the part to the desired position.

World modeling is defined as the process of creating a numerical geometric model of a real world environment or workspace. This model is often used in robotics to plan robot motions which perform a task while avoiding obstacles. In many applications where the world model does not exist ahead of time, structured lighting, laser range finders, and even acoustical sensors have been used to create three dimensional maps of the environment. These maps consist of thousands of range points which are difficult to handle and interpret. This paper presents a least squares technique for fitting range data to planar and quadric surfaces, including cylinders and ellipsoids. Once fit to these primitive surfaces, the amount of data associated with a surface is greatly reduced up to three orders of magnitude, thus allowing for more rapid handling and analysis of world data.

This paper presents a generic robot and motion control applications programmers interface (API) based on generic intelligent systems control (GISC) technology and CORBA communications. The API provides a generic high-level interface for command sequences between a supervisory control system and devices ranging from one degree of freedom motion devices up to six degree of freedom robotic systems. The API manages distributed robotic control using CORBA communications across numerous platforms ranging from Unix workstations to PCs. The paper presents a description of the API functions and classes used to integrate the Sandia-developed GISC control architecture and commercial CORBA software to support device-independent robotic control.

This paper describes an algorithm for determining the optimal placement of a robotic manipulator within a workcell for minimum time coordinated motion. The algorithm uses a simple principle of coordinated motion to estimate the time of a joint interpolated motion. Specifically, the coordinated motion profile is limited by the slowest axis. Two and six degree of freedom (DOF) examples are presented. In experimental tests on a FANUC S-800 arm, the optimal placement of the robot can improve cycle time of a robotic operation by as much as 25%. In high volume processes where the robot motion is currently the limiting factor, this increased throughput can result in substantial cost savings.

This paper describes two methods for controlling the surface of a liquid in an open container as it is being carried by a robot arm. Both methods make use of the fundamental mode of oscillation and damping of the liquid in the container as predicted from a boundary element model of the fluid. The first method uses an infinite impulse response filter to alter an acceleration profile so that the liquid remains level except for a single wave at the beginning and end of the motion. The motion of the liquid is similar to that of a simple pendulum. The second method removes the remaining two surface oscillations by tilting the container parallel to the beginning and ending wave. A double pendulum model is used to determine the trajectory for this motion. Experimental results of a FANUC S-800 robot moving a 230 mm diameter hemispherical container of water are presented.

This paper describes a collision avoidance system using Whole Arm Proximity (WHAP) sensors on a PUMA 560 robot arm. The capacitance-based sensors generate electric fields which can completely encompass the robot arm and detect obstacles as they approach from any direction. The directional obstacle information gathered by the WHAP sensors together with the sensor geometry and robot configuration is used to scale the commanded joint velocities of the robot. A linearized relationship between the WHAP sensor reading and the distance from the obstacle allows direct transformation of perturbations in VHAP readings to perturbations in joint velocities. The VHAP reading is used to directly reduce the component of the command input velocity along the normal axis of the sensor, allowing graceful reductions in speed as the arm approaches the obstacle. By scaling only the component of the velocity vector in the,direction of the nearest obstacles, the control system restricts motion in the direction of obstacles while permitting unconstrained motion in other directions.

The authors address the issue of collision avoidance in unknown or partially modeled environments using a capacitative sensor. An eight channel capacitance-based sensor system which can detect obstacles up to 400 mm away has been developed. This sensor can detect both conductive and nonconductive obstacles of arbitrary color and shape. The sensor hardware was reliable and inexpensive, and may be fabricated using flexible printed circuit boards to provide whole-arm and joint protection for any robot or manipulator. Simple collision avoidance control algorithms have been implemented on a two-link robot arm. The sensor and control system enable the robot arm to avoid a conductive post and a concrete block.>

An analysis is made of the experimental results of a minimum-time trajectory control scheme for a two-link flexible robot. An offline optimization routine determines a minimum-time, straight-line tip trajectory which stays within the torque constraints of the motors and ends with no vibrational transients. An efficient finite-element model is used in the optimization to approximate the flexible arm dynamics. The control strategy described is used to determine the feedback gains for the position, velocity, and strain gage signals from a quadratic cost criterion based on the finite-element model linearized about the straight-line tip trajectory. These feedback signals are added to the modeled torque values obtained from the optimization routine and used to control the robot arm actuators. The results indicate that this combination of model-based and error-driven control strategies, achieves a closer tracking of the desired trajectory and a better handling of modeling errors than either strategy alone.>

The authors develop methodologies for the automatic selection of image features to be used to visually control the relative position and orientation (pose) between the end-effector of an eye-in-hand robot and a workpiece. A resolved motion rate control scheme is used to update the robot's pose based on the position of three features in the camera's image. The selection of these three features depends on a blend of image recognition and control criteria. The image recognition criteria include feature robustness, completeness, cost of feature extraction, and feature uniqueness. The control criteria include system observability, controllability, and sensitivity. A weighted criteria function is used to select the combination of image features that provides the best control of the end-effector of a general six-degrees-of-freedom manipulator. Both computer simulations and laboratory experiments on a PUMA robot arm were conducted to verify the performance of the feature-selection criteria.>

An adaptive method for visually tracking a known moving object with a single mobile camera is described. The method differs from previous methods of motion estimation in that both the camera and the object are moving. The objective is to predict the location of features of the object on the image plane based on past observations and past control inputs and then to determine an optimal control input that will move the camera so that the image features align with their desired positions. A resolved motion rate control structure is used to control the relative position and orientation between the camera and the object. A geometric model of the camera is used to determine the linear differential transformation from image features to camera position and orientation. To adjust for modeling errors and system nonlinearities, a self-tuning adaptive controller is used to update the transformation and compute the optimal control. Computer simulations were conducted to verify the performance of the adaptive feature prediction and control.>

The authors investigate the selection of image features to be used to control visually the position and orientation (pose) of the end-effector of an eye-in-hand robot relative to a workpiece. A resolved-motion-rate control scheme is used to update the robot's pose on the basis of the position of three features in the camera's image. The selection of these three features depends on the condition and sensitivity of the differential relationship between the image features and the control parameters. Both computer simulations and laboratory experiments on a PUMA robot arm were conducted to verify the performance of the feature selection criteria. Experimentally, the PUMA robot arm, with a CCD (charged-coupled device) camera mounted on its end effector, was able to track a randomly moving carburetor gasket with a visual feedback cycle time of 70 ms.>

The authors present a vision module which is able to guide an eye-in-hand robot through general servoing and tracking problems using off-the-shelf image-processing equipment. The vision module uses the location of binary image features from a camera on the robot's end-effector to control the position and one degree of orientation of the robot manipulator. A unique feature-based trajectory generator provides smooth motion between the actual image features and the desired image features even with asynchronous and discontinuous vision updates. By performing the trajectory generation in image feature space, image-processing constraints such as the feature extraction time can be accounted for when determining the appropriate segmentation and acceleration times of the trajectory. Experimental results of a PUMA robot tracking objects with vision feedback are discussed.>

This paper presents an algorithm which may be used to determine a static grasp for an "n" fingered robot hand. A static grasp is one in which the object being manipulated does not move with respect to the gripper throughout the entire operation. The following grasping algorithm is used during the program generation stage of the assembly process. It is based on the available object surfaces at each stage of manipulation, on the gripper characteristics, and on the assembly task to be performed. An example is solved in detail to illustrate the procedure of this algorithm.