Contour Tracking with Force Feedback

In this paper we describe an algorithm to allow a manipulator to track a complex contour without having to teach or program any points on the contour. This is an important problem in many manufacturing situations, when a robot tool such as a deburring tool must follow a complex work piece contour. La such instances it is tedious to teach or program the manipulator to follow such contour additionally calibration of the manipulator or the workpart may be difficult. The algorithm we present has been experimentally demonstrated utilizing a force sensor, a five degree of freedom manipulator and a 68000 single board computer

Analysis of Robot Drive Train Errors and Their Compensation

This paper presents a mathematical model of the kinematic nonlinear drive train errors which reduce absolute static positioning accuracy of robot arms. This kinematic inaccuracy renders robot manipulators ineffective when programmed off-line, though they might be programmed to successfully perform the same task by “Teach Play back” schemes. The kinematic drive train inaccuracy model, presented in this paper can be used to predict and compensate for these second order effects on-line, without resorting to sensor based programming techniques, which are often expensive and difficult to implement in an industrial environment. The drive train error model presented in this paper is based on gear backlash, eccentricity and drive shaft complianc

Sensors: A Key to Successful Robot-Based Assembly

Computer controlled robots offer a number of significant advantages in manufacturing and assembly tasks. These include consistent product reliability and the ability to work in harsh environments. The programmable nature of robotic automation allows the possibility of applying them to a number of tasks. In particular, significant savings can be expected in batch production, if robots can be applied to produce numbers of products successfully without plant re-tooling. Unfortunately, despite considerable progress made in robot programming [Lozano-Perez 83] [Paul 81] ;Ahmad 84] [Graver et al. 84] [Bonner & Shin 82] and in sensing [Gonzalez & Safabakhsh 82] [Fu 82] [Hall et al. 82], [Goto et al. 80], [Hirzinger & Dietrich 86], [Harmon 84], kinematics and control strategies [Whitney 85] [Luh S3] [Lee 82], a number of problems still remain unsolved before en-mass applications take place. In fact, in current applications, the specialized tooling for manufacturing a particular product may make up as much as 80% of the production line cost. In such a production line the robot is often used only as a programmable parts transfer device. Improving robots ability to sense and adapt to different products or environments so as to handle a larger variety of products without retooling is essential. It is just as important to be able to program them easily and quickly, without requiring the user to have a detailed understanding of complex robot programming languages and control schemes such as RCCL [Hayward & Paul 84], VAL-II [Shimano et al., 84], AML [Taylor et al., 83], SR3L-90 [Ahmad 84] or AL [Mujtaba & Goldman 79]. Currently there are a number of Computer Aided Design (CAD) packages available which simplify the robot programming problem. Such packages allow the automation system designer to simulate the assembly workcell which may consist of various machines and robots. The designer can then pick the motion sequences the robot has to execute in order to achieve the desired assembly task. This is done by viewing the motions on a graphical screen from different viewing angles to check for collisions and to ensure the relative positioning is correct, much the same way1 as it is done in on-line teach playback methods (see Figure 1). Off-line robot programming on CAD stations does not always lead to successful results due to two reasons: (i) The robot mechanism is inherently inaccurate due to incorrect kinematic models programmed in their control system [Wu 83] [Hayati 83] [Ahmad 87] [Whitney et ■ al. 84]. (ii) The assembly workcell model represented in the controller is not accurate. As a result parts and tools are not exactly located and their exact position may vary. This causes a predefined kinematic motion sequence program to fail, as it cannot deal with positional uncertainties. Sensors to detect real-time errors in the part and tool positions are obviously required with tailored sensor-based motion strategies to ensure assembly accomplishment. In this chapter we deal with how sensors are used to successfully ensure assembly task accomplishment. We illustrate the use of various sensors by going through an actual assembly of an oil pump. Additionally we illustrate a number of motion strategies which have been developed to deal with assembly errors. Initially, we discuss a number of sensors found in typical robotic assembly systems in Section 1. In Section 2 we discuss how and when sensors are to be used during an assembly operation. Issues relating to sensing and robust assembly systems are discussed very briefly in Section 3. Section 4 details a sensor-based robot assembly to illustrate practical applications

A Laboratory Experiment on Robot Contouring with Force Feedback

In this paper we describe a laboratory experiment which is part of a laboratory orientated robotics class taken by seniors and first year graduate students. This experiment is designed to introduce students to real-time robot control system hardware and software. The experiment attempts to fortify material covered m an introductory (non laboratory orientated) class on robotics. The issues covered by this experiments include: kinematics, dynamics, robot drive mechanisms, interfacing of sensors and force control aspects. Students were also required to learn many aspects of real time programming for control applications. We document this entire experiment so it may be reorganized and repeated. We discuss educational and research value of this experiment

Using Data Mining Techniques to Predict NOx Emissions Levels in Gas Fired Boilers

Natural gas industry becomes one of the largest sectors in the today’s world economy. With the growth of production, pollutants from gas processing plants continue to grow, forcing government and legislative authorities to compel stringent restrictions on the release of emissions. These restrictions become among top vital factors influencing plant operations nowadays in the state of Qatar. One of the most important tasks for gas processing plants is not only to ensure a continuous gas supply to customers, but also to monitor, control and address concerns about the environmental impact of the operations. The use of the gas fired steam boilers in the gas operations has led to a significant increase in the emissions of toxic substances such as NOx. Therefore, significant worries have been raised about the environmental effect on climate and air quality of noxious emissions associated with the steam generation process of the boilers. Data mining is a powerful tool that has been used for decades for advanced process analytics of large quantities of plant data in order to extract useful information and to reach a better understanding of the process. In this research, some of data mining techniques such as artificial neural networks and decision trees are applied on real plant data representing 27 industrial process parameters from a gas fired steam boiler of one gas processing plant at Ras-Laffan Industrial City in order to predict the most important factors impacting the NOx formation in the combustion process of the boiler. Closer attention to those factors can be given promptly in order to enhance the plant environmental performance. The results obtained by the artificial neural network and decision tree models showed that the NOx emissions are directly related to the air flow parameters such as O2 concentration, the excess air level in the boiler and the amount of the flue gas at the boiler outlet. It was also shown that the company is following a traditional technique of lowering the amount of oxygen in the boiler aiming to reduce the NOx emission levels. However, this technique was proven to be limited under certain threshold of boiler load due to other important factors such as the adiabatic flame temperature (AFT) which induces the formation of NOx emissions

Programming of Path Specific Robot Operations with Optimal Part Placement

In this paper we describe a task level programming system for path specific robot operations. We define path specific tasks as those robot tasks in which the path the manipulator end effector has to follow is fixed and is given, such operations may include welding or sealant application. The initial path selection is made through a graphical interface using a pointing device (such as a mouse) to outline the desired path on a CAD model of the workpiece. The final result of the system is the part location, which enables the chosen manipulator to optimally perform the desired task. Optimality is based on maximizing the manipulability of the manipulator performing the task using a function of the jacobian. User defined constraints, joint limit constraints, and collision avoidance constraints are used to guide the optimal location selection. The workable task is then executed using calls to a C language based motion control library outlined in [Guptill88] [Guptill & Stahura 87]. The usefulness of the system described in this paper is indicated by an example of two robotic devices performing a down-hand welding operation

Predictive Adaptive Control of Multiple Robots in Cooperative Motion

In this paper we address the problem of controlling multiple robots manipulating a rigid object cooperatively when the robots and load parameters are uncertain. We propose a controller that takes into account the dynamics of both the load and the manipulators. The linearity of the dynamics of the robots and the load, with respect to the unknown parameters, is exploited during the derivation of the parameter adaptation scheme. In order to design a control and update laws that do not require the measurements of the of the joint accelerations or the load acceleration, the dynamics of both the robots and the load are filtered through a stable first order filter. Then two prediction error vectors are defined as the difference between the measured filtered dynamics and the predicted filtered dynamics of both the robots and the load. The least-squares estimation method is used to estimate the parameters of the multi-robot system from the prediction errors. We then develop a controller that is based on the cancellation of the nonlinearities. The proposed controller guarantees global asymptotic tracking of the robot and load trajectories and also guarantees the asymptotic tracking of the internal forces trajectories

Robot Control Computation in Microprocessor systems with Multiple Arithmetic Processors

In this paper we address the problem of designing a high performance robot controller with multiple arithmetic processing units (APU’s). One attractive feature about this controller is that a minimum number of special purpose hardware components are needed, and in fact off the shelf components can be used. In the controller described in this paper, one main processor (MPU) schedules a number of APU’s to produce the computational throughput. In this design an efficient scheduling algorithm plays the most important role in the system performance. DF/IHS* algorithm [8] is an efficient algorithm that solves strong NP-hard problems of scheduling a set of particularly ordered computational tasks onto a multiprocessor system. When interprocessor communication overheads are appreciable, it is not very effective in providing a practical near optimum schedule. It fails to consider the problem of contention for shared resources. In this paper we present new multiprocessor scheduling algorithm, which minimizes the effect of overhead and by doing so it reduces the effect of contention. We used this scheduling algorithm to derive the operational instructions of the APU’s and the MPU for our multiple APU-based robot controller. Simulations show six Motorola MC 68881 APU’s can be used to generate the robotic control computations in approximately 2.5 milliseconds. The control computations involve inverse dynamic calculations, forward kinematics, inverse kinematics, and trajectory computations. *DF/IHS = Depth First/Initial Heuristic Search, this is a derivative of CP/MISF (critical path/Most Immediate Successor First) scheduling algorithm, see [8]

Mathematical Analysis of the Joint Motion of Redundant Robots Under Pseudo-Inverse Control

Redundant robots that are kinematically controlled using Jacobian pseudo-inverses may not have repeatable joint motions, when the end-effector traces a closed path in the workspace. This phenomenon is known as joint drift. The joint drift problem was initially observed and analyzed by Klein and Huang[lO]. Shamir and Yomdin(l5J also analyzed this problem using differential geometric approach. Klein and Kee[ll.] observed through numerical experiments that the drift had predictable properties. In this paper we present a measure of the drift motion, we show this measure is useful for predicting the stability properties of drifts. We further show that this measure of drift does indeed exhibit the properties numerically observed by Klein and Kee

Adaptive Control of Flexible Joint Robots Derived from Arm Energy Considerations

Almost all industrial robots exhibit joint flexibility due to mechanical compliance of their gear boxes. In this paper we outline a design of an adaptive controller for flexible joint robots based on the arms energy. The desired actuator trajectory in a flexible joint robot is dependent not only on the desired kinematic trajectory of the link but also on the link dynamics. Unfortunately, link dynamic parameters are unknown in most cases, as a result the desired actuator trajectory is also unknown. To overcome this difficulty, a number of control schemes have suggested the use of acceleration and link jerk feedback. In this paper we describe a control scheme which does not use link jerk or acceleration. The control law we derive is based on the energy of the arm deviating from the desired trajectory and it has two stages with two corresponding adaptation laws. The first stage drives the actuator and the joints to a desired manifold, the second controller then seeks to drive the joints to their desired trajectory. On application of our first controller there is an apparent structural reduction of the order of the system. This apparent reduction in the structure is exploited by our second stage controller. Our control scheme does not require link acceleration or jerk measurements, and the numerical differentiation of the velocity signal, or the inversion of the inertial matrices are also unnecessary. Simulations are presented to verify the validity of the control scheme. The superiority of the proposed scheme over existing rigid robot adaptive schemes is also illustrated through simulation