Identification of robotic manipulators' inverse dynamics coefficients via model-based adaptive networks

The values of a given manipulator's dynamics coefficients need to be accurately identified in order to employ model-based algorithms in the control of its motion. This thesis details the development of a novel form of adaptive network which is capable of accurately learning the coefficients of systems, such as manipulator inverse dynamics, where the algebraic form is known but the coefficients' values are not. Empirical motion data from a pair of PUMA 560s has been processed by the Context-Sensitive Linear Combiner (CSLC) network developed, and the coefficients of their inverse dynamics identified. The resultant precision of control is shown to be superior to that achieved from employing dynamics coefficients derived from direct measurement. As part of the development of the CSLC network, the process of network learning is examined. This analysis reveals that current network architectures for processing analogue output systems with high input order are highly unlikely to produce solutions that are good estimates throughout the entire problem space. In contrast, the CSLC network is shown to generalise intrinsically as a result of its structure, whilst its training is greatly simplified by the presence of only one minima in the network's error hypersurface. Furthermore, a fine-tuning algorithm for network training is presented which takes advantage of the CSLC network's single adaptive layer structure and does not rely upon gradient descent of the network error hypersurface, which commonly slows the later stages of network training

Robot Calibration Using Artificial Neural Networks

Robot calibration is an integrated procedure of measurement and data processing to improve and maintain robot positioning accuracy. Existing robot calibration techniques require extensive human intervention and off-line processing, which preclude the techniques from being used to perform on-site calibration in an industrial environment at regular intervals. This thesis investigates and develops intelligent calibration processing algorithms and a novel measurement method toward rapid autonomous robot calibration in a shop-floor environment.Artificial Neural Network (ANN) techniques have been vigorously investigated for calibration data processing (modelling, identification and compensation). A new identification algorithm has been developed for estimating robot kinematic parameter errors using Hopfield continuous-valued type Recurrent Neural Network (RNN). The RNN-based algorithm is computationally more efficient and robust compared with conventional optimisation approaches.A generic accuracy model which accounts for various error sources was introduced. A higher-order neural network was used for implementation of the generic accuracy model. Due to the ANN learning capability, computational power and adaptability, the ANN-based accuracy representation offers an appealing solution to the complex modelling problem.Efficient and robust accuracy compensation algorithms have been developed under the framework of artificial neural networks. The ANN-based algorithms provide constant-time inverse compensation therefore are suitable for on-line implementation. Both path compensation and compensation near robot singularity were tackled using the new algorithm.A novel autonomous calibration tool was developed using a trigger probe and a constraint plane. The new method eliminates any use of external measuring devices to determine robot end-effector location measurements, enabling the robot to perform self-calibration on a production line. Robot accuracy was improved to the level of its repeatability within the local calibration volume using the new calibration scheme, which is consistent with the results from using a precision external measuring device, in this case a Coordinate Measuring Machine (CMM)

NASA Center for Intelligent Robotic Systems for Space Exploration

NASA's program for the civilian exploration of space is a challenge to scientists and engineers to help maintain and further develop the United States' position of leadership in a focused sphere of space activity. Such an ambitious plan requires the contribution and further development of many scientific and technological fields. One research area essential for the success of these space exploration programs is Intelligent Robotic Systems. These systems represent a class of autonomous and semi-autonomous machines that can perform human-like functions with or without human interaction. They are fundamental for activities too hazardous for humans or too distant or complex for remote telemanipulation. To meet this challenge, Rensselaer Polytechnic Institute (RPI) has established an Engineering Research Center for Intelligent Robotic Systems for Space Exploration (CIRSSE). The Center was created with a five year $5.5 million grant from NASA submitted by a team of the Robotics and Automation Laboratories. The Robotics and Automation Laboratories of RPI are the result of the merger of the Robotics and Automation Laboratory of the Department of Electrical, Computer, and Systems Engineering (ECSE) and the Research Laboratory for Kinematics and Robotic Mechanisms of the Department of Mechanical Engineering, Aeronautical Engineering, and Mechanics (ME,AE,&M), in 1987. This report is an examination of the activities that are centered at CIRSSE

Neuro-fuzzy modelling and control of robotic manipulators

The work reported in this thesis aims to design and develop a new neuro-fuzzy control system for robotic manipulators using Machine Learning Techniques, Fuzzy Logic Controllers, and Fuzzy Neural Networks. The main idea is to integrate these intelligent techniques to develop an adaptive position controller for robotic manipulators. This will finally lead to utilising one or two coordinated manipulators to perform upper-limb rehabilitation. The main target is to benefit from these intelligent techniques in a systematic way that leads to an efficient control and coordination system. The suggested control system possesses self-learning features so that it can maintain acceptable performance in the presence of uncertain loads. Simulation and modelling stages were performed using dynamical virtual reality programs to demonstrate the ideas of the control and coordination techniques. The first part of the thesis focuses on the development of neuro-fuzzy models that meet the above requirement of mimicking both kinematics and dynamics behaviour of the manipulator. For this purpose, an initial stage for data collection from the motion of the manipulator along random trajectories was performed. These data were then compacted with the help of inductive learning techniques into two sets of if-then rules that form approximation for both of the inverse kinematics and inverse dynamics of the manipulator. These rules were then used in fuzzy neural networks with differentiation characteristics to achieve online tuning of the network adjustable parameters. The second part of the thesis introduces the proposed adaptive neuro-fuzzy joint-based controller. To achieve this target, a feedback Fuzzy-Proportional-Integral-Derivative incremental controller was developed. This controller was then applied as a joint servo-controller for each robot link in addition to the main neuro-fuzzy feedforward controller used to compensate for the dynamics interactions between robot links. A feedback error learning scheme was applied to tune the feedforward neuro-fuzzy controller online using the error back-propagation algorithm. The third part of the thesis presents a neuro-fuzzy Cartesian internal model control system for robotic manipulators. The neuro-fuzzy inverse kinematics model of the manipulator was used in addition to the joint-based controller proposed and the forward mathematical model of the manipulator in an adaptive internal model controller structure. Feedback-error learning scheme was extended to tune both of the joint-based neuro-fuzzy controller and the neuro-fuzzy internal model controller online. The fourth part of the thesis suggests a simple fuzzy hysteresis coordination scheme for two position-controlled robot manipulators. The coordination scheme is based on maintaining certain kinematic relationships between the two manipulators using reference motion synchronisation without explicitly involving the hybrid position/force control or modifying the existing controller structure for either of the manipulators. The key to the success of the new method is to ensure that each manipulator is capable of tracking its own desired trajectory using its own position controller, while synchronizing its motion with the other manipulator motion so that the differential position error between the two manipulators is reduced to zero or kept within acceptable limits. A simplified test-bench emulating upper-limb rehabilitation was used to test the proposed coordination technique experimentally

Mechatronics of systems with undetermined configurations

This work is submitted for the award of a PhD by published works. It deals with some of the efforts of the author over the last ten years in the field of Mechatronics. Mechatronics is a new area invented by the Japanese in the late 1970's, it consists of a synthesis of computers and electronics to improve mechanical systems. To control any mechanical event three fundamental features must be brought together: the sensors used to observe the process, the control software, including the control algorithm used and thirdly the actuator that provides the stimulus to achieve the end result. Simulation, which plays such an important part in the Mechatronics process, is used in both in continuous and discrete forms. The author has spent some considerable time developing skills in all these areas. The author was certainly the first at Middlesex to appreciate the new developments in Mechatronics and their significance for manufacturing. The author was one of the first mechanical engineers to recognise the significance of the new transputer chip. This was applied to the LQG optimal control of a cinefilm copying process. A 300% improvement in operating speed was achieved, together with tension control. To make more efficient use of robots they have to be made both faster and cheaper. The author found extremely low natural frequencies of vibration, ranging from 3 to 25 Hz. This limits the speed of response of existing robots. The vibration data was some of the earliest available in this field, certainly in the UK. Several schemes have been devised to control the flexible robot and maintain the required precision. Actuator technology is one area where mechatronic systems have been the subject of intense development. At Middlesex we have improved on the Aexator pneumatic muscle actuator, enabling it to be used with a precision of about 2 mm. New control challenges have been undertaken now in the field of machine tool chatter and the prevention of slip. A variety of novel and traditional control algorithms have been investigated in order to find out the best approach to solve this problem

Aspects of parallel processing and control engineering

The concept of parallel processing is not a new one, but the application of it to control engineering tasks is a relatively recent development, made possible by contemporary hardware and software innovation. It has long been accepted that, if properly orchestrated several processors/CPUs when combined can form a powerful processing entity. What prevented this from being implemented in commercial systems was the adequacy of the microprocessor for most tasks and hence the expense of a multi-processor system was not justified. With the advent of high demand systems, such as highly fault tolerant flight controllers and fast robotic controllers, parallel processing became a viable option. Nonetheless, the software interfacing of control laws onto parallel systems has remained somewhat of an impasse. There are no software compilers at present which allow a programmer to specify a control law in pure mathematical terminology and then decompose it into a flow diagram of concurrent processes which may then be implemented on, say, a target Transputer system, liiere are several parallel programming languages with which a programmer can generate parallel processes but, generally, in order to realise a control algorithm in parallel the programmer must have intimate knowledge of the algorithm. Therefore, efficiency is based on the ability of the programmer to recognise inherent parellelism. Some attempts are being made to create intelligent partition and scheduling compilers but this usually means significantly extra overheads on the multiprocessor system. In the absence of an automated technique control algorithms must be decomposed by inspection. The research presented in this thesis is founded upon the application of both parallel and pipelining techniques to particular control strategies. Parallelism is tackled objectively and by creating a tailored terminology it is defined mathematically, and consequently related concepts, such as bounded parallelism and algorithm speedup, are also quantified in a numerical sense. A pipelined explicit Self Tuning Regulator (STR) controller is developed and tested on systems of different order. Under the governance of the parallelism terminology the effectiveness of the parallel STR is evaluated and numerically quantified in terms of relevant performance indices. A parallel simulator is presented for the Puma 560 robotic manipulator. By exploiting parallelism and pipelinability in the robot model a significant increase in execution speed is achieved over the sequential model. The use of Transputers is examined and graphical results obtained for several performance indices, including speedup, processor efficiency and bounded parallelism. By the same analytical technique a parallel computed torque feedforward controller incorporating proportional derivative feedback control for the Puma 560 manipulator is developed and appraised. The performance of a Transputer system in hosting the controller is graphically analysed and as in the case of the parallel simulator the more important performance indices are examined under both optimal conditions and conditions of varying hardware constraints

Neural Network Adaptive Force and Motion Control of Robot Manipulators in the Operational Space Formulation

Ph.DDOCTOR OF PHILOSOPH

Multi-object recognition and retrieval using Puma560 robot

The objective of the research described here is to develop efficient algorithm and software tools for multiobject recognition and retrieval. This research project addresses two major issues: The first issue is the identification of features and efficient methods for feature extraction which can completely describe an object. These features can be acquired using visual and ultra-sonic sensors. The second issue is the development of efficient algorithms for the retrieval of multi-objects based on their features; The methods and algorithms developed in this research are verified on a Unimation PUMA 560 robot. Non contact sensors (a vision and a range sensor) are employed for feature detection. The information from both sensors will be combined for feature extraction and feature mapping (sensor fusion). The sensors and the robot have been integrated for this purpose with a Pentium 133 Mhz Personal Computer

Inverse Kinematic Analysis of Robot Manipulators

An important part of industrial robot manipulators is to achieve desired position and orientation of end effector or tool so as to complete the pre-specified task. To achieve the above stated goal one should have the sound knowledge of inverse kinematic problem. The problem of getting inverse kinematic solution has been on the outline of various researchers and is deliberated as thorough researched and mature problem. There are many fields of applications of robot manipulators to execute the given tasks such as material handling, pick-n-place, planetary and undersea explorations, space manipulation, and hazardous field etc. Moreover, medical field robotics catches applications in rehabilitation and surgery that involve kinematic, dynamic and control operations. Therefore, industrial robot manipulators are required to have proper knowledge of its joint variables as well as understanding of kinematic parameters. The motion of the end effector or manipulator is controlled by their joint actuator and this produces the required motion in each joints. Therefore, the controller should always supply an accurate value of joint variables analogous to the end effector position. Even though industrial robots are in the advanced stage, some of the basic problems in kinematics are still unsolved and constitute an active focus for research. Among these unsolved problems, the direct kinematics problem for parallel mechanism and inverse kinematics for serial chains constitute a decent share of research domain. The forward kinematics of robot manipulator is simpler problem and it has unique or closed form solution. The forward kinematics can be given by the conversion of joint space to Cartesian space of the manipulator. On the other hand inverse kinematics can be determined by the conversion of Cartesian space to joint space. The inverse kinematic of the robot manipulator does not provide the closed form solution. Hence, industrial manipulator can achieve a desired task or end effector position in more than one configuration. Therefore, to achieve exact solution of the joint variables has been the main concern to the researchers. A brief introduction of industrial robot manipulators, evolution and classification is presented. The basic configurations of robot manipulator are demonstrated and their benefits and drawbacks are deliberated along with the applications. The difficulties to solve forward and inverse kinematics of robot manipulator are discussed and solution of inverse kinematic is introduced through conventional methods. In order to accomplish the desired objective of the work and attain the solution of inverse kinematic problem an efficient study of the existing tools and techniques has been done. A review of literature survey and various tools used to solve inverse kinematic problem on different aspects is discussed. The various approaches of inverse kinematic solution is categorized in four sections namely structural analysis of mechanism, conventional approaches, intelligence or soft computing approaches and optimization based approaches. A portion of important and more significant literatures are thoroughly discussed and brief investigation is made on conclusions and gaps with respect to the inverse kinematic solution of industrial robot manipulators. Based on the survey of tools and techniques used for the kinematic analysis the broad objective of the present research work is presented as; to carry out the kinematic analyses of different configurations of industrial robot manipulators. The mathematical modelling of selected robot manipulator using existing tools and techniques has to be made for the comparative study of proposed method. On the other hand, development of new algorithm and their mathematical modelling for the solution of inverse kinematic problem has to be made for the analysis of quality and efficiency of the obtained solutions. Therefore, the study of appropriate tools and techniques used for the solution of inverse kinematic problems and comparison with proposed method is considered. Moreover, recommendation of the appropriate method for the solution of inverse kinematic problem is presented in the work. Apart from the forward kinematic analysis, the inverse kinematic analysis is quite complex, due to its non-linear formulations and having multiple solutions. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network (ANN) can be gainfully used to yield the desired results. Therefore, in the present work several models of artificial neural network (ANN) are used for the solution of the inverse kinematic problem. This model of ANN does not rely on higher mathematical formulations and are adept to solve NP-hard, non-linear and higher degree of polynomial equations. Although intelligent approaches are not new in this field but some selected models of ANN and their hybridization has been presented for the comparative evaluation of inverse kinematic. The hybridization scheme of ANN and an investigation has been made on accuracies of adopted algorithms. On the other hand, any Optimization algorithms which are capable of solving various multimodal functions can be implemented to solve the inverse kinematic problem. To overcome the problem of conventional tool and intelligent based method the optimization based approach can be implemented. In general, the optimization based approaches are more stable and often converge to the global solution. The major problem of ANN based approaches are its slow convergence and often stuck in local optimum point. Therefore, in present work different optimization based approaches are considered. The formulation of the objective function and associated constrained are discussed thoroughly. The comparison of all adopted algorithms on the basis of number of solutions, mathematical operations and computational time has been presented. The thesis concludes the summary with contributions and scope of the future research work