Robot Dynamic Control Research Articles

Enhancing Robot Dynamics Control for Accuracy and Effectiveness using Genetic Algorithms

Enhancing Robot Dynamics Control for Accuracy and Effectiveness using Genetic Algorithms

Self-Organizing Type-2 Fuzzy Double Loop Recurrent Neural Network for Uncertain Nonlinear System Control.

Nonlinear systems, such as robotic systems, play an increasingly important role in our modern daily life and have become more dominant in many industries; however, robotic control still faces various challenges due to diverse and unstructured work environments. This article proposes a double-loop recurrent neural network (DLRNN) with the support of a Type-2 fuzzy system and a self-organizing mechanism for improved performance in nonlinear dynamic robot control. The proposed network has a double-loop recurrent structure, which enables better dynamic mapping. In addition, the network combines a Type-2 fuzzy system with a double-loop recurrent structure to improve the ability to deal with uncertain environments. To achieve an efficient system response, a self-organizing mechanism is proposed to adaptively adjust the number of layers in a DLRNN. This work integrates the proposed network into a conventional sliding mode control (SMC) system to theoretically and empirically prove its stability. The proposed system is applied to a three-joint robot manipulator, leading to a comparative study that considers several existing control approaches. The experimental results confirm the superiority of the proposed system and its effectiveness and robustness in response to various external system disturbances.

Gaussian process inference modelling of dynamic robot control for expressive piano playing.

Piano is a complex instrument, which humans learn to play after many years of practice. This paper investigates the complex dynamics of the embodied interactions between a human and piano, in order to gain insights into the nature of humans' physical dexterity and adaptability. In this context, the dynamic interactions become particularly crucial for delicate expressions, often present in advanced music pieces, which is the main focus of this paper. This paper hypothesises that the relationship between motor control for key-pressing and the generated sound is a manifold problem, with high-degrees of non-linearity in nature. We employ a minimalistic experimental platform based on a robotic arm equipped with a single elastic finger in order to systematically investigate the motor control and resulting outcome of piano sounds. The robot was programmed to run 3125 key-presses on a physical digital piano with varied control parameters. The obtained data was applied to a Gaussian Process (GP) inference modelling method, to train a network in terms of 10 playing styles, corresponding to different expressions generated by a Musical Instrument Digital Interface (MIDI). By analysing the robot control parameters and the output sounds, the relationship was confirmed to be highly nonlinear, especially when the rich expressions (such as a broad range of sound dynamics) were necessary. Furthermore this relationship was difficult and time consuming to learn with linear regression models, compared to the developed GP-based approach. The performance of the robot controller was also compared to that of an experienced human player. The analysis shows that the robot is able to generate sounds closer to humans' in some expressions, but requires additional investigations for others.

Miomir Vukobratovic [History

Miomir Vukobratovic is renowned in the robotics community for his pioneering contributions to both theoretical and applied robotics, particularly in the areas of biped locomotion, anthropomorphic mechanisms, and dynamic robot control. He spent most of his career as the director of the Robotics Laboratory at the Mihajlo Pupin Institute (MPI) in Belgrade. Vukobratovic not only gave the scientific community many ideas that were quite ahead of his time, but he also bridged the East and the West during the Cold War era through his collaborative efforts in the United States, USSR (now Russia), and Japan. Vukobratovic also figured in the careers of many robotics researchers as an educational inspiration, both through his personal contacts and his widely read series of books on robotics.

Hybrid Force-Position Dynamic Control of the Robots Using Fuzzy Applications

The paper presents methods of improving the hybrid force-position dynamic robot control using fuzzy logic for the error control. The implementation of the open architecture control system for robots using fuzzy application allows for the control of the hybrid position and force in Cartesian coordinates through real time processing of the Jacobean matrix obtained out of forward kinematics using the Denevit-Hartenberg method and calculating the Jacobean inverted matrix for control in closed loop. The effectiveness of various fuzzy control structures in controlling the force-position of the robot or mechatronics actuators is presented.

Behaviour based planning and control of leader follower formations in wheeled mobile robots

This paper describes the development of a distributed planning and control strategy for multiple mobile robots in a leader-follower formation framework, which combines together formation planning, navigation and active obstacle avoidance. A layered formation control architecture consisting of functional behaviours based on the relative motion states of the robots, classified into two levels is developed. The supervisor level in the framework handles the higher-level missions such as formation and inter-robot communication, and the lower level deals with the dynamic control of robots during navigation. Dynamic role switching mechanism through the exchange of leadership is incorporated in this work to tackle the problem of obstacle avoidance in the follower path. A state based modelling framework, where the functional behaviours/motion states of the layered approach are modelled using the augmented finite state machine (AFSM) concept, is also presented in this paper. The proposed approach is validated through state based simulations via Simulink/Stateflow environment and experiments using commercially available robot research platforms and the results obtained are discussed.

Study on the Dynamics Control of Industrial Robot Modeling Based on Spatial Operator Algebra Theory

In order to improve the modeling efficiency of the industrial robot dynamics control, the recursive dynamics is studied based on the Spatial Operator Algebra (SOA) theory, and the procedure realization is built in the environment of Matahematica6.0 software. The high effective recursive forward and reward dynamics for the SOA theory, has a simple math expression and a clear physical meaning. The software structure of the forward and inverse dynamics is built and the industrial robot dynamics simulation model is realized based on the integrated procedure VB.NET and Mathematica 6.0. According to the analysis, the example of PUMA560 robot forward and inverse dynamics is studied, and correctness and validity is verified by computed examples.

OFFLINE SERVICE DISCOVERY IN HUMAN, ROBOT, ENVIRONMENT INTERACTION

In this paper, an offline service discovery method is proposed with an interaction framework of humans, robots, and environments. It creates a dynamic robot control system with flexible and uncomplicated configuration. A number of experiments have been conducted successfully. The experiments show that this framework works well and provides some advantages to existing systems.

Real-Time Simulation of Robot Controlled Belt Grinding Processes of Sculptured Surfaces

Industrial robots are introduced to belt grinding processes of free-formed surface with elastic wheel nowadays in order to obtain high quality product and high efficiency. However, it is a laborious task to plan grinding paths and write programs for the robot. To release people from it partially, it is necessary to simulate the belt grinding processes which are useful for path generating and dynamic robot control. In this paper, we present a framework of the robot controlled belt grinding simulation system and some key issues in it. We enhance the global removal model to local process model, which can simulate the grinding process more exactly. We also point out the bottleneck of the real-time simulation and put forward a neural network based regression method to meet this difficulty. At the end of the paper, some simple simulation examples are given.

Searching a Scalable Approach to Cerebellar Based Control

Decades of research into the structure and function of the cerebellum have led to a clear understanding of many of its cells, as well as how learning might take place. Furthermore, there are many theories on what signals the cerebellum operates on, and how it works in concert with other parts of the nervous system. Nevertheless, the application of computational cerebellar models to the control of robot dynamics remains in its infant state. To date, few applications have been realized. The currently emerging family of light-weight robots (Hirzinger, in i>Proc. Second ecpd Int. Conference on Advanced Robotics, Intelligent Automation and Active Systems, 1996) poses a new challenge to robot control: due to their complex dynamics traditional methods, depending on a full analysis of the dynamics of the system, are no longer applicable since the joints influence each other dynamics during movement. Can artificial cerebellar models compete hereq

Contribution to the study of dynamics and dynamic control of robots interacting with dynamic environment

The paper discusses some practical problems of contact dynamics. Modelling the dynamics of contact tasks is carried out in a completely general way. Two dynamic systems, active robot system and passive environment system are brought into contact and the relevant dynamics are analyzed. The effects are: rigid-body contact force, elastodynamics in contact zone, friction in contact points, etc. Simultaneous stabilization of contact force and position is obtained using New Dynamic Position/Force Control. The general model is then applied to some more concrete problems and the simulation results are presented.

Benchmarking cerebellar control

Cerebellar models have long been advocated as viable models for robot dynamics control. Building on an increasing insight in and knowledge of the biological cerebellum, many models have been greatly refined, of which some computational models have emerged with useful properties with respect to robot dynamics control. Looking at the application side, however, there is a totally different picture. Not only is there a robot on the market which uses anything remotely connected with cerebellar control, but also in research labs most testbeds for cerebellar models are restricted to toy problems. Such applications hardly ever exceed the complexity of a 2 DoF simulated robot arm; a task which is hardly representative for the field of robotics, or relates to realistic applications. In order to bring the amalgamation of the two fields forward, we advocate the use of a set of robotics benchmarks, on which existing and new computational cerebellar models can be comparatively tested. It is clear that the traditional approach to solve robotics dynamics loses ground with the advancing complexity of robotic structures; there is a desire for adaptive methods which can compete as traditional control methods do for traditional robots. In this paper we try to lay down the successes and problems in the fields of cerebellar modelling as well as robot dynamics control. By analysing the common ground, a set of benchmarks are suggested which may serve as typical robot applications for cerebellar models.

Composing high-performance schedulers: a case study from real-time simulation

Dynamic, high-performance or real-time applications require scheduling latencies and throughput not typically offered by current kernel or user-level threads schedulers. Moreover, it is widely accepted that it is important to be able to specialize scheduling policies for specific target applications and their execution environments. This paper presents one solution to the construction of such high-performance, application-specific thread schedulers. Specifically, scheduler implementations are composed from modular components, where individual scheduler modules may be specialized to underlying hardware characteristics or implement precisely the mechanisms and policies desired by application programs. The resulting user-level schedulers' implementations can provide resource guarantees by interaction with kernel-level facilities which provide means of resource reservation. This paper demonstrates the concept of composable schedulers by construction of several compositions for highly dynamic target applications, where low scheduling latencies are critical to application performance. Claims about the importance and effectiveness of scheduler composition are validated experimentally on a shared-memory multiprocessor. Scheduler compositions are optimized to take advantage of different low-level hardware attributes and of knowledge about application requirements specific to certain applications, including a Time Warp-based real-time discrete event simulator. Experimental evaluations are based on synthetic workloads, on a real-time simulation blending simulated with implemented control system components, and on a dynamic robot control program. Measurements indicate that schedulers can be composed and specialized to offer performance similar to that of dedicated scheduling co-processors. Copyright © 1999 John Wiley & Sons, Ltd.

時間関数発生器に基づくロボットのオンライン軌道生成

The artificial potential field approach is often used for the on-line path planning of robots because of its simplicity and low computation. However, a little attention has been paid to control of the dynamic behavior of the generated trajectories such as movement time from the initial position to the goal and the velocity profile of the robots. In this paper, a new trajectory generation method based on the artificial potential field approach for dynamic control of robots is proposed by combining a time scale transformation and a time base generator which works as a time scale compressor. The proposed method can control the dynamic behavior of robots actively without any change of the form of the designed potential function itself. Effectiveness of the method is verified through computer simulations with an omnidirectional vehicle.

Cerebellar Control of Robot Arms

Decades of research into the structure and function of the cerebellum have led to a clear understanding of many of its cells, as well as how learning takes place. Furthermore, there are many theories on what signals the cerebellum operates on, and how it works in concert with other parts of the nervous system. Nevertheless, the application of computational cerebellar models to the control of robot dynamics remains in its infant state. To date, a few applications have been realized, but limited to the control of traditional robot structures which, strictly speaking, do not require adaptive control for the tasks that are performed since their dynamic structures are relatively simple. The currently emerging family of light-weight robots (Hirzinger, G. (1996) In Proceedings of the 2nd International Conference on Advanced Robotics, Intelligent Automation, and Active Systems, Vienna, Austria ) poses a new challenge to robot control: owing to their complex dynamics, traditional methods, depending on a full analysis of the dynamics of the system, are no longer applicable since the joints influence each other's dynamics during movement. Can artificial cerebellar models compete here? In this paper, we present a succinct introduction of the cerebellum, and discuss where it could be applied to tackle problems in robotics. Without conclusively answering the above question, an overview of several applications of cerebellar models to robot control is given.

Dynamic control of a robot arm using CMAC neural networks

Neural identification and control techniques are well-suited to the problem of controlling robot dynamics. This paper describes the use of CMAC networks for the adaptive dynamic control of an orange-harvesting robot. Among the various neural-network paradigms available, the CMAC model was chosen in this case because of its fast convergence and on-line adaptation capability. The solution of this dynamic control problem with CMAC is an encouraging demonstration of “experience-based”, as opposed to model-based, control techniques and is a good example of the use of on-line learning in adaptive neural control.

Integrated robot control using manufacturing message specification protocol based on NetBIOS

An implementation of Manufacturing Message Specification (MMS) protocol has been made for robot control based on a token-ring personal computer (PC) network using NetBIOS. This paper discusses the construction of the MMS protocol data unit (PDU) and virtual manufacturing device (VMD) which are essential elements in the protocol. The session layer capability of the NetBIOS protocol provides a good basis for establishing the confirmed services in MMS. The services are implemented by utilising the extensive session control functions in NetBIOS and building on top the required presentation and application elements. The philosophy of multiple-access machine control in MMS is also made possible on a single-user operating system such as DOS by a ring buffer control scheme. The implication of these are further explored in relation to the dynamic control of robot over the network environment using the concepts of domain and program invocation in MMS.

Integrated Robot Control Using Manufacturing Message Specification Protocol Based on Netbios

An implementation of Manufacturing Message Specification (MMS) protocol has been made for robot control based on a token ring personal computer (PC) network using NetBIOS. The session layer stack of NetBIOS provides a good basis for developing the MMS protocol which requires extensive session control in its confirmed services. This paper discusses the construction of MMS protocol data unit (PDU) and virtual manufacturing device (VMD) which are essential elements in the protocol. Implication of these elements are explored in relation to the dynamic control of robots using the concepts of domain and program invocation.

The Operational Space Framework

Robot dynamics has been traditionally viewed from the perspective of a manipulator's joint motions, and significant effort has been devoted to the development of joint space dynamic models and control methodologies. However, the limitations of joint space control techniques, especially in constrained motion tasks, have motivated alternative approaches for dealing with task-level dynamics and control. The operational space formulation, which falls within this line of research, has been driven by the need to develop mathematical models for the description, analysis, and control of robot dynamics with respect to task behavior. In this article, we review the operational space task-level models and discuss the various control methodologies that have been developed in this framework. These include : the unified motion and force control approach ; the notion of dynamic consistency in redundant manipulator control ; the reduced effective inertia property associated with macro-/mini-manipulator systems and the dextrous dynamic coordination strategy proposed for their control ; and the augmented-object model for the control of robot systems involving multiple manipulators.

A compensation method for the dynamic control of robots,

A compensation method for the dynamic control of robots,