Complex Robotic Systems Research Articles

Implementation of real-time distributed control for discrete event robotic systems using Petri nets

This article presents a systematic method of modeling and implementing real-time control for discrete-event robotic systems using Petri nets. Because, in complex robotic systems such as flexible manufacturing systems, the controllers are distributed according to their physical structure, it is desirable to realize real-time distributed control. In this article, the task specification of robotic processes is represented as a system control-level net. Then, based on the hierarchical approach, it is transformed into detailed subnets, which are decomposed and distributed into the local machine controllers. The implementation of real-time distributed control through communication between the system controller and the machine controllers on a microcomputer network is described for a sample robotic system. The proposed implementation method is sufficiently general, and can be used as an effective prototyping tool for consistent modeling, simulation, and real-time control of large and complex robotic systems.

Specification of Robotic Systems on an Example of Visual Servoing

The paper presents a formal method of specifying complex robotic systems. Although the method is general, here it is applied to the description of two, however diverse, robot behaviours, in which the induced motion was computed on the basis of information retrieved from different cameras: a camera integrated with the robot gripper and a camera statically mounted above the scene. The presented experimental results confirm the correctness of the developed systems, thus validate the design method.

Information-driven self-organization: the dynamical system approach to autonomous robot behavior

In recent years, information theory has come into the focus of researchers interested in the sensorimotor dynamics of both robots and living beings. One root for these approaches is the idea that living beings are information processing systems and that the optimization of these processes should be an evolutionary advantage. Apart from these more fundamental questions, there is much interest recently in the question how a robot can be equipped with an internal drive for innovation or curiosity that may serve as a drive for an open-ended, self-determined development of the robot. The success of these approaches depends essentially on the choice of a convenient measure for the information. This article studies in some detail the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process. The PI of a process quantifies the total information of past experience that can be used for predicting future events. However, the application of information theoretic measures in robotics mostly is restricted to the case of a finite, discrete state-action space. This article aims at applying the PI in the dynamical systems approach to robot control. We study linear systems as a first step and derive exact results for the PI together with explicit learning rules for the parameters of the controller. Interestingly, these learning rules are of Hebbian nature and local in the sense that the synaptic update is given by the product of activities available directly at the pertinent synaptic ports. The general findings are exemplified by a number of case studies. In particular, in a two-dimensional system, designed at mimicking embodied systems with latent oscillatory locomotion patterns, it is shown that maximizing the PI means to recognize and amplify the latent modes of the robotic system. This and many other examples show that the learning rules derived from the maximum PI principle are a versatile tool for the self-organization of behavior in complex robotic systems.

Getting Rid of the Wires: Curved Layer Fused Deposition Modeling in Conductive Polymer Additive Manufacturing

This paper describes an additive manufacturing technology that has the potential to print plastic components with integral conductive polymer electronic circuits. This could have a major impact in the fields of robotics and mechatronics as it has the potential to allow large wiring looms, often an issue with complex robotic systems, to be printed as an integral part of the products plastic shell. This paper describes the development of a novel Fused Deposition Modeling (FDM) process in which the layers of material that make up the part are deposited as curved layers instead of the conventional flat layers. This opens up possibilities of building curved plastic parts that have conductive electronic tracks and components printed as an integral part of the plastic component, thereby eliminating printed circuit boards and wiring. It is not possible to do this with existing flatlayer additive manufacturing technologies as the continuity of a circuit could be interrupted between the layers. With curved-layer fused deposition modeling (CLFDM) this problem is removed as continuous filaments in 3 dimensions can be produced, allowing for continuous conductive circuits.

Modeling of the Control Algorithms in Case of a Self Reconfiguring Mobile Robotic System

Self reconfiguring robots are complex robotic systems composed of a finite number of modules, where each module of the system is considered to be a simple agent. Based on this con¬text, the reconfi¬guration represents the process of re-arranging each agent of the robotic system in order to have a working system. One versatile method to determine the most adequate movement and reconfiguration algorithms can be done us¬ing modeling methods and tools. Using this approach, the control algorithms necessary to drive such a system can be modeled, tested and implemented in an embedded system much easier than in a classical approach. This paper presents the case of a self reconfiguring robot for which, a reconfiguration algorithm and a movement algorithm were determined, analyzed, tested and validated.

Learning Control in Robotics

Recent trends in robot learning are to use trajectory-based optimal control techniques and reinforcement learning to scale complex robotic systems. On the one hand, increased computational power and multiprocessing, and on the other hand, probabilistic reinforcement learning methods and function approximation, have contributed to a steadily increasing interest in robot learning. Imitation learning has helped significantly to start learning with reasonable initial behavior. However, many applications are still restricted to rather lowdimensional domains and toy applications. Future work will have to demonstrate the continual and autonomous learning abilities, which were alluded to in the introduction.

Reliability analysis of complex robotic system using Petri nets and fuzzy lambda‐tau methodology

PurposeThe purpose of this paper is to evaluate various reliability parameters of a multi‐robot system, arranged in a complex configuration. The effects of failures and course of action on the system performance have also been investigated.Design/methodology/approachThe present work is based on a multi‐robotic system, in which two robots are working independently with a conveyer unit. Petri net (PN) tool is applied to represent the asynchronous and concurrent processing of the system. To enhance the relevance of the reliability study, fuzzy numbers are developed from available data of the components, using fuzzy possibility theory to define membership functions. The use of fuzzy arithmetic in the PN model increases the flexibility for application to various systems and conditions. Various reliability parameters (such as mean time between failures, ENOF, reliability, availability, etc.) are computed using fuzzy lambda‐tau methodology. As the available data are imprecise, incomplete, vague and conflicting, the fuzzy methodology can deal easily with approximations.FindingsThe adopted methodology in the present work improves the shortcomings/drawbacks of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation.Originality/valueIn an earlier study failure behavior of a single robot was analyzed. This paper is an extension of the previous work, in which failure behavior of multi‐robotic system is analyzed. Also, the interactions among the working units of multi‐robotic system are deeply studied. The paper contains a new idea about the reliability analysis of robotic system. Fuzzy lambda‐tau methodology, a fuzzy PROBIST technique, is used for the proposed robotic system and the results obtained are compared with crisp results. Reliability analysis of a multi‐robotic system is presented in this paper, which may help the system analysts to analyze and predict the system behavior and to reallocate the required resources.

Nonlinear complexity of human biodynamics engine

This paper reviews a nonlinear complexity within the Human Biodynamics Engine (HBE), a world-class human neuro-musculoskeletal simulator, developed at the Department of Defense, Australia. The HBE development is based on an anthropomorphic tree of Euclidean motion groups SE(3), with 270 active degrees of freedom, realistic muscular mechanics and hierarchical neural-like control. The HBE is formulated in the fashion of nonlinear dynamics/control of highly complex biophysical and robotic systems and developed for the purpose of neuro-musculoskeletal injury prediction. The following aspects of the HBE development are described: geometrical, dynamical, control, physiological, biomedical, AI, behavioral and complexity. Several simulation examples are provided.

Software Development for Networked Robot Systems

The design of a complex (Multi-)Robot system is a difficult task for engineers and researchers. Architecture design and software tools help the developers in spending most of their efforts in solving complex research issues. However, the lack of standardized techniques and specifications makes it hard to integrate complex systems, especially when developed by different subjects. In this paper, we propose the use of DDS (Data Distribution Services) as a data-centric middleware to manage information exchange between different components of a robotic system, and/or between different robots or agents. Moreover, we claim that a software framework for robotics should allow for transparent use of data-centric middleware, in order to interoperate with other software components. In this way, the DDS can be seen as the interoperability layer that allows for an easy integration of components that are developed by different groups.1

Design and Development of Control and Communication Systems for Complex Mobile Robot and Manipulator Structure

This paper describes how LEGO kits can be used to teach design and building of the complex robot system composed of the mobile robot platform and the robot arm. Both parts of proposed system are equipped with appropriate microcontrollers, sensors and actuators. All mechanical and electrical components are parts of Lego NXT Mindstroms™ Educational Robotic Kit. The control and communication software supports were building using Java programming language. This system could be controlled in two different modes, direct and Bluetooth controls. The effectiveness of the proposed complex system is demonstrated through several experiments, which provide an innovative use of the LEGO kits in robotics courses.

Development of complex robotic systems using the behavior-based control architecture iB2C

This paper presents a development methodology for complex robotic systems using the behavior-based control architecture iB2C ( integrated Behavior- Based Control). It is shown how architectural principles support several behavior-based mechanisms, e.g. coordination mechanisms, behavior interaction, and hierarchical abstraction. Furthermore, design guidelines and structural patterns are presented which support the design and implementation process. The provided analysis tools and visualization techniques help to manage the complexity of large behavior-based networks. Finally, application examples are presented and a step by step description of constructing a behavior-based control structure for the outdoor robot Ravon is given.

Pseudo-inverse Jacobian control with grey relational analysis for robot manipulators mounted on oscillatory bases

Interest in complex robotic systems is growing in new application areas. An example of such a robotic system is a dexterous manipulator mounted on an oscillatory base. In literature, such systems are known as macro/micro systems. This work proposes pseudo-inverse Jacobian feedback control laws and applies grey relational analysis for tuning outer-loop PID control parameters of Cartesian computed-torque control law for robotic manipulators mounted on oscillatory bases. The priority when modifying controller parameters should be the top ranking importance among parameters. Grey relational grade is utilized to investigate the sensitivity of tuning the auxiliary signal PID of the Cartesian computed-torque law to achieve desired performance. Results of this study can be feasible to numerous mechanical systems, such as mobile robots, gantry cranes, underwater robots, and other dynamic systems mounted on oscillatory bases, for moving the end-effector to a desired Cartesian position.

Integration of humanoid robots in collaborative working environment: a case study on motion generation

This paper illustrates through a practical example an integration of a humanoid robotic architecture, with an open-platform collaborative working environment called BSCW (Be Smart-Cooperate Worldwide). BSCW is primarily designed to advocate a futuristic shared workspace system for humans. We exemplify how a complex robotic system (such as a humanoid robot) can be integrated as a proactive collaborative agent which provides services and interacts with other agents sharing the same collaborative environment workspace. Indeed, the robot is seen as a ‘user’ of the BSCW which is able to handle simple tasks and reports on their achievement status. We emphasis on the importance of using standard software such as CORBA (Common Object Request Broker Architecture) in order to easily build interfaces between several interacting complex software layers, namely from real-time constraints up to basic Internet data exchange.

Guest editorial: selected papers from Robotics: Science and Systems 2008

This volume is one of two special journal issues compiled from the best papers presented at the fourth Robotics: Science and Systems (RSS) conference, held in Zurich in June 2008. As indicated by its title, RSS is a meeting that values research papers focusing on the development of, and experimentation with, new complex robotic systems in real-world environments as much as it values new theoretical and algorithmic advances. The second special issue will be published by the International Journal of Robotics Research. The Zurich meeting of RSS received 163 submitted papers. After a rigorous review process, 40 papers were accepted covering a wide range of topics, from autonomous driving, to micro-robotics, to design of manipulators for use in functional magnetic resonance imaging, to grasping and manipulation. Following the tradition of RSS, there were a number of invited speakers who are global leaders in research areas on the frontiers of robotics. As an indication of the quality and inspirational value of the program, RSS Zurich was well attended, with nearly six times as many attendees as papers. Papers selected for a special issue were selected first, and then partitioned into two groups for the two special issues. The papers selected for this special issue of Autonomous Robots have a common theme close to the focus of this journal: the study of problems related to mobile robots or

Bio-Inspired Control of an Arm Exoskeleton Joint with Active-Compliant Actuation System

This paper presents the methodology followed on the design of a multi-contact point haptic interface that uses a bio-inspired control approach and a novel actuation system. The combination of these components aims at creating a system that increases the operability of the target, and, at the same time, enables an intuitive and safe tele-operation of any complex robotic system of any given morphology. The novelty lies on the combination of a thoughtful kinematic structure driven by an active-compliant actuation system and a bio-inspired paradigm for its regulation. Due to the proposed actuation approach, the final system will achieve the condition of wearable system. On that final solution, each joint will be able to change its stiffness depending on the task to be executed, and on the anatomical features of each individual. Moreover, the system provides a variety of safety mechanisms at different levels to prevent causing any harm to the operator. In future, the system should allow the complete virtual immersion of the user within the working scenario.

New form of the Euler-Bernoulli rod equation applied to robotic systems

This paper presents a theoretical background and an example of extending the Euler-Bernoulli equation from several aspects. Euler-Bernoulli equation (based on the known laws of dynamics) should be supplemented with all the forces that are participating in the formation of the bending moment of the considered mode. The stiffness matrix is a full matrix. Damping is an omnipresent elasticity characteristic of real systems, so that it is naturally included in the Euler-Bernoulli equation. It is shown that Daniel Bernoulli's particular integral is just one component of the total elastic deformation of the tip of any mode to which we have to add a component of the elastic deformation of a stationary regime in accordance with the complexity requirements of motion of an elastic robot system. The elastic line equation mode of link of a complex elastic robot system is defined based on the so-called 'Euler-Bernoulli Approach' (EBA). It is shown that the equation of equilibrium of all forces present at mode tip point ('Lumped-mass approach' (LMA)) follows directly from the elastic line equation for specified boundary conditions. This, in turn, proves the essential relationship between LMA and EBA approaches. In the defined mathematical model of a robotic system with multiple DOF (degree of freedom) in the presence of the second mode, the phenomenon of elasticity of both links and joints are considered simultaneously with the presence of the environment dynamics - all based on the previously presented theoretical premises. Simulation results are presented. .

Image based visual servoing for robot positioning tasks

Visual servoing has become a popular paradigm for the control of complex robotic systems: this sensor based approach exploits the image informations provided by one ore more cameras in a feedback control loop to drive the system to the desired configuration. Here authors will refers to a monocular system where the camera is mounted on the end effector of a 6-DOF manipulator. Among different Visual Servoing approaches Image Based Visual Servoing (IBVS) has been the most investigated in the literature because of its nice properties of robustness with respect to both robot modeling and camera calibration errors: in IBVS the control loop is in fact directly closed in the image; moreover IBVS doesn’t require the knowledge of the target/scene model (model-free approach). Despite its advantages IBVS may be affected by singularity and local minima problems of the control law: these drawbacks arise especially when the initial and the goal camera images respectively corresponding to the actual and desired system configurations are very different (i.e for large system displacements). To overcome these problems an image path planning can be exploited to ensure system convergence. In this paper author presents an off-line image path planning that can be used to execute system positioning task also in presence of large camera displacements: planning trajectories has been developed such as to make the robot end effector move on a 3D helix, connecting the initial and the desired arm configuration, by generating feasible robot twist-screws and keeping the target in the image field of view. During control execution also 3D target informations are retrieved through an adaptive estimation law. Both simulations and experimental results show the feasibility of the proposed approach.

When hard realtime matters: Software for complex mechatronic systems

A still growing number of software concepts and frameworks have been proposed to meet the challenges in the development of more and more complex robotic systems, like humanoids or networked robotics. The issue of hard realtime, however, has not been the main focus of such concepts, but is essential for building and controlling mechatronic systems. Here we discuss the specific demands of complex mechatronic systems and present a software concept, the “agile Robot Development” (aRD) concept, which we developed at our institute to pragmatically address these demands. We show that the performance of current computing and communication hardware allows for a flexible component-based concept with distributed execution, even in hard realtime with rates in the kHz range.

A combined stochastic and greedy hybrid estimation capability for concurrent hybrid models with autonomous mode transitions

Probabilistic hybrid discrete/continuous models, such as Concurrent Probabilistic Hybrid Automata (CPHA) are convenient tools for modeling complex robotic systems. In this paper, we present a novel method for estimating the hybrid state of CPHA that achieves robustness by balancing greedy and stochastic search. To accomplish this, we (1) develop an efficient stochastic sampling approach for CPHA based on Rao–Blackwellised Particle Filtering, (2) perform an empirical comparison of the greedy and stochastic approaches to hybrid estimation and (3) propose a strategy for mixing stochastic and greedy search. The resulting method handles nonlinear dynamics, concurrently operating components and autonomous mode transitions. We demonstrate the robustness of the mixed method empirically.

Using Formal Modeling With an Automated Analysis Tool to Design and Parametrically Analyze a Multirobot Coordination Protocol: A Case Study

Many robot systems employ logic-based or reactive controllers, making them hybrid systems (i.e., mixed discrete continuous). However, designing such control laws in a systematic manner remains a challenging task. In this paper, we apply the formal modeling paradigm to a team of mobile robots. The linear hybrid automata modeling framework is used to describe the high-level design, and the verification software HyTech is used for symbolic analysis of the description. The goal is to symbolically quantify system-level performance as a function of the design parameters, for the purpose of optimizing and synthesizing design parameters, verifying safe operation, and quantitatively exploring tradeoff issues. In order to make the analysis tractable, a series of restrictive assumptions and simplifications must be made-some dictated by the linear hybrid automata model and others necessitated by computational cost. We comment on the restrictiveness of these assumptions and the overall utility of this automated analysis approach in designing complex robotic systems