Off-Line Programming Environment for Robotic Applications

We describe a new method for adaptive model-based control of robotic dynamic systems using a new hybrid neuro-fuzzy-fractal approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. Optimal control of many robotic systems also requires methods which make use of predictions of future behavior. We describe an intelligent system for controlling robot manipulators to illustrate our neuro-fuzzy-fractal hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant selection parameters for the problem. We use neural networks for identification and control of robotic dynamic systems.

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid approach combining neural networks, fuzzy logic, and genetic algorithms. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly non-linear. We describe an intelligent system for controlling robotic systems to illustrate our new hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the genetic algorithm is used to optimize the fuzzy system. We also use neural networks for identification and control of robotic systems.

The manufacture, maintenance and inspection of a ship involve a series of works on the ship shell plate, which were always seen as harmful for human operators and time-consuming work. The shipping industry is looking to replace manual work with automation equipment. A magnetic climbing robot that can omnidirectionally move on ship shell plate was presented in this paper. This article summarized the mechanical structure, control system, kinematic model, and autonomy of robot. The mechanical structure of the robot was inspired by bionics and adopted a wheel-leg hybrid locomotion system. In the control system of this robot, industrial control computer (IPC) was adopted as the core controller and brushless direct current servomotor was chosen as the actuating station. Finally, the motion analysis of the designed robot was performed. The results of the analysis show that the magnetic climbing robot adapted to the ship curved shell plate and crossed obstacles.

Robotic systems have significantly transformed the landscape of minimally invasive surgery, offering enhanced precision, reduced recovery times, and improved patient outcomes. These systems have evolved from simple mechanical tools to sophisticated platforms integrating advanced control algorithms and real-time feedback mechanisms. This paper provides a comprehensive overview of the role of robotic systems in minimally invasive surgery, exploring the various benefits, inherent risks, and the challenges associated with their adoption and development. We examine the technical aspects of robotic control systems, such as kinematics, force control, and haptic feedback, which enable greater surgical accuracy. Despite these advantages, challenges such as system complexity, high costs, and ethical concerns remain significant barriers to widespread adoption. The future of robotic surgery lies in overcoming these obstacles, with advancements in system miniaturization and further refinement of control systems poised to enhance the efficacy and accessibility of robotic platforms. This paper explores the role of robotic systems in minimally invasive surgery, focusing on three key areas: bronchoscopy and ureteroscopy. By reviewing the benefits, risks, case studies, and future directions of these technologies, the paper seeks to provide an in-depth understanding of how robotic systems are shaping the future of surgery. Key words: Robotic Surgery, Minimally Invasive Surgery, Robotic Control Systems, Surgical Precision, PID Control, Kinematics, Inverse Kinematics, Forward Kinematics, Dynamic Control, Force Control, Impedance Control, Surgical Robotics, Haptic Feedback, Robotic Arm, Robotic Surgery Benefits, Robotic Surgery Risks, Robot-Assisted Surgery, Surgeon Console, Real-Time Feedback, Surgical Training, Robotic System Development, System Testing, Clinical Trials, Surgical Robot Performance, Robotics in Urology, Telepresence Surgery, Robotic Surgery Cost, Robotic Surgery Future Directions.

The control system of a free-flying space manipulation robot with force-torque sensing is considered. As part of this work, a control circuit of the remote control system of a free-flying robot with variable delays in the data channel is proposed. The passivation method for TDPA (Time Domain Passivity Approach) communication cannel has been used to ensure the stability of the control system. The TDPA method is a flexible and convenient approach to ensure the passivity of the space manipulation robot's remote control system, but as a usage effect a drift of the position of the processing device happens. The method of compensating the drift of the executive device was applied to eliminate this drawback. To test the proposed control algorithms, a "virtual" stand was created and a simulation of a remote control system of a free-flying space manipulation robot with force-torque sensing was done under variable delays.

Distributed autonomous control of modular robot systems using parallel programming

This thesis focuses on the problem of efficiently allocating resources for enhancing the performance of an autonomous robotic agent. Such an agent is expected to operate in complex dynamic environments by continuously monitoring its internal states and the external events. These requirements give raise to countless problems that have populated research in the autonomous robotics community in the last two decades. Among these issues, one of the most relevant is to coordinate different low and high-level behaviors, giving them, from time to time, different priority values both for resource allocation and for action selection processes. The main problem in achieving this requirement is that the number and complexity of the stimuli received by each behavior may be quite high and also the effects on the emerging activity may be very hard to foresee. It is clear that it is not possible for the robotic control system to process all the incoming information, especially for real-time applications. Thus, it becomes necessary to build mechanisms able to guide this sensory input selection process and to choose the best action to perform, assuring an efficient use of the robot limited sensorial and cognitive resources. For this purpose, attentional mechanisms, balancing sensors elaboration and actions execution, can be very useful since they play two main roles: they focus the attention on salient regions of the space and they distribute resources and activities in time. As a result of the application of these mechanisms within the robotic control system for sensory-motor coordination, the robot behavior is improved: the robot becomes able to react faster to task-related or safety-critical stimuli and to opportunely split resources among concurrent behaviors. Attentional mechanisms applied to autonomous robotic systems have already been proposed, but mainly for vision-based robotics; conversely, the contribution introduced by the present thesis is the use of an artificial attentional mechanism suitable both for optimizing the use of resources and for execution monitoring and control.

The article is devoted to the application of a group of robotic complexes for military purposes. The current state of control systems of single robotic complexes does not allow solving all the tasks assigned to the robot. The analysis of methods of controlling a group of robots in combat conditions is carried out. The necessity of using a multi-level control system for an intelligent combat robot is justified. A multi-level control system for an intelligent robot is proposed. Such a system assumes the possibility of controlling the robot in one of four modes: remote, supervisory, autonomous and group. Moreover, each robot, depending on the external conditions and its condition, can be in any control mode. The application of the technique is shown by the example of the movement of a group of robots with an interval along the front. The problem of the movement of slave robots behind the leader is considered. When forming the robot control algorithm, the method of finite automata was used. The algorithm controls the movement of the RTK in various operating modes: group control mode and autonomous movement mode. In the group control mode, the task is implemented: movement for the leader. For the state of "Movement in formation", an algorithm for forming the trajectory of the movement of guided robots was implemented. An algorithm for approximating the Bezier curve was used. It allows you to build a trajectory for the slave robot. On the basis of the obtained trajectory, the angular and linear velocity were calculated. In the autonomous control mode, two tasks are solved: moving to a given point and avoiding obstacles. Vector Field Histogram was used as an algorithm for detouring an obstacle, which determines the direction of movement without obstacles. The state of "Movement to a given point" is based on Pure Pursuit as a simple and reliable algorithm for solving such problems. A computer model of the movement of a group of robots was developed. The model is implemented in the MATLAB program using the Simulink and Mobile Robotics Simulation Toolbox libraries. Several different variants of the movement of the RTK group are modeled, which differ from each other in the initial location of the robots and the position of obstacles. The conducted computer simulation showed the efficiency and effectiveness of the proposed method of RTC control.

This study explores the incorporation of time-varying sign gain into a parallel iterative learning control (ILC) architecture, augmented by an expert system, to enhance the performance and stability of a robotic arm system. The methodology involves iteratively tuning the learning control gains using time-varying sign gain guided by an expert system. Stability analysis, encompassing asymptotic and monotonic convergence, demonstrates promising results across multiple joints, affirming the effectiveness of the proposed control architecture. In comparison with traditional PID control, fixed gain ILC, and ILC with adaptive learning in the expert system, the analysis focuses on stability, precision, and adaptability, using root mean square error (RMSE) as a key metric. The results show that ILC with adaptive learning from the expert system consistently reduces RMSE, even in the presence of learning transients. This adaptability effectively controls the learning transients, ensuring improved performance in subsequent iterations. In conclusion, the integration of time-varying sign gain with expert system assistance in a parallel ILC architecture holds promise for advancing adaptive control in robotic systems. Positive outcomes in stability, precision, and adaptability suggest practical applications in real-world scenarios. This research provides valuable insights into the implementation of dynamic learning mechanisms for enhanced robotic system performance, laying the groundwork for future refinement in robotic manipulator control systems.

In this paper the procedure for automating the photon quantum experiments for mobile robotic applications is presented. Due to the rapid advances of quantum technologies and quantum engineering, the integration of quantum capabilities in robotic and autonomous systems will be inevitable, and therefore the study and investigation of compatibility and adaptability of quantum systems and classical autonomous systems is of great importance. In a quantum-classical hybrid setup, the source of single photon generation is placed on a leader robot which can send correlated single photons to robot followers. In the case of quantum entanglement, spontaneous parametric down-conversion process using nonlinear paired BBO crystals is implemented which sends entangled photons to the single photon counting modules installed on mobile robots. In the case of quantum cryptography, single photons are sent from Alice robot to Bob robot, where Alice has the course of single photon and Bob has a polarizing beamsplitter and two detectors and that can detect the polarization of photons as vertical and horizontal. Bob then can convert the polarizations to a digital signals as zeros and ones and use them as communication information for control purposes through a classical channel. Motorized optics equipment can automatically align the source of photons to detectors on the mobile robots. The automated alignment procedure is one of the key enabling technologies in integrating quantum capabilities with control of mobile robotic systems. In this paper, in particular, the automated alignment is studied while considering the uncertainties in the dynamic of the system which can potentially cause the alignment task very challenging. The uncertainty analysis in the automated alignment is implemented by Optimal Uncertainty Quantification technique to ensure achieving the quantum control of the robotic systems and presented here for the first time.

PRESTO is a programming system for writing object‐oriented parallel programs in a multiprocessor environment. PRESTO provides the programmer with a set of pre‐defined object types that simplify the construction of parallel programs. Examples of PRESTO objects are threads, which provide fine‐grained control over a program's execution, and synchronization objects, which allow simultaneously executing threads to co‐ordinate their activities.The goals of PRESTO are to provide a programming environment that makes it easy to express concurrent algorithms, to do so efficiently, and to do so in a manner that invites extensions and modifications. The first two goals, which are the focus of this paper, allow a programmer to use parallelism in a way that is naturally suited to the problem at hand, rather than being constrained by the limitations of a particular underlying kernel or hardware architecture. The third goal is touched upon but not emphasized in this paper.PRESTO is written in C++; it currently runs on the Sequent shared‐memory multiprocessor on top of the Dynix operating system. In this paper we describe the system model, its applicability to parallel programming, experiences with the initial implementation, and some early performance measurements.

Control systems for autonomous robots are concurrent, distributed, embedded, real-time and data intensive software systems. A real-world robot control system is composed of tens of software components. For each component providing robotic functionality, tens of different implementations may be available. The difficult challenge in robotic system engineering consists in selecting a coherent set of components, which provide the functionality required by the application requirements, taking into account their mutual dependencies. This challenge is exacerbated by the fact that robotics system integrators and application developers are usually not specifically trained in software engineering. In various application domains, software product line (SPL) development has proven to be the most effective approach to face this kind of challenges. In a previous paper [D. Brugali and N. Hochgeschwender, Managing the functional variability of robotic perception systems, in First IEEE Int. Conf. Robotic Computing, 2017, pp. 277–283.] we have presented a model-based approach to the development of SPL for robotic perception systems, which integrates two modeling technologies developed by the authors: The HyperFlex toolkit [L. Gherardi and D. Brugali, Modeling and reusing robotic software architectures: The HyperFlex toolchain, in IEEE Int. Conf. Robotics and Automation, 2014, pp. 6414–6420.] and the Robot Perception Specification Language (RPSL) [N. Hochgeschwender, S. Schneider, H. Voos and G. K. Kraetzschmar, Declarative specification of robot perception architectures, in 4th Int. Conf. Simulation, Modeling, and Programming for Autonomous Robots, 2014, pp. 291–302.]. This paper extends our previous work by illustrating the entire development process of an SPL for robot perception systems with a real case study.

Robotic technologies for rehabilitating motor impairments from neurological injuries have been the focus of intensive research and capital investment for more than 30 years. However, these devices have failed to convincingly demonstrate greater restoration of patient function compared to conventional therapy. Nevertheless, robots have value in reducing the manual effort required for physical therapists to provide high-intensity, high-dose interventions. In most robotic systems, therapists remain outside the control loop to act as high-level supervisors, selecting and initiating robot control algorithms to achieve a therapeutic goal. The low-level physical interactions between the robot and the patient are handled by adaptive algorithms that can provide progressive therapy. In this perspective, we examine the physical therapist's role in the control of rehabilitation robotics and whether embedding therapists in lower-level robot control loops could enhance rehabilitation outcomes. We discuss how the features of many automated robotic systems, which can provide repeatable patterns of physical interaction, may work against the goal of driving neuroplastic changes that promote retention and generalization of sensorimotor learning in patients. We highlight the benefits and limitations of letting therapists physically interact with patients through online control of robotic rehabilitation systems, and explore the concept of trust in human-robot interaction as it applies to patient-robot-therapist relationships. We conclude by highlighting several open questions to guide the future of therapist-in-the-loop rehabilitation robotics, including how much control to give therapists and possible approaches for having the robotic system learn from therapist-patient interactions.

Binary search trees (BSTs) are one of the most important data structures in the field of computer science. We may easily write a parallel construction program of a BST by extending the sequential algorithm straightly. However, in such conventional approaches, the order of nodes inserted into a BST is determined dynamically, depending on the occasional state of the parallel thread execution. It results in a BST with a different structure (node position) generated on every execution of the parallel program. On the other hand, we have been developing parallel construction schemes of the BST with the same structure as a BST generated by the sequential algorithm. One is the speculatively parallel construction of a BST. And another is the purely (non-speculatively) parallel construction, but it was derived through the concept of thread-level speculation. This paper evaluates the performances of those construction schemes on several types of shared-memory multiprocessors. For the large enough size of BST, our new parallel programs can construct a BST with always the same structure on a little lower or sometimes higher performance than the program that makes a BST with a different structure on every execution. And in contrast with the general expectation that simply enlarging the size of parallel tasks increases misspeculation and damages the performance, we found that it sometimes enhances the performance of speculatively parallel execution.

Edge intelligence and agnostic robotic paradigm in resource synchronisation and sharing in flexible robotic and facility control system