Autonomous robotic rock breaking using a real‐time 3D visual perception system

Hybrid continuous-time and discrete-event system models are developed for applications to intelligent robot and autonomous control systems. Certain general properties, including the interface structure between the discrete event part and the continuous part of hybrid systems, are studied. Petri nets are proposed as the model for the discrete event sub-system (DES), and state equations with a set of binary parameters as the model for the continuous time sub-system (CTS) in a hybrid system. The quantized CTS is formulated in prime event structures, which can also be modeled by a condition/event Petri net. Therefore, a complete hybrid system can be fit into an extended Petri net formalism, which provides a unified framework for hybrid system description. The validity of the proposed model is demonstrated by modeling a two-tank system.

More and more robot control systems require intelligent extensions such as vision or virtual reality (VR) system in order to be capable of easy and flexible interaction with the environment or humans. This paper describes the stereo vision, virtual reality and off-line programming part of the intelligent control system of the Puma 560 robot and the dextrous hand developed at the Technical University of Budapest. With the use of the detailed graphic models of the VR system, efficient off-line robot programming and simulation of the contact forces between the hand and the grasped object is available. A simple robot programming language, which was written specifically for this robot-hand system, is introduced.

BackgroundAdvances in machine learning and robotics have allowed the development of increasingly autonomous robotic systems which are able to make decisions and learn from experience. This distribution of decision-making away from human supervision poses a legal challenge for determining liability.MethodsThe iRobotSurgeon survey aimed to explore public opinion towards the issue of liability with robotic surgical systems. The survey included five hypothetical scenarios where a patient comes to harm and the respondent needs to determine who they believe is most responsible: the surgeon, the robot manufacturer, the hospital, or another party.ResultsA total of 2,191 completed surveys were gathered evaluating 10,955 individual scenario responses from 78 countries spanning 6 continents. The survey demonstrated a pattern in which participants were sensitive to shifts from fully surgeon-controlled scenarios to scenarios in which robotic systems played a larger role in decision-making such that surgeons were blamed less. However, there was a limit to this shift with human surgeons still being ascribed blame in scenarios of autonomous robotic systems where humans had no role in decision-making. Importantly, there was no clear consensus among respondents where to allocate blame in the case of harm occurring from a fully autonomous system.ConclusionsThe iRobotSurgeon Survey demonstrated a dilemma among respondents on who to blame when harm is caused by a fully autonomous surgical robotic system. Importantly, it also showed that the surgeon is ascribed blame even when they have had no role in decision-making which adds weight to concerns that human operators could act as “moral crumple zones” and bear the brunt of legal responsibility when a complex autonomous system causes harm.

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.

With the development of the scale of the power system and the maturity of robotics, the use of inspection robots instead of manual inspection can effectively improve the efficiency of inspection and realize the intelligent management of the power grid. In this paper, a control system for substation inspection robot is designed and implemented for the substation inspection environment. First, a convolutional neural network algorithm under deep learning (DL) is proposed to extract the characteristics of the inspection robot, and then related technologies for inspection robot control are proposed, including navigation and positioning, motion control, power supply management, safety and anti-collision, and finally, it can be obtained through the test of the control system. The inspection robot has good tracking capabilities. In addition, the real trajectory is basically consistent with the set trajectory, which also shows the robustness of the system. Therefore, the use of DL to design the control system of the intelligent inspection robot of the substation is of great research value. In this paper, it is hoped that the deep learning-based convolutional neural network algorithm can be used to extract the features of inspection robots, which can effectively prove that the robots have good tracking ability and promote the power grid inspection to a certain extent. This paper provides reference value for realizing the intelligentization of power grid selection.

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.

The control system of intelligent robot uses 32bit high performance ARM CPU as the control kernel of the whole intelligent robot. The robot is wheel driven, model founded and computer programmed. ARM is adopted as the core controller, and it has low consumption and outstanding performance. The embedded Linux is used as the operating system of the bottom controlling software, and its performance is stable and its real-performance is good. The module extension can be done, which is convenient for the secondary development. The graphic programming interface accelerates the secondary development for the users.

Software plays a crucial role in robot control systems, and its efficient, flexible development is essential for production. Such software must be customized to specific production processes, requiring developers with specialized expertise in these areas—the high development threshold results in reduced efficiency and increased costs. Recently, significant progress has been made in automated problem-solving through societies of agents based on large language models (LLMs). To automate software development for industrial robot control systems, this paper introduces an Industrial Robot Control Software Auto-Development (IRCSAD) framework with multi-agent collaboration, and a Low-code Industrial Software Platform (LISP) for validating IRCSAD-developed software. IRCSAD automates software development and iterative optimization of prompts through the collaboration of multiple LLM-based agents. This work also proposes a software testing methodology for robot control systems based on reliability assessment. An experimental study that develops software for the robot control system for assembly, sorting, and inspection tasks is implemented. The results show that collaboration with LISP enhances IRCSAD's ability to solve complex problems in the development process, saves development costs, and improves development efficiency.

Robotics for lunar surface operations was first studied in 1989 and updated in 2019 to include reusable, modular, and interoperable framework for space-qualifiable space autonomous robotic software systems. There is a clear distinction in autonomous robotic systems between the meanings of a “control system” and a “system under control”, with explicit representation of behavioral models. Autonomous system operational behavior is controlled through software design, best optimized when software engineers translate and code system engineers’ understanding of system behavior. The certification process often requires safety cases showing structured argument of a system's safety in a certain context. Optimal behavior of autonomous robotic system is evidenced by two qualitative characteristics--- robustness and resiliency, when quantitatively measured, help to standardize and certify autonomous robotic systems. The purpose of this paper is to characterize prospective metrics under study –robustness and resilience—and relevancy to the Artemis Mission. Key Terms—Space Robotics and Certification; Multi-Robot Systems; Autonomous Agents

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.

In the control of autonomous mobile robots there exist two types of control: global control and local control. The requirement to solve global and local tasks arises respectively. This chapter concentrates on local tasks and shows that robots can learn to cope with some local tasks within minutes. The main idea of the chapter is to show that, while creating intelligent control systems for autonomous mobile robots, the beginning is most important as we have to transfer as much as possible human knowledge and human expert-operator skills into the intelligent control system. Successful transfer ensures fast and good results. One of the most advanced techniques in robotics is an autonomous mobile robot on-line learning from the experts’ demonstrations. Further, the latter technique is briefly described in this chapter. As an example of local task the wall following is taken. The main goal of our experiment is to teach the autonomous mobile robot within 10 minutes to follow the wall of the maze as fast and as precisely as it is possible. This task also can be transformed to the obstacle circuit on the left or on the right. The main part of the suggested control system is a small Feed-Forward Artificial Neural Network. In some particular cases – critical situations – “If-Then” rules undertake the control, but our goal is to minimize possibility that these rules would start controlling the robot. The aim of the experiment is to implement the proposed technique on the real robot. This technique enables to reach desirable capabilities in control much faster than they would be reached using Evolutionary or Genetic Algorithms, or trying to create the control systems by hand using “If-Then” rules or Fuzzy Logic. In order to evaluate the quality of the intelligent control system to control an autonomous mobile robot we calculate objective function values and the percentage of the robot work loops when “If-Then” rules control the robot.

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.

With the ongoing development of autonomous systems, significant attention has focused on deploying autonomous robotic systems to complete a task under human supervision. An important pillar of this interaction is a notion of human trust in the autonomous system, however the modeling of trust in such scenarios has been hampered by the lack of a standard open-access dataset with longitudinal measurements of trust. By recording measurements of trust at a single point in time (often at the end of interactions), these datasets do not capture the temporal dynamics of trust over the course of interactions. We propose to address these challenges with a novel multi-modal dataset that aggregates autonomous system status and task performance, self-reported human trust, and human interventions in the autonomous system during interactions. We collect the data using a software-based interface that simulates a swarm of autonomous robotic agents performing a foraging task in a rectangular area. By recording metrics pertaining to the autonomous robotic swarm’s task performance and human trust, the dataset provides a foundation for the modeling of human trust dynamics and their influence on human interventions. In addition, the dataset’s inclusion of timestamped human responses to a secondary task permits analysis of human attention allocation between tasks over time. It is anticipated that the dataset will provide a benchmark for human trust models, facilitating the development of more trust-aware autonomous robotic systems. IEEE SOCIETY/COUNCIL Robotics and Automation Society (RAS).

Обоснован перечень задач самообучения интеллектуальных систем управления автономными роботами и мультиагентными робототехническими группировками. Обсуждаются основные проблемы создания интеллектуальных самообучающихся систем управления. Показано, что один из наиболее перспективных подходов к реализации процессов самообучения в интеллектуальных системах управления автономными роботами и многоагентными робототехническими группировками связан с использованием метода автоматического построения деревьев классификации. Приводятся результаты комплексного моделирования.

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.