Complex Robotic Systems Research Articles

Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques

This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier’s capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications.

HoLLiECares - Development of a multi-functional robot for professional care.

Germany's healthcare sector suffers from a shortage of nursing staff, and robotic solutions are being explored as a means to provide quality care. While many robotic systems have already been established in various medical fields (e.g., surgical robots, logistics robots), there are only a few very specialized robotic applications in the care sector. In this work, a multi-functional robot is applied in a hospital, capable of performing activities in the areas of transport and logistics, interactive assistance, and documentation. The service robot platform HoLLiE was further developed, with a focus on implementing innovative solutions for handling non-rigid objects, motion planning for non-holonomic motions with a wheelchair, accompanying and providing haptic support to patients, optical recognition and control of movement exercises, and automated speech recognition. Furthermore, the potential of a robot platform in a nursing context was evaluated by field tests in two hospitals. The results show that a robot can take over or support certain tasks. However, it was noted that robotic tasks should be carefully selected, as robots are not able to provide empathy and affection that are often required in nursing. The remaining challenges still exist in the implementation and interaction of multi-functional capabilities, ensuring ease of use for a complex robotic system, grasping highly heterogeneous objects, and fulfilling formal and infrastructural requirements in healthcare (e.g., safety, security, and data protection).

Design, modeling, and characteristics of ring-shaped robot actuated by functional fluid

The controlled actuation of hydraulic and pneumatic actuators has unveiled fresh and thrilling opportunities for designing mobile robots with adaptable structures. Previously reported rolling robots, which were powered by fluidic systems, often relied on complex principles, cumbersome pump and valve systems, and intricate control strategies, limiting their applicability in other fields. In this investigation, we employed a distinct category of functional fluid identified as Electrohydrodynamic (EHD) fluid, serving as the pivotal element within the ring-shaped actuator. A short stream of functional fluid is placed within a fluidic channel and is then actuated by applying a direct current voltage aiming at shifting the center of mass of the robot and finally pushed the actuator to roll. We designed a ring-shaped fluidic robot, manufactured it using digital machining methods, and evaluated the robot’s characteristics. Furthermore, we developed static and dynamic models to analyze the oscillation and rolling motion of the ring-shaped robots using the Lagrange method. This study is anticipated to contribute to the expansion of current research on EHD flexible actuators, enabling the realization of complex robotic systems.

Brain-inspired biomimetic robot control: a review.

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired control methods to tackle the intricacies associated with controlling complex robotic systems.

Low‐Volume Cores for Fabrication of Compact, Versatile, and Intelligent Soft Systems

AbstractThis study introduces the low‐volume core (LVC) fabrication method, which enables the monolithic molding of compact, complex, versatile, and intelligent soft robotic systems. This method uses thin and flexible thermoplastic sheets to mold internal chambers in soft fluidic actuators, valves, and circuits. The LVC fabrication method creates low‐volume networks in soft actuators (LV‐net actuators) that can be made with compact and complex geometries, enabling both low actuation volume input and multi‐degree‐of‐freedom actuators. LVC fabrication can also be used for compact, completely soft, and monolithic logic components (valves with low‐volume core, also called as LV valves) to provide directional resistance as well as a switching mechanism that enables fluidic logic in soft systems. The compatibility of the fabrication methods for both soft actuators and valves facilitates the creation of compact, integrated, and versatile soft robotic systems with embodied intelligence. This study introduces two examples of such intelligent soft robotic systems that integrate both LV‐net actuators and LV valves to demonstrate capability for complex system fabrication.

Perception and Action Augmentation for Teleoperation Assistance in Freeform Telemanipulation

Teleoperation enables controlling complex robot systems remotely, providing the ability to impart human expertise from a distance. However, these interfaces can be complicated to use as it is difficult to contextualize information about robot motion in the workspace from the limited camera feedback. Thus, it is required to study the best manner in which assistance can be provided to the operator that reduces interface complexity and effort required for teleoperation. Some techniques that provide assistance to the operator while freeform teleoperating include: (1) perception augmentation, like augmented reality visual cues and additional camera angles, increasing the information available to the operator; (2) action augmentation, like assistive autonomy and control augmentation, optimized to reduce the effort required by the operator while teleoperating. In this article, we investigate: (1) which aspects of dexterous telemanipulation require assistance; (2) the impact of perception and action augmentation in improving teleoperation performance; and (3) what factors impact the usage of assistance and how to tailor these interfaces based on the operators’ needs and characteristics. The findings from this user study and resulting post-study surveys will help identify task-based and user-preferred perception and augmentation features for teleoperation assistance.

Physics‐Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation

This work concerns the application of physics‐informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics‐informed neural networks to handle nonconservative effects. These learned models are proposed to combine with model‐based controllers originally developed with first‐principle models in mind. By combining standard and new techniques, precise control performance can be achieved while proving theoretical stability bounds. These validations include real‐world experiments of motion prediction with a soft robot and trajectory tracking with a Franka Emika Panda manipulator.

Experimental and theoretical verification of TLBO and PSO for solving the inverse kinematic model of continuum robots

This paper presents a comprehensive exploration of two meta-heuristic optimization techniques, Teaching Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO), applied to solve the inverse kinematic problem of continuum robots. The study encompasses both theoretical investigations and realistic simulations, including tracking a spiral trajectory and utilizing real measurements to follow a trajectory. TLBO demonstrates exceptional precision in solving the inverse kinematic problem for continuum robots, consistently outperforming PSO in terms of accuracy. On the other hand, PSO showcases notable advantages in terms of computational efficiency, exhibiting faster convergence and reduced time consumption. The research findings suggest promising avenues for the application of meta-heuristic approaches in real-world scenarios involving continuum robots, particularly in domains such as medical devices and industrial automation. However, the challenge remains to develop modified algorithms that strike a balance between accuracy and efficiency to address the diverse requirements of practical applications in this field. Nevertheless, the versatility of meta-heuristic methods in handling complex robotic systems offers exciting prospects for the future of continuum robotics.

Energy structure perspective as an innovative approach to basic study of different analysis of complex robotic systems

In this paper, the idea of using the energy structure perspective for application in the study of complex robotic systems is presented and expanded. The perspective of energy structure can provide a powerful solution for analyzing robotic systems. In fact, among the special capabilities of this approach, it can apply the energy conservation principle simultaneously by considering the effects of related energy wastes during system operation, the possibility of considering movement restrictions in the equation of the energy structure and applying it in the equation explained the movement of the system simultaneously with the first and second laws of thermodynamics, the calculation of the dynamic energy of the system using its movement restrictions, as well as how to apply energy directly, etc. Based on this, the perspective of energy structure can be helpful in studies related to robotic systems, especially systems with specific complexities and different behavioral and movement aspects. This paper develops the fundamental aspects of this issue.

Decentralized Motor Skill Learning for Complex Robotic Systems

Decentralized Motor Skill Learning for Complex Robotic Systems

Software framework concept with visual programming and digital twin for intuitive process creation with multiple robotic systems

With the progressive digitalization in industrial manufacturing, the usage of complex robotic systems in both intralogistics and production is expected to increase. This proposes a challenge for planners and shop floor workers, as programming and interacting with these various systems leads to a high cognitive load. Especially the broad range of different manufacturer specific software leads to a number of problems, e.g. the program-synchronization between different systems and the often necessary workshops for workers. These problems can lead to inefficient programming and planning operations, bad worker satisfaction and human errors. In this paper, we present a modular, system agnostic and human centered software framework that unifies the programming of different systems, to enable centralized and intuitive system programming for non-expert operators. Our software framework utilizes visual programming concepts together with an integrated digital twin of the factory and a novel graph-based programming interface. We explain our concept in detail and describe our validation through integration into a realistic industrial setup with three different systems. In addition, we provide an evaluation of our concept's usability with an experimental user study and discuss the results of the study and the software implementation. Our study results show that even non-technical users are able to use our software after a brief introduction to create complex processes that involve multiple machines working in parallel. All users reported high usability and expert users reported that the visual process editor has enough features to create processes for industrial applications. Finally, we conclude this paper by providing an outlook on future work and use-cases of our software.

Mixed-reality for quadruped-robotic guidance in SAR tasks

Abstract In recent years, exploration tasks in disaster environments, victim localization and primary assistance have been the main focuses of Search and Rescue (SAR) Robotics. Developing new technologies in Mixed Reality (M-R) and legged robotics has taken a big step in developing robust field applications in the Robotics field. This article presents MR-RAS (Mixed-Reality for Robotic Assistance), which aims to assist rescuers and protect their integrity when exploring post-disaster areas (against collapse, electrical, and toxic risks) by facilitating the robot’s gesture guidance and allowing them to manage interest visual information of the environment. Thus, ARTU-R (A1 Rescue Tasks UPM Robot) quadruped robot has been equipped with a sensory system (lidar, thermal, and RGB-D cameras) to validate this proof of concept. On the other hand, Human-Robot interaction is executed by using the Hololens glasses. This work’s main contribution is the implementation and evaluation of a Mixed-Reality system based on a ROS-Unity solution, capable of managing at a high level the guidance of a complex legged robot through different interest zones (defined by a Neural Network and a vision system) of a post-disaster environment (PDE). The robot’s main tasks at each point visited involve detecting victims through thermal, RGB imaging, and neural networks and assisting victims with medical equipment. Tests have been carried out in scenarios that recreate the conditions of PDE (debris, simulation of victims, etc.). An average efficiency improvement of 48% has been obtained when using the immersive interface and a time optimization of 21.4% compared to conventional interfaces. The proposed method has proven to improve rescuers’ immersive experience of controlling a complex robotic system.

Congratulations to Rodney Brooks, Recipient of the 2023 IEEE Founders Medal: Brooks Created Subsumption, an Architecture for Designing Layered Real-Time Control of Complex Robotic Systems That Has Enabled a Wide Range of Robotic Innovations, Including the Human–Robotic Interaction Exemplified by the Roomba Vacuum Cleaner [Society News

Congratulations to Rodney Brooks, Recipient of the 2023 IEEE Founders Medal: Brooks Created Subsumption, an Architecture for Designing Layered Real-Time Control of Complex Robotic Systems That Has Enabled a Wide Range of Robotic Innovations, Including the Human–Robotic Interaction Exemplified by the Roomba Vacuum Cleaner [Society News

CSP2Turtle: Verified Turtle Robot Plans

Software verification is an important approach to establishing the reliability of critical systems. One important area of application is in the field of robotics, as robots take on more tasks in both day-to-day areas and highly specialised domains. Our particular interest is in checking the plans that robots are expected to follow to detect errors that would lead to unreliable behaviour. Python is a popular programming language in the robotics domain through the use of the Robot Operating System (ROS) and various other libraries. Python’s Turtle package provides a mobile agent, which we formally model here using Communicating Sequential Processes (CSP). Our interactive toolchain CSP2Turtle with CSP models and Python components enables plans for the turtle agent to be verified using the FDR model-checker before being executed in Python. This means that certain classes of errors can be avoided, providing a starting point for more detailed verification of Turtle programs and more complex robotic systems. We illustrate our approach with examples of robot navigation and obstacle avoidance in a 2D grid-world. We evaluate our approach and discuss future work, including how our approach could be scaled to larger systems.

Proposal and Implementation of a Procedure for Compliance Recognition of Objects with Smart Tactile Sensors.

This paper presents a procedure for classifying objects based on their compliance with information gathered using tactile sensors. Specifically, smart tactile sensors provide the raw moments of the tactile image when the object is squeezed and desqueezed. A set of simple parameters from moment-versus-time graphs are proposed as features, to build the input vector of a classifier. The extraction of these features was implemented in the field programmable gate array (FPGA) of a system on chip (SoC), while the classifier was implemented in its ARM core. Many different options were realized and analyzed, depending on their complexity and performance in terms of resource usage and accuracy of classification. A classification accuracy of over 94% was achieved for a set of 42 different classes. The proposed approach is intended for developing architectures with preprocessing on the embedded FPGA of smart tactile sensors, to obtain high performance in real-time complex robotic systems.

Latency mitigation using applied HMMs for mixed reality-enhanced intuitive teleoperation in intelligent robotic welding

Latency mitigation is crucial to increasing operational success, ease of use, and product quality in telemanipulation tasks when remotely guiding complex robotic systems. Hardware limitations have created a gap in performance optimization due to large teleoperation delays, which machine learning techniques could fill with lower time, improved performance, and reduced operating costs. Hidden Markov models (HMMs), in particular, have been explored to alleviate the issue due to their relative ease of use. A mixed reality-enhanced intuitive teleoperation framework for immersive and intuitive telerobotic welding is presented. The proposed system implements an HMM generative algorithm to learn and predict human-welder motion to enable a low-cost solution, combining smoothing and forecasting techniques to minimize robotic teleoperation time delay. The predicted welding motion system is simple to implement, can be used as a general solution to solve time delays, and is accurate. More specifically, it provides a 66% RMSE reduction compared to the application without HMM, which may be further optimized by up to 38%. Experiments show the HMM generative algorithm lets humans conduct tele-robot-assisted welding with better performance.

The impact of symmetry violations on determining optimal control trajectories for complex robotic systems

Complex robotic systems, their properties, features and interactions are considered, the article offers the definition of a complex system on the basis of 5 properties: openness, non-morphic variability of three types (structural, spatial and information), Double-code, event aggregation and violation of physical symmetries. The influence of violations of physical symmetries on the determination of optimal control trajectories is reflected in the paradigm approach to the study of complex robotic systems that are not formalized as mathematical objects. The basic concepts, postulates and hypotheses are formulated. Ideal designs of variability of complex systems; energy causal sets; energies; events, causes, effects and evolution; spacetime, quanta and vacuum; interaction of individuals; operators of physical interactions, aggregated events, text and attachments of words. Three basic models for the study of complex systems - the model of physical interactions, the neurolinguistic model and the model of control at incomplete compatibility are proposed and briefly described. The structure of the kernel of the platform of physical simulation modeling for the researches of complex systems is resulted. Three types of space-time quantum modeling - minimal, semantic and evolutionary - are described. The article provides an illustration of results of application of the proposed approach to the study of actions of complex systems. It is noted that the resulting mathematical structure exhibits fractal properties. Typical trajectories of evolution - «homeostat»; «attenuation of actions»; «invariant»; «accident»; «window of possibilities» have been allocated. The article provides a number of principles of research of complex methodological and methodical systems. Recommendations were made on the scope of the proposed approach.

Verification of the ROS NavFn planner using executable specification languages

The Robot Operating System (ROS) is a framework for building robust software for complex robot systems in several domains. The Navigation Stack stands out among the different libraries available in ROS, providing a set of components that can be reused to build robots with autonomous navigation capabilities. This library is a critical component, as navigation failures could have catastrophic consequences for applications like self-driving cars where safety is crucial.Here we devise a general methodology for verifying this kind of complex systems by specifying them in different executable specification languages with verification support and validating the equivalence between the specifications and the original system using differential testing techniques. The complex system can then be indirectly analyzed using the verification tools of the specification languages like model checking, semi-automated functional verification based on Hoare logic, and other formal techniques. In this paper we apply this verification methodology to the NavFn planner, which is the main planner component of the Navigation Stack of ROS, using Maude and Dafny as specification languages. We have formally proved several desirable properties of this planner algorithm like the absence of obstacles in the planned path. Moreover, we have found counterexamples for other concerns like the optimality of the path cost.

Exploring the Most Sectors at the DARPA Subterranean Challenge Finals

Autonomous robot navigation in austere environments is critical to missions like “search and rescue”, yet it remains difficult to achieve. The recent DARPA Subterranean Challenge (SubT) highlights prominent challenges including GPS-denied navigation through rough terrains, rapid exploration in large-scale three-dimensional (3D) space, and interrobot coordination over unreliable communication. Solving these challenges requires both mechanical resilience and algorithmic intelligence. Here, we present our approach that leverages a fleet of custom-built heterogeneous robots and an autonomy stack for robust navigation in challenging environments. Our approach has demonstrated superior navigation performance in the SubT Final Event, resulting in the fastest traversal and most thorough exploration of the environment, which won the “Most Sectors Explored Award.” This paper details our approach from two aspects: mechanical designs of a marsupial ground-and-aerial system to overcome mobility challenges and autonomy algorithms enabling collective rapid exploration. We also provide lessons learned in the design, development, and deployment of complex but resilient robotic systems to overcome real-world navigation challenges.

A Survey on Trust Metrics for Autonomous Robotic Systems

This paper surveys the area of “Trust Metrics” related to security for autonomous robotic systems. As the robotics industry undergoes a transformation from programmed, task oriented, systems to Artificial Intelligence-enabled learning, these autonomous systems become vulnerable to several security risks, making a security assessment of these systems of critical importance. Therefore, our focus is on a holistic approach for assessing system trust which requires incorporating system, hardware, software, cognitive robustness, and supplier level trust metrics into a unified model of trust. We set out to determine if there were already trust metrics that defined such a holistic system approach. While there are extensive writings related to various aspects of robotic systems such as, risk management, safety, security assurance and so on, each source only covered subsets of an overall system and did not consistently incorporate the relevant costs in their metrics. This paper attempts to put this prior work into perspective, and to show how it might be extended to develop useful systemlevel trust metrics for evaluating complex robotic (and other) systems.