Robot Architecture Research Articles

Разработка архитектуры автономно управляемого роя роботов

Purpose: to create an innovative architecture for an autonomously controlled swadm of robots for effective exploration of smoke-filled rooms in designated areas. The problem of smoke-filled premises is relevant during emergencies such as fires and accidents, where a limited incident makes rescue operations difficult. The use of advanced technologies, including artificial intelligence and innovative methods, can improve the efficiency and safety of research in such settings. A promising direction for the development of this area is the use of artificial intelligence to improve the coordination and autonomy of robots, which will increase the accuracy and speed of task completion. Determine the type of robot and the required characteristics of the designed system include limited autonomous robots with highly sensitive sensors and sensors for cost-effective navigation, as well as self-directed coordination capabilities. Methods: comparison of existing methods for studying smokefilled rooms, as well as existing types. Comparison of the main types of sensors and sensors used for navigation in conditions of limited stability. Results: the type of robot was selected that is most suitable in terms of characteristics for the developed automated system, performing the functions of investigating smoke-filled rooms as efficiently as possible. The robot architecture has been developed. A diagram of the interaction of robot control tasks is presented. Practical importance: the architecture of the robot and the interaction scheme for robot control tasks have been developed. This software code for ensuring the safety and control of robots proposes a modular robot architecture, wide adaptation and flexible change of the system under different conditions, limited savings arising in rescue and search operations. The model being developed includes autonomous robots, methods for performing navigation, detecting obstacles, collecting data on the state of premises and ensuring interaction for coordinated research.

Synthesizing the temporal self: robotic models of episodic and autobiographical memory.

Episodic memories are experienced as belonging to a self that persists in time. We review evidence concerning the nature of human episodic memory and of the sense of self and how these emerge during development, proposing that the younger child experiences a persistent self that supports a subjective experience of remembering. We then explore recent research in cognitive architectures for robotics that has investigated the possibility of forms of synthetic episodic and autobiographical memory. We show that recent advances in generative modeling can support an understanding of the emergence of self and of episodic memory, and that cognitive architectures which include a language capacity are showing progress towards the construction of a narrative self with autobiographical memory capabilities for robots. We conclude by considering the prospects for a more complete model of mental time travel in robotics and the implications of this modeling work for understanding human episodic memory and the self in time. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

Adapting to My User, Engaging with My Robot: An Adaptive Affective Architecture for a Social Assistive Robot

Affective feedback from social robots is a useful technique for communicating to people whether they are interacting “well” with the robot or not. However, some users, such as people with physical or cognitive difficulties, may not be able to interact in all the desired ways. In these cases, affective feedback from the robot could be excessively negative—an “unhappy” robot, leading to an unrewarding experience for the user. This paper presents a motivation-based architecture for an autonomous multimodal social robot, that incorporates an affective feedback mechanism which generates an affective state by combining the internal needs of the robot and the social interaction quality. The balance between these two factors can dynamically change, allowing the robot to adapt its affective feedback to the user’s interaction style and capabilities. We have implemented this architecture in a simulation and in a MiRo social robot, and report experiments examining the behavior of the system in interactions with different experimental user profiles. The results show that the adaptive mechanism allows the robot to change its affective feedback to give more positive encouragement to users than in non-adaptive cases.

Sensorimotor control of robots mediated by electrophysiological measurements of fungal mycelia.

Living tissues are still far from being used as practical components in biohybrid robots because of limitations in life span, sensitivity to environmental factors, and stringent culture procedures. Here, we introduce fungal mycelia as an easy-to-use and robust living component in biohybrid robots. We constructed two biohybrid robots that use the electrophysiological activity of living mycelia to control their artificial actuators. The mycelia sense their environment and issue action potential-like spiking voltages as control signals to the motors and valves of the robots that we designed and built. The paper highlights two key innovations: first, a vibration- and electromagnetic interference-shielded mycelium electrical interface that allows for stable, long-term electrophysiological bioelectric recordings during untethered, mobile operation; second, a control architecture for robots inspired by neural central pattern generators, incorporating rhythmic patterns of positive and negative spikes from the living mycelia. We used these signals to control a walking soft robot as well as a wheeled hard one. We also demonstrated the use of mycelia to respond to environmental cues by using ultraviolet light stimulation to augment the robots' gaits.

A microservice based control architecture for mobile robots in safety-critical applications

Mobile robots have become more and more common in public space. This increases the importance of meeting safety requirements of autonomous robots. Simple mechanisms, such as emergency braking, alone do not suffice in these highly dynamic situations. Moreover, actual robotic control approaches in literature and practice do not take safety particularly into account. A more sophisticated situational approach for assessment and planning is needed as part of the high-level process control. This paper presents the concept of a safety-critical Robot Control Architecture for mobile robots based on microservices and a Hierarchical Finite State Machine. It expands already existing architectures by drastically reducing the amount of centralized logic and thus increasing the overall system’s level of concurrency, interruptibility and fail-safety. Furthermore, it introduces new potential for code reuse that allows for straightforward implementation of safety mechanisms such as internal diagnostics systems. In doing so, this concept presents the template of a new type of state machine implementation. It is demonstrated with the application of a delivery robot, which was implemented and operated in real public during a broader research project.

Multimodal tactile sensing fused with vision for dexterous robotic housekeeping

As robots are increasingly participating in our daily lives, the quests to mimic human abilities have driven the advancements of robotic multimodal senses. However, current perceptual technologies still unsatisfied robotic needs for home tasks/environments, particularly facing great challenges in multisensory integration and fusion, rapid response capability, and highly sensitive perception. Here, we report a flexible tactile sensor utilizing thin-film thermistors to implement multimodal perceptions of pressure, temperature, matter thermal property, texture, and slippage. Notably, the tactile sensor is endowed with an ultrasensitive (0.05 mm/s) and ultrafast (4 ms) slip sensing that is indispensable for dexterous and reliable grasping control to avoid crushing fragile objects or dropping slippery objects. We further propose and develop a robotic tactile-visual fusion architecture that seamlessly encompasses multimodal sensations from the bottom level to robotic decision-making at the top level. A series of intelligent grasping strategies with rapid slip feedback control and a tactile-visual fusion recognition strategy ensure dexterous robotic grasping and accurate recognition of daily objects, handling various challenging tasks, for instance grabbing a paper cup containing liquid. Furthermore, we showcase a robotic desktop-cleaning task, the robot autonomously accomplishes multi-item sorting and cleaning desktop, demonstrating its promising potential for smart housekeeping.

An overactuated aerial robot based on cooperative quadrotors attached through passive universal joints: Modeling, control and 6-DoF trajectory tracking

This article discusses a novel aerial robot architecture that overcomes the underactuation of conventional multirotor systems without adding dedicated rotor tilting actuators. The proposed system is based on four quadrotors cooperatively carrying a central body to which they are attached through passive universal joints. While conventional parallel axis multirotors are underactuated, the proposed mechanism makes the system overactuated, enabling independent position and orientation control of the main body. This implies that the payload can be carried in the minimum drag orientation, it enables take-off and landing on inclined surfaces and it provides thrust-vectoring capabilities to the system, leading to high control authority. A detailed dynamic model is derived making use of Lagrangian formalism and a hierarchical control law based on such model is proposed to stabilize the system. This control law is designed to ensure good tracking while minimizing power consumption. The proposed control law and the capabilities of the architecture are evaluated in simulation and in outdoor experimental flights, where the aerial robot shows autonomous tracking of the six degrees of freedom (DoF) of the main body, an inherently unfeasible maneuver for conventional underactuated multirotors.

The Rise of Molecular Image-Guided Robotic Surgery.

Following early acceptance by urologists, the use of surgical robotic platforms is rapidly spreading to other surgical fields. This empowerment of surgical perception via robotic advances occurs in parallel to developments in intraoperative molecular imaging. Convergence of these efforts creates a logical incentive to advance the decades-old image-guided robotics paradigm. This yields new radioguided surgery strategies set to optimally exploit the symbiosis between the growing clinical translation of robotics and molecular imaging. These strategies intend to advance surgical precision by increasing dexterity and optimizing surgical decision-making. In this state-of-the-art review, topic-related developments in chemistry (tracer development) and engineering (medical device development) are discussed, and future scientific robotic growth markets for molecular imaging are presented.

Research on virtual entertainment robots based on machine learning algorithms providing psychological health services for college students

In modern society, college students are facing increasing psychological pressure and mental health problems. In this context, virtual entertainment robots have become a promising form of mental health services, which can utilize machine learning algorithms to provide personalized psychological support and guidance by analyzing a large amount of psychological data and user information. Study the use of sample calculation and screening methods to determine the number of samples and perform feature selection to improve algorithm performance. Then analyze the detection effect and evaluate the effectiveness of the algorithm. By designing the architecture of a virtual entertainment robot and adopting anti-interference strategies to ensure that the robot can accurately recognize mental health information, text recognition technology was implemented, its effectiveness was evaluated, and further multi-source information recognition was carried out to improve recognition accuracy. Finally, a psychological health evaluation system for college students was constructed, and corresponding psychological health service strategies were proposed to meet the needs of college students. The results of this study indicate that virtual entertainment robots based on machine learning algorithms can effectively provide mental health services, providing support and guidance for the mental health problems of college students.

Artificial intelligence based social robots in the process of student mental health diagnosis

This paper in order to achieve the application of artificial intelligence based social robots in the process of student mental health diagnosis. When designing the architecture of social robots, factors such as interactivity, adaptability, and scalability were taken into consideration to ensure that they possess human like interaction characteristics and flexibility. Subsequently, a model was constructed based on deep learning technology to achieve functions such as sentiment classification, text mining, and optimization strategies. The input data set of the training model comes from the user’s interaction records and behavior data on the Internet social platform, as well as the user’s feedback information in the process of using the robot. The research on psychological data classification has constructed corresponding algorithms based on pointer networks and text models to achieve text feature extraction and classification. The psychological emotion mining module extracts emotional states from user discourse and maps them to corresponding categories of psychological problems. Finally, based on the user input question content, classify and optimize psychological problems. Research has shown that the robot has certain accuracy and practicality in data classification and student mental health diagnosis.

Robots in Agriculture: Revolutionizing Farming Practices

The integration of robotics in modern agriculture represents a revolutionary paradigm shift, enhancing efficiency and sustainability in food production. Agricultural robots, designed to automate various tasks, play a pivotal role in addressing the challenges faced by the industry. These robots are purpose-built for activities such as precision planting, weeding, and harvesting, streamlining processes that were traditionally labor-intensive. Their implementation leads to increased productivity, reduced operational costs, and minimized environmental impact through optimized resource utilization. This paper delves into the intricate landscape of robotic structures employed in agriculture, unraveling the diverse mechanisms and designs that underpin their functionality. It meticulously examines and elucidates the structural nuances of agricultural robots, shedding light on the engineering marvels that enable precision farming. From articulated arms to autonomous drones, the paper navigates through a spectrum of robot architectures, dissecting their roles in automating tasks critical to modern agriculture.

Kinematics and spatial structure analysis of TBM gunite robot based on D–H parameter method

In modern tunnel construction, TBM (Tunnel Boring Machine) plays a very important role. In response to the needs of tunnel wall reinforcement and TBM automated construction for tunnel construction, a shotcrete mechanism mounted on the TBM is designed. In order to evaluate the kinematic performance of the mechanism, this paper studies the forward and inverse kinematics and spatial architecture of the TBM shotcrete robot. Firstly, based on the D–H parameter method, the number of joints and links is determined and structural analysis is performed to obtain the robot's forward kinematics equation, achieving the mapping between joint space and pose space. Then, by determining the joint variables, the mapping of the end tool in Cartesian space is achieved. Finally, based on the Monte Carlo random sampling method, the workspace of the robot is constructed, and its reachability and flexibility within the robot workspace are evaluated. The performance of the device is verified by building a prototype, which meets the requirements well. Through the research in this paper, it can provide theoretical basis and guidance for the design and control of the shotcrete robot.

Distributed real-time control architecture for electrohydraulic humanoid robots

PurposeThis paper aims to develop an adaptable control architecture for electrohydraulic humanoid robots (HYDROïD) that emulate the functionality of the human nervous system. The developed control architecture overcomes the limitations of classical centralized and decentralized systems by distributing intelligence across controllers.Design/methodology/approachThe proposed solution is a distributed real-time control architecture with robot operating system (ROS). The joint controllers have the intelligence to make decisions, dominate their actuators and publish their state. The real-time capabilities are ensured in the master controller by using a Preempt-RT kernel beside open robot control software middleware to operate the real-time tasks and in the customized joint controllers by free real-time operating systems firmware. Systems can be either centralized, where all components are connected to a central unit or decentralized, where distributed units act as interfaces between the I/Os and the master controller when the master controller is without the ability to make decisions.FindingsThe proposed architecture establishes a versatile and adaptive control framework. It features a centralized hardware topology with a master PC and distributed joint controllers, while the software architecture adapts based on the task. It operates in a distributed manner for precise, force-independent motions and in a decentralized manner for tasks requiring compliance and force control. This design enables the examination of the sensorimotor loop at both low-level joint controllers and the high-level master controller.Originality/valueIt developed a control architecture emulating the functionality of the human nervous system. The experimental validations were performed on the HYDROïD. The results demonstrated 50% advancements in the update rate compared to other humanoids and 30% in the latency of the master processor and the control tasks.

A framework for neurosymbolic robot action planning using large language models.

Symbolic task planning is a widely used approach to enforce robot autonomy due to its ease of understanding and deployment in engineered robot architectures. However, techniques for symbolic task planning are difficult to scale in real-world, highly dynamic, human-robot collaboration scenarios because of the poor performance in planning domains where action effects may not be immediate, or when frequent re-planning is needed due to changed circumstances in the robot workspace. The validity of plans in the long term, plan length, and planning time could hinder the robot's efficiency and negatively affect the overall human-robot interaction's fluency. We present a framework, which we refer to as Teriyaki, specifically aimed at bridging the gap between symbolic task planning and machine learning approaches. The rationale is training Large Language Models (LLMs), namely GPT-3, into a neurosymbolic task planner compatible with the Planning Domain Definition Language (PDDL), and then leveraging its generative capabilities to overcome a number of limitations inherent to symbolic task planners. Potential benefits include (i) a better scalability in so far as the planning domain complexity increases, since LLMs' response time linearly scales with the combined length of the input and the output, instead of super-linearly as in the case of symbolic task planners, and (ii) the ability to synthesize a plan action-by-action instead of end-to-end, and to make each action available for execution as soon as it is generated instead of waiting for the whole plan to be available, which in turn enables concurrent planning and execution. In the past year, significant efforts have been devoted by the research community to evaluate the overall cognitive capabilities of LLMs, with alternate successes. Instead, with Teriyaki we aim to providing an overall planning performance comparable to traditional planners in specific planning domains, while leveraging LLMs capabilities in other metrics, specifically those related to their short- and mid-term generative capabilities, which are used to build a look-ahead predictive planning model. Preliminary results in selected domains show that our method can: (i) solve 95.5% of problems in a test data set of 1,000 samples; (ii) produce plans up to 13.5% shorter than a traditional symbolic planner; (iii) reduce average overall waiting times for a plan availability by up to 61.4%.

A Review and Evaluation of Control Architectures for Modular Legged and Climbing Robots.

Robotic control is a fundamental part of autonomous robots. Modular legged and climbing robots are complex machines made up of a variety of subsystems, ranging from a single robot with simple legs to a complex system composed of multiple legs (or modules) with computing power and sensitivity. Their complexity, which is increased by the fact of needing elements for climbing, makes a correct structure crucial to achieve a complete, robust, and versatile system during its operation. Control architectures for legged robots are distinguished from other software architectures because of the special needs of these systems. In this paper, we present an original classification of modular legged and climbing robots, a comprehensive review of the most important control architectures in robotics, focusing on the control of modular legged and climbing robots, and a comparison of their features. The control architecture comparison aims to provide the analytical tools necessary to make informed decisions tailored to the specific needs of your robotic applications. This article includes a review and classification of modular legged and climbing robots, breaking down each category separately.

Control framework of the ROBILAUT soil sampling robot: system overview and experimental results

Abstract The paper presents the control architecture of a crawler mobile robot designed and developed to sample potentially contaminated lands. The robot, developed in the framework of an Italian national project named ROBILAUT, carries a driller with a customized sampling mechanism to implement on-site the required quartering, and it is controlled to move the drilling device on specific points acquired in real time before the mission starts. The paper describes the software architecture for the navigation and control, focusing on the control framework of the robotic platform. Specifically, the robot exhibits a differential drive kinematics with actuators’ constraints, and two different control strategies have been experimentally tested for comparison both in a structured environment and in the real site in May 2023.

A containerised approach for multiform robotic applications.

As the area of robotics achieves promising results, there is an increasing need to scale robotic software architectures towards real-world domains. Traditionally, robotic architectures are integrated using common frameworks, such as ROS. Therefore, systems with a uniform structure are produced, making it difficult to integrate third party contributions. Virtualisation technologies can simplify the problem, but their use is uncommon in robotics and general integration procedures are still missing. This paper proposes and evaluates a containerised approach for designing and integrating multiform robotic architectures. Our approach aims at augmenting preexisting architectures by including third party contributions. The integration complexity and computational performance of our approach is benchmarked on the EU H2020 SecondHands robotic architecture. Results demonstrate that our approach grants simplicity and flexibility of setup when compared to a non-virtualised version. The computational overhead of using our approach is negligible as resources were optimally exploited.

Breaking the left-right symmetry in fluttering artificial cilia that perform nonreciprocal oscillations

Recent investigations on active materials have introduced a new paradigm for soft robotics by showing that a complex response can be obtained from simple stimuli by harnessing dynamic instabilities. In particular, polyelectrolyte hydrogel filaments actuated by a constant electric field have been shown to exhibit self-sustained oscillations as a consequence of flutter instability. Owing to the nonreciprocal nature of the emerging oscillations, these artificial cilia are able to generate flows along the stimulus. Building upon these findings, in this paper we propose a design strategy to break the left-right symmetry in the generated flows, by endowing the filament with a natural curvature at the fabrication stage. We develop a mathematical model based on morphoelastic rod theory to characterize the stability of the equilibrium configurations of the filament, proving the persistence of flutter instability. We show that the emerging oscillations are nonreciprocal and generate thrust at an angle with the stimulus. The results we find at the level of the single cilium open new perspectives on the possible applications of artificial ciliary arrays in soft robotics and microfluidics.

Enhancing Robotic Perception through Synchronized Simulation and Physical Common-Sense Reasoning.

We introduce both conceptual and empirical findings arising from the amalgamation of a robotics cognitive architecture with an embedded physics simulator, aligning with the principles outlined in the intuitive physics literature. The employed robotic cognitive architecture, named CORTEX, leverages a highly efficient distributed working memory known as deep state representation. This working memory inherently encompasses a fundamental ontology, state persistency, geometric and logical relationships among elements, and tools for reading, updating, and reasoning about its contents. Our primary objective is to investigate the hypothesis that the integration of a physics simulator into the architecture streamlines the implementation of various functionalities that would otherwise necessitate extensive coding and debugging efforts. Furthermore, we categorize these enhanced functionalities into broad types based on the nature of the problems they address. These include addressing challenges related to occlusion, model-based perception, self-calibration, scene structural stability, and human activity interpretation. To demonstrate the outcomes of our experiments, we employ CoppeliaSim as the embedded simulator and both a Kinova Gen3 robotic arm and the Open-Manipulator-P as the real-world scenarios. Synchronization is maintained between the simulator and the stream of real events. Depending on the ongoing task, numerous queries are computed, and the results are projected into the working memory. Participating agents can then leverage this information to enhance overall performance.

Numerical Simulation and Influence of Non-Gaussian Vibrations on Flexible Robotic Systems

Random vibration excitation is a standard method for obtaining the dynamic properties of aerospace structures and simulating loading conditions. Shaker-based random excitation and traditional finite element analysis techniques use random vibrations, assuming a Gaussian distribution of excitation impulse magnitudes. While Gaussian signals may sufficiently describe some vibrational environments, many real-world excitations have non-Gaussian distributions. These signals may contain vibrational impulses larger than those observed in a Gaussian signal and could cause damage if not considered. This paper examines the influence of non-Gaussian signals on flexible vs. rigid robotic architectures using NASA JPL’s Pop-Up Flat Folding Explorer Robot (PUFFER) and Cooperative Autonomous Distributed Robotic Explorer (CADRE). The results indicate that flexible systems can be significantly impacted by a non-Gaussian excitation profile and that neither a typical enveloping of a Gaussian excitation nor a superposition of the responses to a worst-case impulse load with the response to a Gaussian signal was sufficient to bound the response due to non-Gaussian excitation. The driving mechanism for increased sensitivity of flexible robotic systems appears to be inertial, and designers may consider shifting the relative flexibility of the system or its constraints to force particular deformation modes or designing hinge mechanisms with increased damping to minimize the influence of non-Gaussian signals on the response of a flexible robotic structure.