State Of Robot Research Articles

State Estimation for Quadruped Robots on Non-Stationary Terrain via Invariant Extended Kalman Filter and Disturbance Observer

Quadruped robots possess significant mobility in complex and uneven terrains due to their outstanding stability and flexibility, making them highly suitable in rescue missions, environmental monitoring, and smart agriculture. With the increasing use of quadruped robots in more demanding scenarios, ensuring accurate and stable state estimation in complex environments has become particularly important. Existing state estimation algorithms relying on multi-sensor fusion, such as those using IMU, LiDAR, and visual data, often face challenges on non-stationary terrains due to issues like foot-end slippage or unstable contact, leading to significant state drift. To tackle this problem, this paper introduces a state estimation algorithm that integrates an invariant extended Kalman filter (InEKF) with a disturbance observer, aiming to estimate the motion state of quadruped robots on non-stationary terrains. Firstly, foot-end slippage is modeled as a deviation in body velocity and explicitly included in the state equations, allowing for a more precise representation of how slippage affects the state. Secondly, the state update process integrates both foot-end velocity and position observations to improve the overall accuracy and comprehensiveness of the estimation. Lastly, a foot-end contact probability model, coupled with an adaptive covariance adjustment strategy, is employed to dynamically modulate the influence of the observations. These enhancements significantly improve the filter’s robustness and the accuracy of state estimation in non-stationary terrain scenarios. Experiments conducted with the Jueying Mini quadruped robot on various non-stationary terrains show that the enhanced InEKF method offers notable advantages over traditional filters in compensating for foot-end slippage and adapting to different terrains.

Image‐based backbone reconstruction for non‐slender soft robots

AbstractOne advantage of soft robots to conventional robots is their continuous deformation, which makes them highly adaptable to their surroundings. To describe the kinematics of soft robots it is not sufficient to only know the position of the end‐effector. Instead, the steady state of a soft robot can be described by its backbone curvature. An image‐based backbone reconstruction method is used and adopted for a non‐slender soft robot to reconstruct the backbone curve using polynomials to describe an arc length‐dependent curvature. A constant curvature and cubic curvature polynomial have been tested to reconstruct the backbone curve. In the case of a pressurized soft robot without externally applied loads, the constant curvature polynomial can be used to reconstruct the backbone of the soft robot using an image‐based approach, yielding good agreement with the captured images. However, some care has to be put into the consideration of the soft robot geometry in this method, to guide the optimization procedure. From our findings, we can conclude that it is not necessary to apply markers to the soft robot to reconstruct the backbone, nor is it necessary to have a detailed geometric model of the soft robot to be used in a differential rendering process. An image‐based iterative‐closest‐point optimization method, with some carefully placed estimate points, to integrate the volumetric structure of the soft robot can yield good results for the reconstructed backbones.

PUMA: Deep Metric Imitation Learning for Stable Motion Primitives

Imitation learning (IL) facilitates intuitive robotic programming. However, ensuring the reliability of learned behaviors remains a challenge. In the context of reaching motions, a robot should consistently reach its goal, regardless of its initial conditions. To meet this requirement, IL methods often employ specialized function approximators that guarantee this property by construction. Although effective, these approaches come with some limitations: 1) they are typically restricted in the range of motions they can model, resulting in suboptimal IL capabilities, and 2) they require explicit extensions to account for the geometry of motions that consider orientations. To address these challenges, we introduce a novel stability loss function that does not constrain the function approximator's architecture and enables learning policies that yield accurate results. Furthermore, it is not restricted to a specific state space geometry; therefore, it can easily incorporate the geometry of the robot's state space. Proof of the stability properties induced by this loss is provided and the method is empirically validated in various settings. These settings include Euclidean and non‐Euclidean state spaces, as well as first‐order and second‐order motions, both in simulation and with real robots. More details about the experimental results can be found at https://youtu.be/ZWKLGntCI6w.

Application of Barycentric Coordinates and the Jacobian Matrix to the Analysis of a Closed Structure Robot

A new approach is presented to study the kinematic properties of stationary robots with a closed structure. It combines the application of conventional methods from kinematics with geometric parameters represented in a barycentric coordinate system. This allows examining the influence of the proportions of the robot’s links on its basic mechanical characteristics. Each point from the newly introduced barycentric space corresponds to a set of robots with the same link proportions. The proposed approach is used to study three aspects: the link proportions for which the robot can exist; the shape of the robot’s workspace; and the possible singular configurations. This is valuable when evaluating the qualities of existing robots and could be applied to the design of new mechanical systems. An example of a 5-link robot with a closed structure is considered. The conditions for the existence of the mechanism and the conditions under which certain types of singular configurations can occur are defined. The example reveals the great potential of combining barycentric coordinates and Jacobian properties. The barycentric coordinates of 10 robots with a 5-link closed structure known from the literature are determined, and their properties are analyzed. The results are presented graphically. An extension of the application area of the approach is discussed.

Energy-Aware Hierarchical Control of Joint Velocities

Nowadays, robots are applied in dynamic environments. For a robust operation, the motion planning module must consider other tasks besides reaching a specified pose: (self) collision avoidance, joint limit avoidance, keeping an advantageous configuration, etc. Each task demands different joint control commands, which may counteract each other. We present a hierarchical control that, depending on the robot and environment state, determines online a suitable priority among those tasks. Thereby, the control command of a lower-prioritized task never hinders the control command of a higher-prioritized task. We ensure smooth control signals also during priority rearrangement. Our hierarchical control computes reference joint velocities. However, the underlying concepts of hierarchical control differ when using joint accelerations or joint torques as control signals instead. So, as a further contribution, we provide a comprehensive discussion on how joint velocity control, joint acceleration control, and joint torque control differ in hierarchical task control. We validate our formulation in an experiment on hardware.

Industrial robotics in Russia under the conditions of sanctions

The article in question is a brief analytical review based on the materials of open publications of the state of indus-trial robotics in Russia on the eve and after the introduction of sanctions by the West related to the implementation of Special military operation. Despite the lack of official information, the estimated quantitative characteristics of the domestic industrial robotics market in the specified period are given and the main factors influencing its devel-opment are listed with appropriate conclusions.

Robot-assisted pedestrian evacuation in fire scenarios based on deep reinforcement learning

Indoor fires pose a significant challenge to the safe evacuation of pedestrians. In response to fire hazards, pedestrians instinctively seek alternative evacuation routes to avoid the hazard zone. However, the specific location and intensity of the fire hazard zone can influence pedestrians' decisions, leading to varying congestion levels in different areas. To address this challenge and enhance overall evacuation efficiency, this paper introduces an improved social force model to depict pedestrian movement in fire scenarios and proposes a methodology that leverages dynamic robot for pedestrian evacuation, employing Deep Reinforcement Learning (DRL) and Human-Robot Interaction (HRI). The results show that in the no-robot scenario, pedestrians will detour according to the varying locations of fire hazard zones and emergency levels, resulting in congestion at different positions. In the static robot scenario, robots placed in different locations exhibit varied effects on evacuation depending on the fire hazard zones' locations and intensities. In the DRL-control robot scenario, the robot controlled by DRL and HRL can always navigate to the appropriate position to promote evacuation, regardless of the fire's location and emergency levels or the robot's initial placement. Furthermore, our findings reveal that strategically positioned robots can enhance evacuation efficiency by alleviating crowding and increasing the distance between pedestrians and fire hazard zones in most cases, thereby improving pedestrian safety. This study offers practical guidance for managing pedestrian evacuation during fire incidents and establishes a theoretical foundation for refining evacuation strategies and safety measures at fire scenes.

Online tree-based planning for active spacecraft fault estimation and collision avoidance.

Autonomous robots operating in uncertain or hazardous environments subject to state safety constraints must be able to identify and isolate faulty components in a time-optimal manner. When the underlying fault is ambiguous and intertwined with the robot's state estimation, motion plans that discriminate between simultaneous actuator and sensor faults are necessary. However, the coupled fault mode and physical state uncertainty creates a constrained optimization problem that is challenging to solve with existing methods. We combined belief-space tree search, marginalized filtering, and concentration inequalities in our method, safe fault estimation via active sensing tree search (s-FEAST), a planner that actively diagnoses system faults by selecting actions that give the most informative observations while simultaneously enforcing probabilistic state constraints. We justify this approach with theoretical analysis showing s-FEAST's convergence to optimal policies. Using our robotic spacecraft simulator, we experimentally validated s-FEAST by safely and successfully performing fault estimation while on a collision course with a model comet. These results were further validated through extensive numerical simulations demonstrating s-FEAST's performance.

Conductive hydrogels‐based self‐sensing soft robot state perception and trajectory tracking

AbstractSoft robots face significant challenges in proprioceptive sensing and precise control due to their highly deformable and compliant nature. This paper addresses these challenges by developing a conductive hydrogel sensor and integrating it into a soft robot for bending angle measurement and motion control. A quantitative mapping between the hydrogel resistance and the robot's bending gesture is formulated. Furthermore, a nonlinear differentiator is proposed to estimate the angular velocity for closed‐loop control, eliminating the reliance on conventional sensors. Meanwhile, a controller is designed to track both structural and nonstructural trajectories. The proposed approach integrates advanced soft sensing materials and intelligent control algorithms, significantly improving the proprioception and motion accuracy of soft robots. This work bridges the gap between novel material design and practical control applications, opening up new possibilities for soft robots to perform delicate tasks in various fields. The experimental results demonstrate the effectiveness of the proposed sensing and control approach in achieving precise and robust motion control of the soft robot.

BA-CLM: A Globally Consistent 3D LiDAR Mapping Based on Bundle Adjustment Cost Factors.

Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrate that the proposed method achieves accurate trajectory estimation and consistent mapping.

Advancing Robotics and Control Systems in West Africa: Opportunities, Challenges, and Future Directions

Robotics and control systems are poised to significantly impact West Africa's socio-economic landscape, offering transformative potential across various sectors. This review examines the current state, challenges, and future directions of robotics and control systems in the region. Robotics technology, while still in its nascent stage in West Africa, presents opportunities for substantial advancements in agriculture, industry, and healthcare. Key areas of focus include the fundamental algebraic methods used in robotics, such as kinematics, dynamic modeling, and control theory, which underpin the design and optimization of robotic systems. The review highlights the potential applications of robotics in agriculture, where automation can enhance productivity and efficiency, and in healthcare, where surgical robots and rehabilitation devices offer improved patient outcomes. However, significant barriers such as limited access to advanced technology, high costs, and inadequate infrastructure pose challenges to widespread adoption. Training and capacity building are critical for fostering a skilled workforce and advancing the robotics industry. Investment in education, specialized training, and international collaborations can address current limitations and support the development of a robust robotics ecosystem. Looking ahead, emerging technologies like artificial intelligence, collaborative robots, and advanced sensing are expected to drive future innovations. Strategic initiatives, including government policies, infrastructure development, and innovation hubs, are essential to realizing the full potential of robotics in West Africa. By overcoming existing challenges and leveraging opportunities for growth, the region can harness robotics and control systems to drive economic development and improve quality of life. Keywords: Robotics, Control Systems, West Africa, Opportunities, Challenges, Future Directions.

Exploring the Role of Robotics in Maritime Technology: Innovations, Challenges, and Future Prospects

Robotics has emerged as a disruptive force in the maritime sector, offering promising solutions for various applications, including underwater exploration, offshore operations, port logistics, and vessel maintenance. The paper begins by presenting an overview of the current state of robotics in the maritime industry, highlighting the key advancements and the impact they have had on traditional practices. It then delves into the various innovative applications of robotics, such as autonomous underwater vehicles (AUVs) for oceanographic research and inspection robots for offshore structures, discussing their capabilities and potential benefits. However, the integration of robotics in maritime technology also brings forth a unique set of challenges. These challenges encompass technical, regulatory, and ethical aspects, including issues related to safety, cyber security, legal frameworks, and public acceptance. The paper critically examines these challenges and discusses potential strategies to overcome them, emphasizing the importance of collaboration between industry stakeholders, policymakers, and researchers. It discusses emerging trends, such as swarm robotics, human-robot collaboration, and the utilization of artificial intelligence for decision-making. The potential benefits of these advancements, including increased efficiency, reduced costs, and enhanced safety, are highlighted.

Robotic environmental state recognition with pre-trained vision-language models and black-box optimization

In order for robots to autonomously navigate and operate in diverse environments, it is essential for them to recognize the state of their environment. On the other hand, the environmental state recognition has traditionally involved distinct methods tailored to each state to be recognized. In this study, we perform a unified environmental state recognition for robots through the spoken language with pre-trained large-scale vision-language models. We apply Visual Question Answering and Image-to-Text Retrieval, which are tasks of vision-language models. We show that with our method, it is possible to recognize not only whether a room door is open/closed, but also whether a transparent door is open/closed and whether water is running in a sink, without training neural networks or manual programing. In addition, the recognition accuracy can be improved by selecting appropriate texts from the set of prepared texts based on black-box optimization. For each state recognition, only the text set and its weighting need to be changed, eliminating the need to prepare multiple different models and programs, and facilitating the management of source code and computer resources. We experimentally demonstrate the effectiveness of our method and apply it to the recognition behavior on a mobile robot, Fetch.

MAZE SLOVING ROBOT WITH LIVE MONITORING

This project presents a sophisticated maze-solving robot, equipped with advanced live monitoring capabilities, meticulously designed to autonomously navigate and solve intricate maze environments.The robot's navigation prowess is underpinned by an array of sensors, prominently featuring ultrasonic and infrared technologies, which are adept at detecting and avoiding obstacles while simultaneously mapping the layout of the maze. These sensors feed data into an embedded microcontroller, which processes the information in real-time. To determine the optimal path to the maze's exit, the microcontroller employs sophisticated algorithms such as Depth-First Search (DFS) or Breadth-First Search (BFS), renowned for their efficiency in solving complex mazes.A key feature of the robot is its live monitoring capability, enabled by a wireless communication module. This module is responsible for transmitting critical data, including the robot's status, position, and sensory inputs, to a remote monitoring station. The real-time feedback provided through this system allows users to observe the robot's progress dynamically and make necessary adjustments on the fly, ensuring optimal performance and adaptability in diverse environments. The robot’s compact chassis design ensures superior maneuverability, even in the most confined spaces, while a robust power management system guarantees extended operational periods, making the robot both efficient and reliable.The integration of live monitoring capabilities not only significantly enhances the robot's functionality but also offers invaluable insights into its performance and decision-making processes. This feature is particularly advantageous for educational purposes, providing students and researchers with a tangible demonstration of theoretical concepts in robotics and automation. Additionally, the real-time data acquisition and analysis capabilities make the robot a potent tool for research, especially in fields requiring precise navigation and obstacle avoidance, such as search and rescue operations. Here, the ability to receive immediate feedback and monitor the robot’s actions in real-time is crucial, as it can directly influence the success of missions in dynamic and potentially hazardous environments.The development of this maze-solving robot represents a significant leap forward in the realms of robotics, automation, and remote monitoring technologies. By addressing real-world challenges with enhanced efficiency, this project underscores the potential of advanced robotics to transform a variety of applications. The robot's design, characterized by a harmonious blend of sophisticated sensor arrays, intelligent pathfinding algorithms, and robust real-time monitoring systems, exemplifies the cutting-edge advancements in technology. This convergence of features not only demonstrates the practical applications of theoretical research but also paves the way for future innovations in autonomous systems, highlighting their capability to operate effectively in real-world scenarios and to provide critical insights that can drive further technological progress. Keywords: maze-solving robot, autonomous navigation, live monitoring, ultrasonic sensors, infrared sensors, flood-fill algorithm, real-time processing, wireless communication.

Kinematic Analysis of a Wheeled-Leg Small Pipeline Robot Turning in Curved Pipes

A wheeled-leg pipeline robot suitable for operation in small pipes is proposed to address the challenges of detecting the condition of pipelines, such as solution corrosion and crack defects, which cannot be conducted externally due to the pre-buried pipe system embedded in other structures. Inspired by existing pipeline robots, the proposed robot employs a mechanical structure with six wheeled legs arranged in an alternating pattern. To analyze the motion state of the pipeline robot turning in curved pipes, kinematic analysis based on geometry is conducted to figure out the kinematic characteristics of the robot navigating in curved pipes. The relationship between the motion trajectories of each contact wheel and the posture angle of the robot in the pipeline is the focal point. Additionally, a turning method preventing wheel slippage is proposed specifically for this type of robot. Finally, an experiment with the pipeline robot navigating in the curved pipeline is implemented and demonstrates successful passing through curved pipes with an inner diameter of 120 mm as well as a turning radius of 240 mm, with the effectiveness of the kinematic analysis validated.

POMDP-based probabilistic decision making for path planning in wheeled mobile robot

Path Planning in a collaborative mobile robot system has been a research topic for many years. Uncertainty in robot states, actions, and environmental conditions makes finding the optimum path for navigation highly challenging for the robot. To achieve robust behavior for mobile robots in the presence of static and dynamic obstacles, it is pertinent that the robot employs a path-finding mechanism that is based on the probabilistic perception of the uncertainty in various parameters governing its movement. Partially Observable Markov Decision Process (POMDP) is being used by many researchers as a proven methodology for handling uncertainty. The POMDP framework requires manually setting up the state transition matrix, the observation matrix, and the reward values. This paper describes an approach for creating the POMDP model and demonstrates its working by simulating it on two mobile robots destined on a collision course. Selective test cases are run on the two robots with three categories – MDP (POMDP with belief state spread of 1), POMDP with distribution spread of belief state over ten observations, and distribution spread across two observations. Uncertainty in the sensor data is simulated with varying levels of up to 10 %. The results are compared and analyzed. It is demonstrated that when the observation probability spread is increased from 2 to 10, collision reduces from 34 % to 22 %, indicating that the system's robustness increases by 12 % with only a marginal increase of 3.4 % in the computational complexity.

Advances in the Application of AI Robots in Critical Care: Scoping Review.

In recent epochs, the field of critical medicine has experienced significant advancements due to the integration of artificial intelligence (AI). Specifically, AI robots have evolved from theoretical concepts to being actively implemented in clinical trials and applications. The intensive care unit (ICU), known for its reliance on a vast amount of medical information, presents a promising avenue for the deployment of robotic AI, anticipated to bring substantial improvements to patient care. This review aims to comprehensively summarize the current state of AI robots in the field of critical care by searching for previous studies, developments, and applications of AI robots related to ICU wards. In addition, it seeks to address the ethical challenges arising from their use, including concerns related to safety, patient privacy, responsibility delineation, and cost-benefit analysis. Following the scoping review framework proposed by Arksey and O'Malley and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a scoping review to delineate the breadth of research in this field of AI robots in ICU and reported the findings. The literature search was carried out on May 1, 2023, across 3 databases: PubMed, Embase, and the IEEE Xplore Digital Library. Eligible publications were initially screened based on their titles and abstracts. Publications that passed the preliminary screening underwent a comprehensive review. Various research characteristics were extracted, summarized, and analyzed from the final publications. Of the 5908 publications screened, 77 (1.3%) underwent a full review. These studies collectively spanned 21 ICU robotics projects, encompassing their system development and testing, clinical trials, and approval processes. Upon an expert-reviewed classification framework, these were categorized into 5 main types: therapeutic assistance robots, nursing assistance robots, rehabilitation assistance robots, telepresence robots, and logistics and disinfection robots. Most of these are already widely deployed and commercialized in ICUs, although a select few remain under testing. All robotic systems and tools are engineered to deliver more personalized, convenient, and intelligent medical services to patients in the ICU, concurrently aiming to reduce the substantial workload on ICU medical staff and promote therapeutic and care procedures. This review further explored the prevailing challenges, particularly focusing on ethical and safety concerns, proposing viable solutions or methodologies, and illustrating the prospective capabilities and potential of AI-driven robotic technologies in the ICU environment. Ultimately, we foresee a pivotal role for robots in a future scenario of a fully automated continuum from admission to discharge within the ICU. This review highlights the potential of AI robots to transform ICU care by improving patient treatment, support, and rehabilitation processes. However, it also recognizes the ethical complexities and operational challenges that come with their implementation, offering possible solutions for future development and optimization.

Set-valued rigid-body dynamics for simultaneous, inelastic, frictional impacts

Robotic manipulation and locomotion often entail nearly-simultaneous collisions—such as heel and toe strikes during a foot step—with outcomes that are extremely sensitive to the order in which impacts occur. Robotic simulators and state estimation commonly lack the fidelity and accuracy to predict this ordering, and instead pick one with a heuristic. This discrepancy degrades performance when model-based controllers and policies learned in simulation are placed on a real robot. We reconcile this issue with a set-valued rigid-body model which generates a broad set of outcomes to simultaneous frictional impacts with any impact ordering. We first extend Routh’s impact model to multiple impacts by reformulating it as a differential inclusion (DI), and show that any solution will resolve all impacts in finite time. By considering time as a state, we embed this model into another DI which captures the continuous-time evolution of rigid-body dynamics, and guarantee existence of solutions. We finally cast simulation of simultaneous impacts as a linear complementarity problem (LCP), and develop an algorithm for tight approximation of the post-impact velocity set with probabilistic guarantees. We demonstrate our approach on several examples drawn from manipulation and legged locomotion, and compare the predictions to other models of rigid and compliant collisions.

Oscillating latent dynamics in robot systems during walking and reaching.

Sensorimotor control of complex, dynamic systems such as humanoids or quadrupedal robots is notoriously difficult. While artificial systems traditionally employ hierarchical optimisation approaches or black-box policies, recent results in systems neuroscience suggest that complex behaviours such as locomotion and reaching are correlated with limit cycles in the primate motor cortex. A recent result suggests that, when applied to a learned latent space, oscillating patterns of activation can be used to control locomotion in a physical robot. While reminiscent of limit cycles observed in primate motor cortex, these dynamics are unsurprising given the cyclic nature of the robot's behaviour (walking). In this preliminary investigation, we consider how a similar approach extends to a less obviously cyclic behaviour (reaching). This has been explored in prior work using computational simulations. But simulations necessarily make simplifying assumptions that do not necessarily correspond to reality, so do not trivially transfer to real robot platforms. Our primary contribution is to demonstrate that we can infer and control real robot states in a learnt representation using oscillatory dynamics during reaching tasks. We further show that the learned latent representation encodes interpretable movements in the robot's workspace. Compared to robot locomotion, the dynamics that we observe for reaching are not fully cyclic, as they do not begin and end at the same position of latent space. However, they do begin to trace out the shape of a cycle, and, by construction, they are driven by the same underlying oscillatory mechanics.

Research Status and Development Trend of Multi-arm Collaborative Robots

Abstract: Industrial robots are mainly used in metal forming, automotive, and electrical and electronics industries. After decades of unremitting efforts, industrial robots have achieved great success. However, problems such as low safety, weak environmental adaptability, and insufficient humancomputer interaction ability of industrial robots have greatly limited the application of industrial robots, and multi-arm collaborative robots came into being in this case. Multi-arm collaborative robots offer high flexibility and precision. Over the years, the application and development of multiarm collaborative robots have been paid more and more attention. This paper aims to summarize the patents related to multi-arm cooperative robots, including their principles, classification, latest progress and future development, in order to meet the needs of the real world. According to the structural characteristics of multi-arm collaborative robots and the requirements of multi-arm collaborative robots application field, the most typical mobile collaborative robots and stationary dual-arm collaborative robots in multi-arm collaborative robots are summarized. By investigating various patents of multi-arm collaborative robots the main problems of multi-arm collaborative robots are summarized and analyzed, such as collisions or conflicts between robotic arms, low power-to-weight ratio, loose structures, and heavy weight of the robot body. In addition, the development trend of multi-arm collaborative robots is also discussed. Multi-arm robots have broad prospects and potential in future applications and development. With the advancement of new technologies, multi-arm collaborative robots will become more efficient, intelligent, safe, and flexible. More relevant patents are expected to be invented in the future.