Mobile Robot System Research Articles

Design an omnidirectional autonomous mobile robot based on non‐linear optimal control to track a specified path

AbstractThis paper explores two non‐linear control techniques for designing an effective control system for an omnidirectional autonomous mobile robot with four Mecanum wheels. Due to the unique wheel structure and four separate wheels, the robot has non‐linear dynamics, multiple inputs and outputs. The first technique uses the state‐dependent Riccati equation (SDRE) to address optimal non‐linear control while considering energy and time constraints. The second technique, using an intermediate variable , has expanded the Hamilton‐Jacobi‐Belman equation in terms of the power series. Consequently, these equations are reduced to a set of recursive Lyapunov algebraic equations, leading to a closed‐form solution for solving the non‐linear optimal control problem. Finally, the maneuverability and path‐tracking capability of the robot are examined by highlighting the non‐linear term through numerical simulation.

Разработка автоматизированной системы управления мобильным роботом

In modern conditions, when automation is becoming universal, there are many different names and manufacturers of automated control systems on the market. These systems include various devices such as communication facilities, multifunctional signal input/output boards, a variety of controllers, intelligent and non-intelligent sensors, actuators, single-board computers for industrial use and other devices. There are many companies and names in this field offering their products and solutions. Companies such as Siemens, Schneider Electric, ABB, Rockwell Automation, Honeywell and many others. The automated control system of the mobile robot allows for manual and automatic control under the direct supervision of a qualified employee. The main idea in the development of an automatic control system for mobile robots is that the robot moves through the warehouse premises of the enterprise without human intervention, transporting loads of different sizes and choosing the optimal trajectory of movement. The main advantage of mobile robotic platforms is their versatility. Mobile platforms can be used to perform a variety of tasks, such as transporting goods in a confined space or mapping an area. Depending on the characteristics of each mobile platform, you can create control systems to perform a specific task.

Optimizing path planning in mobile robot systems using motion capture technology

Recent advancements in mechatronics and robotics have led to the emergence of a wide range of technologies with potential applications across various industries. This progress has been observed in recent years and is expected to continue as the integration of robots into daily life becomes more widespread. Despite these developments, deploying robots in industrial environments, particularly in assembly operations, still presents several challenges, including effectively distributing skills-based tasks between human and robotic workers. This paper proposes an approach to improve the performance of mobile robot systems for optimal path planning. The technique utilizes motion capture technology to collect real-time data on the robot's movements, generate optimal path planning strategies, and enable remote control and monitoring of the robot's activities. The proposed approach can significantly enhance mobile robot systems' capabilities in various industrial settings. The results of our study demonstrate that the integration of motion capture technology can substantially improve the accuracy and efficiency of path planning in mobile robot systems and enhance their overall performance. A series of experiments demonstrate its effectiveness in generating optimal path-planning strategies while minimizing the risk of collisions and other hazards.

Comparison of proactive and reactive interaction modes in a mobile robotic telecare study

Mobile robotic telepresence systems require that information about the environment, the task, and the robot be presented to a remotely located user (operator) who controls the robot for a specific task. In this study, two interaction modes, proactive and reactive, that differ in the way the user receives information from the robot, were compared in an experimental system simulating a healthcare setting. The users controlled a mobile telepresence robot that delivered and received items (medication, food, or drink), and also obtained metrics (vital signs) from a simulated patient while the users performed a secondary healthcare-related task (they compiled health records which were displayed to them on the screen and answered related questions). The effect of the two interaction modes on overall performance and user perception was evaluated through a within-participant study design conducted with 50 participants belonging to two different types of populations (with and without a technological background). Efficiency, effectiveness, understanding, satisfaction, and situation awareness were defined as the dependent variables measured both objectively and subjectively. The proactive mode increased user performance, and understanding of the system and reduced the workload compared to the reactive mode. However, several of the users valued the option of increased user control experienced in the reactive mode. We, therefore, proposed design suggestions to highlight some of the benefits of factoring the reactive mode into the design as a hybrid mode.

Development of Autonomous Mobile Robot with 3DLidar Self-Localization Function Using Layout Map

In recent years, there has been growing interest in autonomous mobile robots equipped with Simultaneous Localization and Mapping (SLAM) technology as a solution to labour shortages in production and distribution settings. SLAM allows these robots to create maps of their environment using devices such as Lidar, radar, and sonar sensors, enabling them to navigate and track routes without prior knowledge of the environment. However, the manual operation of these robots for map construction can be labour-intensive. To address this issue, this research aims to develop a 3D SLAM autonomous mobile robot system that eliminates the need for manual map construction by utilizing existing layout maps. The system includes a PC for self-position estimation, 3DLidar, a camera for verification, a touch panel display, and the mobile robot itself. The proposed SLAM method extracts stable wall point cloud information from 3DLidar, matches it with the wall surface information in the layout map, and uses a particle filter to estimate the robot’s position. The system also includes features such as route creation, tracking, and obstacle detection for autonomous movement. Experiments were conducted to compare the proposed system with conventional 3D SLAM methods. The results showed that the proposed system significantly reduced errors in self-positioning and enabled accurate autonomous movement on specified routes, even in the presence of slight differences in layout maps and obstacles. Ultimately, this research demonstrates the effectiveness of a system that can transport goods without the need for manual environment mapping, addressing labour shortages in such environments.

Adaptive region-reaching control for a non-holonomic mobile robot via system decomposition approach

This article focuses on the region-reaching control for a non-holonomic mobile robot system with two actuated wheels. The region-reaching control task consists of three basic control objectives: reaching an objective region, maintaining a rest state in the objective region, and stabilising at a desired posture. The constraints imposed on the motion of the mobile robot system are not integrable and have no inverse resolution, and thus increase the complexity of the controller design for non-holonomic systems. A system decomposition approach is proposed to convert the non-holonomic constraint control system to the cascade holonomic constraint control subsystems, and an adaptive control gain technique is presented to guarantee the stability of the non-holonomic system. Then, a novel torque controller is presented to implement the region-reaching control task. Specially, the objective region can be considered as a parking spot for a mobile robot.

Autonomous Navigation System for a Differential Drive Mobile Robot

Abstract Mobile robotics is a rapidly expanding field of research because of its large number of applications. Autonomous mobile robots (AMRs) are used in multiple areas, such as industrial automation, logistics and warehouse management, museums, hospitals and restaurant assistance, space and ocean exploration, and many others. In this work, a navigation system for a low-cost mobile robot is proposed. It relies on state-of-the-art software suited for designing automated systems, robot applications, and computer vision algorithms. The robot is driven by two wheels powered by Beckhoff motors, controlled using Beckhoff’s TwinCAT 3 automation software in an industrial computer. A separate small-sized Raspberry Pi computer handles the environment perception by processing the information acquired by a Lidar module and a webcam and the localization and planning through the Robot Operating System (ROS). Communication between the two computers yields a complete robot navigation system. This system is tested and subsequently tuned to achieve good performance. Experimental tests show that the navigation system is globally effective, and some limitations related to the robot’s design and its navigation subsystem are discussed. The approach proposed in this work can be extended, adjusted, and used in other types of mobile robots.

Ground-based response robotics for the safe operation of TV journalists in emergency zone

The article considers an approach to ensuring safe working conditions for TV journalists conducting TV reports from the emergency zone by means of ground-based response robotics. It is proposed to use a remote controlled (teleoperated) ground-based mobile service robots equipped with vision systems and professional television film-ing equipment. Remote-controlled robots developed at the Russian State Scientific Center for Robotics and Tech-nical Cybernetics (St. Petersburg) can be used as such robotic systems. Equipping robots with a stereoscopic vi-sion systems and a special TV camera to organize teleoperation of a mobile robot’s movement requires a broad-band communication channel, since it is necessary to transmit several video streams simultaneously. To solve this problem, it is proposed to use an image compression methods and the experience of using a network technolo-gies obtained during the development of the multiple-camera vision systems for installation on the mobile robotic systems BROKK-110D and BROKK-330 (Sweden) at the instructions of the Center for Special Risk Rescue Opera-tions «Leader» (Russia). The technical solution is presented to provide TV journalists with special remote-controlled ground-based robotics, which allows them to conduct television reports directly from the scene of hostili-ties, while being at a safe distance from the scene of events and the location of radio antennas.

Design of a robotic system to assist in the treatment of severe COVID‐19 patients

AbstractThe article presents the conceptualization, development, and implementation of a sophisticated mobile robotic system with the purpose of providing aid in the medical care of those afflicted with severe cases of COVID‐19. The robotic system will engage in verbal communication with the patient and provide updates regarding the external environment. Additionally, it will facilitate the delivery of food, beverages, and other consumable items to the isolation room. The robot has the capability to navigate to its intended location using two distinct modes: autonomous mode and online control mode. The hardware is constructed within a mobile robot system that is interconnected to the Internet over a 4G mobile network. The system employs a client–server software architecture wherein the transmission of data between the client and the server occurs through various transport protocols. Experimental simulations were conducted in an active treatment room to evaluate the performance of autonomous operating mechanisms, including obstacle avoidance positioning, safe destination navigation, and feedback display for remote robot operation.

BI-RRT*: An improved path planning algorithm for secure and trustworthy mobile robots systems

The optimal RRT in elliptic space sampling (Informed-RRT*) is an extension of RRT that provides asymptotic optimality, however, it experiences gradual progress and close to obstacles. In the paper, we propose a novel path planning algorithm guided bidirectional Informed-RRT* (BI-RRT*), that introduces extension range, dual-direction exploration, and refinement in trajectory design. The growth range refers to maintaining an additional area from the obstacle to enhance the dependability of the path through preventing impacts. Bidirectional search is a search strategy using both start and target points for a initial solution. Smoothing improves path robustness by using cubic spline. Furthermore, simulation tests for the BI-RRT* algorithm are executed, and the efficacy of the suggested algorithm is confirmed through its application in a robot operating system (ROS). Simulations and experimental tests verify that the proposed algorithm improves the path planning capability. We emphasize the importance of safety, privacy, and reliability in the deployment of AI systems. Our algorithm ensures that the planned paths maintain a safe distance from obstacles, reducing the risk of collisions. Additionally, we prioritize privacy by adhering to data protection regulations and implementing secure communication protocols within the AI system. Moreover, we have applied rigorous testing and validation processes to enhance the reliability of our algorithm, ensuring consistent and accurate path planning outcomes.

Distributed Fixed-Time Formation Tracking Control for the Multi-Agent System and an Application in Wheeled Mobile Robots

This work addresses the issue of multi-agent system (MAS) formation control under external disturbances and a directed communication topology. Firstly, a new disturbance observer is proposed to effectively reconstruct and compensate for external disturbances within a short period of time. Then, the integral terminal sliding mode technology is introduced to devise a novel distributed formation control protocol, ultimately realizing the stability of the MAS within a fixed time. Moreover, by means of rigorous Lyapunov theory analyses, a faster formation convergence rate and more accurate consensus accuracies are achieved in the proposed fixed-time strategy with variable exponent form. Finally, the formation tracking control scheme is applied to a multi-wheeled mobile robot (WMR) system. The experimental results strongly support the fine effectiveness of the control scheme designed in this work.

A heterogeneous robots collaboration for safety, security, and rescue robotics: e-ASIA joint research program for disaster risk and reduction management

In the face of humanitarian crises such as torrential rainfall, resulting floods, and landslides, rapid rescue operations are often imperative. However, owing to the inherent dangers and unpredictability of such circumstances, immediate on-site aid delivery is frequently unfeasible. In such challenging scenarios, mobile robot systems have emerged as the optimal solution for aiding search and rescue efforts. The 'Informational system for management of flood and landslide disaster areas using a distributed heterogeneous robotic team' project, initiated by the International e-ASIA Joint Research Program, united research teams from Japan, Russia, and Thailand, each contributing unique expertize and experience towards common objectives. Drawing upon our extensive theoretical and practical knowledge in disaster response research, we developed an integrated international operational framework for disaster site management, centered on dispersed, heterogeneous semi-autonomous Unmanned Aerial Vehicles (UAVs), Semi-Autonomous Rough Terrain Vehicles (UGVs), and Semi-Autonomous Surface Vehicles (USVs). This paper provides an overview of the methodologies, models, and algorithms employed throughout the successful three-year project conducted by the Thailand team while also directing interested readers to supplementary research papers for in-depth insights.

INTELLIGENT CONTROL SYSTEM OF MULTI-MOTION MOBILE ROBOT

Technical devices with irregular motion are described by complex non-linear differential equations, as they have an uncertain environment. Due to interactions and simplifications in these types of control objects, their mathematical models may have certain errors. For this reason, by known methods, for example, the linear matrix inequality method, Lyapunov's quadratic function, etc. The synthesized automatic control systems (ACS) for dynamic objects written by nonlinear mathematical models based on the quadratic matrix determined by The synthesis of regulators for the management of dynamic objects described in nonlinear and uncertain conditions is based on fuzzy logic theory. Since the controllers synthesized by fuzzy logic are based on knowledge, their application to other objects (robots) is limited. In such systems, it is difficult or not at all to determine the dependencies between ACS quality indicators, stability and object parameters. Taking into account the above, a method of analytically parametric synthesis of regulators that ensures the degree of stability and accuracy in ACS’s designed for fuzzy TS-type dynamic objects is proposed. In the article, the solution to the problem of the synthesis of the control system of the mobile robot was considered, and it was successfully applied in the multi-motion mobile robot system. Modeling and experimental results confirm that the operation and stability of the obtained system fully meet the technical requirements. The proposed method has led to an increase in both tracking and control accuracy during high-speed movements. Keywords: multi-motion mobile robot, mathematical model, mechatronic modules, fuzzy TS model, robust controller, fuzzy logic.

Dual-Event-Triggered Intelligence Security Control for Multiagent Systems Against DoS Attacks With Applications in Mobile Robot Systems

The intelligence security control problem of multiagent systems in the presence of actuator faults is investigated against denial-of-service (DoS) attacks. Noticing that the DoS attacks usually make the actuator signal not available for distributed control protocols, a novel intelligence dual-eventtriggered control strategy is developed to eliminate the impact of DoS attacks. This control strategy includes two related event triggers respectively set on different channels, in which the second event-triggered condition is related to the triggering sequence of the previous event. Through the coordination between two nested event triggers set on different channels, the controller is avoided by being affected by wrong signals caused by DoS attacks. The developed distributed control protocol can not only guarantee the realization of the tracking control task but also ensure that no ‘Zeno behavior” occurs in the process of the event triggering. Moreover, the obtained results are applied to the mobile robot systems. Finally, a numerical simulation example and a mobile robot system application are given to verify the feasibility and effectiveness of the proposed control strategy.

Navigation System of a Differential-drive Wheeled Mobile Robot Using a GA-Tuned Kinematic Controller

Navigation System of a Differential-drive Wheeled Mobile Robot Using a GA-Tuned Kinematic Controller

Robot Docking and Charging Techniques in Real Time Deep Learning Model

This article describes various approaches that utilize computer vision and Lidar technology. These approaches include, but not limited to, vision-based algorithms such as the Faster RCNN model and AprilTag; and single shot detectors (SSD). In carrying out docking and recharging operations, the aforementioned approaches have shown varying degrees of success and accuracy. In order to make it easier for mobile robot systems to perform autonomous docking and recharging (ADaR) in industrial settings, this study presents a new method that employs vision and Lidar technology. In this study, we propose the YOLOv7 deep learning model to find charging stations. To further simplify docking with the specified wireless charging station, a Lidar-based approach is used to precisely modify the robot's position. An account of the assessment standards and training procedure used for the adjusted YOLOv7 model is provided in the results and discussion section. In this research, it was found that the model's 86.5% mean Average Precision (mAP) within the IoU range of 0.5 to 0.9 is evidence of its efficacy. In addition, the detection and identification of charging stations had an average accuracy rate of 95% in the studies conducted in real-world settings.

Boosting the hospital by integrating mobile robotic assistance systems: a comprehensive classification of the risks to be addressed

Mobile service robots are a promising technology for supporting workflows throughout the hospital. Combined with an understanding of the environment and the current situation, such systems have the potential to become invaluable tools for overcoming personal shortages and streamlining healthcare workflows. However, few robotic systems have actually been translated to practical application so far, which is due to many challenges centered around the strict and unique requirements imposed by the different hospital environments, which have not yet been collected and analyzed in a structured manner. To address this need, we now present a comprehensive classification of different dimensions of risk to be considered when designing mobile service robots for the hospital. Our classification consists of six risk categories – environmental complexity, hygienic requirements, interaction with persons and objects, workflow flexibility and autonomy – for each of which a scale with distinct risk levels is provided. This concept, for the first time allows for a precise classification of mobile service robots for the hospital, which can prove useful for certification and admission procedures as well as for defining architectural and safety requirements throughout the design process of such robots.

Experimental Assessment of Chance-Constrained Motion Planning for Small Uncrewed Aircraft

This work extends the experimental evaluation of chance-constrained motion planning algorithms to fielded fixed-wing small uncrewed aircraft systems (sUAS). Despite advances in planning algorithms, certain challenges remain to producing trajectories for nonholonomic mobile robotic systems such as sUAS. These challenges include nonlinear dynamics which create a complex mapping from inputs to outputs, initialization uncertainty in online motion planning due to compute time of planners and latency in data transfer, environmental uncertainty that has non-Gaussian impact on the robot’s trajectory, and system uncertainty that arises from incomplete models of complex systems. Small UAS often have proprietary components such as commercial, off-the-shelf autopilots which prevent motion planning models from accurately capturing system behavior in all regions of the state space. These challenges can be addressed by leveraging probabilistic motion planners that accurately represent and reason over dynamics and uncertainty. Chance-constrained motion planning offers a method of reasoning over uncertainty as feasibility constraints, and Monte Carlo sampling within a motion planning algorithm offers a method of representing complex uncertainty that may not have a closed form representation. This work extends the chance-constrained motion planning problem to reason over constraint on the trajectory and constraints on the state which ensure that the system model remains valid. Dynamical and systems models are formulated to extend to a broad class of fixed-wing UAS through the experimental derivation of input distributions and system parameters. Uncertainty is quantified using a data-driven approach to modeling input distributions, and Monte Carlo uncertainty sampling deployed within a Rapidly-exploring Random Tree motion planning algorithm is used to plan a trajectory containing a representation of uncertainty. The motion planning algorithm’s ability to accurately reason over layered chance constraints is evaluated experimentally in 61 fielded missions. The results show that when the flight conditions fall within the domain of the uncertainty distributions, the motion planning system is able to accurately reason over the chance constraints.

Learning Selective Sensor Fusion for State Estimation.

Autonomous vehicles and mobile robotic systems are typically equipped with multiple sensors to provide redundancy. By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e.g., locations and orientations. Although deep learning (DL) approaches for multimodal odometry estimation and localization have gained traction, they rarely focus on the issue of robust sensor fusion--a necessary consideration to deal with noisy or incomplete sensor observations in the real world. Moreover, current deep odometry models suffer from a lack of interpretability. To this extent, we propose SelectFusion, an end-to-end selective sensor fusion module that can be applied to useful pairs of sensor modalities, such as monocular images and inertial measurements, depth images, and light detection and ranging (LIDAR) point clouds. Our model is a uniform framework that is not restricted to specific modality or task. During prediction, the network is able to assess the reliability of the latent features from different sensor modalities and to estimate trajectory at both scale and global pose. In particular, we propose two fusion modules--a deterministic soft fusion and a stochastic hard fusion--and offer a comprehensive study of the new strategies compared with trivial direct fusion. We extensively evaluate all fusion strategies both on public datasets and on progressively degraded datasets that present synthetic occlusions, noisy and missing data, and time misalignment between sensors, and we investigate the effectiveness of the different fusion strategies in attending the most reliable features, which in itself provides insights into the operation of the various models.

Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems

Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems