Position Of Robot Research Articles

Impact of Tire Shape on Localization Accuracy in Piping Inspection Robots

The sewerage pipes laid in Japan are extensive, spanning approximately 470,000 km, with most of them constructed during the high economic growth period from around 1955 to around 1973. According to indicators from the Ministry of Land, Infrastructure, Transport, and Tourism, the service life of sewerage pipes is estimated at 50 years. This suggests that many sewer pipes across Japan, installed over 50 years ago, are becoming obsolete. Consequently, piping inspections using robots have commenced in Japan. Currently, stand-alone types that can be inspected by a single robot are garnering attention. Meanwhile, we have been conducting research and development with the aim of implementing a small, easily portable, stand-alone piping inspection robot. Furthermore, numerous stand-alone types have been employed to prevent falls by adjusting the tire shape or the distance between the axles. However, this hardware approach does not completely prevent falls. Therefore, we have opted for a software approach to explore measures to prevent falls by controlling driving, aiming to achieve the advanced localization required for this purpose. We are currently in the stage of verifying the localization. However, accurately measuring the robot's position and orientation using general measuring instruments is challenging due to the curved piping. Hence, we developed a specialized three-dimensional position-measuring instrument for piping inspection robots. In this paper, we utilize the instrument to examine the influence of tire shape on localization. Additionally, we demonstrate the effectiveness of the localization method in an environment where tire shape does not affect the outcome.

A BLE beacon global tracking strategy for small robot using an ESP32 beacon system and GPS

This study aims to estimate the position of a small robot in both outdoor and indoor environments using a combination of RSSI-to-distance calculation, trilateration, and MQTT protocol for communication between ESP32 stations and the server. RSSI is utilized to approximate the distance between the ESP32 and the beacon, while trilateration is employed to determine the precise position of the beacon. The ratio of the imaginary circles is calculated by various signal measurement techniques, and the MQTT protocol is used to set up communication between ESP32 stations and the server. Once the beacon device is visible to all three ESP32 modules, trilateration takes place, and the position of the BLE beacon is displayed as a web page. The server collects the largest RSSI values from the nearest three ESP32 stations, and trilateration is performed from the distances obtained, with the coordinates being sent to the dashboard for visual representation. This strategy is supported by a GPS in outdoor environments, and indoors when structures allow it. Furthermore, the study includes the implementation of an experimental procedure and findings that involve utilizing ESP32 modules as scanning stations to track a BLE beacon.

Navigation system for orchard spraying robot based on 3D LiDAR SLAM with NDT_ICP point cloud registration

Autonomous navigation of orchard spraying robots can effectively improve operational efficiency and reduce workload. The conventional robot navigation solutions based on the Global Navigation Satellite System (GNSS) are susceptible to signal loss in orchards due to interference from the canopy of fruit trees. In this paper, an autonomous navigation scheme for orchard spraying robots based on multi-sensor devices and the three-dimensional (3D) LiDAR Simultaneous Localization and Mapping (SLAM) technology. The initial step of the scheme involved the construction of a 3D point cloud map of the orchard using the 3D LiDAR SLAM algorithm. Subsequently, a designed point cloud map transformation algorithm was used to convert the 3D point cloud map of the orchard into a two-dimensional (2D) grid map containing key feature information of the orchard. To enhance the mapping accuracy of the orchard point cloud, a point cloud registration algorithm combining Iterative Closet Point (ICP) and Normal Distributions Transform (NDT) was proposed. This algorithm initially utilized the NDT algorithm for coarse point cloud registration to obtain transformation parameters. Then, the ICP algorithm was applied in conjunction with the obtained parameters for fine registration. Regarding obstacle avoidance, a cooperative obstacle avoidance algorithm based on the Robot Operating System (ROS) multithreaded communication mechanism was introduced. Experimental trials conducted in a standardized spindle-shaped peach orchard validated the successful completion of the SLAM mapping trajectory, which was achieved through the utilization of the proposed NDT_ICP point cloud registration algorithm. The average absolute pose error between the mapped trajectory and the reference trajectory was 1.173 m, with a standard deviation of 0.498 m. The robot's positioning and navigation errors increased with higher speeds, with the largest positioning deviation observed at 1.2 m/s speed, with a maximum lateral positioning error of 7.04 cm. The robot's average lateral navigation error did not exceed 16 cm, and the average heading deviation was less than 8° at different speeds of 0.4, 0.8 and 1.2 m/s. Both the positioning and navigation accuracies of the robot were sufficient to meet the autonomous operational requirements of orchard spraying robots.

Research on Medical Robots Based on Convolutional Neural Network: Drug Transportation System and E-commerce for Neurodigestive Diseases

The research team designed medical robots for patients with neurodigestive diseases, mainly using convolutional neural networks to achieve robot positioning. We designed the primary model of the drone and used a spatial Cartesian coordinate system as a reference system to conduct positioning research. Finally, by analyzing the current situation of multiple disease relapses and conducting drug related questionnaire surveys, we verified the necessity of drug delivery robots, Convolutional neural networks have been implemented to achieve spatial positioning of robots.

Optimization of stiffness performance of six-axis industrial robots based on posture stiffness evaluation index

Aiming at the problem that the stiffness performance of industrial robots depends on the posture, a method to improve the stiffness performance by optimizing the robot posture is proposed based on the robot posture stiffness evaluation index. The robot joint stiffness identification model is established by using the principle of virtual work and Jacobi matrix, and the stiffness values of six joints are obtained by least-squares. On the basis of revealing the mapping relationship between robot joints and end, the robot end flexibility ellipsoid is obtained, and the volume of the end flexibility ellipsoid is utilized as the global stiffness coefficient of the robot, so as to obtain the robot posture stiffness evaluation index. According to the posture stiffness evaluation index, the robot posture optimization model is established, and the optimized posture is obtained by using genetic algorithm. Taking the QJR6-1 robot as the experimental object, under the maximum load, the deformation of the robot end center is reduced from 0.6238 to 0.3984 mm, which is 36.13%, and the deformation of the end is greatly reduced compared with that before the optimization, which indicates that the robot’s stiffness is improved after the posture optimization, and it verifies the feasibility of the optimization model, and provides a reference to improve the stiffness performance of industrial robots further.

Modeling and control of magnetically actuated micro/milli robots position under laminar flow

The goal of this study is to model and control the position of micro- and milli-scale robots (MMRs) under laminar flow for multiscale robotic applications. A conceptual design depending on Lagrange point-based manipulation of the MMRs is proposed. The design includes a channel between two cylindrical permanent magnets that are used as the magnetic actuator. Spherical magnetic MMRs are placed in this channel with a diameter range of 0.25–1 mm. First, an analytical permanent magnet model is implemented. Following this, an adaptive mathematical model is built considering the dynamic magnetic forces on the MMRs. The position of the MMRs is analyzed for different parameters such as the channel dimension, the magnet diameter and distance, the type of the surrounding liquid, and the Re number. It is observed that there are considerable fluctuations during the positioning. Also, there is a deviation from the Lagrange point due to the significant drag forces caused by the laminar flow. To overcome these fluctuations and the deviation, an I-PD controller is implemented. Then, the position of the MMRs is analyzed with similar parameters in the presence of the controller. Results indicated that it is possible to control the MMR position at the Lagrange point by the I-PD controller, with a 14 % maximum overshoot.

Bioinspired Flexible Capacitive Sensor for Robot Positioning in Unstructured Environments.

The evolution of bionic machines into intelligent robots to adapt to real scenarios is inseparable from positioning sensors. However, traditional positioning methods such as camera arrays, ultrasound, or GPS are limited in narrow concealed spaces, harsh temperatures, or dynamic light fields, which hinder the practical application of special robots. Here, we report a flexible sensor inspired by Gnathonemus petersii that enables robots to achieve contactless and high-precision spatial localization independent of the unstructured features of the environment. Sensors are obtained from low-cost materials (carbon nanotubes and polyimides) and simple structures (fibers) and preparation processes (spin-coating). Experiments and simulations confirmed the high resolution (<1 mm) of the sensor over a large distance detection range (>150 mm) and high bandwidth (0-520 MPa) of contact force. Moreover, the sensing capability is still feasible when the sensor is bent to various curvatures and not affected under harsh conditions such as ultralow temperatures (below -78 °C), ultrahigh temperatures (over 250 °C), darkness, or brightness. We demonstrate the practical potential of the proposed sensors for a biomimetic hyper-redundant continuum robot to locate and avoid collisions in unstructured environments.

Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR

In recent years, with the rapid growth in technology and demand for industrial robots, Automated Guided Vehicles (AGVs) have found widespread application in industrial workshops and smart logistics, emerging as a global hot research topic. Due to the volatile and complex working environments, the positioning technology of AGV robots is of paramount importance. To address the challenges associated with AGV robot positioning, such as significant accumulated errors in wheel odometer and Inertial Measurement Unit (IMU), susceptibility of Ultra Wide Band (UWB) positioning accuracy to Non Line of Sight (NLOS) errors, as well as the distortion points and drift in point clouds collected by LiDAR during robot motion, a novel positioning method is proposed. Initially, Weighted Extended Kalman Filter (W-EKF) is employed for the loosely coupled integration of wheel odometer and Ultra Wide Band (UWB) data, transformed into W-EKF pose factors. Subsequently, appropriate addition of W-EKF factors is made during the tight coupling of pre-integrated Inertial Measurement Unit (IMU) with 3D-LiDAR to counteract the distortion points, drift, and accumulated errors generated by LiDAR, thereby enhancing positioning accuracy. After experimentation, the algorithm achieved a final positioning error of only 6.9cm, representing an approximately 80% improvement in positioning accuracy compared to the loosely coupled integration of the two sensors.

A curvature fiber optic sensor with expandable measurement points based on anti-resonant hollow-core optical fiber

This work presents an expandable fiber optic curvature sensor. The sensor achieves multi-point curvature measurements by cascading different lengths of anti-resonant hollow-core fiber sensing units. The sensing unit is formed by connecting the anti-resonant hollow-core fiber between two single-mode fibers. The highest curvature sensitivity achieved by a single sensing unit is −11.86 nm/m−1, with a low temperature sensitivity of only 3 pm/°C. This sensing unit is readily manufacturable and exhibits good curvature sensitivity, temperature insensitivity, and stability. In contrast to traditional multi-point distributed sensors, this sensor can be flexibly installed at various positions, providing curvature sensing at desired locations. Moreover, it demonstrates the potential for multi-point curvature measurements, offering a promising solution for practical applications such as structural monitoring and robotic posture detection.

Passive Visual Odometry of Mobile Transport Robots in Production Areas Using Anchor Points

The article considers the problem of determining the orientation of mobile transport robots, which are now actively used in almost all areas of industry. They make it possible to intensify production, free personnel from performing routine operations, and exclude the influence of the human factor from work. Based on the analysis of various variants of active orientation methods involving the use of binocular video systems installed on the vehicles themselves, it is proposed to use a passive orientation detection system. It includes a single stationary monocular video system, the replacement of onboard video systems on mobile robots with simple radiation sensors, as well as the use of anchor points in the area of their movement. The order of choice of anchor points in solving the problem of orientation of a mobile transport robot using a monocular video system, in particular for a rectangular interior space, is considered. Calculation formulas for determining the coordinates of the sensor from the pixel coordinates of its image obtained from a monocular video camera are determined. The general sequence of actions for determining the orientation of a mobile robotic platform is also considered. Unlike active systems, this method makes it possible to significantly simplify the hardware, significantly facilitate the analysis of the current position of the mobile robot and thereby reduce the computational complexity of calculations due to the fact that there is no need to use a complex mathematical apparatus. It is being replaced by simpler two-dimensional geometric calculations. This approach, due to the unified management, makes it possible to effectively coordinate the actions of a group of mobile transport robots when they are used together, greatly simplifies the solution of a number of tasks for optimizing the intra-shop movement of mobile vehicles.

Accuracy Compensation Based on Optimized Neural Network for a Robotic Patient Positioning System with 6 Degrees of Freedom Used in Proton Therapy System

Proton therapy for tumor treatment is a typical application of nuclear technology. For proton therapy systems, robotic patient positioning systems (PPSs) are increasingly used because of their high flexibility and efficiency. Most robotic PPSs are developed based on industrial robots, which have good repeatability but low absolute position accuracy (1 to 3 mm) and do not satisfy the requirement of highly precise treatment. In this study, an optimized algorithm, named the Back Propagation Neural Network (BPNN) algorithm based on particle swarm optimization, is proposed to improve the performance of absolute positioning accuracy. A comparison of the training for the traditional BPNN and the optimized algorithm is presented. A series of experiments with different payload weights and tools is implemented to validate the performance of the proposed method. The training results show that the proposed method can improve the average predicted positioning error from 0.55 to 0.38 mm. The results of the experiment with a calibration tool show that the average position error is reduced from 4.10 to 0.32 mm. The results of the experiment with a carbon fiber couch top show that the average and maximal positioning errors are 0.35 and 0.77 mm, respectively. All the results verify the feasibility of the proposed method in this study in improving the position accuracy of the robotic PPS.

Design and Construction of A Remotely Controlled Multy-Tasking Chain-Wheel Combat Robot

Troops in the context of reconnaissance or search tasks to find out the enemy in difficult, protected and disguised terrain is an element of vulnerability that needs to be taken into account. If you insist on finding out the enemy's condition, there is a possibility that contact will occur early and cause casualties which could thwart the main task. Therefore, a tool or robot is needed to help obtain data about protected enemies by diverting the enemy's attention and providing resistance. The aim of this research is to create a prototype chain-wheeled combat robot platform system that is capable of maneuvering, firing SS2 and rockets, detecting mine-prone areas, and detecting body thermals. The robot control system consists of a mobile controller and a base station. The mobile controller functions to control robot movements such as forward, backward, right turn, left turn, weapon azimuth movement, weapon elevation movement and shooting using 2.4 GHz radio control and target monitoring using FPV ((First person view) directly in the field on a frequency of 5.6 GHz, base station functions to control the robot's movement and monitor targets using a laptop via the internet. The robot system is equipped with GPS and a metal detector, so that the robot's position can be monitored and report the presence of mines to the base station. Thus, the robot created is expected to be able to help to combat duty in the context of war military operations.

A Review of Underwater Robot Localization in Confined Spaces

Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in confined underwater environments, researchers have studied different underwater positioning methods and have selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have developed some bio-inspired autonomous positioning methods. This article introduces applicable robot positioning methods and sensors in confined underwater environments and discusses the research directions of robot positioning methods in such environments.

A novel vision-based multi-functional sensor for normality and position measurements in precise robotic manufacturing

Cobots play an essential role in the fourth industrial revolution and the automation of complex manufacturing processes. However, cobots still face challenges in achieving high precision, which obstructs their usage in precise applications such as the aerospace industry. Nonetheless, advances in perception systems unlock new cobot manufacturing capabilities. This paper presents a novel multi-functional sensor that combines visual and tactile feedback using a single optical sensor, featuring a moving gate mechanism. This work also marks the first integration of Vision-Based Tactile Sensing (VBTS) into a robotic machining end-effector. The sensor provides vision-based tactile perception capabilities for precise normality control and exteroceptive perception for robot localization and positioning. Its performance is experimentally demonstrated in a precise robotic deburring application, where the sensor achieves the high-precision requirements of the aerospace industry with a mean normality error of 0.13° and a mean positioning error of 0.2 mm. These results open a new paradigm for using vision-based sensing for precise robotic manufacturing, which surpasses conventional approaches in terms of precision, weight, size, and cost-effectiveness.

Optomagnetic Coordination Helical Robot with Shape Transformation and Multimodal Motion Capabilities.

Soft robots with magnetic responsiveness exhibit diverse motion modes and programmable shape transformations. While the fixed magnetization configuration facilitates coupling control of robot posture and motion, it limits individual posture control to some extent. This poses a challenge in independently controlling the robot's transformation and motion, restricting its versatile applications. This research introduces a multifunctional helical robot responsive to both light and magnetism, segregating posture control from movements. Light fields assist in robot shaping, achieving a 78% maximum diameter shift. Magnetic fields guide helical robots in multimodal motions, encompassing rotation, flipping, rolling, and spinning-induced propulsion. By controlling multimodal locomotion and shape transformation on demand, helical robots gain enhanced flexibility. This innovation allows them to tightly grip and wirelessly transport designated payloads, showcasing potential applications in drug delivery, soft grippers, and chemical reaction platforms. The unique combination of structural design and control methods holds promise for intelligent robots in the future.

Research on Human-Robot Interaction Methods Based on Audience Cognition

There are various ways of information transmission, but due to factors such as temporal and spatial differences, semantic expressions, and cultural differences, some information would inevitably be lost during the transmission process. Fortunately, in the information age, various modern information technologies can effectively compensate for the shortcomings of human communication, and social robots have become emerging communication tools. This article analyzes the dilemma of human-robot communication from the perspective of audience cognition and finds that there are still problems with the positioning of social robots and their technology, as well as individual human cognitive limitations and usage challenges. These factors significantly impede the innovative evolution of human-robot communication modes. To address these challenges, the article proposes several response strategies aimed at offering insights and inspiration for the advancement of human-robot communication.

A Synchronous Localization Method for Multiple Mobile Robots Based on Visual SLAM Algorithm in the Context of the Internet of Things

Abstract Aiming at the problem that traditional methods cannot meet the problem of synchronous positioning of multi-mobile robots in different motion states, a synchronous positioning method of multi-mobile robots based on visual simultaneous localization and mapping (SLAM) algorithm is proposed. Analyze the general model of SLAM problem, realize visual SLAM feature detection through scale invariant feature transform algorithm, and match image features based on active vision. A multi-mobile robot motion state estimation model is constructed, an octree model is used to construct a multi-robot synchronous localization map, and the camera pose tracking is realized by using the oriented fast accelerated segment test and rotated binary robust independent elementary features points that are not in the target bounding box. The three-dimensional points in the environment recovered by visual SLAM separate the background target and the moving target, and match the corresponding target in the previous frame through the feature to realize the synchronous positioning of the multi-mobile robot. The experimental results show that the method can realize the synchronous positioning of multi-robots under the state of multi-robot linear motion, curvilinear motion and mixed motion, and the positioning accuracy is high.

Multimodal Sensors Enabled Autonomous Soft Robotic System with Self-Adaptive Manipulation.

Human hands are amazingly skilled at recognizing and handling objects of different sizes and shapes. To date, soft robots rarely demonstrate autonomy equivalent to that of humans for fine perception and dexterous operation. Here, an intelligent soft robotic system with autonomous operation and multimodal perception ability is developed by integrating capacitive sensors with triboelectric sensor. With distributed multiple sensors, our robot system can not only sense and memorize multimodal information but also enable an adaptive grasping method for robotic positioning and grasp control, during which the multimodal sensory information can be captured sensitively and fused at feature level for crossmodally recognizing objects, leading to a highly enhanced recognition capability. The proposed system, combining the performance and physical intelligence of biological systems (i.e., self-adaptive behavior and multimodal perception), will greatly advance the integration of soft actuators and robotics in many fields.

Research on the Anchor-Rod Recognition and Positioning Method of a Coal-Mine Roadway Based on Image Enhancement and Multiattention Mechanism Fusion-Improved YOLOv7 Model

A drill-anchor robot is an essential means of efficient drilling and anchoring in coal-mine roadways. It is significant to calculate the position of the drill-anchor robot based on the positioning information of the supported anchor rod to improve tunneling efficiency. Therefore, identifying and positioning the supported anchor rod has become a critical problem that needs to be solved urgently. Aiming at the problem that the target in the image is blurred and cannot be accurately identified due to the low and uneven illumination environment, we proposed an improved YOLOv7 (the seventh version of the You Only Look Once) model based on the fusion of image enhancement and multiattention mechanism, and the self-made dataset is used for testing and training. Aiming at the problem that the traditional positioning method cannot guarantee accuracy and efficiency simultaneously, an anchor-rod positioning method using depth image and RGB image alignment combined with least squares linear fitting is proposed, and the positioning accuracy is improved by processing the depth map. The results show that the improved model improves the mAP by 5.7% compared with YOLOv7 and can accurately identify the target. Through the positioning method proposed in this paper, the error between the positioning coordinate and the measurement coordinate of the target point on each axis does not exceed 11 mm, which has high positioning accuracy and improves the positioning accuracy and robustness of the anchor rod in the coal-mine roadway.

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

In the logistics warehouse environment, the autonomous navigation and environment perception of the logistics sorting robot are two key challenges. To deal with the complex obstacles and cargo layout in a warehouse, this study focuses on improving the robot perception and navigation system to achieve efficient path planning and safe motion control. For this purpose, a scheme based on an improved Gmapping algorithm is proposed to construct a high-precision map inside a warehouse through the efficient scanning and processing of environmental data by robots. While the improved algorithm effectively integrates sensor data with robot position information to realize the real-time modeling and analysis of warehouse environments. Consequently, the precise mapping results provide a reliable navigation basis for the robot, enabling it to make intelligent path planning and obstacle avoidance decisions in unknown or dynamic environments. The experimental results show that the robot using the improved Gmapping algorithm has high accuracy and robustness in identifying obstacles and an effectively reduced navigation error, thus improving the intelligence level and efficiency of logistics operations. The improved algorithm significantly enhances obstacle detection rates, increasing them by 4.05%. Simultaneously, it successfully reduces map size accuracy errors by 1.4% and angle accuracy errors by 0.5%. Additionally, the accuracy of the robot’s travel distance improves by 2.4%, and the mapping time is reduced by nine seconds. Significant progress has been made in achieving high-precision environmental perception and intelligent navigation, providing reliable technical support and solutions for autonomous operations in logistics warehouses.