Orientation Of Robot Research Articles

Position and reduced attitude trajectory tracking control of quadrotors: Theory and experiments

Multirotor aerial vehicles are versatile flying robots that perform hovering, vertical take-off and landing, and aggressive maneuvers in a 3D environment. Due to their underactuated nature, the aerial vehicles' position and orientation cannot be controlled independently. For this reason, most of the quadrotors' tasks involved position tracking or regulation tasks. This paper focuses on the position-tracking problem of quadrotors using the reduced orientation of the vehicle, meaning that only two degrees of freedom of the robot's orientation are controlled. We propose an almost global exponential reduced attitude control law that aligns the aerial robot's thrust direction with the desired force that drives the robot along the desired position trajectory. For the translational subsystem, we propose a dynamic control law that drives the position and velocity of the quadrotors asymptotically to the desired trajectories. The proposed attitude control law is computationally simple, and thus, it is suitable to run on board. Finally, we provide experimental results performed on a low-cost quadrotor and a comparison study with a full-attitude controller to illustrate the performance and advantages of the proposed control laws.

Impact of Antenna Orientation on Localization Accuracy Using RSSI-based Trilateration

The goal of the indoor localization is to determine the position and orientation of people, devices, and mobile robots. With the rise of Industry 4.0, wireless communication technologies have emerged as a rapidly evolving and crucial area for achieving this goal. Various radiocommunication-based technologies, including Bluetooth, Bluetooth Low Energy (BLE), Wi-Fi, Ultra-Wideband (UWB), and ZigBee offer means to indirectly estimate distance. These methods leverage diverse principles such as time-based measurements, signal strength, and angle of arrival. Indoor positioning systems can be categorized into two approaches: distance-based and distance-independent techniques. The Free Space Path Loss (FSPL) model describes the connection between distance and Received Signal Strength Indicator (RSSI). The parameters within this model significantly impact distance estimation and localization accuracy. Therefore, a method that accurately characterizes the model is critical. This work proposes an orientation-based localization technique utilizing RSSI and trilateration. Measurements were conducted between two ESP32 units in various orientations to obtain optimal parameters for each specific scenario. To assess the effectiveness of this approach, two scenarios were evaluated: one considering orientation and another neglecting it. The results show that incorporating orientation information leads to significantly more accurate positioning compared to the orientation-agnostic approach.

Lidar pose estimation method based on factor graph optimization

Abstract In the realm of computer vision and robotics, lidar SLAM is an important technical means. Lidar SLAM frequently serves to ascertain the spatial coordinates and orientation of mobile robots amidst their surroundings. In this paper, an adaptive factor graph optimization of the lidar pose estimation method using only point-to-line constraints is proposed. The algorithm only employs the measure of distance between a point and a line to construct the constraint problem and solves the pose through point-to-line ICP and the pose with higher accuracy by optimizing the factor graph. The robustness of mobile robots in indoor scenes is improved. According to different application scenarios, including street and gate, comparative experiments are carried out on the open-source data set M2DGR containing lidar data. Empirical findings demonstrate that the algorithm put forth enhances the precision of the lidar odometer when confronted with outdoor scenarios, thereby bolstering the resilience of mobile robots traversing urban thoroughfares.

Locomotion analysis of a crawling wave robot in circular canal

In this paper, we analyze the locomotion of a wave robot crawling in a curved canal between rigid walls. We determine the conditions for crawling, considering the robot geometry, orientation, wall curvature, canal width, and friction coefficients. Initially, we present an analytical model of the different cases of crawling and conditions for advancement. We found four cases of crawling: one where the robot touches only the outer wall, two with one contact point each on the outer and inner walls, and a third where the robot has two contact points with the outer wall and one with the inner wall. Subsequently, we introduce a multibody numerical simulation, which accounts for sliding along the wall and over the ground surface. The simulation allows one to validate our analytical results and analyze the robot's behavior under different conditions. Finally, we present validating results of multiple experiments conducted with our SAW robot. This analysis encompasses all types of crawling robots with internal thrust mechanisms that are not based on contact with the side walls of the pipe or canal.

Offline robot programming assisted by task demonstration: an AutomationML interoperable solution for glass adhesive application and welding

ABSTRACT Robots have been successfully deployed in both traditional and novel manufacturing processes. However, they are still difficult to program by non-experts, which limits their accessibility to a wider range of potential users. Programming robots requires expertise in both robotics and the specific manufacturing process in which they are applied. Robot programs created offline often lack parameters that represent relevant manufacturing skills when executing a specific task. These skills encompass aspects like robot orientation and velocity. This paper introduces an intuitive robot programming system designed to capture manufacturing skills from task demonstrations performed by skilled workers. Demonstration data, including orientations and velocities of the working paths, are acquired using a magnetic tracking system fixed to the tools used by the worker. Positional data are extracted from CAD/CAM. Robot path poses are transformed into Cartesian space and validated in simulation, subsequently leading to the generation of robot programs. PathML, an AutomationML-based syntax, integrates robot and manufacturing data across the heterogeneous elements and stages of the manufacturing systems considered. Experiments conducted on the glass adhesive application and welding processes showcased the intuitive nature of the system, with path errors falling within the functional tolerance range.

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.

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.

A Neuromorphic Vision and Feedback Sensor Fusion Based on Spiking Neural Networks for Real‐Time Robot Adaption

For some years now, the locomotion mechanisms used by vertebrate animals have been a major inspiration for the improvement of robotic systems. These mechanisms range from adapting their movements to move through the environment to the ability to chase prey, all thanks to senses such as sight, hearing, and touch. Neuromorphic engineering is inspired by brain problem‐solving techniques with the goal of implementing models that take advantage of the characteristics of biological neural systems. While this is a well‐defined and explored area in this field, there is no previous work that fuses analog and neuromorphic sensors to control and modify robotic behavior in real time. Herein, a system is presented based on spiking neural networks implemented on the SpiNNaker hardware platform that receives information from both analog (force‐sensing resistor) and digital (neuromorphic retina) sensors and is able to adapt the speed and orientation of a hexapod robot depending on the stability of the terrain where it is located and the position of the target. These sensors are used to modify the behavior of different spiking central pattern generators, which in turn will adapt the speed and orientation of the robotic platform, all in real time. In particular, experiments show that the network is capable of correctly adapting to the stimuli received from the sensors, modifying the speed and heading of the robotic platform.

Enhance the Balance of Quadruped Robot using CMPS12

Quadruped is a robot that can move stably and flexibly on various types of surfaces. However, when quadruped encounters an uneven surface, it needs a good navigation system to get through it. The analysis of increasing the performance of the quadruped robot is based on the addition of the CMPS12 sensor for navigation. The CMPS12 sensor is used to measure the direction of the robot's orientation. Tests were carried out on four types of obstacles, namely broken road obstacles, sloping road obstacles, rocky road obstacles, and muddy road obstacles. The results of testing the robot on broken road obstacles obtained a maximum slope for the pitch axis of 13◦ forward, −21◦ to the rear and for the roll axis at a slope of 24◦ to the left and −19◦ to the right. On inclined road obstacles, the robot can pass through obstacles with an average travel time of 8.07 seconds with a maximum slope of 25◦ on the pitch axis. Then, on the rocky road obstacle, the robot can pass the obstacle with an average travel time of 8.13 seconds, with a maximum slope of 9◦ on the pitch axis and 8◦ on the roll axis. Then, on a muddy road obstacle, the robot can pass the obstacle with an average travel time of 11.67 seconds, with a maximum slope of 15◦ on the pitch axis and −6◦ on the roll axis.

Neural Dynamic Fault-Tolerant Scheme for Collaborative Motion Planning of Dual-Redundant Robot Manipulators.

To avoid the task failure caused by joint breakdown during the collaborative motion planning of dual-redundant robot manipulators, a neural dynamic fault-tolerant (NDFT) scheme is proposed and applied. To do so, a joint fault-tolerant strategy is first designed, and it is formulated as a time-varying equality constraint. Second, combining the robot position and orientation control, joint limit constraint, joint fault-tolerant equality constraint, and considering the repetitive motion optimization criterion, a fault-tolerant framework for the dual-redundant robot manipulators based on quadratic programming (QP) is constructed. Then, a varying-parameter recurrent neural network (VP-RNN) is designed to solve the QP issue. The fault-tolerant framework and the VP-RNN constitute NDFT scheme. With the NDFT scheme, the impact of faulty joints on the whole system can be remedied by healthy joints, thereby the end-effectors of the robot can complete the given end-effector task. Finally, computer simulations and physical experiments are implemented to verify the availability, physical realizability, and accuracy of the proposed NDFT scheme in the collaborative execution of end-effector tasks. Comparative experimental results with conventional repetitive motion planning schemes based on neural networks show higher accuracy and smaller joint angle drift.

Simplified System Integration of Robust Mobile Robot for Initial Pose Estimation for the Nakanoshima Robot Challenge

This paper describes a study of the simple system integration of a mobile robot in the Nakanoshima Robot Challenge 2022. To improve the operability of the robot at the start of its journey, we studied the solution to the problem of initial localization during the experimental run and the setting of virtual obstacles on the map to be used by the mobile robot. This method reduces the amount of time and manual operation required to estimate the initial position and orientation of a mobile robot in mobile robot experiments. In this study, a mobile robot is implemented using open-source products such as robot operating system (ROS) and i-Cart mini. Experimental runs in the Extra Challenge of the Nakanoshima Robot Challenge 2022 demonstrate the feasibility of the method.

The Impact of LiDAR Configuration on Goal-Based Navigation within a Deep Reinforcement Learning Framework.

Over the years, deep reinforcement learning (DRL) has shown great potential in mapless autonomous robot navigation and path planning. These DRL methods rely on robots equipped with different light detection and range (LiDAR) sensors with a wide field of view (FOV) configuration to perceive their environment. These types of LiDAR sensors are expensive and are not suitable for small-scale applications. In this paper, we address the performance effect of the LiDAR sensor configuration in DRL models. Our focus is on avoiding static obstacles ahead. We propose a novel approach that determines an initial FOV by calculating an angle of view using the sensor's width and the minimum safe distance required between the robot and the obstacle. The beams returned within the FOV, the robot's velocities, the robot's orientation to the goal point, and the distance to the goal point are used as the input state to generate new velocity values as the output action of the DRL. The cost function of collision avoidance and path planning is defined as the reward of the DRL model. To verify the performance of the proposed method, we adjusted the proposed FOV by ±10° giving a narrower and wider FOV. These new FOVs are trained to obtain collision avoidance and path planning DRL models to validate the proposed method. Our experimental setup shows that the LiDAR configuration with the computed angle of view as its FOV performs best with a success rate of 98% and a lower time complexity of 0.25 m/s. Additionally, using a Husky Robot, we demonstrate the model's good performance and applicability in the real world.

Three-degrees-of-freedom orientation manipulation of small untethered robots with a single anisotropic soft magnet

Magnetic actuation has been well exploited for untethered manipulation and locomotion of small-scale robots in complex environments such as intracorporeal lumens. Most existing magnetic actuation systems employ a permanent magnet onboard the robot. However, only 2-DoF orientation of the permanent-magnet robot can be controlled since no torque can be generated about its axis of magnetic moment, which limits the dexterity of manipulation. Here, we propose a new magnetic actuation method using a single soft magnet with an anisotropic geometry (e.g., triaxial ellipsoids) for full 3-DoF orientation manipulation. The fundamental actuation principle of anisotropic magnetization and 3-DoF torque generation are analytically modeled and experimentally validated. The hierarchical orientation stability about three principal axes is investigated, based on which we propose and validate a multi-step open-loop control strategy to alternatingly manipulate the direction of the longest axis of the soft magnet and the rotation about it for dexterous 3-DoF orientation manipulation.

Heading control for quadruped stair climbing based on PD controller for the KRSRI competition

Quadruped, a robot that resembles four-legged animals, is developed for many purposes, such as surveillance and rescue. Such a caveat requires the robot to have the capability to overcome various terrain and obstacles. When moving across such a landscape, it is essential to maintain the robot's orientation steadily. Inclined terrains such as stairs have posed another challenge to the control strategy as the robot is unstable while climbing. Therefore, the contribution of this work is to address the need for heading control because of the relatively longer stairs used for the current competition compared to the past. The proposed control system simultaneously maintains the heading while keeping the body stable. The inertial measurement unit sensor carried by the robot would provide the pose needed for heading control calculations. The robot's heading becomes the base for the PD controller calculation. The final pose that stabilizes the robot while tackling heading error is a combination of correction from the PD controller and the stabilization part of the control strategy. Then, the leg servo angle determination deployed the inverse kinematics calculation from the suitable robot pose. The proposed method enabled the designed robot to maintain its heading with a 4.4-degree margin of error and stabilize the body. The quadruped also completes the stair climbing at the shortest time of 20 seconds with a speed of up to 5.5 centimeters per second.

Fiber optic sensor embedded in robotic systems for 3-D orientation assessment using polymer fiber

The present work is considered to installed sensor system to the robotics system. The structure of 3-D rotational sensor is based on polymer fiber, where two fibers are twisted to couple the light. Twisted fibers are kept in a bending shape after twisting and installed on the robot where three scenarios are carried out. The bending radius variation leads to the bending loss in the first fiber, and the loss is being coupled in the second fiber. Robot's orientation causes the change in the macro-bend radius of the sensor. Our proposed sensor analyzes the robot's orientation where the robot is moved in the full rotational cycle from 0° to 360° in both clock and anti-clockwise rotation directions. Furthermore, the robot is also rotated at different speeds to analyze the dynamic response and also analyzed for simultaneous response of roll, pitch, and yaw's orientation. The results indicate that the proposed sensor of present work is a timely response with the robot moments. The simple structure and flexible 3-D rotational sensor structure pave to easily integrate into the robotics system.

Noncontacting Position and Orientation Estimation of a Centimeter-Scale Robot Using an Active Electromagnet

This article focuses on estimating the position and orientation of a three degree-of-freedom (DoF) robot using a novel electromagnet-based active control system. The electromagnet is mounted on a servo motor and its orientation is controlled in real time so that it always points at the robot. A two-axis magnetic sensor on the robot helps determine both its radial distance from the electromagnet and its orientation. The active pointing control of the electromagnet highly simplifies the position estimation problem. Instead of requiring a magnetic field model that is a function of all three DoF, the radial distance and robot orientation estimation tasks become decoupled and independent problems. The radial distance is estimated using an asymptotically stable nonlinear observer designed using a linear matrix inequality. The analytical principles of the estimation system are first presented using key technical lemmas and proofs. Extensive experimental results are then presented on the performance of the estimation system, including real-time estimation of the moving robot's position and orientation.

Calibration Considering the Direction of Rotation for Contact Type Three-Dimensional Position-Measuring Instruments

In Japan, the aging of sewage facilities due to long-term use has become a significant social issue. As a result, there has been a growing interest in portable and easy-to-operate standalone pipe inspection robots in recent years. However, the pipes being inspected not only have level differences due to joint connections but also pose a high risk of toppling over due to deterioration, collapses, and sludge caused by external disturbances. Many standalone robots address this by suppressing tipping through tire shape and axle adjustments. However, this method is for suppression and not complete prevention. Therefore, we are exploring a software-based approach to prevent tipping through control of robot movement and aiming to achieve advanced self-position estimation. Currently, we are in the stage of verifying this self-position estimation. However, due to the curved nature of the pipes, it is difficult to accurately measure the robot's position and orientation using conventional measurement instruments. Thus, we have developed a specialized three-dimensional position measurement instrument for pipe inspection robots. Furthermore, although we performed calibration in the translational direction of the instrument, while improvements in accuracy were observed in the translational direction, the accuracy in the rotational direction deteriorated. In this paper, we propose a method to simultaneously calibrate the translational and rotational accuracy of the instrument to address this issue. We discuss the mechanism model, calibration parameters, elimination of redundant parameters by introducing redundancy, and parameter estimation using the Newton method. Additionally, through comparative validation using the instrument, we confirm that the position accuracy after calibration is within the range of approximately ±1.0 mm, and the orientation accuracy is within the range of approximately ±0.3 degrees. This demonstrates the effectiveness of the proposed method.

Artificial Neural Networks for inverse kinematics problem in articulated robots

The inverse kinematics problem in articulated robots implies to obtain joint rotation angles using the robot end effector position and orientation tool. Unlike the problem of direct kinematics, in inverse kinematics there are no systematic methods for solving the problem. Moreover, solving the inverse kinematics problem is particularly complicated for certain morphologies of articulated robots. Machine learning techniques and, more specifically, artificial neural networks (ANNs) have been proposed in the scientific literature to solve this problem. However, there are some limitations in the performance of ANNs. In this study, different techniques that involve ANNs are proposed and analyzed. The results show that the proposed original bootstrap sampling and hybrid methods can substantially improve the performance of approaches that use only one ANN. Although all of these improvements do not solve completely the inverse kinematics problem in articulated robots, they do lay the foundations for the design and development of future more effective and efficient controllers. Therefore, the source code and documentation of this research are also publicly available to practitioners interested in adapting and improving these methods to any industrial robot or articulated robot.

Smooth Autonomous Patrolling for a Differential-Drive Mobile Robot in Dynamic Environments.

Today, mobile robots have a wide range of real-world applications where they can replace or assist humans in many tasks, such as search and rescue, surveillance, patrolling, inspection, environmental monitoring, etc. These tasks usually require a robot to navigate through a dynamic environment with smooth, efficient, and safe motion. In this paper, we propose an online smooth-motion-planning method that generates a smooth, collision-free patrolling trajectory based on clothoid curves. Moreover, the proposed method combines global and local planning methods, which are suitable for changing large environments and enabling efficient path replanning with an arbitrary robot orientation. We propose a method for planning a smoothed path based on the golden ratio wherein a robot's orientation is aligned with a new path that avoids unknown obstacles. The simulation results show that the proposed algorithm reduces the patrolling execution time, path length, and deviation of the tracked trajectory from the patrolling route compared to the original patrolling method without smoothing. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity, and its performance was validated through the results of an experiment employing a differential-drive mobile robot.

METHOD OF SIMULTANEOUS LOCALIZATION AND MAPPING FOR CONSTRUCTION OF 2.5D MAPS OF THE ENVIRONMENT USING ROS

SLAM (Simultaneous Localization and Mapping) is a relevant topic of research and development in the field of robotics and computer vision. SLAM finds wide applications in various areas such as autonomous navigation of intelligent robots, solving problems in augmented and virtual reality, UAVs, and other systems. In recent years, SLAM has made significant progress due to the gradual development of its algorithms, the use of advanced sensors, and improvements in computational power of computers. The subject of this study is modern methods of real-time simultaneous localization and mapping. The goal of the research is to model the developed algorithm for constructing maps of the surrounding environment and determining the position and orientation of the intelligent robot in space in real-time using ROS packages. The purpose of this article is to demonstrate the results of combining SLAM methods and developing new approaches to solve simultaneous localization and mapping problems. In order to achieve the set objectives, a collaboration of laser scanning (2D LRF) and depth image reconstruction (RGB-D) methods was utilized for simultaneous localization and mapping of the intelligent robot and construction of a 2.5D environment map. The obtained results are promising and demonstrate the potential of the integrated SLAM methods, which collaborate to ensure accurate execution of simultaneous localization and mapping for intelligent robots in real-time mode. The proposed method allows for considering obstacle heights in constructing the map of the surrounding environment while requiring less computational power. In conclusion, this approach expands technologies without replacing existing working propositions and enables the use of modern methods for comprehensive detection and recognition of the surrounding environment through an efficient localization and mapping approach, providing more accurate results with fewer resources utilized.