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The focus in this book is on mobile robots that move on a surface. When the robot moves for a period of time its new position can be determined by odometry: integrating the velocity of the robot over the period of its motion to obtain distance or integrating the acceleration to get velocity and integrating again to obtain distance. If the robot cha...
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Abstract. In the assembly process of large-size aerospace products, the leveling and horizontal alignment of the large components are essential works before the installation of Inertial Navigation System (INS) and the final quality inspection. In general, the inherent coordinate systems of the large-scale coordinate measuring
machines are not coinc...
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... The choice must be made upon careful consideration of the intended implementation and the specialized missions that will be assigned to the robot. The literature is abundant on Figure 2. Examples of wheeled UGVs with specialized functionalities: (a) four-wheeled UGV drives through seedlings, picking weeds between rows [13]; (b) six-wheeled UCV traverses Mars' surface. ...
... When one is constructing a customized wheeled UGV, the design will be focused upon a number of determinative characteristics that include, but are not limited to: the number of wheels, the power source of torque (electric batteries, combustion engines, environmental energy harvesting techniques, etc.), the wheel position on the robot's frame, the orientation of the wheels during normal operation, removability, retractability, the The movement of a wheeled robot, and thus its capabilities in each working space, is determined by the number and configuration of the wheels and the wheel axles. For example, a two-wheeled robot with differential drive (see Figure 3a) has three degrees of freedom (DOF), i.e., the position (x, y) and the heading (φ) and two actuators, i.e., the motors on the two wheels [13]. These motors enable the robot to move forwards and backward and rotate in place, but the robot cannot move laterally without first rotating. ...
... The motor enables the robot to move forwards and backward, while the steering wheel can only orient the former movement. As a result, the four-wheeled robot can neither rotate in place nor move laterally [13]. ...
Subterranean openings, including mines, present a unique and challenging environment for robots and autonomous exploration systems. Autonomous robots that are created today will be deployed in harsh and unexplored landscapes that humanity is increasingly encountering in its scientific and technological endeavors. Terrestrial and extraterrestrial environments pose significant challenges for both humans and robots: they are inhospitable and inaccessible to humans due to a lack of space or oxygen, poor or no illumination, unpredictable terrain, a GPS-denied environment, and a lack of satellite imagery or mapping information of any type. Underground mines provide a good physical simulation for these types of environments, and thus, can be useful for testing and developing highly sought-after autonomous navigation frameworks for autonomous agents. This review presents a collective study of robotic systems, both of individual and hybrid types, intended for deployment in such environments. The prevalent configurations, practices for their construction and the hardware equipment of existing multi-agent hybrid robotic systems will be discussed. It aims to provide a supplementary tool for defining the state of the art of coupled Unmanned Ground Vehicle (UGV)–Unmanned Aerial Vehicle (UAV) systems implemented for underground exploration and navigation purposes, as well as to provide some suggestions for multi-agent robotic system solutions, and ultimately, to support the development of a semi-autonomous hybrid UGV–UAV system to assist with mine emergency responses.
... Degrees of freedom (DOF) are the modes in which a mechanical device or system can move. The number of degrees of freedom is equal to the total number of independent displacements of aspects of motion that a mobile robot or the end effector of a robotic manipulator can make [20]. A robot has three major axes. ...
This research is based on the design of a robotic arm to remove pallets from the palletizer station to a queuing conveyor. It is a case study for ABC Beverages, a manufacturer, bottler and distributer of carbonated soft drinks (CSD), and bottled water. The root cause analysis techniques of the fishbone diagram and 5 whys analysis showed that pallet accumulation at the palletizer was the major cause of line stoppages, accounting for 36% of the overall causes of line stoppages. Time studies showed that an average of 30 minutes was lost at every line stop and start interval. The study focuses on eliminating the main problem, which is pallet accumulation. The robot has a payload of 300 kg. The results from the study show that pallet accumulation was eliminated and 120 minutes of production time was saved.
... A new measurement of the tunnel length, L(k)′, is computed when a robot successfully reaches the digging site and excavates pellets. At this time, the tunnel length is derived from the x-component of the robot's location as computed by the robot odometry [32]. The robots use the kinematic model of a differential drive mobile robot based on wheel encoder counts to estimate their absolute displacements in the tunnel. ...
Social organisms which construct nests consisting of tunnels and chambers necessarily navigate confined and crowded conditions. Unlike low density collectives like bird flocks and insect swarms in which hydrodynamic and statistical phenomena dominate, the physics of glasses and supercooled fluids is important to understand clogging behaviors in high density collectives. Our previous work revealed that fire ants flowing in confined tunnels utilize diverse behaviors like unequal workload distributions, spontaneous direction reversals and limited interaction times to mitigate clogging and jamming and thus maintain functional flow; implementation of similar rules in a small robophysical swarm led to high performance through spontaneous dissolution of clogs and clusters. However, how the insects learn such behaviors and how we can develop “task capable” active matter in such regimes remains a challenge in part because interaction dynamics are dominated by local, potentially time-consuming collisions and no single agent can survey and guide the entire collective. Here, hypothesizing that effective flow and clog mitigation could be generated purely by collisional learning dynamics, we challenged small groups of robots to transport pellets through a narrow tunnel, and allowed them to modify their excavation probabilities over time. Robots began excavation with equal probabilities to excavate and without probability modification, clogs and clusters were common. Allowing the robots to perform a “reversal” and exit the tunnel when they encountered another robot which prevented forward progress improved performance. When robots were allowed to change their reversal probabilities via both a collision and a self-measured (and noisy) estimate of tunnel length, unequal workload distributions comparable to our previous work emerged and excavation performance improved. Our robophysical study of an excavating swarm shows that despite the seeming complexity and difficulty of the task, simple learning rules can mitigate or leverage unavoidable features in task capable dense active matter, leading to hypotheses for dense biological and robotic swarms.
... Extended author information available on the last page of the article. on odometry or inertial measurement units (IMUs) have cumulative errors [1], not guaranteeing consistent results when used without other sources of information. On the other hand, relying exclusively on global positioning system (GPS) receivers may not be entirely possible for indoor environments and result in poor performance for outdoor locations where buildings or natural topology can degrade the GPS signal. ...
This paper addresses the problem of simultaneous localization, mapping, and moving object tracking (SLAMMOT) with application to unmanned aerial vehicles inside uncertain environments with both static and moving objects. Although this problem has been addressed by the community, the usual approach is to identify the moving objects and remove them from contributing to the localization and mapping algorithm. Conversely, the proposed strategy integrates the moving objects in the vehicle motions estimation, without compromising its accuracy. The proposed solution is based on the design of: i) an extended Kalman filter (EKF); ii) a modified version of the multiple hypothesis tracking method to perform data association and track moving objects; and iii) and the interacting multiple model algorithm to identify the motion models described by the environment’s objects. The performance of the devised SLAMMOT filter is studied in simulation and validated with a new public experimental dataset using an RGB-D camera on-board an instrumented quadrotor with ground-truth.
... Moreover, as the robot moves, the onboard sensors provide a local pose estimation, y ⋆ (t+1). Typically, these measurements come from odometry calculations and concern distance travelled and changes in orientation [26,Chapter 5]. Hence, in the context of this work, we consider that the local estimation is of the following form: ...
The evolution of the Industrial Internet of Things (IIoT) and Edge Computing enables resource-constrained mobile robots to offload the computationally intensive localization algorithms. Naturally, utilizing the remote resources of an edge server to offload these tasks, encounters the challenge of a joint co-design in communication, control, estimation and computing infrastructure. We introdce a set-based estimation offloading framework, for the specific case of the navigation of a unicycle robot towards a target position. The robot is subject to modeling and measurement uncertainties, and the estimation set is calculated using overapproximation techniques that alleviate additional computations. A switching set-based control mechanism provides accurate navigation and triggers more precise estimation algorithms when needed. To guarantee the convergence of the system and optimize the utilization of remote resources, a utility-based offloading mechanism is designed, which takes into account both the dynamic network conditions and the available computing resources at the network edge. The performance of the proposed framework is demonstrated through simulations and comparison with alternative offloading schemes.
... To deal with the issue, controllers are designed and tuned to achieve the results close to the desired ones. This paper addresses the issue by stating the method of designing and tuning the PI controller for 3 wheeled differential drive custom made robot for which robot's wheel speed is used as a feedback [11]- [13]. Feedback is something that's automatic and makes the motion competent. ...
Mobile robots due to their ability of movement have proven to find their applications in different fields over the
years. Their applications in different fields are due to easy design and manufacturing of the custom platform. Manufacturing of the custom chassis/platforms may result in synchronization issue of motors resulting in detouring of the robot chassis from the path or complexity of the algorithm. To address this issue, design and tuning of a PI controller for a custom built three wheeled mobile robot for synchronization of the motors is discussed in this paper. Results achieved are very close to the desired response of the motors to move the mobile robot in a straight path. Paper highlights the method of designing and tuning a PI controller for wheeled mobile robots.
... Kecepatan putaran roda atau jumlah perputaran roda dapat diukur menggunakan rotary encoder. Jika rotary encoder menggunakan tipe incremental (menghitung jumlah perputaran roda dalam satuan pulsa / ticks), perpindahan roda dalam satuan meter menggunakan Persamaan (3) dengan i merupakan roda kiri (l) atau roda kanan (r), R adalah jari-jari roda, ∆ adalah perbedaan pulsa pada waktu t dan t -1, dan resolusi_sensor adalah jumlah pulsa per revolusi roda [7]. ...
... Setelah masing-masing perpindahan roda dihitung, perpindahan sudut orientasi robot dapat dihitung menggunakan Persamaan (4) dengan b adalah jarak antar roda. Perpindahan robot dalam koordinat x dapat dihitung menggunakan Persamaan (5) dan perpindahan robot dalam koordinat y dihitung menggunakan Persamaan (6), dengan variabel dc pada Persamaan (5) dan Persamaan (6) dihitung dengan Persamaan (7) serta variabel merupakan sudut orientasi awal robot [7]. ...
Wheeled robots are widely used in many industrial fields. The wheeled robot needs to have implemented an autonomous navigation system to improve work efficiency. In this research, a map-based indoor navigation system is implemented on wheeled robot with Robotics Operating System (ROS) platform using Hector Mapping algorithm. The algorithm Multisensor Data Fusion using Extended Kalman Filter (EKF) which fuses Wheel Odometry data with IMU sensor data for localization, Field Dynamic A-Star algorithm for path planning, and ON-OFF controller for trajectory tracking. Field Dynamic A-Star algorithm is chosen because it solves general path planning algorithm’s main issue that limits robot’s orientation movement for every 45o (suboptimal and subnatural path). The robot has ODROID-XU4 as controller to perform map-based indoor navigation, Arduino Mega 2560 to drive motors, RPLIDAR A2 LASER rangefinder for mapping, and VEX Integrated Encoder with Sparkfun Razor 9DoF IMU for localization. The navigation system is successfully implemented on wheeled robot with ROS platform. Robot has successfully mapped indoor environment with 0.174 meter error rate, and has successfully done localization with average error rate of 0.05m on x coordinate, 0.028m on y coordinate, and 1.506o on orientation angle. Path planner is proved capable of generating path that is not limited every 45o orientation. Path planner yields 62.5% success rate in generating traversable path and the robot yields 75% success rate in following the path. Robot yields average error rate of 0.046m in moving towards target’s x coordinate, 0.072m in moving towards target’s y coordinate, and 5.163o in turning towards target’s orientation angle. Robot beroda banyak digunakan di banyak bidang industri. Pada robot beroda tersebut perlu diimplementasikan sistem navigasi autonomous untuk meningkatkan efisiensi kerja. Pada penelitian ini, sistem navigasi indoor berbasis peta diimplementasikan pada robot beroda dengan platform Robot Operating System (ROS) menggunakan algoritma Hector Mapping untuk pemetaan. Algoritma ini menggunakan Data Fusion dengan algoritma Extended Kalman Filter (EKF) yang menggabungkan data Wheel Odometry dengan data sensor IMU untuk lokalisasi, algoritma Field Dynamic A-Star untuk path planning, dan pengontrol ON-OFF untuk trajectory tracking. Algoritma Field Dynamic A-Star dipilih karena algoritma tersebut dapat mengatasi permasalahan algoritma path planning pada umumnya yang membatasi arah orientasi pergerakan robot setiap kelipatan 45o (jalur tidak optimal dan tidak natural). Robot memiliki ODROID-XU4 sebagai pengontrol utama yang bertugas untuk melakukan navigasi indoor berbasis peta, Arduino Mega 2560 untuk menggerakkan motor, LASER rangefinder RPLIDAR A2 untuk pemetaan, dan VEX Integrated Encoder serta Sparkfun Razor 9DoF IMU untuk lokalisasi. Sistem navigasi berhasil diimplementasikan pada robot beroda dengan platform ROS. Robot berhasil melakukan pemetaan lingkungan indoor dengan tingkat kesalahan rata-rata 0,174 meter serta berhasil melakukan lokalisasi dengan tingkat kesalahan rata-rata 0,05m pada koordinat x, 0,028m pada sumbu y, dan 1,506o pada sudut orientasi. Path planner terbukti menghasilkan jalur yang tidak terbatas pada kelipatan orientasi 45o. Path planner memiliki tingkat keberhasilan 62,5% dalam menghasilkan jalur yang dapat dilewati robot dan robot memiliki tingkat keberhasilan 75% dalam mengikuti jalur. Robot memiliki tingkat kesalahan rata-rata 0,046m dalam bergerak menuju koordinat x target, 0,072m dalam bergerak menuju koordinat y target, dan 5,163o dalam berputar menuju sudut orientasi target.
... Odometry is the estimation of the motion of an object. Some proposal founded in the literature use wheel encoders to measure the displacement of the robot [1], other approaches employs SLAM with sensors such as lidar to performs relocalisation. In the last decades, the problem to camera pose has been studied with the use of visual information obtained from cameras. ...
... Thus, as described in previous sections, the Kalman filter is employed. Before the blind spot, it is used the Kalman filter in the case (1). When the drone enters the blind spot, the state machine goes to the next state, and the Kalman filter is used with case (2). ...
In recent years Autonomous Drone Racing has become a significant challenge due to the problems involved in developing an algorithm for autonomous navigation. One of the major problems is the estimation of the camera pose; several approaches can be founded to achieve the estimation of the camera pose. In particular, it is possible to estimates the position based on specific object detection. However, object detection at the same time of navigation entails the problem of a blind spot area when the camera is closest to the object. We propose a methodology that overcomes the blind spot in autonomous navigation based on CNN gate detection to perform pose estimation with a stochastic algorithm for distance estimation. We achieve over 95 % in gate detection and a mean error of around 35 cm in 1D pose estimation into the blind spot zone.
... Odometry is the estimation of the motion of an object. Some proposal founded in the literature use wheel encoders to measure the displacement of the robot [1], other approaches employs SLAM with sensors such as lidar to performs relocalisation. In the last decades, the problem to camera pose has been studied with the use of visual information obtained from cameras. ...
... Thus, as described in previous sections, the Kalman filter is employed. Before the blind spot, it is used the Kalman filter in the case (1). When the drone enters the blind spot, the state machine goes to the next state, and the Kalman filter is used with case (2). ...
In recent years Autonomous Drone Racing has become a significant challenge due to the problems involved in developing an algorithm for autonomous navigation. One of the major problems is the estimation of the camera pose; several approaches can be founded to achieve the estimation of the camera pose. In particular, it is possible to estimates the position based on specific object detection. However, object detection at the same time of navigation entails the problem of a blind spot area when the camera is closest to the object. We propose a methodology that overcomes the blind spot in autonomous navigation based on CNN gate detection to perform pose estimation with a stochastic algorithm for distance estimation. We achieve over 95 % in gate detection and a mean error of around 35 cm in 1D pose estimation into the blind spot zone.
... In this study, odometry is picked up with the information obtained from the wheel encoders of the robot to set the position. This method is one of the basic methods used for robot navigation [13]. According to this method, it is possible to get information about how far distance has been taken from a certain number of wheel turns. ...
