Reliable Tracking Research Articles

Parallel structure of six wheel-legged robot trajectory tracking control with heavy payload under uncertain physical interaction

PurposeThis paper aims on the trajectory tracking of the developed six wheel-legged robot with heavy load conditions under uncertain physical interaction. The accuracy of trajectory tracking and stable operation with heavy load are the main challenges of parallel mechanism for wheel-legged robots, especially in complex road conditions. To guarantee the tracking performance in an uncertain environment, the disturbances, including the internal friction, external environment interaction, should be considered in the practical robot system.Design/methodology/approachIn this paper, a fuzzy approximation-based model predictive tracking scheme (FMPC) for reliable tracking control is developed to the six wheel-legged robot, in which the fuzzy logic approximation is applied to estimate the uncertain physical interaction and external dynamics of the robot system. Meanwhile, the advanced parallel mechanism of the electric six wheel-legged robot (BIT-NAZA) is presented.FindingsCo-simulation and comparative experimental results using the BIT-NAZA robot derived from the developed hybrid control scheme indicate that the methodology can achieve satisfactory tracking performance in terms of accuracy and stability.Originality/valueThis research can provide theoretical and engineering guidance for lateral stability of intelligent robots under unknown disturbances and uncertain nonlinearities and facilitate the control performance of the mobile robots in a practical system.

Labeling PLA-PEG nanocarriers with IR780: physical entrapment versus covalent attachment to polylactide.

Near-infrared fluorescent dyes, such as IR780, are promising theranostics, acting as photosensitizers for photodynamic therapy and in vivo tracers in image-guided diagnosis. This work compared the uptake by macrophage-like cells of IR780 either physically associated or covalently attached to poly(D,L-lactide) (PLA) formulated as polymeric nanocapsules (NC) from a blend of PLA homopolymer and PLA-PEG block copolymer. The physicochemical characterization of both NC was conducted using asymmetric flow field-flow fractionation (AF4) analysis with static and dynamic light scattering and atomic force microscopy. The interaction of IR780 with serum proteins was evidenced by AF4 with fluorescence detection and flow cytometry in cell uptake studies. The average diameters of NC were around 120 nm and zeta potentials close to -40 mV for all NC. NC uptake by cells in different media and experimental conditions shows significantly lower fluorescence intensities for IR780 covalently linked to PLA and correspondingly low quantitative uptake. Different mechanisms of internalization were evidenced depending on the IR780 type of association to NC. Serum proteins mediate IR780 interaction with cells in a dose-dependent manner. Our results show that non-covalently linked IR780 was released from NC and accumulated in macrophage cells. Oppositely, IR780 conjugated to PLA provides stable association with NC, and its fluorescence is representative of cell uptake of the nanocarrier itself. This work strongly reinforces the importance of covalent attachment of a fluorescence dye such as IR780 to the nanocarrier to study their interaction with cells in vitro and to obtain reliable tracking in image-guided therapy. Graphical abstract.

Automated Safety Diagnosis Based on Unmanned Aerial Vehicle Video and Deep Learning Algorithm

This paper presents an automated traffic safety diagnostics solution named “Automated Roadway Conflict Identification System” (ARCIS) that uses deep learning techniques to process traffic videos collected by unmanned aerial vehicle (UAV). Mask region convolutional neural network (R-CNN) is employed to improve detection of vehicles in UAV videos. The detected vehicles are tracked by a channel and spatial reliability tracking algorithm, and vehicle trajectories are generated based on the tracking algorithm. Missing vehicles can be identified and tracked by identifying stationary vehicles and comparing intersect of union (IOU) between the detection results and the tracking results. Rotated bounding rectangles based on the pixel-to-pixel manner masks that are generated by mask R-CNN detection are introduced to obtain precise vehicle size and location data. Based on the vehicle trajectories, post-encroachment time (PET) is calculated for each conflict event at the pixel level. By comparing the PET values and the threshold, conflicts with the corresponding pixels in which the conflicts happened can be reported. Various conflict types: rear-end, head on, sideswipe, and angle, can also be determined. A case study at a typical signalized intersection is presented; the results indicate that the proposed framework could significantly improve the accuracy of the output data. Moreover, safety diagnostics for the studied intersection are conducted by calculating the PET values for each conflict event. It is expected that the proposed detection and tracking method with UAVs could help diagnose road safety problems efficiently and appropriate countermeasures could then be proposed.

Neural fuzzy approximation enhanced autonomous tracking control of the wheel-legged robot under uncertain physical interaction

The accuracy of trajectory tracking and stable operation with heavy load are the main challenges of parallel mechanism for wheel-legged robots, especially in complex road conditions. To guarantee the tracking performance in an uncertain environment, the disturbances, including the internal-robot friction and external-robot and environment interaction forces, should be considered in the robot’s dynamical system. In this article, a neural fuzzy-based model predictive tracking scheme (NFMPC) for reliable tracking control is proposed to the developed four wheel-legged robot, and the fuzzy neural network approximation is applied to estimate the unknown physical interaction and external dynamics of the robot system. Meanwhile, the advanced parallel mechanism of the four wheel-legged robot (BIT-NAZA) is introduced. Finally, co-simulation and experiment results using the BIT-NAZA robot derived from the proposed hybrid control strategy indicate that the methodology can achieve satisfactory tracking performance in terms of accuracy and stability. This research can provide theoretical and engineering guidance for lateral stability of intelligent robots under unknown disturbances and uncertain nonlinearities, and facilitate the control performance of the wheel-legged robot in a practical system.

Real-Time Detection of Road Lane-Lines for Autonomous Driving

Background: Enabling fast and reliable lane-lines detection and tracking for advanced driving assistance systems and self-driving cars. Methods: The proposed technique is mainly a pipeline of computer vision algorithms that augment each other and take in raw RGB images to produce the required lane-line segments that represent the boundary of the road for the car. The main emphasis of the proposed technique in on simplicity and fast computation capability so that it can be embedded in affordable CPUs that are employed by ADAS systems. Results: Each used algorithm is described in details, implemented and its performance is evaluated using actual road images and videos captured by the front mounted camera of the car. The whole pipeline performance is also tested and evaluated on real videos. Conclusion: The evaluation of the proposed technique shows that it reliably detects and tracks road boundaries under various conditions.

Dynamics-Based Algorithm for Reliable Assembly Mode Tracking in Parallel Robots

Finding the current pose of the end-effector of a parallel robot is a problem, since its forward geometric model generally has several solutions. Current methods to address this problem operate mainly under the assumption that the robot never changes its assembly mode nor gets close to Type 2 singularities. Nonetheless, recent works proved that a parallel robot can change its assembly mode, thanks to dedicated trajectory generation and control. Such a feature allows increasing the operational workspace of such manipulators. Hence, correctly tracking the end-effector pose while crossing Type 2 singularities is mandatory for a practical usage of this workspace enhancement method. However, on Type 2 singularities, several solutions of the forward geometric model merge, making current tracking methods ineffective. To fill this gap, we propose a two-step pose tracking methodology. First, a differential inclusion based on kinematics and dynamics is solved. Second, joint measurements are used to tighten resulting enclosures. The effectiveness of this method is discussed, thanks to experimental data gathered on a planar parallel robot.

Preparing construction supply chains for blockchain technology: An investigation of its potential and future directions

Blockchain, a peer-to-peer, controlled, distributed database structure, has the potential to profoundly affect current business transactions in the construction industry through smart contracts, cryptocurrencies, and reliable asset tracking. The construction industry is often criticized for being slow in embracing emerging technologies and not effectively diffusing them through its supply chains. Often, the extensive fragmentation, traditional procurement structures, destructive competition, lack of collaboration and transparency, low-profit margins, and human resources are shown as the main culprits for this. As blockchain technology makes its presence felt strongly in many other industries like finance and banking, this study investigates the preparation of construction supply chains for blockchain technology through an explorative analysis. Empirical data for the study were collected through semi-structured interviews with 17 subject experts. Alongside presenting a strengths, weaknesses, opportunities, and threats analysis (SWOT), the study exhibits the requirements for and steps toward a construction supply structure facilitated by blockchain technology.

A Novel Approach for Direct Numerical Simulation of Hydraulic Fracture Problems

Hydraulic fracturing is a non-linear and multi-physics problem involving the break up of a solid medium due to the action of hydrodynamic forces. Fluid and solid mechanics are involved at the same time together with fracture mechanics. Despite its relevance in many scientific and engineering fields, the theoretical and numerical description of hydraulic fracturing remains a challenging matter and the capabilities of existing models for applications are still limited. In this context, we propose a novel numerical approach to the Direct Numerical Simulation of hydraulic fracturing based on the Navier–Stokes equations coupled with peridynamic theory of solid mechanics through a multi-direct Immersed Boundary Method. The main advantage of this approach consists in the reliable crack-detection and tracking capabilities of peridynamics together with the capability of the Immersed Boundary Method of managing no-slip and no-penetration boundary conditions on complex and time-evolving interfaces. A massive-parallel solver based on this model has been implemented. We present a detailed theoretical description of the proposed methodology as well as the results of an extensive validation campaign for the new solver. Different benchmarking tests are provided together with the qualitative results of a simulation reproducing the fracture of a solid structure in a laminar, unsteady flow.

Centroidal-momentum-based trajectory generation for legged locomotion

This paper presents a trajectory optimization framework for planning dynamic legged locomotion based on a robot’s centroidal momentum (CM), which is the aggregation of all the links’ momenta at the robot’s Center of Mass (CoM). This new framework is built around CM dynamic model driven by Ground Reaction Forces (GRFs) parameterized with Bézier polynomials. Due to the simple form of CM dynamics, the closed-form solution of the robot’s CM can be obtained by directly integrating the Bézier polynomials of GRFs. The CM can be also calculated from the robot’s generalized coordinates and velocities using Centroidal Momentum Matrices (CMM). For dynamically feasible motions, these CM values should match, thereby providing equality constraints for the proposed trajectory optimization framework. Direct collocation methods are utilized to obtain feasible GRFs and joint trajectories simultaneously under kinematic and dynamic constraint. With the closed-form solutions of CM due to the parameterization of GRFs in the formulation, numerical error induced by collocation methods in the solution of trajectory optimization can be reduced, which is crucial for reliable tracking control when applied to real robotic systems. Using the proposed framework, jumping trajectories of legged robots are obtained in the simulation. Experimental validation of the algorithm is performed on a planar robot testbed, proving the effectiveness of the proposed method in generating dynamic motions of the legged robots.

Body size but not age influences phototaxis in bumble bee (Bombus terrestris, L.) workers

We studied phototaxis, the directional movement relative to light, in the bumble bee Bombus terrestris. We first developed and validated a MATLAB-based system enabling reliable high-resolution tracking of a bee and a measurement of her distance relative to a changing LED light source. Using this system, we found in all our experiments that workers show positive phototaxis. The strength of the phototactic response was influenced by body size but not age, and this effect was significant when the light source was weak. In a separate experiment, foragers showed stronger phototactic response compared with nurses only in one of two trials in which they were larger and tested with weak light intensity. The evidence that phototaxis is associated with size-based division of labor in the bumble bee and with age-related division of labor in the honey bee lends credence to response threshold models implicating the response to light in the organization of division of labor in cavity dwelling social insects.

Effective time interval for railway track geometry inspection

A proper decision-making scheme for track geometry maintenance requires a knowledge of the real condition of track geometry. Therefore, the track must be inspected by measurement cars at different time intervals. The frequency of track geometry inspection plays a crucial role in decision-making and has always been a big concern for infrastructure managers. The inspection interval should be chosen properly, it means that the small period can decrease the capacity of line and affect the operation of network and the big period can result in low quality of track and in some cases derailments and possible loss of human lives. The aim of this paper is to determine the effective inspection interval such that the total maintenance cost is minimized. In the proposed cost model, the costs of inspection, preventive maintenance, corrective maintenance and the penalty for exceeding the corrective maintenance level are considered. A case study is performed on a real dataset collected from a railway line in Iran. The standard deviation of longitudinal level is considered to measure track geometry degradation. A widely applied linear model is used to model track geometry degradation over time. Monte Carlo technique is used to simulate the track geometry behavior under various track geometry inspection intervals. In addition, a set of sensitivity analyses are carried out to assess the effect of various inspection intervals on different terms of maintenance cost. The results indicate that not only can substantial costs be saved by setting effective inspection intervals, but also the time during which the track suffers from bad conditions is dramatically reduced. The result of this study has shown the appropriate inspection interval for the studied case can result in 13.6 percent decrease in maintenance cost in comparison with the current maintenance policy. Besides, it would lead to more reliable railway track by preventing the system exceed the corrective threshold.

Discovering Customer Paths from Location Data with Process Mining

customer paths can be used for several purposes, such as understanding customer needs, defining bottlenecks, improving system performance. Two of the principal difficulties depend on discovering customer paths due to dynamic human behaviors and collecting reliable tracking data. Although machine learning methods have contributed to individual tracking, they have complex iterations and problems to produce understandable visual results. Process mining is a methodology that can rapidly create process flows and graphical representations. In this study, customer flows are created with process mining in a supermarket. The differences between the paths of customers purchased and non-purchased are discussed. The results show that both groups have almost similar visit duration, which is 87.5 minutes for purchased customers and 86.6 minutes for non-purchased customers. However, the duration of aisles is relatively small in non-purchased customer flows because customers aim to return or change the item instead of buying.

Event-triggered adaptive reliable guaranteed performance control for uncertain nonlinear systems with abrupt non-affine faults

In this paper, the issue of neural-network-approximation-based adaptive reliable event-triggered tracking for a class of strict-feedback systems with abrupt non-affine fault is investigated. The mean-value theorem is first used to decouple non-affine fault into affine form and then the Nussbaum function is introduced to overcome the difficulty of unknown control gains encountered in controller design process. Under the framework of adaptive backstepping technique, a novel event-triggered fault-tolerant control scheme is proposed, which ensures the stability of the system and reduces the waste of transmission resources. It should be mentioned that abrupt actuator fault and the discontinuous control signals under the event-triggered mechanism may lead to poor transient performance. Therefore, the prescribed performance bounds (PPB) method is used to constrain the tracking error within the desired specified range, which successfully improves the transient performance of the system. Finally, it is proved that all closed-loop signals under the proposed control method are semi-globally bounded and the Zeno behavior is avoided. The simulation results illustrate the effectiveness of the proposed method.

Deep Reinforcement Learning for Data Association in Cell Tracking.

Accurate target detection and association are vital for the development of reliable target tracking, especially for cell tracking based on microscopy images due to the similarity of cells. We propose a deep reinforcement learning method to associate the detected targets between frames. According to the dynamic model of each target, the cost matrix is produced by conjointly considering various features of targets and then used as the input of a neural network. The proposed neural network is trained using reinforcement learning to predict a distribution over the association solution. Furthermore, we design a residual convolutional neural network that results in more efficient learning. We validate our method on two applications: the multiple target tracking simulation and the ISBI cell tracking. The results demonstrate that our approach based on reinforcement learning techniques could effectively track targets following different motion patterns and show competitive results.

Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.

Volumetric Calibration Refinement of a Multi-Camera System Based on Tomographic Reconstruction of Particle Images

The calibration of a multi-camera system for volumetric measurements is a basic requirement of reliable 3D measurements and object tracking. In order to refine the precision of the mapping functions, a new, tomographic reconstruction-based approach is presented. The method is suitable for Volumetric Particle Image Velocimetry (PIV), where small particles, drops or bubbles are illuminated and precise 3D position tracking or velocimetry is applied. The technique is based on the 2D cross-correlation of original images of particles with regions from a back projection of a tomographic reconstruction of the particles. The off-set of the peaks in the correlation maps represent disparities, which are used to correct the mapping functions for each sensor plane in an iterative procedure. For validation and practical applicability of the method, a sensitivity analysis has been performed using a synthetic data set followed by the application of the technique on Tomo-PIV measurements of a jet-flow. The results show that initial large disparities could be corrected to an average of below 0.1 pixels during the refinement steps, which drastically improves reconstruction quality and improves measurement accuracy and reliability.

Reliable Breathing Tracking with Wearable Mask Device.

Breathing tracking is critical for the assessment of lung functions, exercise physiologies, and energy expenditure. Conventional methods require using a face mask or mouthpiece that is connected to a stationary equipment through a tube, restricting the location, movement, or even the posture. To obtain accurate breathing physiology parameters that represent the true state of the patient during different scenarios, a wearable technology that has less intervention to patient's activities in free-living conditions is highly preferred. Here, we propose a miniaturized, reliable, and wide-dynamic ranged flow sensing technology that is immune to orientation, movement, and noise. As far as we know, this is the first work of introducing a fully integrated mask device focusing on breath tracking in free-living conditions. There are two key challenges for achieving this goal: miniaturized flow sensing and motion-induced artifacts elimination. To address these challenges, we come up with two technical innovations: 1) in hardware wise, we have designed an integrated flow sensing technique based on differential pressure Pneumotach approach and motion sensing; 2) in software wise, we have developed comprehensive algorithms based baseline tracking and orientation and motion compensation. The effectiveness of the proposed technology has been proven by the experiments. Experimental results from simulator and real breath conditions show high correlation (R2 = 0.9994 and 0.9964 respectively) and mean error within 2.5% for Minute Volume (VE), when compared to values computed from reference methods. These results show that the proposed method is accurate and reliable to track the key breath parameters in free-living conditions.

Autonomous collision detection and avoidance for ARAGON USV: Development and field tests

AbstractThis study addresses the development of algorithms for multiple target detection and tracking in the framework of sensor fusion and its application to autonomous navigation and collision avoidance systems for the unmanned surface vehicle (USV) Aragon. To provide autonomous navigation capabilities, various perception sensors such as radar, lidar, and cameras have been mounted on the USV platform and automatic ship detection algorithms are applied to the sensor measurements. The relative position information between the USV and nearby objects is obtained to estimate the motion of the target objects in a sensor‐level tracking filter. The estimated motion information from the individual tracking filters is then combined in a central‐level fusion tracker to achieve persistent and reliable target tracking performance. For automatic ship collision avoidance, the combined track data are used as obstacle information, and appropriate collision avoidance maneuvers are designed and executed in accordance with the international regulations for preventing collisions at sea (COLREGs). In this paper, the development processes of the vehicle platform and the autonomous navigation algorithms are described, and the results of field experiments are presented and discussed.

Discovering Customer Paths from Location Data with Process Mining

customer paths can be used for several purposes, such as understanding customer needs, defining bottlenecks, improving system performance. Two of the principal difficulties depend on discovering customer paths due to dynamic human behaviors and collecting reliable tracking data. Although machine learning methods have contributed to individual tracking, they have complex iterations and problems to produce understandable visual results. Process mining is a methodology that can rapidly create process flows and graphical representations. In this study, customer flows are created with process mining in a supermarket. The differences between the paths of customers purchased and non-purchased are discussed. The results show that both groups have almost similar visit duration, which is 87.5 minutes for purchased customers and 86.6 minutes for non-purchased customers. However, the duration of aisles is relatively small in non-purchased customer flows because customers aim to return or change the item instead of buying.

A Review on Automatic Bi-directional Smart Meter along with a Proposed Model of Second Layer Grid Protection System Based on Solid State Relay

In order to avoid the labour – cost, poor flexibility, reliability, accuracy and live tracking system; upgradation of traditional energy meter is required. Automatic Metering Interface (AMI) can be provided better billing accuracy, live energy consumption monitoring and economic power supply with the help of Zigbee, GSM based wireless and PLC based wired data transfer mode. This can be helpful to calculate the net amount of power transfer between suppliers and consumers with dynamic pricing through bidirectional communication system containing securely paired master slave mode during installation. AMI offers reliable protection and maintain the stability of power system with the preliminary fault detection through an inbuilt tripping circuit. This paper presents a brief review on the internal construction of the AMI, recent pros and cons of AMI along with the new approach of the tripping mechanism to handle the preliminary faults in the power system intelligently.