Conventional Tracking Techniques Research Articles

Smart Sensors for Real-time Monitoring in a Sustainable Logistics

The new logistics landscape needs openness, efficiency, and agility. Conventional tracking techniques are unable to keep up with the complex dance of items traveling across countries. Intelligent and communicative smart sensors have changed the game by allowing for real-time process monitoring and revolutionizing the logistics process from start to finish. This study explores how smart sensors can revolutionize the logistics industry. First, smart devices were study in-depth and their various features were equally examined, such as GPS tracking, RFID scanning, temperature and humidity sensors, package integrity assessment, vibration and acoustic sensors, and environmental monitoring. By conducting a comprehensive investigation, this research paper aims to scrutinize the data collecting ability of these sensors. The study will focus on their potential for gathering an abundance of information regarding cargo whereabouts, surrounding environment conditions, physical soundness, and equipment efficiency. This stream of real-time current data helps in active problem-solving, and imparts all the relevant insights one needs to know about the complete sustainable logistics operation. Their critical role toward improving productivity, transparency, and environmental sustainability in the dynamic logistics environment. For this reason, they can offer all perspectives needed (which are the most complete) to foster a proactive analysis and enhance any aspect over time.

Global Maximum Power Point Tracking of Solar Photovoltaic Strings under Partial Shading Conditions Using Cat Swarm Optimization Technique

The power versus voltage curves of solar photovoltaic panels form several peaks under fractional (partial) shading conditions. Traditional maximum output power tracking (MPPT) techniques fail to achieve global peak power at the output terminals. The proposed Cat Swarm Optimization (CSO) method intends to apply MPPT techniques to extract the global maxima from the shaded photovoltaic systems. CSO is a robust and powerful metaheuristic swarm-based optimization technique that has received very positive feedback since its emergence. It has been used to solve a variety of optimization issues, and several variations have been developed. The CSO-based maximum power tracking technique can successfully tackle two major issues of the PV system during shading conditions, including random oscillations caused by conventional tracking techniques and power loss. The proposed techniques have been extensively used in comparison to conventional algorithms like the Perturb and the Observe (P and O) technique. The main objective is to achieve a tracking speed for extracting the Maximum Power Point (MPP) from the solar Photovoltaic (PV) system under fractional shading conditions by using CSO. Modeling of the solar photovoltaic array in the MATLAB/Simulink platform comprises a photovoltaic module, a switching converter (Boost Converter), and the load. The PSO and CSO techniques are applied to the PV module under different weather conditions. The PSO algorithm is compared to the CSO algorithm according to simulation results, revealing that the CSO algorithm can provide better accuracy and a faster tracking speed.

Experimental evaluation of adaptive maximum power point tracking for a standalone photovoltaic system

The adaptability of maximum power point tracking (MPPT) of a solar PV system is important for integration to a microgrid. Depending on what fixed step-size the MPPT controller implements, there is an impact on settling time to reach the maximum power point (MPP) and the steady state operation for conventional tracking techniques. This paper presents experimental results of an adaptive tracking technique based on Perturb and Observe (P&O) and Incremental Conductance (IC) for standalone Photovoltaic (PV) systems under uniform irradiance and partial shading conditions. Analysis and verification of measured and MATLAB/Simulink simulation results have been carried out. The adaptive tracking technique splits the operational region of the solar PV’s power–voltage characteristic curve into four and six operational sectors to understand the MPP response and stability of the technique. By implementing more step-sizes at sector locations based on the distance of the sector from the MPP, the challenges associated with fixed step-size is improved on.The measured and simulation results clearly indicate that the proposed system tracks MPP faster and displays better steady state operation than conventional system. The proposed system’s tracking efficiency is over 10% greater than the conventional system for all techniques. The proposed system has been under partial shading condition has been and it outperforms other techniques with the GMPP achieved in 0.9 s which is better than conventional techniques.

Robust Robot Tracking for Next-Generation Collaborative Robotics-Based Gaming Environments

The collaboration between humans and robots is one of the most disruptive and challenging research areas. Even considering advances in design and artificial intelligence, humans and robots could soon ally to perform together a number of different tasks. Robots could also became new playmates. In fact, an emerging trend is associated with the so-called phygital gaming, which builds upon the idea of merging the physical world with a virtual one in order to let physical and virtual entities, such as players, robots, animated characters and other game objects interact seamlessly as if they were all part of the same reality. This paper specifically focuses on mixed reality gaming environments that can be created by using floor projection, and tackles the issue of enabling accurate and robust tracking of off-the-shelf robots endowed with limited sensing capabilities. The proposed solution is implemented by fusing visual tracking data gathered via a fixed camera in a smart environment with odometry data obtained from robot's on-board sensors. The solution has been tested within a phygital gaming platform in a real usage scenario, by experimenting with a robotic game that exhibits many challenging situations which would be hard to manage using conventional tracking techniques.

Adaptive patch feature matching and scale estimation for visual object tracking

In computer vision, the object tracking technique is a complex task to predict the object target under challenging task occlusion, scaling, and illumination. A robust object tracking algorithm is proposed based on adaptive patch feature matching and scale estimation. Initially, tracking technique extracts geometric locations using defined directional distances and object location of the previous frame. The feature matching is carried out between the previous frame featured vector and overlapping patches feature vectors of the present frame. The matched patch is bounded by the identified location in the current frame. For object scaling, speeded up robust features point matching technique is applied to an enlarged patch of the present frame and the tracked patch of the previous frame. Finally, the proposed algorithm updates motion, feature, and geometric location vectors to keep tracking between successive frames. The feature vector update between successive frames solves the aforementioned challenging issues in object tracking. For experimentation, the proposed algorithm is tested with available tracking benchmarks. The performance evaluation results show that the proposed technique is better in terms of computational time and accuracy compared to conventional tracking techniques.

A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals.

For global navigation satellite system receivers, Kalman filter (KF)-based tracking loops show remarkable advantages in terms of tracking sensitivity and robustness compared with conventional tracking loops. However, to improve the tracking sensitivity further, increasing the coherent integration time is necessary, but it is typically limited by the navigation data bit sign transition. Moreover, for standard KF-based tracking receivers, the KF parameters are initialized by the acquired results. However, especially under weak signal conditions, the acquired results have frequency errors that are too large for KF-based tracking to converge rapidly to a steady state. To solve these problems, a two-stage KF-based tracking architecture is proposed to track weaker signals and achieve faster convergence. In the first stage, coarse tracking refines the acquired results and achieves bit synchronization. Then, in the second stage, fine tracking initializes the KF-based tracking by using the coarse tracking results and extends the coherent integration time without the bit sign transition limitation. This architecture not only utilizes the self-tuning technique of the KF to improve the tracking sensitivity, but also adopts the two-stage to reduce the convergence time of the KF-based tracking. Simulation results demonstrate that the proposed method outperforms conventional tracking techniques in terms of tracking sensitivity. Furthermore, the proposed method is compared with the standard KF-based tracking approach, proving that the proposed method converges more rapidly.

Maximum Power Point Tracking for Photovoltaic Systems under Partial Shading Conditions Using Bat Algorithm

The vibrant, noiseless, and low-maintenance characteristics of photovoltaic (PV) systems make them one of the fast-growing technologies in the modern era. This on-demand source of energy suffers from low-output efficiency compared with other alternatives. Given that PV systems must be installed in outdoor spaces, their efficiency is significantly affected by the inevitable complication called partial shading (PS). Partial shading occurs when different sections of the solar array are subjected to different levels of solar irradiance, which then leads to a multiple-peak function in the output characteristics of the system. Conventional tracking techniques, along with some nascent/novel approaches used for the tracking maximum power point (MPP), are unsatisfactory when subjected to PS, eventually leading to the reduced efficiency of the PV system. This study aims at investigating the use of the bat algorithm (BA), a nature-inspired metaheuristic algorithm for MPP tracking (MPPT) subjected to PS conditions. A brief explanation of the behavior of the PV system under the PS condition and the advantages of using BA for estimating the MPPT of the PV system under PS condition is discussed. The deployment of the BA for the MPPT in PV systems is then explained in detail highlighting the simulation results which verifies whether the proposed method is faster, more efficient, sustainable and more reliable than conventional and other soft computing-based methods. Three testing conditions are considered in the simulation, and the results indicate that the proposed technique has high efficiency and reliability even when subjected to an acute shading condition.

Optimal parameter design of fractional order control based INC-MPPT for PV system

A comprehensive and straightforward methodology for optimal parameter design of Fractional Order control based Incremental Conductance (FOINC)-Maximum Power Point Tracking (MPPT) is developed in this paper. The main objective is to develop a more efficient, robust MPPT algorithm based on the integration between the fractional-order control and Incremental Conductance (INC) method. The integration between INC robustness and the accuracy of fractional-order can enhance the overall tracking performance in comparison to the conventional tracking techniques. Such integration ensures fast dynamics and high tracking accuracy for the Maximum Power Point (MPP) under tremendous climate variations. A small signal model for the whole system has been built to design the most appropriate order and gain of the fractional integrator for variable step size FOINC-MPPT. The ultimate gain (upper limit) of the controller has been specified properly via root locus plot before starting the optimization process to avoid falling in instability region. Radial Movement Optimization, as an optimization tool, is used for obtaining the optimal parameters of the fractional controller. The feasibility and effectiveness of the proposed FOINC-MPPT algorithm are validated under different climate conditions with slow and rapid changes in solar irradiance. Simulation results show that the proposed FOINC-MPPT algorithm is able to track the MPP accurately and rapidly in comparison with the conventional INC-based tracker.

Ghost: Voronoi-based tracking in sparse wireless networks using virtual nodes

Conventional tracking techniques for wireless networks locate a target by using at least three non-collinear tracker nodes. However, having such a high density of trackers over the monitored area is not always possible. This paper presents Ghost, a new tracking method based on Voronoi tessellations able to track a target by using less than three tracker nodes in wireless networks. In Ghost, different locations of the target create different Voronoi diagrams of the monitored area by placing virtual nodes around tracker nodes. These diagrams are used to estimate the current location of the target by intersecting the previous and current Voronoi diagrams. The target's route is constructed by systematically searching the most likely estimated target's locations over time. Simulation results validate that the proposed method has better tracking accuracy compared with existing proposals. Moreover, our approach is not tied to a specific technology, thus it can be applied in different platforms (e.g., WLAN and WSN).

Visual Tracking in Occlusion Environments by Autonomous Switching of Targets

Visual tracking is required by many vision applications such as human-computer interfaces and human-robot interactions. However, in daily living spaces where such applications are assumed to be used, stable tracking is often difficult because there are many objects which can cause the visual occlusion. While conventional tracking techniques can handle, to some extent, partial and short-term occlusion, they fail when presented with complete occlusion over long periods. They also cannot handle the case that an occluder such as a box and a bag contains and carries the tracking target inside itself, that is, the case that the target invisibly moves while being contained by the occluder. In this paper, to handle this occlusion problem, we propose a method for visual tracking by a particle filter, which switches tracking targets autonomously. In our method, if occlusion occurs during tracking, a model of the occluder is dynamically created and the tracking target is switched to this model. Thus, our method enables the tracker to indirectly track the “invisible target” by switching its target to the occluder effectively. Experimental results show the effectiveness of our method.

Navigation-Guidance-Based Robotic Interception of Moving Objects in Industrial Settings

An important task for autonomous industrial robotic systems is the interception of moving objects. In order to achieve this objective, an on-line robot-motion planning technique that utilizes real-time sensory feedback about the object's motion is needed. In this paper, an Ideal Proportional Navigation Guidance (IPNG) based technique is utilized for on-line robot-motion planning. One must note, however, that navigation-guidance techniques were originally developed for bringing the interceptor into a collision course with (hostile) airborne targets. Therefore, in our case, a conventional tracking technique must be utilized as a subsequent phase to an initial IPNG-based robot-motion planning phase in order to ensure smooth interception. The implementation of the hybrid scheme in industrial settings, where one may not have access to the robot's dynamic model nor to the joints' controllers, is discussed. Real-time experimental results using an industrial robot and a computer-vision system are presented, confirming the (interception-time) superiority of our proposed scheme over conventional tracking techniques.