Trajectory Path Research Articles

Automated Flight Technology for Integral Path Planning and Trajectory Tracking of the UAV

In view of the problem that path planning and trajectory tracking are rarely solved simultaneously in the current research, which hinders their practical implementation, this paper focuses on enhancing the autonomous flight planning capability of unmanned aerial vehicles (UAVs) by investigating integrated path planning and trajectory tracking technologies. The autonomous flight process is divided into two sub-problems: waypoint designing/optimizing and waypoint tracking. Firstly, an improved DB-RRT* algorithm is proposed for waypoint planning to make the algorithm have higher planning efficiency, better optimization results, and overcome the defects of accidental and low reliability of single RRT* planning results. Secondly, the scheme of “offline design + online flight” is adopted to lead the UAV to fly online according to the waypoints’ instructions by using the sliding mode guidance based on angle constraint with finite-time convergence so that it can fly to the destination autonomously. In order to check the performance of the proposed algorithm, a variety of simulations are conducted to verify the feasibility of the proposed algorithm.

Path Planning and Trajectory Tracking for Autonomous Obstacle Avoidance in Automated Guided Vehicles at Automated Terminals

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system’s capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme.

Path Planning and Trajectory Tracking Control for Two-Wheel Mobile Robot

The mobile robot is a system that can work in various environments. This means that the robot must be able to navigate without delay and avoid any obstacles placed within the boundaries of its movement. Designing mobile robots that can be intelligently managed and operate autonomously when traveling from one place to another requires at least two steps. To start with, path planning is required to prevent motion collisions. Tracking the robot's trajectory is a crucial second task. The primary goal of this study is to find the quickest and safest path between the two positions. In this work, we investigated the path planning of a mobile robot with dynamic, and dynamic obstacles with moving goal environments using RRT, BiRRT, and HA* algorithms. These algorithms are easy, computationally inexpensive, and simple to use. They have been chosen for numerous real-time path-planning applications. The DDMR's kinematic model has been utilized in this paper to control path tracking, and a PID controller has been proposed to reduce tracking deviations between the robot's actual route and the reference trajectory. This work introduced the PSO, FPA, CSA, SSA, BWOA, and proposed HBPO optimization techniques for obtaining PID parameters (k_p,k_i,k_d) for improved mobile robot trajectory tracking. The simulation results have been examined using three trajectory shapes: step, circular, and infinite. The simulation findings reveal that HA* outperforms the other algorithms by generating collision-free pathways that are smoother and shorter than their RRT and BiRRT equivalents. On the other hand, the proposed HBPO outperforms the other methods. The HBPO method converges quicker than the other proposed algorithms.

Vision-based autonomous navigation stack for tractors operating in peach orchards

Recently, camera-based approaches have been proposed to replace expensive LiDAR or real-time kinematic (RTK) based solutions for the unmanned tractors operating in an orchard-like environment. However, constructing a completely autonomous navigation stack with a low-costly camera and onboard computer presents extra difficulties, such as a computationally efficient perception model, rapid path planning without costly SLAM, and the capacity to avoid obstacles. In this paper, a novel vision-based autonomous navigation stack is devised to address challenges of the wholly autonomous tractor using stereo camera and inertial measurement unit (IMU) that are inexpensive. The computational pipeline consists primarily of three modules: (1) the multi-task perception network, (2) the frame transformation algorithm, and (3) the motion planning module. The multi-task perception network detects tree trunks, obstacles, and traversable areas simultaneously from the RGB image with high efficiency (69 FPS) and accuracy (mAP@.5 of 96.7% and mIoU of 98.1%). For global path planning and trajectory prediction, the frame transformation algorithm integrates the downsized the navigational features and transforms them to the tractor frame from the image frame. The motion planning module then uses the fused data to plan the appropriate path (i.e., the center of the tree row, U-turn path, or J-turn path) and search for the optimal trajectory utilizing the optimized dynamic window approach (DWA) for path tracking. In the peach orchard, the proposed stack is implemented on our autonomous retrofitted tractor and its efficacy is demonstrated compared with the human-driving tractor.

A backup plan for life? Alternative Life paths facilitate disengagement in an action crisis

While there is anecdotal evidence and some scientific support for the value of having multiple paths to reach one’s life goals, recent work concerning backup plans argues that their mere availability undermines commitment to and performance in the originally chosen path. In this paper, we evaluated this phenomenon amongst college students (N = 345) entering their first term with an already available family-based alternative life path. As expected, entering into college with an available family-based alternative life path led to a decrease in study commitment over the first semester and indirectly predicted lower end-of-semester grades through this reduction in commitment. However, results indicate that this only occurred when students reported experiencing an action crisis at the end of their first semester. If students did not report having an action crisis, an available family-based alternative life path did not influence study commitment and predicted a higher end-of-semester GPA. Ultimately, findings highlight the major role action crisis plays in the influence an alternative life path has on path trajectory.

A Class of Parameter Interpolation Iteration Algorithm based on Clem- Equations

The Newdon Iterative method and its deformation mostly require calculating the derivative of nonlinear equations. In practice, many functions are complex, and there are limitations in finding high-order derivatives. This paper fits an existing iterative format by establishing n times interpolated polynomials, n conditions are used to construct a system of linear equations with n equations and n + 1 unknowns, in which the previous n unknowns can be expressed by the n + 1 th unknown by Clem’s rule, to achieve the purpose of reducing the number of derivative calculations. Convergence analysis and numerical examples verify that the algorithm is better than Newdon iteration, and can be better used in path trajectory optimization and fractional time delay control.

Path planning for mobile robots in unstructured orchard environments: An improved kinematically constrained bi-directional RRT approach

In complex orchard environments, efficient path-planning algorithms for mobile robots are crucial for achieving accurate autonomous navigation and mobile operations. The existence of kinematic constraints for mobile robots in orchards and the unstructured layouts of orchard environments, which primarily feature irregular obstacles, pose considerable challenges for path-planning algorithms. Ideal path-planning algorithms must overcome the above problems and plan navigation paths that are safe, efficient, and consistent with the kinematic constraints of mobile robots in an obstacle-filled environment. In this study, to satisfy the above requirements, the Improved Kinematically Constrained Bi-directional Rapidly Exploring Random Tree (IKB-RRT) algorithm is proposed, which includes three innovations. First, an improved artificial potential field method is designed for guidance-node generation in complex orchard environments; hence, the nodes provide the path guidance for the random expansion trees that are subject to the kinematic constraints, avoiding obstacle regions and expanding towards the target point. Second, a motion-control parameter sampling method for the robot configuration is introduced to control the random expansion tree to generate path nodes and trajectories that satisfy the mobile robot constraints. Third, a path optimization strategy for dual-tree connected regions is proposed to avoid sudden changes in the heading angle at dual-tree connected waypoints and to improve the overall path smoothness. Path planning is performed in four orchard environments with different characteristics. The algorithm test results show that, in different scenarios, the proposed IKB-RRT algorithm reduces the path length by a maximum of 13.94% and 12.20% and the planning time by a maximum of 61.41% and 68.84%, significantly improving the overall smoothness of the path relative to the KB-RRT* and KB-RRT algorithms, respectively. The results fully verify that the IKB-RRT algorithm has obvious advantages in handling the path-planning problem that arises for mobile robots in complex orchard environments. The proposed algorithm can be implemented in future orchard mobile robots to enable effective path planning.

TRACE: Transformer-based continuous tracking framework using IoT and MCS

Target tracking, a critical application in the Internet of Things (IoT) and Mobile Crowd Sensing (MCS) domains, is a complex task that involves the continuous estimation of the positions of an object by using efficient and accurate algorithms. Some potential applications of target tracking include surveillance systems, asset tracking, wildlife monitoring, and cross-border security. The existing target tracking solutions are either energy-inefficient or are only effective for fixed-length trajectories, making them impractical for real-world applications. For robust predictive tracking, with irregular trajectory lengths, energy efficiency and accuracy are vital to ensure system’s longevity and reliability. In this work, using a combination of trajectory prediction and path correction techniques, a novel approach, TRACE, is proposed for continuously tracking a target in an environment. TRACE uses locations offered by IoT/MCS localization systems to make predictions about the target’s future movement. A transformer neural network is implemented to learn mobility patterns to predict the target’s future trajectory. To ensure accurate predictions, a path correction mechanism is devised, by updating the predicted trajectory using polynomial regression. Experiments are conducted using a real-world GeoLife dataset to evaluate the performance of the proposed approach. The results demonstrate that TRACE performs better than existing tracking techniques with an improvement in accuracy of about 50% while using 85% less energy, supporting the potential of the proposed approach for enhancing target tracking in IoT/MCS applications.

Risk Assessment Procedure of Final Approach to Landing Using Deep Learning

Flight safety is the highest priority of all involved in aviation. All civil aviation authorities around the world have the responsibility to guarantee flight safety, but the flight data are considered confidential and regarded as trade secrets by airlines. Therefore, the publicly available sources are employed in the study to investigate the risk and related factors of flight safety during the approach to landing. Automatic Dependent Surveillance–Broadcast flight trajectory data and the Meteorological Terminal Aviation Routine Weather Report are used to analyze the risk associated with the final approach to landing. This study presents a procedure to conduct the data mining and risk assessment of flights landing at Taipei Songshan Airport (TSA). A deep learning method is adopted to tackle the problems associated with nonlinear regression that cannot be addressed by traditional statistical regression methods. The trajectory and glide path angle deviations of each flight are determined and applied to the model training. These two deviation variables are regarded as the evaluation standards, while the weather conditions are used as risk factors. Each weather factor in the deep learning models is weighted by the permutation feature importance analysis to capture how much influence each factor has on predictions from the model. The results show that the estimation accuracy of trajectory deviation with the 10-input model and glide path angle deviation with the 11-input model is over 90 and 95%, respectively. The trajectory deviations of runway 10 have higher impacts from temperature, precipitations, and crosswind.

Aye-aye middle finger kinematic modeling and motion tracking during tap-scanning

The aye-aye (Daubentonia madagascariensis) is a nocturnal lemur native to the island of Madagascar with a unique thin middle finger. Its slender third digit has a remarkably specific adaptation, allowing them to perform tap-scanning to locate small cavities beneath tree bark and extract wood-boring larvae from it. As an exceptional active acoustic actuator, this finger makes an aye-aye’s biological system an attractive model for pioneering Nondestructive Evaluation (NDE) methods and robotic systems. Despite the important aspects of the topic in the aye-aye’s unique foraging and its potential contribution to the engineering sensory, little is known about the mechanism and dynamics of this unique finger. This paper used a motion-tracking approach for the aye-aye’s middle finger using simultaneous video graphic capture. To mimic the motion, a two-link robot arm model is designed to reproduce the trajectory. Kinematics formulations were proposed to derive the motion of the middle finger using the Lagrangian method. In addition, a hardware model was developed to simulate the aye-aye’s finger motion. To validate the model, different motion states such as trajectory paths and joint angles, were compared. The simulation results indicate the kinematics of the model were consistent with the actual finger movement. This model is used to understand the aye-aye’s unique tap-scanning process for pioneering new tap-testing NDE strategies for various inspection applications.

Safe Trajectory Path Planning Algorithm Based on RRT* While Maintaining Moderate Margin From Obstacles

Safe Trajectory Path Planning Algorithm Based on RRT* While Maintaining Moderate Margin From Obstacles

Robot path planning based on improved A* algorithm

To address the issues of excessive polyline paths and the increased number of search nodes in the A* algorithm, a weighted WA* algorithm is proposed based on the A* algorithm. Firstly, a new distance function is introduced to reduce computational resource usage and improve search efficiency. Then, the heuristic function is improved by searching all feasible paths to find the shortest path. When the lengths of feasible paths are very similar, an additional value is added to the heuristic function along with weights to minimize frequent searches. Lastly, Bezier curves are introduced to enhance the smoothness of the global path. Simulation results show that the improved WA* algorithm reduces the number of turns, time consumption, search space, and turning angles by 25.0%, 65.4%, 53.5%, and 18.7%, respectively, compared to the A* algorithm. Compared to the bidirectional A* algorithm, these reductions are 7.6%, 34.4%, 43.5%, and 7.1%, respectively. In addition, the WA* algorithm uses Bezier curves to curve-fit the generated path points, making the path trajectory smoother.

Generating rectilinear, elliptical, and circular oscillations of a single-mass vibratory system equipped with an enhanced twin crank-type exciter

One of the most intensively developing fields of vibratory technologies is focused on improving the design and operational parameters of the excitation mechanisms and drives. The present research is devoted to a novel twin crank-type exciter driven by a permanent-magnet direct-current motor. The research methodology consists of mathematical modeling and computer simulation of the dynamic behavior of a single-mass oscillatory system equipped with the proposed exciter. The obtained results substantiate the possibilities of generating rectilinear, elliptical, and circular oscillations of the working member by applying the corresponding design changes of the transformable vibration exciter. The major scientific novelty of the carried-out investigations consists in determining the influence of specific design parameters of the exciter on the trajectory (path) of the working member motion. The obtained results can have a significant practical value while designing new and improving existent drives of various vibratory equipment (screens, conveyors, sieves, compactors, technological (lapping, polishing) machines, etc.).

Multi-UAV Data Collection and Path Planning Method for Large-Scale Terminal Access.

In the context of the relentless evolution of network and communication technologies, the need for enhanced communication content and quality continues to escalate. Addressing the demands of data collection from the abundance of terminals within Internet of Things (IoT) scenarios, this paper presents an advanced approach to multi-Unmanned Aerial Vehicle (UAV) data collection and path planning tailored for extensive terminal accessibility. This paper focuses on optimizing the complex interplay between task completion time and task volume equilibrium. To this end, a novel strategy is devised that integrates sensor area partitioning and flight trajectory planning for multiple UAVs, forming an optimization framework geared towards minimizing task completion duration. The core idea of this work involves designing an innovative k-means algorithm capable of balancing data quantities within each cluster, thereby achieving balanced sensor node partitioning based on data volume. Then, the UAV flight trajectory paths are discretely modeled, and a grouped, improved genetic algorithm is used to solve the Multiple Traveling Salesman Problem (MTSP). The algorithm introduces a 2-opt optimization operator to improve the computational efficiency of the genetic algorithm. Empirical validation through comprehensive simulations clearly underscores the efficacy of the proposed approach. In particular, the method demonstrates a remarkable capacity to rectify the historical issue of diverse task volumes among multiple UAVs, all the while significantly reducing task completion times. Moreover, its convergence rate substantially outperforms that of the conventional genetic algorithm, attesting to its computational efficiency. This paper contributes an innovative and efficient paradigm to improve the problem of data collection from IoT terminals through the use of multiple UAVs. As a result, it not only augments the efficiency and balance of task distribution but also showcases the potential of tailored algorithm solutions for realizing optimal outcomes in complex engineering scenarios.

Hybrid Layer of Improved Interfered Fluid Dynamic System and Nonlinear Model Predictive Control for Navigation and Control of Autonomous Underwater Vehicles

This study introduces a hybrid control structure called Improved Interfered Fluid Dynamic System Nonlinear Model Predictive Control (IIFDS-NMPC) for the path planning and trajectory tracking of autonomous underwater vehicles (AUVs). The system consists of two layers; the upper layer utilizes the Improved Interfered Fluid Dynamic System (IIFDS) for path planning, while the lower layer employs Nonlinear Model Predictive Control (NMPC) for trajectory tracking. Extensive simulation experiments are conducted to determine optimal parameters for both static and dynamic obstacle scenarios. Additionally, real-world testing is performed using the BlueRov2 platform, incorporating multiple dynamic and static obstacles. The proposed approach achieves real-time control at a frequency of 100 Hz and exhibits impressive path tracking accuracy, with a root mean square (RMS) of 0.02 m. This research provides a valuable framework for navigation and control in practical applications.

Geodesic equations for guided wave helical path separation for a pipe bend

The sections of pipe bends are susceptible to wall thinning by flow-accelerated corrosion due to the sudden change in the fluid flow direction and velocity. Compared to existing screening approaches, Guided Wave Tomography (GWT) has been demonstrated to provide more accurate information about the defect. In GWT an inversion problem is solved, in which a forward model is used to predict a synthetic dataset for a given physical model. Two-dimensional (2D) forward models are preferred for the inversion scheme due to the feasibility of combining these with tomographic algorithms. Since 2D models resemble the GW propagation of a plate, they do not consider the cyclic nature of the pipe. To overcome this limitation, a robust helical path separation was introduced in Huthwaite and Seher (2015) for a straight pipe. However, it relies on time-reversing the measured wave field ϕ(t) as a function of the propagation distance xs,r from the source to the receiver. Although ray-tracing and grid-based methods are capable of computing the distance xs,r for complex geometries such as the pipe bend, they are complex and time-consuming. Hence, in this paper a straight forward approach is proposed to compute xs,r based on the pipe geometry and the geodesic equations of a torus. Then the computed distance xs,r is used for removing the helical path trajectories. Compared to the distance obtained by using the Hilbert envelope, the results show that the geodesic distances preserve the information of the wavefront of interest, including the focusing effect and the scattered wave field from a Hann-shaped defect. The technique is applied to experimental data and demonstrated to separate the wavefront of interest.

Climate change, counter-terrorism and capitalist development in Somalia

ABSTRACT Somalia is often referred to as a ‘failed state’. In addition to ineffective governance, feeble economic development, and a large anti-government insurgency, it faces increasingly severe climate change-induced devastation. This article offers a critical discussion of the role of capitalist interests and ideology as a factor in the climate crisis. It explores interlocking issues of (1) the relationship between the ruling political class of Somalia and capitalist mining interests; (2) the largely covert US-backed ‘war on terror’ in Somalia; and (3) the ongoing, capitalism-induced climate crisis. The article analyses current US and Somali proposals to address these issues. Many of these proposals remain trapped in the politico-ideological deadlock of capitalist developmentalism, oriented towards fossil fuel extraction and militarised accumulation. The trajectory of this current path in Somalia is leading to immiseration, oppression, displacement for millions of people, and the destruction of an already deteriorating environment. Alternative paths to avert these catastrophes require transnational solidarity, cooperation and assistance.

A Distributed Cooperative Continuous Coverage Strategy of UAVs for MEC Services of Smart Grids Considering Energy Consumption

<p>Unmanned aerial vehicles (UAVs) play an essential role in scenarios such as smart grid data collection and power line inspection for their maneuverability and low cost. However, the most obvious defect of UAVs is still the energy problem. The battery life of UAVs is too short of completing long-term tasks. Therefore, this paper proposes a distributed cooperative and continuous coverage strategy for UAVs. When the UAV is short of power, we select the path through the relay algorithm and send backhaul information such as the remaining power to the macro base station (mBS). mBS will send auxiliary UAVs to replace UAVs with weak power to complete the task of UAV replacement. We jointly optimize the relay information transmission path and UAV trajectory to achieve the goal of minimizing the total energy consumption of the system. The simulation results show that the optimization algorithm can significantly reduce the energy consumption of UAVs.</p> <p> </p>

Spatio-Temporal Distributions of the Natural Non-Sea-Salt Aerosol Over the Southern Ocean and Coastal Antarctica and Its Potential Source Regions

More than 40 years of aerosol data including concentrations of particle number and of nine major ions collected over the Southern Ocean and coastal stations have been aggregated and filtered with back trajectories to reduce the risk of influence from adjacent continents. That provided a rich dataset including latitudinal distribution and seasonality of physical and chemical aerosol parameters that allow insights into aerosol sources over the Southern Ocean. These data together with statistics of back trajectory paths of high (75% percentile) and low (25% percentile) concentrations of the studied aerosol parameters were used to identify potential source regions of the respective compounds. For particle number concentrations, MSA, and the non-sea-salt fractions of Ca and potassium the most prominent source regions were found in high DMS-areas close to Antarctica, whereas the potential source regions of NH4 and the non-sea-salt fraction of Mg were located in part further north over the Southern Ocean. These geographical differences would reflect differences in the marine biota.

Effect of notch location on fatigue crack growth behavior for inhomogeneous material domains in friction stir welded magnesium alloy

The fatigue crack growth behavior of friction stir welded joints of AZ31 Mg alloy is investigated for three different notch locations. The extended finite element method based simulation is developed to capture the effect of stir zone on fatigue crack growth behavior. The notch locations with asymmetric material domain experience an asymmetric stress field, which leads to crack path deviation from the orthogonal direction to the applied force. This leads to a longer crack path. The near-tip stress field from the simulation enhances the understanding of unusual crack path. The experimental results including crack path trajectories from experiments compare well against the numerical predictions.