Trajectory Path Research Articles

Path Planning and Trajectory Tracking for Automatic Guided Vehicles.

Automated guided vehicle technology has become a hot area of scientific research due to its increasing use in manufacturing and logistics. Its main features are programming and control, remote computer eye tracking, command receiving and execution, autonomous route planning, and autonomous driving execution of tasks, with the advantages of high intelligence and flexibility. In this work, a simple vehicle model is used to study the route planning and tracking control of automatic guided vehicles. This paper uses wireless communication to find the optimal route planning problem. Using geometric methods, we develop a model of the working environment of the mobile automatic guided vehicle and develop a route finding algorithm. Based on the kinematic model, an advanced routing controller is designed to conduct experimental simulation of two trajectories and verify the effectiveness of the trajectory tracking controller. When the time is after 2 s, the position error is almost completely zero. In the path planning, when the number of iterations is greater than 10, the path length remains constant, verifying the effectiveness of the method in this paper.

OPERATION FEATURES OF AN ULTRASONIC FLOW TRANSDUCER WITH A COMPLEX TRAJECTORY OF THE MEASURING PATH

To measure the flowrate of fuel and energy resources, different methods are used, which determine the range of measuring instruments. Instruments based on ultrasonic measurement methods are widely used in measurement practice due to the provision of high accuracy, a wide range of measured flowrates, the absence of additional pressure losses and the design simplicity. Such instruments are easily integrated into automated readout systems for collecting and transmitting information. The asymmetry of the measured medium flow is a significant problem in the use of single-channel ultrasonic meters. This issue is successfully solved by using multi-channel flow transducers.
 The disadvantages of multi-channel ultrasonic flow transducers include a significant complication of the hydraulic channel design, the need to use more complex mathematical algorithms for processing the obtained output signal. This requires the use of high performance electronic elements, first of all microprocessors.
 The use of a transducer with multiple reflection of one measuring beam from the wall of the measuring path acts as an alternative to multi-channel measurement.
 The aim of the work is to create a mathematical model of an ultrasonic flow transducer with a complex measuring path trajectory. For research, a scheme with sounding along three chords was chosen, which implements the time-pulse measurement method, it provides for determining the flow rate by the difference in the time of passage of the measuring path along the flow and against it. In this case, the projections of the chords on the cross section of the measuring section create an equilateral triangle. An analytical expression for the conversion response is obtained for the case of several beam reflections in different planes.
 The authors reproduced the hydraulic part of the investigated flow transducer using simulation modeling implemented on the basis of the finite element method. Transducer’s operation simulation in the range of measured flow rates in the conditions of an ideally formed profile of the gas flow velocity in the transducer inlet cross-section has been carried out. The results obtained confirmed the absence of influence of the measuring circuit elements (beam reflectors) on the flow profile and the measuring beam trajectory.

Anti-jamming trajectory design for UAV-enabled wireless sensor networks using communication flight corridor

This paper investigates the anti-jamming trajectory design to safeguard the effective data collection, where a unmanned aerial vehicle (UAV) is dispatched to collect data over multiple sensor nodes(SNs) in jamming environment. Under the limited power and transmission range of SNs, we aim to minimize the UAV's flight energy consumption in a finite task period, by jointly optimizing SNs collection sequence and UAV flight trajectory. Firstly, we propose a general optimization framework which consists of path planning and trajectory optimization for the formulated non-convex problem. In the path planning phase, a dynamic programming (DP) algorithm is used to provide the initial path of the UAV, which is the shortest path to visit each SN. In the trajectory optimization phase, we introduce the concept of Communication Flight Corridor (CFC) to meet the non-convex constraints and apply a piecewise Bezier curve, based on Bernoulli polynomial, to represent the flight trajectory of the UAV, which can transform the optimization variables from infinite time variables to polynomial coefficients of finite order. Finally, we simulate the flight trajectory of UAV in hovering mode and continuous flight mode under different parameters, and the simulation results demonstrate the effectiveness of the proposed method.

Path Planning of Multi-Robot Systems With Boolean Specifications Based on Simulated Annealing

In this letter, we address the path planning of multi-robot systems (i.e., a team of identical mobile robots) with a global high-level specification that is given as a Boolean formula over some regions of the environment. The task is composed of logical requirements on the trajectories and the final states of the robots for some interest regions. First, a preprocessing algorithm based on Dijkstra algorithm is developed to calculate the shortest path between each interest region according to the global map information and the Boolean specification. Then, a simulated annealing based approach is developed to obtain a path trajectory of each robot such that the Boolean specification is satisfied on the final state, while the total travel distance is minimized. Finally, several numerical studies are investigated to show that the developed approach is superior to the existing methods in terms of both the computational cost and the quality of the obtained solution.

Energy Performance Analysis of a Multi-Story Building Using Building Information Modeling (BIM)

There is an enormous rise in building construction to meet serious demands of population increase. Besides its benefits, certain negative impacts on climate change and environments are associated with the built environment due to substantial energy requirements during operational phase. The current work aims to assess the energy consumption pattern of a residential facility based upon solar path analysis using simulation technique. A multi-story conventional building has been developed in a virtual 3D parametric environment using building information modeling. The BIM model was converted into the energy model using cloud computing. The energy model, at the proposed current orientation, was analyzed using insight 360 and solar energy analysis performed accordingly. Based upon the solar path analysis, the study observed that, at the present trajectory of solar path, provision of solar panels arrangements on 106,221 ft2 Photovoltaic panel area can produce the energy of 2,163,417 kwh/year with a payback period of 0.8 years.Keyword: Building Information Modelling, Energy analysis, Energy Optimization, Architecture 2030 Challenge

Planning the Shortest Carrying Trajectory Including Path and Attitude Change Considering Gripping Constraints

Carrying gripped objects at high speed can lead to gripping failure owing to loads applied to the gripper by its acceleration. When carrying an object at high speed, it is important to carry it with accelerations at which gripping is maintained. Previously proposed methods generated the shortest carrying trajectory for a predetermined trajectory path. In this study, focusing on the object-carrying trajectory and the hand attitude of the gripper, we propose a method to generate the shortest carrying trajectory by varying the state quantities. The proposed method considers the gripping force and the deformation of the gripper. During planning, we estimated the deformation volume of the gripper from its inputs and generated the shortest carrying trajectory with the allowable force and deformation values as constraints. Using the proposed method, we generated a trajectory capable of carrying objects in a shorter time (0.28 times) than in a straight-line trajectory and conducted experiments with a robot arm. The trajectory was generated while keeping the hand attitude unchanged. Through experiments, we have proved the effectiveness of keeping objects gripped on the generated shortest carrying trajectory. Compared with a success ratio of 0.47 in keeping objects gripped on the trajectory with the same time as the generated trajectory, the success ratio on the generated trajectory is as high as 1.0.

Coupled versus decoupled visuomotor feedback: Differential frontoparietal activity during curved reach planning on simultaneous functional near-infrared spectroscopy and electroencephalography.

IntroductionInteracting with the environment requires the planning and execution of reach‐to‐target movements along given reach trajectory paths. Human neural mechanisms for the motor planning of linear, or point‐to‐point, reaching movements are relatively well studied. However, the corresponding representations for curved and more complex reaching movements require further investigation. Additionally, the visual and proprioceptive feedback of hand positioning can be spatially and sequentially coupled in alignment (e.g., directly reaching for an object), termed coupled visuomotor feedback, or spatially decoupled (e.g., dragging the computer mouse forward to move the cursor upward), termed decoupled visuomotor feedback. During reach planning, visuomotor processing routes may differ across feedback types.MethodsWe investigated the involvement of the frontoparietal regions, including the superior parietal lobule (SPL), dorsal premotor cortex (PMd), and dorsolateral prefrontal cortex (dlPFC), in curved reach planning under different feedback conditions. Participants engaged in two delayed‐response reaching tasks with identical starting and target position sets but different reach trajectory paths (linear or curved) under two feedback conditions (coupled or decoupled). Neural responses in frontoparietal regions were analyzed using a combination of functional near‐infrared spectroscopy and electroencephalography.ResultsThe results revealed that, regarding the cue period, curved reach planning had a higher hemodynamic response in the left SPL and bilateral PMd and a smaller high‐beta power in the left parietal regions than linear reach planning. Regarding the delay period, higher hemodynamic responses during curved reach planning were observed in the right dlPFC for decoupled feedback than those for coupled feedback.ConclusionThese findings suggest the crucial involvement of both SPL and PMd activities in trajectory‐path processing for curved reach planning. Moreover, the dlPFC may be especially involved in the planning of curved reaching movements under decoupled feedback conditions. Thus, this study provides insight into the neural mechanisms underlying reaching function via different feedback conditions.

Numerical modeling and experimental evaluation of machining parameters for 2-dimensions ultrasonic-assisted micro-milling at low and high-speed machining

Vibration assisted machining (VAM) is one of the hybrid machining processes for improving the machined surface quality. VAM performance is mainly influenced by the combination of machining and vibration control parameters, where surface roughness value (Ra) became the benchmarking indicator. It is difficult to determine the optimum parameter combination to produce high precision products, especially for micro-milling, due to the interconnected correlation among parameters. The benefits of high-speed machining with VAM are high material removal rate and shorter machining time than low-speed machining. VAM operation at high-speed machining is still limited due to the high possibility of chatter occurrence. Therefore, this research aims to evaluate the 2D VAM resonant performance at low-speed and high-speed machining, operated at ultrasonic vibration and amplitude below one μm. The mathematical model and experimental evaluate the vibration effect based on machining mode, amplitude, and spindle speed variation. The mathematical modelling and experiment result complement each other, where the mathematical model can characterize the effect of resonant vibration, amplitude, and spindle speed increment on the tool path trajectory. The 2D resonant vibration at the feed direction causes interrupting cutting and transforms the tool path trajectory from linear to wavy. The mathematical model and experiment result show the dominant influence of spindle speed and feed rate on the toolpath trajectory and Ra, where low spindle speed and feed rate result in better machine surface roughness. The low-speed machining with VAM results in Ra value between 0.1–0.155 μm, which is below the high-speed machining result, between 0.2–0.38 μm

DEEP LEARNING-BASED ROAD SEGMENTATION OF 3D POINT CLOUDS FOR ASSISTING ROAD ALIGNMENT PARAMETERIZATION

Abstract. The need for transportation infrastructure digitalization is becoming more important, and efficient data collection and processing workflows have to be established and pose a great research challenge. This paper presents a fully automated method for the geometric parametrization of the road alignment from 3D point clouds acquired with a low-cost mobile mapping system. It exploits the Point Transformer Deep Learning architecture in order to segment the 3D point cloud in four different classes, which include road markings. Those markings are then used as a reference to extract the alignment trajectory path, classify its geometries (straight lines, circular arcs, and clothoids) and then parametrize it, extracting data to easily generate alignment data that may follow the standard schema of the Industry Foundation Classes (IFC). Both the deep learning architecture and the geometry parametrization process show promising results to develop automatic workflows that extract precise as-built data of the infrastructure from 3D point clouds.

RRR-MF: Time-domain feature reconstruction of road roughness in front of anchor machine via multimodal fusion

Road recognition technology extracts roadway roughness information to provide decision information for active control, which is an extremely important issue to be solved in the autonomous operation of anchor machines. However, the reality of road recognition technology in elevation detection of coal machine equipment is far behind earlier expectations. The RRR-MF with SINS as the backbone is proposed to realize the localization and mapping of anchor machines. In addition, The RRR-MF is designed with a feature distribution controller to enhance the roughness features of the floor point cloud and an improved Bezier curve fitting to achieve road roughness reconstruction. Meanwhile, we design an immovable high-precision measurement platform to collect standard values. LeGO-LOAM and LIO-SAM are set as control groups, and the result of RRR-MF is the closest to the standard value with half the error of the other control groups, which meets the needs of active control such as path planning and trajectory tracking.

Integration of Deep Learning Network and Robot Arm System for Rim Defect Inspection Application.

Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric vehicles. RobotStudio software was used to simulate the environment and path trajectory for a camera installed on an ABB robot arm to capture 3D images of the rims. Four types of surface defects were examined: (1) dirt spots, (2) paint stains, (3) scratches, and (4) dents. Generative adversarial network (GAN) and deep convolutional generative adversarial networks (DCGAN) were used to generate additional images to expand the depth of the training dataset. We also developed a graphical user interface and software system to mark patterns associated with defects in the images. The defect detection algorithm based on YOLO algorithms made it possible to obtain results more quickly and with higher mean average precision (mAP) than that of existing methods. Experiment results demonstrated the accuracy and efficiency of the proposed system. Our developed system has been shown to be a helpful rim defective detection system for industrial applications.

A life course view on depression: Social determinants of depressive symptom trajectories over 25 Years of Americans’ Changing Lives

ObjectivesThis study investigates heterogeneity in trajectories of depressive symptomatology in a national sample of American adults followed over 25 years. Using an innovative combination of data and methods, we sought to illuminate how depressive symptoms change over adulthood in terms of their levels and severity across 25 years, and how the social determinants of health influence differences in those trajectory paths. MethodsData come from the Americans’ Changing Lives (ACL) study, a national sample of 3617 adults (age 25+) followed over 25 years (1986–2011). Depressive symptoms were assessed with an 11-item abbreviated version of the Center for Epidemiologic Studies-Depression scale (CESD). A second-order growth mixture model was used to assess measurement invariance (addressing questions of different or changing meaning of depression) and latent trajectories of change (addressing questions of changing severity levels with aging) in depressive symptomatology over adulthood. ResultsResults indicate that the CESD was invariant across time, and depressive symptomatology followed a U-shaped form across the life course. Two latent trajectories of depressive symptoms were identified across the life course; one was a normative trajectory (60% of the sample) and the other (40% of the sample) had persistently high depressive symptoms over adulthood. Women, race/ethnic minorities, and those of lower socioeconomic position were more likely to be in the persistently depressed class. DiscussionResults are consistent with those of other studies demonstrating a U-shaped form of depressive symptoms across the life course. However, a substantial sub-population with persistent depressive symptomatology over adulthood was also identified, whose predictors suggest the need to take a social determinants of health approach to disparities related to serious and persistent mental illness.

Motion control for a differential vehicle with variable point of interest. Application: Smart cane control

This article addresses an unified solution to the trajectory tracking and path following problems for differential drive mobile robots (DDMR) considering a point of interest (PoI) with variable location relative to the vehicle. The mobile robot is modeled with an extended kinematic model avoiding typical singularities of this kind of vehicles, and allowing a straightforward definition of the corresponding inverse kinematics controller (IKC). This classical IKC fulfills the control objective with exponential error convergence but with the shortcoming of generating backward navigation when the PoI is located behind the DDMR, which is undesirable in some practical applications where the forward navigation must be preserved. This situation is theoretically analyzed, concluding that even though both forward and backward navigations correspond to equilibrium points of the closed loop, the stability of the forward navigation requires a PoI located in front of the DDMR, and the stability of the backward navigation requires a PoI located behind the DDMR. Finally, the article presents novel alternative controllers in order to always fulfill the motion objectives with stable forward navigation. Simulation results are presented to show the performance of the proposed controllers, and a real application of a robotic cane guiding its user is experimentally developed.

Using Bidirectional Long-Term Memory Neural Network for Trajectory Prediction of Large Inner Wheel Routes

When a large car turns at an intersection, it often leads to tragedy because the driver does not pay attention to the incoming car or the dead corner of the line of sight of the car body. On the market, the wheel difference warning system used in large cars generally adds sensors or lenses to confirm whether there are incoming vehicles in the dead corner of the line of sight. However, the accident rate of large vehicles has not been reduced due to the installation of a vision subsidy system. The main reason is that motorcycle and bicycle drivers often neglect to pay attention to the inner wheel difference formed when large vehicles turn, resulting in accidents with large vehicles at intersections. This paper proposes a bidirectional long-term memory neural network for the prediction of the inner wheel path trajectory of large cars, mainly from the perspective of motorcycle riders, through the combination of YOLOv4 and the stacked Bi-LSTM model used in this study to analyze the motion of large cars and predict the inner wheel path trajectory. In this study, the turning trajectory of large vehicles at the intersection is predicted by using an object detection algorithm and cyclic neural network model. Finally, the experiment shows that this study uses the stacked Bi-LSTM trajectory prediction model to predict the next second trajectory with one second trajectory data, and the prediction accuracy is 87.77%; it has an accuracy of 75.75% when predicting the trajectory data of two seconds. In terms of prediction error, the system has a better prediction error than LSTM and Bi-LSTM models.

Well Path Design and Optimization Using Composite Cubic Bezier Curves

Summary Well path design is a primary part of the well plan for well drilling. The main objective of well path design is to generate a continuous and smooth trajectory from a start point to a target point under various design constraints. The conventional approaches usually consist of a number of circular arcs and straight lines, representing build and turn sections and hold sections, respectively. This study presents a new methodology to generate a continuous and smooth well path trajectory with low dogleg severity (DLS) using composite Bezier curves. The trajectory consists of a number of piecewise cubic Bezier curves that satisfy C1 and/or C2 continuity conditions, meaning that the joining curves share the first and second derivatives at the joining points, respectively. The comprehensive methodology for determining the composite curve and the primary properties of the well trajectory is presented. Their advantages regarding feasibility and efficiency are demonstrated in case studies. The proposed method is also applied in a field case, where the automatically generated well path using composite Bezier curves is smoother with a smaller DLS. It can also be quickly updated if a real-time anticollision adjustment is required. The presented work has strong potential to be an efficient and automatic tool for path design and optimization in well planning.

An Efficient Online Trajectory Generation Method Based on Kinodynamic Path Search and Trajectory Optimization for Human-Robot Interaction Safety.

With the rapid development of robot perception and planning technology, robots are gradually getting rid of fixed fences and working closely with humans in shared workspaces. The safety of human-robot coexistence has become critical. Traditional motion planning methods perform poorly in dynamic environments where obstacles motion is highly uncertain. In this paper, we propose an efficient online trajectory generation method to help manipulator autonomous planning in dynamic environments. Our approach starts with an efficient kinodynamic path search algorithm that considers the links constraints and finds a safe and feasible initial trajectory with minimal control effort and time. To increase the clearance between the trajectory and obstacles and improve the smoothness, a trajectory optimization method using the B-spline convex hull property is adopted to minimize the penalty of collision cost, smoothness, and dynamical feasibility. To avoid the collisions between the links and obstacles and the collisions of the links themselves, a constraint-relaxed links collision avoidance method is developed by solving a quadratic programming problem. Compared with the existing state-of-the-art planning method for dynamic environments and advanced trajectory optimization method, our method can generate a smoother, collision-free trajectory in less time with a higher success rate. Detailed simulation comparison experiments, as well as real-world experiments, are reported to verify the effectiveness of our method.

Multi-segment trajectory tracking of the redundant space robot for smooth motion planning based on interpolation of linear polynomials with parabolic blend

This work presents multi-segment trajectory tracking of the redundant space robot for smooth motion planning based on interpolation of linear polynomials with the parabolic blend. First, direct kinematics equation is inhibited with cuckoo search algorithm (CSA) to avoid orientation singularity. Second, the Bezier curve has been used to depict the shape of the joint trajectory by interpolating linear polynomials with the parabolic blend. Third, CSA is used to find the optimal solution of joint trajectories by satisfying the imposed constraints (joint velocity and acceleration).Fourth, various influencing variables, such as joint angles, joint rates, joint accelerations, end-effector attitude quaternion, end-effector position, and base attitude orientation are optimized so that the end-effector smoothly achieves the desired pose by stabilizing the base attitude. At last, the jerk minimized motion planning scheme is executed concerning imposed constraints such as (joint angles, angular velocities, angular accelerations, and torque). The proposed methodology of a redundant 7 Degree of Freedom (DOF) space robot has validated the efficacy of the proposed method. Using this method, a smooth trajectory path is achieved by controlling base attitude orientation while maintaining the end-effector pose during task completion. Additionally, with proposed jerk minimized motion planning, the manipulator vibration is reduced.

Flight dynamics of aerial vehicle considering time-varying ambient wind

Novel equations of motion for aerial vehicles considering the effect of continuously differentiable time-varying ambient winds are proposed. The equations of motion consist of the aerodynamic angles and the inertial flight path angles as state variables. They cover a large range of ambient wind speeds without any approximation or linearization. Two unique angles of sequential rotations called the path-relative wind angles are proposed to parametrize the difference between the air-relative velocity and the inertial velocity caused by ambient wind terms. Moreover, since the conventional aerodynamic roll angle is no longer well-defined in a wind condition, a compatible modified version is proposed as well. The resulting state equations are structured to form a cascade system, which helps designers interpret the physical and geometrical meaning of individual subsystems and efficiently design a corresponding feedback control law. The model particularly fits motion control problems such as trajectory tracking or path following control of fixed-wing-type aerial vehicles in the presence of time-varying ambient wind. The properties and potential of the proposed formulation are discussed in depth by focusing on the meaning and use of each proposed angle and the wind estimation techniques. Numerical examples demonstrate the usefulness of the proposed model by showing how existing high-performance flight controllers initially developed for windless environments can be reused for windy environments while keeping their overall performance and formulation.

Design and Experimental Evaluation of a Hierarchical Controller for an Autonomous Ground Vehicle With Large Uncertainties

Autonomous ground vehicles (AGVs) are receiving increasing attention, and the motion planning and control problem for these vehicles has become a hot research topic. In real applications such as material handling, an AGV is subject to large uncertainties and its motion planning and control become challenging. In this paper, we investigate this problem by proposing a hierarchical control scheme, which is integrated by a model predictive control (MPC) based path planning and trajectory tracking control at the high level, and a reduced-order extended state observer (RESO) based dynamic control at the low level. The control at the high level consists of an MPC-based improved path planner, a velocity planner, and an MPC-based tracking controller. Both the path planning and trajectory tracking control problems are formulated under an MPC framework. The control at the low level employs the idea of active disturbance rejection control (ADRC). The uncertainties are estimated via a RESO and then compensated in the control in real-time. We show that, for the first-order uncertain AGV dynamic model, the RESO-based control only needs to know the control direction. Finally, simulations and experiments on an AGV with different payloads are conducted. The results illustrate that the proposed hierarchical control scheme achieves satisfactory motion planning and control performance with large uncertainties.

Asynchronous Multithreading Reinforcement-Learning-Based Path Planning and Tracking for Unmanned Underwater Vehicle

The underwater unmanned vehicle (UUV) is widely used in various marine operations, in which path planning and trajectory tracking are the critical technologies to achieve autonomous motion planning. Unlike previous research methods, this article proposes the asynchronous multithreading proximal policy optimization-based path planning (AMPPO-PP) and trajectory tracking (AMPPO-TT) algorithms and applies these two methods to different task scenarios of UUVs. Taking advantage of the AMPPO, the expensive online computational procedure is converted to an offline training process. The proposed algorithms enable the UUV to learn autonomous planning, tracking, and emergency obstacle avoiding. Besides, the algorithm architecture of the AMPPO-PP and the AMPPO-TT is described in detail. By refining the reward in each timestep and utilizing the reward-shaping trick, the reward sparsity is avoided. The goal-distance heuristic reward function is used to make the UUV explore more directionally. Various simulation environments are developed from simple to complex, along with multiple comparative experiments to verify the effectiveness of the proposed algorithms.