Trajectory Generation Scheme Research Articles

Modelling of Control Strategy for Sun Tracking of Concentrated Photovoltaic (CPV) Systems and Performance Analysis of Optical Losses Due to Tracking Errors.

Abstract Among the wide range of solar energy technologies, CPV remains a promising technology worldwide. The photovoltaic concentrator maximizes its efficiency through the use of optical concentration systems. But since optical concentration depends on direct solar radiation, maximizing the quantity of useful solar radiation received requires using a solar tracker that tracks the direction of the sun. To maximize collector efficiency, it’s crucial to choose tracking systems that align with the optical concentration factor. Inadequate focusing of direct solar radiation on the concentrator’s receiver can result in high optical losses, regardless of quality. This paper focuses on improving the accuracy and lowering the cost of the tracker trajectory generation strategy. A sun tracker simulator is first developed, allowing for the investigation of the impact of sun tracking performance a traditional control strategy is initially implemented that uses an astronomical calculation to follow the path of the sun. This strategy uses a sun position algorithm from the National Renewable Energy Laboratory to generate the tracker trajectory and track the sun’s path. When tested on the simulator, as a result, this strategy demonstrated a level of accuracy that ensured a good balance between intricacy and accuracy. Then, An investigation study was conducted to determine how this solar tracker’s precision might affect its optical efficiency. the optical losses resulting from the tracking were calculated using the provided location, The optical loss calculated for this collector’s maximum tracking error is approximately 9%. The research methodology showed that the tracker precision satisfied the requirements of the optical system under study.

A Novel Learning-Based Trajectory Generation Strategy for a Quadrotor.

In this article, a learning-based trajectory generation framework is proposed for quadrotors, which guarantees real-time, efficient, and practice-reliable navigation by online making human-like decisions via reinforcement learning (RL) and imitation learning (IL). Specifically, inspired by human driving behavior and the perception range of sensors, a real-time local planner is designed by combining learning and optimization techniques, where the smooth and flexible trajectories are online planned efficiently in the observable area. In particular, the key problems in the framework, temporal optimality (time allocation), and spatial optimality (trajectory distribution) are solved by designing an RL policy, which provides human-like commands in real-time (e.g., slower or faster) to achieve better navigation, instead of generating traditional low-level motions. In this manner, real-time trajectories are calculated using convex optimization according to the efficient and accurate decisions of the RL policy. In addition, to improve generalization performance and to accelerate the training, an expert policy and IL are employed in the framework. Compared with existing works, the kernel contribution is to design a real-time practice-oriented intelligent trajectory generation framework for quadrotors, where human-like decision-making and model-based optimization are integrated to plan high-quality trajectories. The results of comparative experiments in known and unknown environments illustrate the superior performance of the proposed trajectory generation strategy in terms of efficiency, smoothness, and flexibility.

Unified framework for geometric error compensation and shape-adaptive scanning in five-axis metrology systems

In response to the escalating demand for precise shape metrology of complex optical surfaces, this study unveils a unified geometric error compensation and trajectory planning framework tailored for high-accuracy five-axis scanning metrology systems, which remains a notably underexplored field compared to error compensation in machine tools. Founded on a unified geometric model, the proposed framework seamlessly integrates a versatile shape-adaptive trajectory planning strategy, a thorough global error sensitivity analysis approach, and an exhaustive geometric error compensation scheme. Leveraging inverse kinematics, an innovative shape-adaptive scanning trajectory generation strategy is mathematically formulated, thereby facilitating adaptable measurement trajectory generation for diverse surface geometries. Employing forward kinematics, an exhaustive geometric error model is established to extensively address the 53 distinct geometric errors in the metrology system. This proposed error model fundamentally augments conventional geometric error models in machine tool by managing not only the geometric errors from the motion system, but also those from the probe and workpiece. To streamline the error compensation procedure, a novel global error sensitivity analysis approach is introduced, identifying both system-oriented and process-oriented sensitive geometric errors for targeted compensation. Experimental validation using a standard ball, which achieved an exceptional 89.35% reduction in the root mean square of the measurement errors, further confirms the feasibility and effectiveness of the proposed framework. By offering an universal trajectory planning, sensitivity analysis and error compensation trinity for five-axis scanning metrology systems, this study sets the stage for precision advancements and design optimization across diverse configurations of metrology systems.

GeoPM-DMEIRL: A deep inverse reinforcement learning security trajectory generation framework with serverless computing

Vehicle trajectory data is essential for traffic management and location-based services. However, the release of trajectories raises serious privacy concerns because they contain sensitive information, such as home addresses and workplaces, etc, making it indispensable to consider privacy protection when releasing trajectory data. It is urgent to reconcile data utility with privacy in trajectory generation calls for advanced methods that adhere to real-world traffic rules. Leveraging serverless computing for deep learning provides an efficient solution, bypassing infrastructure complexities to handle the scale trajectory data. In this paper, a Geo-Piecewise Mechanism Deep Maximum Entropy Inverse Reinforcement Learning (GeoPM-DMEIRL) is proposed for secure trajectory generation, which consists of three key components, namely GeoPM, A2C reinforcement learning and DMEIRL. Firstly, GeoPM is a novel local differential privacy mechanism that incorporates Geo-aware gridding techniques. The Piecewise Mechanism is used to perturb the user trajectory while ensuring that the perturbed trajectory conforms to real-world traffic rules. Secondly, an A2C reinforcement learning network is refined to train the optimal trajectory generation strategy. Thirdly, Deep Maximum Entropy Inverse Reinforcement Learning is improved to train the weights of the A2C reward function. Finally, real-world data are collected in the experiments and the serverless computing platform AWS Lambda is used for training the reinforcement learning models. Experimental results show that our proposed GeoPM-DMEIRL framework can effectively resist user re-identification attacks, which can improve the utility by an average of 54.605% and enhance the privacy by an average of 30.678%. Meanwhile, GeoPM-DMEIRL is able to maintain the utility of the data while protecting the privacy of the user’s trajectories.

Stable Walking Feedback Control for a Self-Balanced Lower Limb Exoskeleton for Paraplegics

Self-Balanced lower limb exoskeleton (SBLLE) is designed for rehabilitation and walking assistance for dyskinesia persons with spinal or lower limb muscle injury to regain the locomotion ability in daily activities. In this paper, an innovative feedback control strategy for a fully actuated SBLLE is presented, which helps users to walk stably without crutches or other assistive devices. Exoskeleton is approximated as a simplified center of mass (CoM) model. Based on this simplified dynamic model, the trajectory generation and walking feedback control strategy, which enhances the locomotion stability, is introduced. The proposed strategy is implemented on our exoskeleton, AutoLEE-II, and its stability and dynamic performance are demonstrated in the stable walking simulations and experiments for exoskeleton with a female subject.

Development of a hierarchical control strategy for a soft knee exoskeleton based on wearable multi-sensor system

Soft exoskeleton is a newly emerging approach for providing assistive forces to the movement of disabled individuals, which has aroused the interest of many researchers due to its promising prospects in rehabilitation training and motion assistance. In this article, a wearable wireless multi-sensor system and a hierarchical control architecture are developed for a soft knee exoskeleton. First, the major components of the soft knee exoskeleton are introduced, including the flexible Bowden-cable actuators, the soft wearable components, and the sensing modules and the control system. The wearable wireless multi-sensor system consists of an inertial measurement unit–based knee flexion–extension angle sensing module, a surface electromyogram–based joint torque estimation module, a miniature pulling force sensing module, and a central communication unit. And then, a three-level hierarchical control strategy—which fuses a high-level motion recognition and torque estimation algorithm, a mid-level knee trajectory generation strategy, and a low-level fuzzy impedance controller—is proposed to realize human motion assistance. Finally, experiments are carried out to verify the effectiveness of the proposed control strategy. The experimental setup is established with the soft knee exoskeleton, a treadmill, a wearable robotic rotary joint with an encoder, and an inelastic rope. The experimental result indicates that the root mean square errors of the joint angle estimation strategy and joint torque estimation strategy are less than 4.3° and 4.8 Nm, while the surface electromyogram–based assistance efficiency and torque-based assistance efficiency under different loading conditions are higher than 10.2% and 18.2%, respectively.

A Convex Programming Approach to Multipoint Optimal Motion Planning for Unicycle Robots

An important extension of point-to-point motion planning deals with navigating a robot optimally through a sequence of waypoints. The resulting multipoint problem is typically nonconvex due to the requirement to optimize the waypoint passage time, which has hindered the determination of globally optimal solutions. In this paper, a new motion planning technique is presented for multipoint problems involving unicycle robots. Our main finding is that, by adopting a cost function that weights a combination of path length and maneuver time, together with a suitable tightening of the control input constraints, such type of problems can be cast as a convex program. This can be solved in polynomial time to obtain a global optimum. The proposed motion planner is compared to time-optimal trajectory generation scheme based on nonlinear programming, and it can find better trajectories than this method while being computationally faster.

Dummy trajectory generation scheme based on generative adversarial networks

Dummy trajectory generation scheme based on generative adversarial networks

Tool center point control of a large-scale manipulator using absolute position feedback

This work presents a trajectory generation and control scheme for point-to-point motion of a hydraulically driven large-scale manipulator considering various constraints such as joint position, joint velocity, task space, and volumetric flow rate constraints. Trajectories for the absolute position and orientation of the tool center point are generated by a constrained quadratic optimization problem based on differential kinematics. The use of an efficient quadratic problem solver renders the algorithm real-time capable. The control loop uses a high-quality measurement feedback in Cartesian coordinates from a robotic total station network to precisely position the tool center point. The proposed control scheme is compared to a time-optimal solution in simulations. Simulations further show the redundancy exploitation and robustness of the algorithm. Experiments give insight into the tracking quality and show that the achieved positioning accuracy is in the sub-centimeter range.

Pick–and–Place Trajectory Planning and Robust Adaptive Fuzzy Tracking Control for Cable–Based Gangue–Sorting Robots with Model Uncertainties and External Disturbances

A suspended cable–based parallel robot (CBPR) composed of four cables and an end–grab is employed in a pick–and–place operation of moving target gangues (MTGs) with different shapes, sizes, and masses. This paper focuses on two special problems of pick–and–place trajectory planning and trajectory tracking control of the cable–based gangue–sorting robot in the operation space. First, the kinematic and dynamic models for the cable–based gangue–sorting robots are presented in the presence of model uncertainties and unknown external disturbances. Second, to improve the sorting accuracy and efficiency of sorting system with cable–based gangue–sorting robot, a four-phase pick–and–place trajectory planning scheme based on S-shaped acceleration/deceleration algorithm and quintic polynomial trajectory planning method is proposed, and moreover, a robust adaptive fuzzy tracking control strategy is presented against inevitable uncertainties and unknown external disturbances for trajectory tracking control of the cable–based gangue–sorting robot, where the stability of a closed-loop control scheme is proved with Lyapunov stability theory. Finally, the performances of pick–and–place trajectory planning scheme and robust adaptive tracking control strategy are evaluated through different numerical simulations within Matlab software. The simulation results show smoothness and continuity of pick–and–place trajectory for the end–grab as well as the effectiveness and efficiency to guarantee a stable and accurate pick–and–place trajectory tracking process even in the presence of various uncertainties and external disturbances. The pick–and–place trajectory generation scheme and robust adaptive tracking control strategy proposed in this paper lay the foundation for accurate sorting of MTGs with the robot.

Traffic and Pollution Modelling for Air Quality Awareness: An Experience in the City of Zaragoza

Air pollution due to the presence of small particles and gases in the atmosphere is a major cause of health problems. In urban areas, where most of the population is concentrated, traffic is a major source of air pollutants (such as nitrogen oxides or hbox {NO}_x and carbon monoxide or CO). Therefore, for smart cities, carrying out an adequate traffic monitoring is a key issue, since it can help citizens to make better decisions and public administrations to define appropriate policies. Thus, citizens could use these data to make appropriate mobility decisions. In the same way, a city council can exploit the collected data for traffic management and for the establishment of suitable traffic policies throughout the city, such as restricting the traffic flow in certain areas. For this purpose, a suitable modelling approach that provides the estimated/predicted values of pollutants at each location is needed. In this paper, an approach followed to model traffic flow and air pollution dispersion in the city of Zaragoza (Spain) is described. Our goal is to estimate the air quality in different areas of the city, to raise awareness and help citizens to make better decisions; for this purpose, traffic data play an important role. In more detail, the proposal presented includes a traffic modelling approach to estimate and predict the amount of traffic at each road segment and hour, by combining historical measurements of real traffic of vehicles and the use of the SUMO traffic simulator on real city roadmaps, along with the application of a trajectory generation strategy that complements the functionalities of SUMO (for example, SUMO’s calibrators). Furthermore, a pollution modelling approach is also provided, to estimate the impact of traffic flows in terms of pollutants in the atmosphere: an R package called Vehicular Emissions INventories (VEIN) is used to estimate the amount of hbox {NO}_x generated by the traffic flows by taking into account the vehicular fleet composition (i.e., the types of vehicles, their size and the type of fuel they use) of the studied area. Finally, considering this estimation of hbox {NO}_x, a service capable of offering maps with the prediction of the dispersion of these atmospheric pollutants in the air has been established, which uses the Graz Lagrangian Model (GRAL) and takes into account the meteorological conditions and morphology of the city. The results obtained in the experimental evaluation of the proposal indicate a good accuracy in the modelling of traffic flows, whereas the comparison of the prediction of air pollutants with real measurements shows a general underestimation, due to some limitations of the input data considered. In any case, the results indicate that this first approach can be used for forecasting the air pollution within the city.

Autonomous Vehicle Overtaking: Modeling and an Optimal Trajectory Generation Scheme

Traffic congestion or accidents may occur as a consequence of the difficulty of performing a safe, comfortable, and efficient overtaking in a timely manner when there is a slow or stopped vehicle, cyclist, or partial lane blockage on the road. Specifically, most drivers find it challenging to overtake a sluggish vehicle on a single-lane road in the presence of vehicles coming from other directions. To resolve such overtaking concerns, this paper proposes a novel optimal trajectory generating scheme for autonomous vehicle overtaking that is both smooth and safe and can be used in a variety of traffic scenarios. The proposed scheme is based on the solution of an optimal predictive problem with the goal of minimizing driving costs while limiting collision risks in the presence of any opposite vehicle on the overtaking lane. The computational burden of the scheme is almost negligible and can be implemented in real-time. The scheme is evaluated in a variety of traffic conditions, including stopped and slow vehicles in the lane, as well as the presence or absence of a nearby opposite vehicle. The simulation results show that the proposed scheme effectively obtains the optimal trajectories even in the difficult overtaking contexts considering various constraints imposed by the road curve, opposite vehicles, and slow preceding vehicles. Finally, the optimal overtaking costs are obtained for various states of the associated vehicles, which provide an indication of the best state to initiate the overtake. The proposed technology can be employed as a fully automated system or an advanced driver assistance system (ADAS) to improve the vehicle flows at challenging driving conditions and enhance transportation sustainability.

Accurate prediction of machining cycle times by data-driven modelling of NC system's interpolation dynamics

Accurate prediction of cycle times of machining part programs plays a crucial role in process planning and part flow optimization on shop floors. This paper presents a data-driven approach to model the trajectory generation (interpolation) strategy embedded in the Numerical Control (NC) system of CNC machine tools to accurately predict their machining cycle times. Artificial Neural Networks (ANN) are trained to learn and accurately mimic how the CNC plans its feedrate profile as it accelerates, decelerates, and interpolates along complex part programs. Proposed approach is validated experimentally and shown to predict machining cycle times with >95% accuracy along tested complex machining toolpaths.

Universal Path-Following of Wheeled Mobile Robots: A Closed-Form Bounded Velocity Solution.

This paper presents a nonlinear, universal, path-following controller for Wheeled Mobile Robots (WMRs). This approach, unlike previous algorithms, solves the path-following problem for all common categories of holonomic and nonholonomic WMRs, such as omnidirectional, unicycle, car-like, and all steerable wheels. This generality is the consequence of a two-stage solution that tackles separately the platform path-following and wheels’ kinematic constraints. In the first stage, for a mobile platform divested of the wheels’ constraints, we develop a general paradigm of a path-following controller that plans asymptotic paths from the WMR to the desired path and, accordingly, we derive a realization of the presented paradigm. The second stage accounts for the kinematic constraints imposed by the wheels. In this stage, we demonstrate that the designed controller simplifies the otherwise impenetrable wheels’ kinematic and nonholonomic constraints into explicit proportional functions between the velocity of the platform and that of the wheels. This result enables us to derive a closed-form trajectory generation scheme for the asymptotic path that constantly keeps the wheels’ steering and driving velocities within their corresponding, pre-specified bounds. Extensive experimental results on several types of WMRs, along with simulation results for the other types, are provided to demonstrate the performance and the efficacy of the method.

Grinding trajectory generator in robot-assisted laminectomy surgery.

Grinding trajectory planning for robot-assisted laminectomy is a complicated and cumbersome task. The purpose of this research is to automatically obtain the surgical target area from the CT image, and based on this, formulate a reasonable robotic grinding trajectory. We propose a deep neural network for laminae positioning, a trajectory generation strategy, and a grinding speed adjusting strategy. These algorithms can obtain surgical information from CT images and automatically complete grinding trajectory planning. The proposed laminae positioning network can reach a recognition accuracy of 95.7%, and the positioning error is only 1.12mm in the desired direction. The simulated surgical planning on the public dataset has achieved the expected results. In a set of comparative robotic grinding experiments, those using the speed adjustment algorithm obtained a smoother grinding force. Our work can automatically extract laminar centers from the CT image precisely to formulate a reasonable surgical trajectory plan. It simplifies the surgical planning process and reduces the time needed for surgeons to perform such a cumbersome operation manually.

Fast Generation of Chance-Constrained Flight Trajectory for Unmanned Vehicles

In this article, a fast chance-constrained trajectory generation strategy incorporating convex optimization and convex approximation of chance constraints is designed so as to solve the unmanned vehicle path planning problem. A path-length-optimal unmanned vehicle trajectory optimization model is constructed with the consideration of the pitch angle constraint, the curvature radius constraint, the probabilistic control actuation constraint, and the probabilistic collision avoidance constraint. Subsequently, convexification technique is introduced to convert the nonlinear problem formulation into a convex form. To deal with the probabilistic constraints in the optimization model, convex approximation techniques are introduced such that the probabilistic constraints are replaced by deterministic ones while simultaneously preserving the convexity of the optimization model. Numerical results, obtained from a number of case studies, validate the effectiveness and reliability of the proposed approach. A number of comparative studies were also performed. The results confirm that the proposed design is able to produce more optimal flight paths and achieve enhanced computational performance than other chance-constrained optimization approaches investigated in this article.

Robust force tracking control via backstepping sliding mode control and virtual damping control for hydraulic quadruped robots

In order to improve the force tracking performance of hydraulic quadruped robots in uncertain and unstructured environments, an impedance-based adaptive reference trajectory generation scheme is used. Secondly, in order to improve the robustness to environmental changes and reduce the contact force errors caused by trajectory tracking errors, the backstepping sliding mode controller is combined with the adaptive reference trajectory generator. Finally, a virtual damping control based on velocity and pressure feedback is proposed to solve the problem of contact force disappearance and stall caused by sudden environmental change. The simulation results show that the proposed scheme has higher contact force tracking accuracy when the environment is unchanged; the contact force error can always be guaranteed within an acceptable range when the environment is reasonably changed; when the environment suddenly changes, the drive unit can move slowly until the robot re-contacts the environment.

Real-Time Trajectory Generation and Reachability Determination in Autorotative Flare

Autorotation maneuvers inherently offer little margin for error in execution and induce high pilot workload, particularly as the aircraft nears the ground in an autorotative flare. Control augmentation systems may potentially reduce pilot workload while simultaneously improving the likelihood of a successful landing by offering the pilot appropriate cues. This paper presents an initial investigation of a real-time trajectory generation scheme for autorotative flare based on time-to-contact theory. The algorithm exhibits deterministic runtime performance and provides a speed trajectory that can be tracked by a pilot or inner-loop controller to bring the vehicle to a desired landing point at the time of touchdown. A low-order model of the helicopter dynamics in autorotation is used to evaluate dynamic feasibility of the generated trajectories. By generating and evaluating trajectories to an array of candidate landing points, the set of reachable landing points in front of the aircraft is determined. Simulation results are presented in which the trajectory generator is coupled with a previously derived autorotation controller. Example cases and trade studies are conducted in a six degree-of-freedom simulation environment to demonstrate overall performance as well as robustness of the algorithm to variations in target landing point, helicopter gross weight, and winds. The robustness of the reachability determination portion of the algorithm is likewise evaluated through trade studies examining off-nominal flare entry conditions and the effects of winds.

A tool path generation method for quasi-intermittent vibration assisted swing cutting

During the machining of freeform surfaces, the tool path will directly affect the machining accuracy of the surface, the execution of each axis of the machine tool, and the machining efficiency. Therefore, tool path planning is a very critical link in all types of diamond turning processes. In this paper, a new tool path generation strategy is proposed for machining freeform surfaces by quasi-intermittent vibration assisted swing cutting (QVASC) method. Due to the unique tool swing motion law of QVASC, the effective central angle of tool nose arc participating in the cutting is a parameter that is ignored by traditional cutting and is considered. This makes the generation of tool trajectories, tool geometry selection and freeform surfaces very different from traditional diamond cutting. According to the principle of QVASC, the tool parameters are analysed, and the tool position is designed in the cylindrical coordinate system. Interpolation was then performed by the Hermite spline interpolation theorem. The application of this strategy is discussed, and the sinusoidal surface, sinusoidal mesh surface and toric surface are taken as examples to simulate. The simulation succeeded in obtaining the tool path corresponding to the three curved surfaces processed by the QVASC method. The results prove that the tool trajectory generation strategy proposed in this paper is feasible. The proposed tool path generation strategy can provide a new reference for future freeform surfaces processing.

Linear Interpolation of machining tool-paths with robust vibration avoidance and contouring error control

This paper presents a reference trajectory (command) generation scheme to robustly avoid residual vibrations that occur due to rapid acceleration of machine axes in high-speed machining and positioning tasks. A robust Finite impulse response (FIR) filter is designed to generate reference motion commands for accurate real-time interpolation of linear toolpaths along point-to-point (P2P) and non-stop contouring trajectories. The designed FIR filter generates reference acceleration profiles with wide attenuation bands in the frequency spectrum to avoid unwanted vibrations. Design and tuning principles for the robust FIR filter are presented, and kinematics of the generated reference trajectories are analyzed. Contour errors that occur during rapid non-stop linear interpolation are estimated from the filter dynamics. A feedrate scheduling method is introduced to analytically confine those interpolation contour errors within user specified tolerances. Effectiveness of the proposed trajectory generation scheme is validated in actual machining tests, and it is benchmarked against the state-of-the-art technique. It is shown that developed FIR-based trajectory generator can robustly avoid unwanted inertial vibrations to produce smoother surfaces without elongating cycle times.