Trajectory Adjustment Research Articles

Application of Mobile Mapping System for a Modern Topography

Abstract The mobile 3D mapping systems (MMS) and the integration with Geographical Information Systems (GIS) represent an innovative and efficient approach to modern topography. It is recommended and useful for infrastructure analysis and urban planning. This modern technology provides accurate, fast, and detailed data, facilitating well-informed decision-making and contributing to the development of sustainable infrastructure in the 21st century. The purpose of this research was to use a mobile mapping system for land surveying measurement of an urban area, independent of pre-established ground control points. On the other hand, the research is supposed to demonstrate the usefulness of kinematic trajectory, digital imaging, and laser scanning in acquiring, storing, and processing point cloud data. This paper shows an affordable method and a modern scanning instrument used for mobile mapping scanning, to create a georeferenced point cloud (PC). The obtained result has centimetric precision of georeferenced point cloud data, even if the average speed of the car was about 30km/h. The trajectory adjustment with static GNSS measurements increases the accuracy of ground control points (GCPs) up to ±5cm for horizontal positioning and vertical positioning. Although this study was carried out while the system was mounted on a car the result followed the national accuracy requirements for land surveying and cadastre fields. In other words, the mobile mapping system is a faster and cost-effective in time alternative to static laser scanning and it is important to continue to explore and adopt these technological solutions to face the future challenges in the field of urban development and infrastructure.

Traffic safety improvement method for highway tunnel entrances based on linear guiding − An engineering practice from China

To improve traffic safety at the entrance of highway tunnels, a comparative analysis of the traffic environment inside and outside the tunnel was conducted, and an improvement scheme based on “linear visual guiding” was developed by considering the human factors. Before and after the improvement of nine experimental tunnels, two batches of vehicle experiments were conducted to analyze the improvement effect of linear guiding scheme on traffic safety. The results show that the linear guiding scheme warns drivers and enables them to make preparations to enter the tunnel earlier. After improvement, the starting position for deceleration (SPD) was 61% earlier and the length of the deceleration section (LDS) has increased by 42%; the starting position for trajectory change (SPTC) was 153% earlier and the length of the trajectory change section (LTCS) has increased by 44%. The improved deceleration and trajectory change are earlier and smoother, and the dramatic changes in speed and trajectory in the tunnel entrance are resolved. When entering the tunnel, drivers sequentially complete deceleration, vehicle trajectory adjustment, and visual adaptation to black-hole effect, disperses driving tasks, reducing the coupling of driving risks. And use linear guiding facilities to enhance the local brightness of the tunnel, enhancing the driver’s visual perception ability in the black-hole effect area. Linear guiding scheme has good results during day, night, and fog, and is able to improve traffic safety at tunnel entrances in all-weather and multiple time periods.

CGDS near-bit geosteering drilling system and its first application in Niger

Abstract The Niger project is one of the important exploration and development cooperation projects of China National Petroleum Corporation in West Africa. The Dibeilla N-1 block of Agadem oilfield in Niger faces challenges such as frequent interlayers and susceptibility to encountering faults during drilling. According to the deployment requirements of the plan, the block will be developed using a mixed well network of horizontal and vertical wells, and the upper sand body will need to be developed using horizontal wells. The dogleg angle of the inclined wellbore should be controlled within 2∼4.5 (°)/30m, and the target thickness should be only 3m. Conventional guiding tools represented by MWD(Measurement While Drilling) cannot achieve effective and efficient reservoir identification and trajectory adjustment in thin oil layers due to their zero length of around 10 meters. Therefore, a geological guiding tool that can achieve near drill bit measurement is needed. Therefore, in order to successfully complete the horizontal section drilling and guidance engineering, maximize the drilling encounter rate of high-quality reservoirs, communicate favorable layers to achieve oil reservoir benefit development, it is decided to use CGDS(CNPC Geological Drilling System)near bit geological guidance tool to implement horizontal section drilling. The system is equipped with gamma, resistivity, and engineering parameter measurements at a distance of 3 meters from the drill bit, providing multiple basis for precise directional guidance of the formation. In the Dibeilla N-1 block of Niger, a total of 6 wells were applied, with an average reservoir drilling encounter rate of 85.38% and a mechanical drilling speed of 12.31 m/h. The first application of this system overseas has provided valuable experience for similar domestic work.

Continuous measurement assists dynamic adjustment of wellbore trajectory

Abstract In the process of directional well drilling, how to accurately model the real-time well trajectory is of great significance to improving the reservoir encounter rate and recovery rate. This paper provides a method to improve the accuracy of well trajectory by cleaning and correcting the continuous measurement data in combination with fixed measurement data, and then using the continuous measurement data for trajectory modeling. The continuous measurement data comes from the CG-STEER rotary steerable system. The well trajectory calculated by the continuous measurement data corrected by the above method greatly reduces the vertical depth calculation error during the drilling process, and effectively reduces the vertical depth error accumulation caused by the large spacing between fixed measurement points. It can effectively improve the drilling target accuracy and horizontal section penetration rate, and reduce invalid drilling.

Effect of the cone-beam CT acquisition trajectory on image quality in spine surgery: experimental cadaver study

BACKGROUNDIntraoperative 3D imaging with cone-beam CT (CBCT) improves assessment of implant position and reduces complications in spine surgery. It is also used for image-guided surgical techniques, resulting in improved quality of care. However, in some cases, metal artifacts can reduce image quality and make it difficult to assess pedicle screw position and reduction. PURPOSEThe objective of this study was to investigate whether a change in CBCT acquisition trajectory in relation to pedicle screw position during dorsal instrumentation can reduce metal artifacts and consequently improve image quality and clinical assessability. STUDY DESIGNExperimental cadaver study. METHODSA human cadaver was instrumented with pedicle screws in the thoracic and lumbar spine region (Th11 to L5). Then, the acquisition trajectory of the CBCT (Cios Spin, Siemens, Germany) to the pedicle screws was systematically changed in 5° steps in angulation (−30° to +30°) and swivel (−25° to +25°). Subsequently, radiological evaluation was performed by 3 blinded, qualified raters on image quality using 9 questions (including anatomical structures, implant position, appearance of artifacts) with a score (1–5 points). For statistical evaluation, the image quality of the different acquisition trajectories was compared to the standard acquisition trajectory and checked for significant differences. RESULTSThe angulated acquisition trajectory significantly increased the score for subjective image quality (p<.001) as well as the clinical assessability of pedicle screw position (p<.001) with particularly strong effects on subjective image quality in the vertebral pedicle region (d=1.61). Swivel of the acquisition trajectory significantly improved all queried domains of subjective image quality (p<.001) as well as clinical assessability of pedicle screw position (p<.001). CONCLUSIONSIn this cadaver study, the angulation as well as the swivel of the acquisition trajectory led to a significantly improved image quality in intraoperative 3D imaging (CBCT) with a constant isocenter. The data show that maximizing the angulation/swivel angle towards 30°/25° provides the best tested subjective image quality and enhances clinical assessability. Therefore, a correct adjustment of the acquisition trajectory can help to make intraoperative revision decisions more reliably. CLINICAL SIGNIFICANCEThe knowledge of enhanced image quality by changing the acquisition trajectory in intraoperative 3D imaging can be used for the assessment of critical screw positions in spine surgery. The implementation of this knowledge requires only a minor change of the current intraoperative imaging workflow without additional technical equipment and could further reduce the need for revision surgery.

The Continuous Jump Control of a Locust-Inspired Robot With Omnidirectional Trajectory Adjustment

The Continuous Jump Control of a Locust-Inspired Robot With Omnidirectional Trajectory Adjustment

Multi-Object Trajectory Prediction Based on Lane Information and Generative Adversarial Network.

Nowadays, most trajectory prediction algorithms have difficulty simulating actual traffic behavior, and there is still a problem of large prediction errors. Therefore, this paper proposes a multi-object trajectory prediction algorithm based on lane information and foresight information. A Hybrid Dilated Convolution module based on the Channel Attention mechanism (CA-HDC) is developed to extract features, which improves the lane feature extraction in complicated environments and solves the problem of poor robustness of the traditional PINet. A lane information fusion module and a trajectory adjustment module based on the foresight information are developed. A socially acceptable trajectory with Generative Adversarial Networks (S-GAN) is developed to reduce the error of the trajectory prediction algorithm. The lane detection accuracy in special scenarios such as crowded, shadow, arrow, crossroad, and night are improved on the CULane dataset. The average F1-measure of the proposed lane detection has been increased by 4.1% compared to the original PINet. The trajectory prediction test based on D2-City indicates that the average displacement error of the proposed trajectory prediction algorithm is reduced by 4.27%, and the final displacement error is reduced by 7.53%. The proposed algorithm can achieve good results in lane detection and multi-object trajectory prediction tasks.

Energy-Efficient Speed Planning for Autonomous Driving in Dynamic Traffic Scenarios

In the field of autonomous driving, velocity planning is of paramount importance for handling dynamic obstacle scenarios. To avoid unnecessary acceleration and deceleration, self-driving vehicles need to find an energy-optimized velocity trajectory. Moreover, in complex traffic environments, the vehicle trajectory must consider the spatio-temporal coupling problem to avoid unrealistic driving paths. To address these challenges, this paper proposes a hierarchical planner that first plans the path and then performs speed planning based on the already planned path. Specifically, we focus on the energy consumption factor and use dynamic programming for speed planning while combining safety and comfort considerations. The optimal energy-saving trajectory is obtained by combining the speed profile with the optimal path. To cope with complex scenarios on real roads, we propose an adaptive trajectory adjustment strategy based on model predictive control to track by adaptively selecting tracking modes. Finally, hardware-in-the-loop experimental validation demonstrates that our proposed method significantly reduces energy consumption compared with the traditional decoupling method while ensuring that the autonomous vehicle adapts well to complex traffic scenarios.

A Theoretical Method and Model for TBM Tunnelling Trajectory Adjustment

TBM tunnelling trajectory adjustment is a significant issue in guaranteeing a tunnel project. This paper proposes a theoretical method of trajectory adjustment aimed at synchronously reducing bias angle and distance. The proposed method can be divided into three steps. First, the need for manual intervention in the tunnelling trajectory is determined based on the current bias angle and distance. Next, if manual intervention is required, a theoretical relationship is established using the current bias angle and distance as an input and the rotated angle of the TBM main beam as an output. Finally, a series of constraints according to the normal working conditions of the TBM are incorporated into the method. These constraints ensure that the method remains within the acceptable range of TBM operation. Using mechanical parameters such as the cutterhead diameter and cutter spacing of the TBM used in the sixth section of the Qingdao Metro Project, a model suitable for the project is established, and the model is modified by the tunnelling trajectory data collected in the field. This method provides a reference for TBM drivers to use to correct the tunnelling trajectory while considering the specific conditions of the project.

A High-Efficient Reinforcement Learning Approach for Dexterous Manipulation.

Robotic hands have the potential to perform complex tasks in unstructured environments owing to their bionic design, inspired by the most agile biological hand. However, the modeling, planning and control of dexterous hands remain unresolved, open challenges, resulting in the simple movements and relatively clumsy motions of current robotic end effectors. This paper proposed a dynamic model based on generative adversarial architecture to learn the state mode of the dexterous hand, reducing the model's prediction error in long spans. An adaptive trajectory planning kernel was also developed to generate High-Value Area Trajectory (HVAT) data according to the control task and dynamic model, with adaptive trajectory adjustment achieved by changing the Levenberg-Marquardt (LM) coefficient and the linear searching coefficient. Furthermore, an improved Soft Actor-Critic (SAC) algorithm is designed by combining maximum entropy value iteration and HVAT value iteration. An experimental platform and simulation program were built to verify the proposed method with two manipulating tasks. The experimental results indicate that the proposed dexterous hand reinforcement learning algorithm has better training efficiency and requires fewer training samples to achieve quite satisfactory learning and control performance.

Optimal delta-V-based strategies in orbital pursuit-evasion games

This research investigates the orbital pursuit-evasion games with impulsive maneuvers in long-distance interception scenarios. Specifically, this paper examines the influences of escape impulses, with different directions, applied by the evader on the intercept-correction delta-v (ΔVCorr) of the pursuer that is already on an interception orbit. After obtaining the evader’s strategies based on maximizing the ΔVCorr under different circumstances, it is found that, with the knowledge of the evader’s best strategy, the pursuer could make ΔVCorr smaller by adjusting its initial interception orbit. Then, a method to modify the initial orbit with energy constraints is designed so that the pursuer can match the evader’s escape maneuver with less delta-v to prepare. Finally, this paper summarizes the best strategies of both sides after considering the initial trajectory adjustment and discusses the impact of incomplete information on tactics in orbital pursuit-evasion games.

RadarPDR: Radar-Assisted Indoor Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measurement errors and sensing drift, the accuracy of walking direction, step detection, and step length estimation cannot be guaranteed, leading to large accumulative tracking errors. In this paper, we propose a radar-assisted PDR scheme, called RadarPDR, which integrates a frequency-modulation continuous-wave (FMCW) radar to assist the inertial sensors-based PDR. We first establish a segmented wall distance calibration model to deal with the radar ranging noise caused by irregular indoor building layouts and fuse wall distance estimation with acceleration and azimuth signals measured by the inertial sensors of a smartphone. We also propose a hierarchical particle filter(PF) together with an extended Kalman filter for position and trajectory adjustment. Experiments have been conducted in practical indoor scenarios. Results demonstrate that the proposed RadarPDR is efficient and stable and outperforms the widely used inertial sensors-based PDR scheme.

A Method for a Compliant Robot Arm to Perform a Bandaging Task on a Swaying Arm: A Proposed Approach

Bandaging is a regular and indispensable nursing task in daily medical care. The task is challenging for robots because it requires simultaneous control of the bandaging trajectory and tension. The patient’s involuntary swaying due to pain and other reasons may cause further complications. To solve these problems, a compliant robot arm based on the serial elastic actuator (SEA) is selected as the hardware solution for this task. In this article, a method for a force–position-decoupling control strategy is developed for achieving the full process of bandage tension control as well as bandaging trajectory adjustment and following. Experiments are conducted to evaluate the performance of the proposed design and methods, using five subjects and a collaborative robot. The results show that the proposed robot arm successfully performs the bandaging task and that the proposed methods can effectively enable the robot to estimate the movement direction and position of the injured arm. The bandage tension can be effectively maintained even when the arm is swaying.

Trajectory planning method with grinding compensation strategy for robotic propeller blade sharpening application

A propeller is the main part of a water-borne carrier's drive system. Geometrical shape accuracy and sharpness on the cutting side of the propeller's blade play vital roles to minimize fluid resistance and improve transmission efficiency. For Jet Ski and small boats, the significant thickness variation along the blade edge is embedded in the lost-wax casting process. The automatic trajectory planning for the robotic propeller blade's sharpening operation is challenging due to its complicated curved shape and the tiny gaps between two connected blades. The trajectory planning for robotic grinding based on the edge thickness distribution of the standard model may result in over- or under-cutting due to the highly inconsistent shape deformation caused by the casting process. To address the aforementioned challenges, this study proposes a fully automated robot grinding system. The proposed method combines a novel trajectory planning approach based on a parametric and point cloud model with a 3D scanning on edge-based trajectory adjustment approach. Moreover, to improve the grinding accuracy, a laser-vision sensor-based detection algorithm is also developed for grinding compensation to counteract the geometrical variation caused by the casting process. The effectiveness of the proposed system is validated through numerous experimental trials using a 6-DOF industrial robot. The research findings showcase that the proposed technique is feasible, stable, and effective for robotic propeller blade edge sharpening operations.

A policy optimization algorithm based on sample adaptive reuse and dual-clipping for robotic action control

When applying deep reinforcement learning in the real physical environment for decision-making, how to improve the sample efficiency while ensuring training stability is an urgent problem that needs to be solved. In order to solve this problem, some of on-policy algorithms are proposed and have achieved state-of-the-art performance. However, these on-policy algorithms, such as proximal policy optimization (PPO) algorithm, have the drawback of extremely low sample efficiency. In this study, we proposed a novel policy optimization method named improved proximal policy optimization algorithm based on sample adaptive reuse and dual-clipping (SARD-PPO) for robotic action control, which combines the advantage of the on-policy methods in training stability with the advantage of the off-policy methods in sample efficiency. First, we analyzed the clipping mechanism of the PPO algorithm, devised a more constrained clipping mechanism based on the analysis of the relationship between the clipping mechanism and the objective constraints, and developed a policy updating method that reuses the old samples of the prior policy in a more principle-based way. Second, we ensured the training stability of the algorithm through element-level dual-clipping, as well as adaptive adjustment and reuse of the entire policy trajectory. The experimental results on six tasks in the MuJoCo benchmark indicate that SARD-PPO can significantly improve policy performance while balancing policy training stability and sample efficiency, outperforming the baseline PPO algorithm and other SOTA policy gradient methods using on- and off-policy samples in terms of overall performance.

High-Performance Compact Pre-Lens Retarding Field Energy Analyzer for Energy Distribution Measurements of an Electron Gun.

The energy distribution of an electron gun is one of the most important characteristics determining the performance of electron beam-based instruments, such as electron microscopes and electron energy loss spectroscopes. For accurate measurements of the energy distribution, this study presents a novel retarding field energy analyzer (RFEA) with the feature of an additional integrated pre-lens, which enables an adjustment of beam trajectory into the analyzer. The advantages of this analyzer are its compact size and simple electrode configuration. According to trajectory simulation theories, the optimum condition arises when the incident electron beam inside the RFEA is focused on the center of a retarding electrode. Comparing I–V curves depending on whether the pre-lens working or not, it is confirmed that the use of the pre-lens dramatically improves the energy resolution and efficiency of the signal acquisition process. The pre-lens RFEA was applied to characterize a Schottky electron gun under various temperatures and extraction voltages as operational conditions. When the tip temperature was increased by 50 K, we were able to measure an energy distribution broadening of 13.8 meV with the proposed pre-lens RFEA. The relative standard deviation of energy distribution was 0.7% for each working condition.

Collective Conditioned Reflex: A Bio-Inspired Fast Emergency Reaction Mechanism for Designing Safe Multi-Robot Systems

A multi-robot system (MRS) is a group of coordinated robots designed to cooperate with each other and accomplish given tasks. Due to the uncertainties in operating environments, the system may encounter emergencies, such as unobserved obstacles, moving vehicles, and extreme weather. Animal groups such as bee colonies initiate collective emergency reaction behaviors such as bypassing obstacles and avoiding predators, similar to muscle conditioned reflex which organizes local muscles to avoid hazards in the first response without delaying passage through the brain. Inspired by this, we develop a similar collective conditioned reflex mechanism for multi-robot systems to respond to emergencies. In this study, Collective Conditioned Reflex (CCR), a bio-inspired emergency reaction mechanism, is developed based on animal collective behavior analysis and multi-agent reinforcement learning (MARL). The algorithm uses a physical model to determine if the robots are experiencing an emergency; then, rewards for robots involved in the emergency are augmented with corresponding heuristic rewards, which evaluate emergency magnitudes and consequences and decide local robots' participation. CCR is validated on three typical emergency scenarios: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">turbulence, strong wind, and hidden obstacle</i> . Simulation results demonstrate that CCR improves robot teams' emergency reaction capability with faster reaction speed and safer trajectory adjustment compared with baseline methods.

Biased Stackelberg game‐based UAV relay anti‐jamming communications: Exploiting trajectory optimization and transmission mode selection

Although unmanned aerial vehicle (UAV) relay can provide auxiliary communication due to its flexible mobility, it is vulnerable to jamming attacks. This paper considers the UAV relay anti-jamming communication issue under the threat of a malicious jammer with beam-forming jamming capability. To prevent the relay link from deteriorating, UAV trajectory adjustment and transmission mode switching between half-duplex and full-duplex are two available schemes, while they will incur the additional flying costs and continuous mode switching, respectively. To balance the trade-off between trajectory optimization and mode selection, this paper investigates the joint trajectory optimization and mode selection anti-jamming approach. First, an anti-jamming utility considering the cost-efficient and end-to-end capacity gains is designed. Second, to model the bounded rationality of both the UAV relay and the jammer due to the adversarial context, a biased Stackelberg game to analyse the competitive system interactions is proposed. Moreover, the existence of Stackelberg equilibrium (SE) in the problem is proved. Finally, a joint mode selection and trajectory optimization (JMSTO) algorithm based on the multi-armed bandit is proposed to obtain the SE. It is further demonstrated that the JMSTO algorithm has a logarithmic regret. The results show that our proposed JMSTO algorithm is superior to non-joint optimization methods.

A 45-degree ion-extraction photoelectron velocity map imaging apparatus coupled to the DUV-IR mass spectrometry and spectroscopy instrument

A photoelectron velocity-map imaging (VMI) apparatus has been developed from the DUV-IR photoionization mass spectrometry and spectroscopy instrument. A 45-degree plate-set is designed and combined with the upright reflection time-of-flight mass spectrometer (Re-TOFMS) to pick up target cluster anions and change their trajectory after the vertical reflection. Following the ion-extraction zone, the mass-selected cluster anions then pass through up-down and left-right trajectory adjustment and beam focus until the arrival at the photoelectron detachment zone to encounter the normal incidence of a pulsed UV laser. The detached electrons are subject to acceleration and detected by a dual micro-channel plate detector and imaging on the phosphor screen. This 45-degree ion-extraction photoelectron VMI setup not only expands the function of our DUV-IR instrument for structural chemistry but also facilitates the compatibility with the conventional upright reflection of TOFMS.

Practical Part-Specific Trajectory Optimization for Robot-Guided Inspection via Computed Tomography

Robot-guided computed tomography enables the inspection of parts that are too large for conventional systems and allows, for instance, the non-destructive and volumetric evaluation of mechanical joining components within already assembled cars in the automotive industry. However, the typical scan time required by such setups is still significant and represents a major barrier for its industrial large-scale application. As an approach to mitigate the necessary time demand, we propose a part-specific adjustment of the acquisition trajectory. Common circular standard trajectories are inherently inefficient, since they are applied independently of the considered inspection task, while the use of acquisition orbits tailored particularly to the investigated object effectively allows a reduction of the required number of projections, which in turn has the potential to directly decrease the scan time significantly. In contrast to former simulation-guided approaches, this work is considered to be the first successful task-specific trajectory optimization being performed on a robot-based industrial CT platform and aims towards providing a first proof of concept that such methods can be practically applied in a shop floor environment. Based on representative results, a reduction of the number of required projections by approx. 55 % or an image quality improvement according to the root-mean squared error by approx. 40 % compared to the conventionally applied planar acquisition trajectory was achieved.