Precise Trajectory Research Articles

Enhanced three-dimensional trajectory tracking control for AUVs in variable operating conditions using FMPC-FTTSMC

In addressing the variable operating conditions encountered in complex underwater tasks, an enhanced three-dimensional tracking control method is proposed for autonomous underwater vehicle (AUV) based on fuzzy model predictive control-finite time terminal sliding mode control (FMPC-FTTSMC). Firstly, to enhance adaptability to tasks and environments, fuzzy model predictive control method is designed to achieve precise three-dimensional trajectory tracking. Additionally, a fuzzy weight allocator is employed to enable AUV to autonomously adjust optimization strategies based on real-time states such as task type, speed, and distance from the seabed, thus better coping with changing conditions and improving the universality of the control method. Secondly, a dynamic controller is designed using the finite-time terminal sliding mode control method, combined with a finite-time radial basis function neural network (FTRBFNN) disturbance observer to accurately estimate disturbances. Finally, simulation results demonstrate that the proposed method effectively reduces optimization solving time compared to traditional model predictive control, achieves trajectory tracking control under various conditions, meets preset state constraints, and demonstrates excellent tracking accuracy and robustness.

A versatile door opening system with mobile manipulator through adaptive position-force control and reinforcement learning

The ability of robots to navigate through doors is crucial for their effective operation in indoor environments. Consequently, extensive research has been conducted to develop robots capable of opening specific doors. However, the diverse combinations of door handles and opening directions necessitate a more versatile door opening system for robots to successfully operate in real-world environments. In this paper, we propose a mobile manipulator system that can autonomously open various doors without prior knowledge. By using convolutional neural networks, point cloud extraction techniques, and external force measurements during exploratory motion, we obtained information regarding handle types, poses, and door characteristics. Through two different approaches, adaptive position-force control and deep reinforcement learning, we successfully opened doors without precise trajectory or excessive external force. The adaptive position-force control method involves moving the end-effector in the direction of the door opening while responding compliantly to external forces, ensuring safety and manipulator workspace. Meanwhile, the deep reinforcement learning policy minimizes applied forces and eliminates unnecessary movements, enabling stable operation across doors with different poses and widths. The RL-based approach outperforms the adaptive position-force control method in terms of compensating for external forces, ensuring smooth motion, and achieving efficient speed. It reduces the maximum force required by 3.27 times and improves motion smoothness by 1.82 times. However, the non-learning-based adaptive position-force control method demonstrates more versatility in opening a wider range of doors, encompassing revolute doors with four distinct opening directions and varying widths.

Real-time deep learning-based model predictive control of a 3-DOF biped robot leg

Our research utilized deep learning to enhance the control of a 3 Degrees of Freedom biped robot leg. We created a dynamic model based on a detailed joint angles and actuator torques dataset. This model was then integrated into a Model Predictive Control (MPC) framework, allowing for precise trajectory tracking without the need for traditional analytical dynamic models. By incorporating specific constraints within the MPC, we met operational and safety standards. The experimental results demonstrate the effectiveness of deep learning models in improving robotic control, leading to precise trajectory tracking and suggesting potential for further integration of deep learning into robotic system control. This approach not only outperforms traditional control methods in accuracy and efficiency but also opens the way for new research in robotics, highlighting the potential of utilizing deep learning models in predictive control techniques.

MRI-based axis-referenced morphometric model corresponding to lamellar organization for assessing hippocampal atrophy in dementia.

Research on the local hippocampal atrophy for early detection of dementia has gained considerable attention. However, accurately quantifying subtle atrophy remains challenging in existing morphological methods due to the lack of consistent biological correspondence with the complex curving regions like the hippocampal head. Thereby, this article presents an innovative axis-referenced morphometric model (ARMM) that follows the anatomical lamellar organization of the hippocampus, which capture its precise and consistent longitudinal curving trajectory. Specifically, we establish an "axis-referenced coordinate system" based on a 7 T ex vivo hippocampal atlas following its entire curving longitudinal axis and orthogonal distributed lamellae. We then align individual hippocampi by deforming this template coordinate system to target spaces using boundary-guided diffeomorphic transformation, while ensuring that the lamellar vectors adhere to the constraint of medial-axis geometry. Finally, we measure local thickness and curvatures based on the coordinate system and boundary surface reconstructed from vector tips. The morphometric accuracy is evaluated by comparing reconstructed surfaces with those directly extracted from 7 T and 3 T MRI hippocampi. The results demonstrate that ARMM achieves the best performance, particularly in the curving head, surpassing the state-of-the-art morphological models. Additionally, morphological measurements from ARMM exhibit higher discriminatory power in distinguishing early Alzheimer's disease from mild cognitive impairment compared to volume-based measurements. Overall, the ARMM offers a precise morphometric assessment of hippocampal morphology on MR images, and sheds light on discovering potential image markers for neurodegeneration associated with hippocampal impairment.

GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data

Signaling data are records of the interactions of users’ mobile phones with their nearest cellular stations, which could provide long-term and continuous-time location data of large-scale citizens, and therefore have great potential in intelligent transportation, smart cities, and urban sensing. However, utilizing the raw signaling data often suffers from two problems: (1) Low positioning accuracy. Since the signaling data only describes the interaction between the user and the mobile base station, they can only restore users’ approximate geographical location. (2) Poor data quality. Due to the limitations of mobile signals, user signaling may be missing and drifting. To address the above issues, we propose a graph-enhanced trajectory recovery network, GenTrajRec, to recover precise trajectories from signaling data. GenTrajRec encodes signaling data through spatiotemporal encoders and enhances the traveling semantics by constructing a signaling transition graph. In fusing the spatiotemporal information as well as the deep traveling semantics, GenTrajRec can well tackle the challenge of poor data quality, and recover precise trajectory from raw signaling data. Extensive experiments have been conducted on two real-world datasets from Mobile Signaling and Geolife, and the results confirm the effectiveness of our approach, and the positioning accuracy can be improved from 315 m per point to 82 m per point for signaling data using our network.

Performance-prescribed optimal neural control for hypersonic vehicles considering disturbances: An adaptive dynamic programming approach

The precise trajectory tracking control of hypersonic vehicles plays a crucial role in secure flight. To address this challenge, this article develops an optimal neural control framework with prescribed performance constraints for height-velocity control tasks with disturbances. To this end, the control-oriented error model is firstly established and transformed into an affine nonlinear form to provide a foundation for the design of controller. Then, in order to balance performance constraints and convergence speed, a performance-constraint function is introduced to convert the constrained tracking error into an unconstrained one. After that, an improved sliding mode compensation observer is proposed by combining low-fidelity approximation data with high-fidelity compensation model for eliminating disturbances. The developed control scheme is organized by an adaptive dynamic programming (ADP) algorithm based on the actor-critic policy structure to achieve the near-optimal control strategy. Meanwhile, the designed neural network (NN) weight updating laws are provided to obtain the solution of Hamiltonian-Jacobi-Bellman (HJB) equation rapidly without high calculation consumption. Finally, the stability conditions of the closed-loop system are performed and ensure the entire framework is semi-global uniformly ultimately bounded (SGUUB), two simulation cases verify the effectiveness and superiority of the proposed approach.

The flashy escape: support for dynamic flash coloration as anti-predator defence.

Dynamic flash coloration is a type of antipredator coloration where intermittently appearing colour patterns in moving animals misdirect predator attacks by obscuring the precise location and trajectory of the moving prey. Birds and butterflies with differing dorsoventral wing coloration or iridescent surface structures may potentially benefit from such effects. However, we lack an understanding of what makes for an effective dynamic flash colour design and how much it benefits the carrier. Here, we test the effect of colour flashing using small passerine birds preying upon colourful, moving, virtual 'prey' stimuli on a touchscreen. We show that at fast speeds, green-to-blue flashing colour patterns can reduce the likelihood of pecks hitting the target, induce greater error in targeting accuracy and increase the number of pecks at a stimulus relative to similarly coloured non-flashing targets. Our results support the idea that dynamic flash coloration can deflect predatory attacks at fast speeds, but the effect may be the opposite when moving slowly.

Predicted Trajectory Accuracy Requirements to Reduce Aviation Impact of Space Launch Operations

Thousands of aircraft flight plans are affected by space launch and reentry operations each year, increasing the distances flown, causing flight delays, and increasing air traffic controllers workload. Due to regulations and procedures, predefined Aircraft Hazard Areas (AHAs) are used to protect aircraft from the risks of space launch and reentry debris, thus constraining the available airspace for other airspace users. While disruptive, there is no less impactful approach at present that adequately protects the flying public. In this paper, we explore the application of trajectory-based operations to evaluate the impact of an AHA on commercial aircraft, with the aim of reducing the number of flights that must be rerouted or rescheduled. This approach relies on precise trajectory predictions to the AHA boundary to determine which flights are expected to clear the AHA before its activation or remain clear of the AHA until after its deactivation. This paper derives the required predicted trajectory accuracy for air traffic automation systems to effectively predict flights impacted by an AHA. The required accuracy is derived based on a model for managing flights relative to the AHA using speed changes alone (as opposed to reroutes or holding) in the context of operational uncertainties like departure time delays and flight characteristics. Additionally, we derived a model to relate scheduling buffers to the AHA activation time, delivery accuracy at the AHA boundary, and AHA violation probability.

ABDALAMEER. DEVELOPMENT OF AUTONOMOUS MOBILE ROBOTS CONTROL SYSTEMS

The development of an efficient control system for mobile autonomous robots is explored. It delineates key stages in this process, starting with the necessity of precise trajectory planning followed by the creation of a mathematical model for controlling the robotic device. Subsequently, the implementation of navigation algorithms in the management system is emphasized. The importance of conducting comprehensive error analysis to address discrepancies during robot movement is underscored. Post the primary phase of control system creation, evaluating the cost-effectiveness and ensuring workplace safety are deemed crucial management steps. Localization emerges as a pivotal factor enabling the robot's effective autonomous movement relative to its target position. This, in turn, aids in orienting the robot within its external environment. Spatial information acquisition is underscored through the significance of traffic planning, cartography and specialized robotics sensors. Furthermore, the article delves into proprioceptive sensors, detailing their components, such as optical and electrical conductor sensors operating via the Hall effect discovered in 1879. It provides a formula to accurately ascertain conductivity state, involving parameters like hole and electron concentration, electron and hole mobility and elementary charge. In essence, the control system parallels the human brain, requiring a recognition system, action planning and execution functions. Navigation is identified as a critical element facilitating the robot's movement in three-dimensional space. The article integrates technical intricacies about proprioceptive sensors, highlighting their pivotal role within robot control systems.

Iterative flexibility compensation based high precision trajectory tracking for industrial manipulators

High precision industrial manipulators are widely demanded in precision machining and advanced manufacturing. To realize high precision trajectory tracking in task space, feedback plus model based feedforward control strategies are widely used in modern industrial manipulators. Furthermore, the accuracy of feedforward model determines the accuracy of trajectory tacking to a great extent, especially for elastic manipulators. In traditional feedforward controllers, the flexibilities out of the rotational plane are often neglected and thus the end-effector tracking performance can be degraded a lot. Therefore, an iterative flexibility compensation based feedforward controller is proposed to improve the end-effector trajectory tracking accuracy of industrial manipulators in this paper. First, the flexible deformations in all directions are described accurately by using the extended flexible joint model (EFJM) for industrial manipulators. Second, a feedforward controller is designed by using the dynamic inversion of EFJM. In the feedforward controller, an iterative flexibility compensation scheme is designed to transform the reference Cartesian trajectory into joint space and thus the limitation of the nonminimum phase characteristics of EFJM can be avoided. Moreover, the flexible deformations are predicted and compensated based on EFJM. After that, the feedforward torque is derived in joint space by using the efficient feedforward torque computation algorithm for EFJM. Simulation and experimental results demonstrate the effectiveness of the proposed method.

Application of Improved Sliding Mode and Artificial Neural Networks in Robot Control

Mobile robots are autonomous devices capable of self-motion, and are utilized in applications ranging from surveillance and logistics to healthcare services and planetary exploration. Precise trajectory tracking is a crucial component in robotic applications. This study introduces the use of improved sliding surfaces and artificial neural networks in controlling mobile robots. An enhanced sliding surface, combined with exponential and hyperbolic tangent approach laws, is employed to mitigate chattering phenomena in sliding mode control. Nonlinear components of the sliding control law are estimated using artificial neural networks. The weights of the neural networks are updated online using a gradient descent algorithm. The stability of the system is demonstrated using Lyapunov theory. Simulation results in MATLAB/Simulink R2024a validate the effectiveness of the proposed method, with rise times of 0.071 s, an overshoot of 0.004%, and steady-state errors approaching zero meters. Settling times were 0.0978 s for the x-axis and 0.0902 s for the y-axis, and chattering exhibited low amplitude and frequency.

Assessing risk of acute respiratory infectious diseases in crowded indoor settings with digital twin and precision trajectory approach

Indoor environments can pose a substantial respiratory infection risk, especially in densely populated areas. It is therefore vital to evaluate and predict infection risks for mobile individuals engaging in indoor activities. However, previous studies have primarily focused on assessing viral transmission probabilities between relatively stationary individuals, disregarding the dynamic features of pedestrian trajectories. To address this issue, we propose an approach that uses precise trajectory data and digital twin technology to evaluate and forecast indoor infection risks for mobile individuals. Our approach involves the utilization of high-precision trajectory data to develop a time-varying virus density field map (VDFM), forming the basis for infection risk assessment. Additionally, we present a deep-learning model that employs the transformer method to predict future time-varying VDFMs and associated infection risks. Furthermore, we introduce digital twin to comprehend real-time interactions between the physical and digital realms within the structure. To validate our approach, we conducted a case study at the Wuhan International Exposition Center, serving as the control room. We performed a sensitivity analysis on various preventive measures, including mask-wearing, social distancing, and indoor infection control. The results highlighted the significant impact of these measures on individual infection risks associated with indoor activities. Our research contributes to the development of tailored prevention and control strategies, which play a critical role in mitigating the spread of respiratory infections. Thus, this study holds significant implications for technology-facilitated public health services.

Enhancing Mobile Robot Navigation: Optimization of Trajectories through Machine Learning Techniques for Improved Path Planning Efficiency

Efficient navigation is crucial for intelligent mobile robots in complex environments. This paper introduces an innovative approach that seamlessly integrates advanced machine learning techniques to enhance mobile robot communication and path planning efficiency. Our method combines supervised and unsupervised learning, utilizing spline interpolation to generate smooth paths with minimal directional changes. Experimental validation with a differential drive mobile robot demonstrates exceptional trajectory control efficiency. We also explore Motion Planning Networks (MPNets), a neural planner that processes raw point-cloud data from depth sensors. Our tests demonstrate MPNet’s ability to create optimal paths using the Probabilistic Roadmap (PRM) method. We highlight the importance of correctly setting parameters for reliable path planning with MPNet and evaluate the algorithm on various path types. Our experiments confirm that the trajectory control algorithm works effectively, consistently providing precise and efficient trajectory control for the robot.

Precise trajectory tracking of mecanum-wheeled omnidirectional mobile robots via a novel fixed-time sliding mode control approach

Precise trajectory tracking of mecanum-wheeled omnidirectional mobile robots via a novel fixed-time sliding mode control approach

Using camera-guided electrode microdrive navigation for precise 3D targeting of macaque brain sites.

Spatial accuracy in electrophysiological investigations is paramount, as precise localization and reliable access to specific brain regions help the advancement of our understanding of the brain's complex neural activity. Here, we introduce a novel, multi camera-based, frameless neuronavigation technique for precise, 3-dimensional electrode positioning in awake monkeys. The investigation of neural functions in awake primates often requires stable access to the brain with thin and delicate recording electrodes. This is usually realized by implanting a chronic recording chamber onto the skull of the animal that allows direct access to the dura. Most recording and positioning techniques utilize this implanted recording chamber as a holder of the microdrive or to hold a grid. This in turn reduces the degrees of freedom in positioning. To solve this problem, we require innovative, flexible, but precise tools for neuronal recordings. We instead mount the electrode microdrive above the animal on an arch, equipped with a series of translational and rotational micromanipulators, allowing movements in all axes. Here, the positioning is controlled by infrared cameras tracking the location of the microdrive and the monkey, allowing precise and flexible trajectories. To verify the accuracy of this technique, we created iron deposits in the tissue that could be detected by MRI. Our results demonstrate a remarkable precision with the confirmed physical location of these deposits averaging less than 0.5 mm from their planned position. Pilot electrophysiological recordings additionally demonstrate the accuracy and flexibility of this method. Our innovative approach could significantly enhance the accuracy and flexibility of neural recordings, potentially catalyzing further advancements in neuroscientific research.

Iterative Learning Tracking Control for Nonlinear Systems Based on Data Driven Control

This paper introduces a novel methodology for tracking control of general nonlinear systems. Unlike traditional methods, which rely on linearization or nonlinear cancellation. This article starts from the iterative domain and utilizes the concept of iterative learning control to improve the control performance of the system. It employs a data-driven approach for model-free adaptive control, addressing the challenges posed by strong system nonlinearity, difficulties in control, and even modeling issues caused by disturbances and other factors. It enables precise trajectory tracking without the need for complex computations, making it suitable for real-time control applications. The effectiveness of the methodology is demonstrated through examples.

A time-varying shockwave speed model for reconstructing trajectories on freeways using Lagrangian and Eulerian observations

Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from connected vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, driver dynamics while reconstructing the trajectories are ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction on freeways. The approach introduces a calibration algorithm for time-varying shockwave speed. The shockwave speed calibrated by the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionally, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption by applying a vehicle movement model. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption.

Expressway Vehicle Trajectory Prediction Based on Fusion Data of Trajectories and Maps from Vehicle Perspective

Research on vehicle trajectory prediction based on road monitoring video data often utilizes a global map as an input, disregarding the fact that drivers rely on the road structures observable from their own positions for path planning. This oversight reduces the accuracy of prediction. To address this, we propose the CVAE-VGAE model, a novel trajectory prediction approach. Initially, our method transforms global perspective map data into vehicle-centric map data, representing it through a graph structure. Subsequently, Variational Graph Auto-Encoders (VGAEs), an unsupervised learning framework tailored for graph-structured data, are employed to extract road environment features specific to each vehicle’s location from the map data. Finally, a prediction network based on the Conditional Variational Autoencoder (CVAE) structure is designed, which first predicts the driving endpoint and then fits the complete future trajectory. The proposed CVAE-VGAE model integrates a self-attention mechanism into its encoding and decoding modules to infer endpoint intent and incorporate road environment features for precise trajectory prediction. Through a series of ablation experiments, we demonstrate the efficacy of our method in enhancing vehicle trajectory prediction metrics. Furthermore, we compare our model with traditional and frontier approaches, highlighting significant improvements in prediction accuracy.

Vision-based adaptive LT sliding mode admittance control for collaborative robots with actuator saturation

Abstract In this paper, we propose a novel vision-based adaptive leakage-type (LT) sliding mode admittance control for actuator-constrained collaborative robots to realize the synchronous control of the precise path following and compliant interaction force. Firstly, we develop a vision-admittance-based model to couple the visual feedback and force sensing in the image feature space so that a reference image feature trajectory can be obtained concerning the contact force command and predefined trajectory. Secondly, considering the system uncertainty, external disturbance, and torque constraints of collaborative robots in reality, we propose an adaptive sliding mode controller in the image feature space to perform precise trajectory tracking. This controller employs a leakage-type (LT) adaptive control law to reduce the side effects of system uncertainties without knowing the upper bound of system uncertainties. Moreover, an auxiliary dynamic is considered in this controller to overcome the joint torque constraints. Finally, we prove the convergence of the tracking error with the Lyapunov stability analysis and operate various semi-physical simulations compared to the conventional adaptive sliding mode and parallel vision/force controller to demonstrate the efficacy of the proposed controller. The simulation results show that compared with the controller mentioned above, the path following accuracy and interaction force control precision of the proposed controller increased by 50% and achieved faster convergence.

Tunable viscoelastic size-based particle separation in straight microchannels with triangular cross-sections

In recent years, viscoelastic particle manipulation within microfluidic systems has received much attention due to the ease with which micron-sized objects may be maneuvered and isolated on the basis of size. While several factors, including both fluid and particle properties, regulate the precise locations and trajectories of micron-sized species flowing along a microchannel, the role of channel cross-section shape is critical, since it directly influences the fluid velocity profile and thus the direction and magnitude of hydrodynamic forces. It is therefore surprising that this parameter has not been comprehensively investigated for cell-based separations, especially since most viscoelastic microfluidic systems are only able to efficiently sperate cells over limited size ranges. To address this shortcoming, we present a viscoelastic microfluidic system integrating a triangular cross-section microchannel, for efficient and tunable size-based separations of micron-sized species. We find that particle focusing patterns can be controlled by simple variation of volumetric flow rates, which allows for the efficient separation of particles and cells of variable size. To showcase the efficacy of the approach, we present a size-based separation of various blood components, including white blood cells, platelets, and rare cells. By quantifying the number of particles collected at the outlets, we achieve recovery efficiencies of over 98%.