Target Position Research Articles

Learning-Based Control for Soft Robot-Environment Interaction with Force/Position Tracking Capability.

Soft robotics promises to achieve safe and efficient interactions with the environment by exploiting its inherent compliance and designing control strategies. However, effective control for the soft robot-environment interaction has been a challenging task. The challenges arise from the nonlinearity and complexity of soft robot dynamics, especially in situations where the environment is unknown and uncertainties exist, making it difficult to establish analytical models. In this study, we propose a learning-based optimal control approach as an attempt to address these challenges, which is an optimized combination of a feedforward controller based on probabilistic model predictive control and a feedback controller based on nonparametric learning methods. The approach is purely data-driven, without prior knowledge of soft robot dynamics and environment structures, and can be easily updated online to adapt to unknown environments. A theoretical analysis of the approach is provided to ensure its stability and convergence. The proposed approach enabled a soft robotic manipulator to track target positions and forces when interacting with a manikin in different cases. Moreover, comparisons with other data-driven control methods show a better performance of our approach. Overall, this work provides a viable learning-based control approach for soft robot-environment interactions with force/position tracking capability.

Automatic Dependent Surveillance-Broadcast Deceptive Jamming Detection Method Based on Track Data

The characteristics of automatic dependent surveillance-broadcast (ADS-B) technology, such as unencrypted channels and open data protocols, make it extremely vulnerable to various intentional or unintentional spoofing and jamming, which seriously threatens air traffic safety. Traditional approaches do not update their high-bit data for a long time, which causes the situations of the same encoding results to have different aircraft positions and it is difficult to determine the real position of the aircraft and effectively detect ADS-B jamming. Therefore, it is urgent to propose more effective detection methods for ADS-B spoofing and jamming. First, the methods and principles of frequency shift calculation and automatic monitoring algorithm are introduced. Then, signal consistency detection and error robustness are analyzed, and a track-data-based detection model for abnormal ADS-B data are proposed. The track-data-based detection methods can use multi-point sampling judgment algorithm to realize the reception and processing of data bits. This method makes full use of the information of multiple sampling values of each data bit and the reference power value obtained in the preamble detection, and determines the data value and confidence value through the relationship between the two. The detection methods for ADS-B spoofing and jamming distinguishes the true and false signals and the authenticity of target positions by comparing the arrival time difference of each signal at the ground station. Finally, the research results show that, with the increase of signal to noise ratio, no matter the estimation based on virtual array translation or the estimation based on circular stationary signal, their estimation accuracies are improved with gradually increasing trends.

Orthogonal Translation for Site-Specific Installation of Post-translational Modifications.

Post-translational modifications (PTMs) endow proteins with new properties to respond to environmental changes or growth needs. With the development of advanced proteomics techniques, hundreds of distinct types of PTMs have been observed in a wide range of proteins from bacteria, archaea, and eukarya. To identify the roles of these PTMs, scientists have applied various approaches. However, high dynamics, low stoichiometry, and crosstalk between PTMs make it almost impossible to obtain homogeneously modified proteins for characterization of the site-specific effect of individual PTM on target proteins. To solve this problem, the genetic code expansion (GCE) strategy has been introduced into the field of PTM studies. Instead of modifying proteins after translation, GCE incorporates modified amino acids into proteins during translation, thus generating site-specifically modified proteins at target positions. In this review, we summarize the development of GCE systems for orthogonal translation for site-specific installation of PTMs.

Neurons in the monkey frontopolar cortex encode learning stage and goal during a fast learning task.

The frontopolar cortex (FPC) is, to date, one of the least understood regions of the prefrontal cortex. The current understanding of its function suggests that it plays a role in the control of exploratory behaviors by coordinating the activities of other prefrontal cortex areas involved in decision-making and exploiting actions based on their outcomes. Based on this hypothesis, FPC would drive fast-learning processes through a valuation of the different alternatives. In our study, we used a modified version of a well-known paradigm, the object-in-place (OIP) task, to test this hypothesis in electrophysiology. This paradigm is designed to maximize learning, enabling monkeys to learn in one trial, which is an ability specifically impaired after a lesion of the FPC. We showed that FPC neurons presented an extremely specific pattern of activity by representing the learning stage, exploration versus exploitation, and the goal of the action. However, our results do not support the hypothesis that neurons in the frontal pole compute an evaluation of different alternatives. Indeed, the position of the chosen target was strongly encoded at its acquisition, but the position of the unchosen target was not. Once learned, this representation was also found at the problem presentation, suggesting a monitoring activity of the synthetic goal preceding its acquisition. Our results highlight important features of FPC neurons in fast-learning processes without confirming their role in the disengagement of cognitive control from the current goals.

Learning to resist distraction by spatially predictable luminance transients and color singletons: same or different mechanisms?

ABSTRACT Stimuli that appear abruptly in the visual field or differ from the surrounding stimuli based on a given visual feature can capture attention and interfere with the visual search process if they are not targets. When both types of distractors appear with higher likelihood at a given location, observers can learn to reduce their impact (distractor-location effect). In the case of feature-singleton distractors, this can imply a cost for processing targets that appear at locations associated with a high distractor probability (target-location effect). This has been proposed as evidence that distractor interference originates in the competition between distractors and targets within an attentional map, whose inputs can be modified by experience. In this study, we conduct a series of experiments that confirmed previous finding related to feature-singleton distractors, but consistently showed that learning to predict the spatial occurrence of luminance transients induces a distractor-location effect in the absence of a target-location effect. Combining this finding with the fact that interference by luminance transients were larger when distractors were far from the target position, we suggest that different mechanisms are responsible for the reduction of distractor interference in the case of color singletons and luminance transients.

Improved Detection Method for Micro-Targets in Remote Sensing Images

With the exponential growth of remote sensing images in recent years, there has been a significant increase in demand for micro-target detection. Recently, effective detection methods for small targets have emerged; however, for micro-targets (even fewer pixels than small targets), most existing methods are not fully competent in feature extraction, target positioning, and rapid classification. This study proposes an enhanced detection method, especially for micro-targets, in which a combined loss function (consisting of NWD and CIOU) is used instead of a singular CIOU loss function. In addition, the lightweight Content-Aware Reassembly of Features (CARAFE) replaces the original bilinear interpolation upsampling algorithm, and a spatial pyramid structure is added into the network model’s small target layer. The proposed algorithm undergoes training and validation utilizing the benchmark dataset known as AI-TOD. Compared to speed-oriented YOLOv7-tiny, the mAP0.5 and mAP0.5:0.95 of our improved algorithm increased from 42.0% and 16.8% to 48.7% and 18.9%, representing improvements of 6.7% and 2.1%, respectively, while the detection speed was almost equal to that of YOLOv7-tiny. Furthermore, our method was also tested on a dataset of multi-scale targets, which contains small targets, medium targets, and large targets. The results demonstrated that mAP0.5:0.95 increased from “9.8%, 54.8%, and 68.2%” to “12.6%, 55.6%, and 70.1%” for detection across different scales, indicating improvements of 2.8%, 0.8%, and 1.9%, respectively. In summary, the presented method improves detection metrics for micro-targets in various scenarios while satisfying the requirements of detection speed in a real-time system.

Hybrid 3D Reconstruction of Indoor Scenes Integrating Object Recognition

Indoor 3D reconstruction is particularly challenging due to complex scene structures involving object occlusion and overlap. This paper presents a hybrid indoor reconstruction method that segments the room point cloud into internal and external components, and then reconstructs the room shape and the indoor objects in different ways. We segment the room point cloud into internal and external points based on the assumption that the room shapes are composed of some large external planar structures. For the external, we seek for an appropriate combination of intersecting faces to obtain a lightweight polygonal surface model. For the internal, we define a set of features extracted from the internal points and train a classification model based on random forests to recognize and separate indoor objects. Then, the corresponding computer aided design (CAD) models are placed in the target positions of the indoor objects, converting the reconstruction into a model fitting problem. Finally, the indoor objects and room shapes are combined to generate a complete 3D indoor model. The effectiveness of this method is evaluated on point clouds from different indoor scenes with an average fitting error of about 0.11 m, and the performance is validated by extensive comparisons with state-of-the-art methods.

SAR Multi-Angle Observation Method for Multipath Suppression in Enclosed Spaces

Synthetic aperture radar (SAR) is a powerful tool for detecting and imaging targets in enclosed environments, such as tunnels and underground garages. However, SAR performance is degraded by multipath effects, which occur when electromagnetic waves are reflected by obstacles, such as walls, and interfere with the direct signal. This results in the formation of multipath ghost images, which obscure the true target and reduce the image quality. To overcome this challenge, we propose a novel method based on multi-angle observation. This method exploits the fact that the position of ghost images changes depending on the angle of the radar, while the position of the true target remains stable. By collecting and processing multiple data sets from different angles, we can eliminate the ghost images and enhance the target image. In addition, we introduce a center vector distance algorithm to address the complexity and computational intensity of existing multipath suppression algorithms. This algorithm, which defines the primary direction of multi-angle vectors from stable scattering centers as the center vector, processes and synthesizes multiple data sets from multi-angle observations. It calculates the distance of pixel intensity sequences in the composite data image from the center vector. Pixels within a specified threshold are used for imaging, and the final result is obtained. Simulation experiments and real SAR data from underground garages confirm the effectiveness of this method in suppressing multipath ghost images.

Analysis of the Technical Accuracy of a Patient-Specific Stereotaxy Platform for Brain Biopsy.

The use of stereotactic frames is a common practice in neurosurgical interventions such as brain biopsy and deep brain stimulation. However, conventional stereotactic frames have been shown to require modification and adaptation regarding patient and surgeon comfort as well as the increasing demand for individualized medical treatment. To meet these requirements for carrying out state-of-the-art neurosurgery, a 3D print-based, patient-specific stereotactic system was developed and examined for technical accuracy. Sixteen patient-specific frames, each with two target points, were additively manufactured from PA12 using the Multi Jet Fusion process. The 32 target points aim to maximize the variability of biopsy targets and depths for tissue sample retrieval in the brain. Following manufacturing, the frames were measured three-dimensionally using an optical scanner. The frames underwent an autoclave sterilization process prior to rescanning. The scan-generated models were compared with the planned CAD models and the deviation of the planned target points in the XY-plane, Z-direction and in the resulting direction were determined. Significantly lower (p < 0.01) deviations were observed when comparing CAD vs. print and print vs. sterile in the Z-direction (0.17 mm and 0.06 mm, respectively) than in the XY-plane (0.46 mm and 0.16 mm, respectively). The resulting target point deviation (0.51 mm) and the XY-plane (0.46 mm) are significantly higher (p < 0.01) in the CAD vs. print comparison than in the print vs. sterile comparison (0.18 mm and 0.16 mm, respectively). On average, the results from the 32 target positions examined exceeded the clinically required accuracy for a brain biopsy (2 mm) by more than four times. The patient-specific stereotaxic frames meet the requirements of modern neurosurgical navigation and make no compromises when it comes to accuracy. In addition, the material is suitable for autoclave sterilization due to resistance to distortion.

Improved artificial potential field method for mobile robot path planning

Path planning has already been used in areas like robots and unmanned vehicles to prevent collisions in certain environments. A Path planning algorithm is needed to achieve such tasks and Artificial Potential Field (APF) method is one of the methods. However, APF has limitations facing various situations like being stuck in a local minimum such as a dead-end or a narrow path. To solve the problem, First, a side force is added to the algorithm along with two types of definitions of the force direction. Then a variable is proposed to prevent the dead-end situation. Finally, the variable step size is used to improve the efficiency of the algorithm. The simulation results demonstrate the effectiveness of the method. In comparison to APF, the improved APF could prevent the local minimum and reach the target position with fewer processing steps and better performance.

Spatial Decoding for Gaze Independent Brain-Computer Interface Based on Covert Visual Attention Shift Using Electroencephalography.

The gaze-independent brain-computer interface (BCI) device is used to re-establish interaction for individuals who have abnormal eye movement. It may be possible to control the BCI by shifting your attention spatially. However, spatial attention is rarely employed to increase the effectiveness of target detection and is typically used to provide a simple "yes" or "no" response to the target recognition inquiry. To improve the effectiveness of detecting target, it is crucial to take advantage of the possible advantages of spatial attention. N2-posterior-contralateral (N2pc) component reflects correlates of visual spatial attention and is used to determine target position. In this study, a long-short-term memory (LSTM) network is used to answer "yes/no" questions by decoding covert spatial attention based on N2pc characteristics using EEG signals. The proposed LSTM-based model's average decoding accuracy is 92.79%. The target detection efficiency was successfully increased by about 4% when compared to conventional machine learning algorithms. The proposed model is tested on the independent dataset to validate its performance. The results of this work show that N2pc characteristics can be employed in gaze-independent BCIs for tracking covert attention shifts, which may help persons with poor eye mobility to connect with their environment.

Fusion of Time-of-Flight Based Sensors with Monocular Cameras for a Robotic Person Follower

Human-robot collaboration (HRC) is becoming increasingly important in advanced production systems, such as those used in industries and agriculture. This type of collaboration can contribute to productivity increase by reducing physical strain on humans, which can lead to reduced injuries and improved morale. One crucial aspect of HRC is the ability of the robot to follow a specific human operator safely. To address this challenge, a novel methodology is proposed that employs monocular vision and ultra-wideband (UWB) transceivers to determine the relative position of a human target with respect to the robot. UWB transceivers are capable of tracking humans with UWB transceivers but exhibit a significant angular error. To reduce this error, monocular cameras with Deep Learning object detection are used to detect humans. The reduction in angular error is achieved through sensor fusion, combining the outputs of both sensors using a histogram-based filter. This filter projects and intersects the measurements from both sources onto a 2D grid. By combining UWB and monocular vision, a remarkable 66.67% reduction in angular error compared to UWB localization alone is achieved. This approach demonstrates an average processing time of 0.0183s and an average localization error of 0.14 meters when tracking a person walking at an average speed of 0.21 m/s. This novel algorithm holds promise for enabling efficient and safe human-robot collaboration, providing a valuable contribution to the field of robotics.

Distributed Neural Networks Training for Robotic Manipulation With Consensus Algorithm.

In this article, we propose an algorithm that combines actor-critic-based off-policy method with consensus-based distributed training to deal with multiagent deep reinforcement learning problems. Specifically, convergence analysis of a consensus algorithm for a type of nonlinear system with a Lyapunov method is developed, and we use this result to analyze the convergence properties of the actor training parameters and the critic training parameters in our algorithm. Through the convergence analysis, it can be verified that all agents will converge to the same optimal model as the training time goes to infinity. To validate the implementation of our algorithm, a multiagent training framework is proposed to train each Universal Robot 5 (UR5) robot arm to reach the random target position. Finally, experiments are provided to demonstrate the effectiveness and feasibility of the proposed algorithm.

A Machine Learning Approach Based on Indoor Target Positioning by Using Sensor Data Fusion and Improved Cosine Similarity

A Machine Learning Approach Based on Indoor Target Positioning by Using Sensor Data Fusion and Improved Cosine Similarity

Visual guidance fine-tunes probing movements of an insect appendage

Visually guided reaching, a regular feature of human life, comprises an intricate neural control task. It includes identifying the target's position in 3D space, passing the representation to the motor system that controls the respective appendages, and adjusting ongoing movements using visual and proprioceptive feedback. Given the complexity of the neural control task, invertebrates, with their numerically constrained central nervous systems, are often considered incapable of this level of visuomotor guidance. Here, we provide mechanistic insights into visual appendage guidance in insects by studying the probing movements of the hummingbird hawkmoth's proboscis as they search for a flower's nectary. We show that visually guided proboscis movements fine-tune the coarse control provided by body movements in flight. By impairing the animals' view of their proboscis, we demonstrate that continuous visual feedback is required and actively sought out to guide this appendage. In doing so, we establish an insect model for the study of neural strategies underlying eye-appendage control in a simple nervous system.

Controlling Musculoskeletal System with Spinal Cord-Inspired Constraint Force Field Primitives

Organisms naturally have extraordinary motor ability. They can autonomously control the highly redundant and nonlinear musculoskeletal system to complete fast and flexible movements. Motor primitive theory proposed that a complex movement can be realized by modular organization of simple movement patterns. This theory provides a convincing theoretical basis for explaining the extraordinary motor ability of organisms, and has been supported by plenty of experimental evidences in neurosciences. As a kind of typical motor primitive related to the modular regulation of the spinal motor system, convergent force field was thought to be significant for reducing the complexity of controlling numerous muscles and joints simultaneously. Inspired by the primitive property of convergent force field, in this paper, we proposed a new algorithm to efficiently construct constraint force field on musculoskeletal system with highly redundant actuators by taking optimized parameters of constraint force field as motor primitives. Compared with previous methods of constructing constraint force field, the proposed algorithm is able to reduce the dimension of the solution space so as to effectively improve the computational efficiency of constructing constraint force field in musculoskeletal system with redundant muscles. Validation experiments were carried out in a musculoskeletal system with 10 redundant muscles. The number of optimization iterations and computational time required for constructing constraint force field at a new position were significantly reduced. The system can accurately reach the target position using constant activations. This work may bring in new inspiration for realizing efficient and accurate motion control of musculoskeletal robots with limited feedback accuracy.

An Acoustic Array Sensor Signal Recognition Algorithm for Low-Altitude Targets Using Multiple Five-Element Acoustic Positioning Systems with VMD

To solve the problem in target acoustic signal processing and recognition when the target flies at a low altitude based on the acoustic positioning system, which is often affected by external interference and brings false information, this paper proposes a target signal processing and recognition algorithm for low-altitude target acoustic positioning based on variational modal decomposition and the test method of multiple five-element acoustic arrays. This algorithm uses VMD to decompose the target signal into modal components with different central frequencies and then performs wavelet threshold processing on the low-frequency part of the signal. After determining the remaining signal components and the low-frequency part’s threshold, the residual component is reconstructed. Based on the test principle and calculation model of the five-element acoustic positioning system, following processing of the low-altitude target acoustic positioning signal using variational modal decomposition, the cross-correlation function method is introduced to perform correlation operations on the basic array of five acoustic sensors and then obtain the time value and time difference of the target acoustic information in each acoustic sensor, ultimately determining the spatial position of the target. Finally, we used the data fusion processing method for target coordinates in multi-acoustic basic arrays to determine the actual target position. By comparing the results obtained using the high-speed camera method with those of the proposed approach, it was found that the average error in the test area of 100 × 100 m was less than 1 m.

A heparin-based nanogel system for redox and pH dual-responsive delivery of cisplatin

Heparin recently has been discovered as a novel anti-cancer agent. The combinations of heparin with other agents was reported not only to reduce the undesired effects of free heparin and increase the cellular uptake of the delivered molecules, but also is the basis for the design and development of multi-stimulation response systems to improve their killing cancer cell efficiency at the target positions. This study aimed to design a redox and pH dual-responsive anticancer system based on heparin for cisplatin (CPT) therapy. Heparin was first cross-linked with Poloxamer 407 chains via disulfide bridges to form a redox-sensitive system Hep-P407. CPT was then encapsulated into the Hep-P407 system via the complex of Platin and carboxyl groups to form the redox/pH-responsive system CPT@Hep-P407. The obtained Hep-P407 systems were proved and characterized using specific techniques including 1H-NMR, zeta potential, Dynamic Light Scattering (DLS) and Fourier-transform infrared spectroscopy. The dual-responsive behavior to redox and pH of CPT@Hep-P407 was proved through DLS, zeta and in vitro release analysis meanwhile its cytotoxicity was investigated using Resazurin assay. The CPT@Hep-P407 system is expected to be a promising redox/pH-responsive anticancer system based on heparin for CPT therapy.

Underwater target correlation detection and location method based on normalized magnetic moment

For the dynamic magnetic detection platform represented by airborne magnetic anomaly detection, the magnetic field measured by the vector magnetometer is easily affected by the rapid attitude change. Therefore, traditional anti-submarine aircraft are equipped with scalar magnetometers that are less affected by changes in the carrier attitude. However, the scalar magnetometer can only measure the total magnetic field signal, and its measurement information is single. It contains a time-varying environmental magnetic field signal, including the geomagnetic field, which is difficult to effectively reject. This brings great difficulties to the single real-time location of a magnetic anomaly target. This paper proposes a target correlation detection method based on the normalized magnetic moment that can effectively solve the above problems, considering the rotational invariance of magnetic anomaly measurements for dynamic magnetic exploration platforms and the efficient detection methods required for the single real-time positioning of targets. Correlation detection based on the normalized magnetic moment characteristic function and characteristic time search based on the correlation detection are included. Simulation and experimental results verify the single-point real-time positioning ability of the improved detection method within an effective operating distance. This method has strong usability for actual magnetic anomaly detection of underwater targets.

Station layout optimization for underwater acoustic positioning system based on combined cone configuration

The theoretical minimum of the geometric dilution of precision (GDOP) for target positioning cannot be achieved because of the coplanar constraint for underwater station configurations, and the conventional single-cone configuration leads to an infinite GDOP. Therefore, this study extends the single-cone configuration to the combined cone configuration. The proposed configuration is theoretically proven to be superimposable, rotatable, and GDOP-rotation-invariant. A criterion based on the global GDOP for underwater targets is constructed to address the problem of a single criterion for station layout optimization. However, traditional optimization algorithms are prone to local optimality and low convergence efficiency. Therefore, an improved adaptive particle swarm optimization (APSO) algorithm is proposed to optimize the configuration of the underwater acoustic positioning systems. Finally, it is verified that the proposed APSO can enhance the global optimality and improve the convergence efficiency of the algorithm. Combined with the measured data of the long baseline system, it is shown that the optimal combined cone configuration significantly reduces the global GDOP and improves the positioning accuracy compared with traditional configurations.