Componentized PNT: An Improved Plug-and- Play Framework for Multisensor Fusion

The ALICE experiment will undergo a major upgrade during the second long shutdown of the CERN LHC. As part of this program, the present Inner Tracking System (ITS), which employs different layers of hybrid pixels, silicon drift and strip detectors, will be replaced by a completely new tracker composed of seven layers of monolithic active pixel sensors. The upgraded ITS will have more than twelve billion pixels in total, producing 300Gbit/s of data when tracking 50kHz Pb–Pb events. Two families of pixel chips realized with the TowerJazz CMOS imaging process have been developed as candidate sensors: the ALPIDE, which uses a proprietary readout and sparsification mechanism and the MISTRAL-O, based on a proven rolling shutter architecture. Both chips can operate in continuous mode, with the ALPIDE also supporting triggered operations. As the communication IP blocks are shared among the two chip families, it has been possible to develop a common Readout Electronics. All the sensor components (analog stages, state machines, buffers, FIFOs, etc.) have been modelled in a system level simulation, which has been extensively used to optimize both the sensor and the whole readout chain design in an iterative process. This contribution covers the progress of the R&D efforts and the overall expected performance of the ALICE-ITS readout system.

In various practical applications, such as autonomous vehicle and unmanned aerial vehicle navigation, Global Navigation Satellite Systems (GNSSs) are commonly used for positioning. However, traditional GNSS positioning methods are often affected by disturbances due to external observational conditions. For instance, in areas with dense buildings, tree cover, or tunnels, GNSS signals may be obstructed, resulting in positioning failures or decreased accuracy. Therefore, improving the accuracy and stability of GNSS positioning in these complex environments is a critical concern. In this paper, we propose a novel multi-sensor fusion framework based on multi-frame residual optimization for GNSS/INS/LiDAR to address the challenges posed by complex satellite environments. Our system employs a novel residual detection and optimization method for continuous-time GNSS within keyframes. Specifically, we use rough pose measurements from LiDAR to extract keyframes for the global system. Within these keyframes, the multi-frame residuals of GNSS and IMU are estimated using the Median Absolute Deviation (MAD) and subsequently employed for the degradation detection and sliding window optimization of the GNSS. Building on this, we employ a two-stage factor graph optimization strategy, significantly improving positioning accuracy, especially in environments with limited GNSS signals. To validate the effectiveness of our approach, we assess the system’s performance on the publicly available UrbanLoco dataset and conduct experiments in real-world environments. The results demonstrate that our system can achieve continuous decimeter-level positioning accuracy in these complex environments, outperforming other related frameworks.

While automated systems have been shown to improve safety and efficiency in operational environments, automation failures can lead to abrupt shifts in workload. Subjective workload scales have been shown to be sensitive to differences in workload, but they are limited in their ability to assess dynamic, moment-to-moment workload variations. Physiological measures may be better suited to assess dynamic workload in complex environments. This study explored the feasibility of a relatively new physiological measure, Transcranial Doppler Sonography (TCD), as a candidate for adaptive automation studies. Participants performed long duration, supervisory control tasks under varying levels of taskload. In one group, enemy threats increased once late in the simulation, and in another group enemy threats increased at two points; once early and once late within the simulation. All participants completed a comparison condition in which there was no variation in the number of incoming enemy threats. Cerebral blood flow velocity (CBFV), as measured by TCD, was measured during task performance. Performance was assessed by the ability of the operator to protect a no-fly zone from enemy incursion. Subjective mental workload was assessed using the NASA-TLX. As performance decreased during periods of high load, CBFV increased, and there was a close parallel between the CBFV and performance measures. The NASA-TLX was sensitive in detecting differences in workload between the two conditions, but the patterns of results of this subjective measure were insensitive to specific task elements. The results are interpreted in terms of a resource theory of task performance and show that the CBFV measure is sensitive to dynamic changes in taskload in complex environments.

Disparate bilingual experiences modulate task-switching advantages: A diffusion-model analysis of the effects of interactional context on switch costs

Intrinsically soft and fully recyclable robotic sensors with quadruple sensing functions for reliable human-robot interactions.

This paper proposes an uncalibrated adaptive visual servoing (VS) control framework based on dual-camera fusion to address key technical challenges in robotic visual servoing systems, including real-time state estimation, multi-space coordination, and dynamic target tracking. By combining the complementary advantages of “eye-in-hand” and “eye-to-hand” camera configurations, an adaptive switching mechanism is designed to achieve coordinated control between image space and Cartesian space, addressing convergence problems of traditional methods during target occlusion or field-of-view loss. Key features of the framework include: an uncalibrated control method based on the image Jacobian matrix; adaptive parameter adjustment based on Kalman filtering (KF); and a dual-camera fusion switching strategy. Experiments show the method achieves a positioning accuracy of 1.197 mm and an orientation accuracy of 0.149° in a representative static positioning task; demonstrates effective performance in scenarios involving out-of-view target acquisition and occlusion recovery; and reduces tracking errors by 13%–28% while shortening convergence time by 5%–32% in dynamic tracking tasks. This framework provides a practical technical approach for visual servoing systems in complex environments, showing potential for broad industrial applications.

Jumping is a critical capability for quadruped robots, especially for navigating obstacles and gaps in complex environments. For successful jump, accurate trajectory tracking and robust feedback mechanism are essential, as cumulative deviations from the desired jumping trajectory can lead to instability or landing failure. Existing controllers often rely on fixed joint-level PD control or simplified inverse dynamics, which often fall short in tracking accuracy and robustness. In this paper, we propose a phase-aware iterative Linear Quadratic Regulator (iLQR) framework tailored for dynamic quadruped jumping tasks. By segmenting the jumping motion into distinct phases, we define phase-wise optimal control problem that respects the unique characteristics and requirements of each stage. Moreover, by leveraging a planar full-body dynamics of quadruped in each iLQR sub-problem, we derive a tracking controller consisting time-varying, full-state feedback gains, which shows better performance in tracking accuracy and disturbances rejection over traditional baseline controllers. Extensive simulation and hardware experiments on the Deeprobotics Lite3 quadruped validate the effectiveness and reliability of our proposed method in a number of dynamic jumping scenarios.

Context. Direct detection of exoplanets around nearby stars requires advanced adaptive optics (AO) systems. High-order systems are needed to reach a high Strehl ratio (SR) in near-infrared and optical wavelengths on future giant segmented-mirror telescopes (GSMTs). Direct detection of faint exoplanets with the European Southern Observatory (ESO) Extremely Large Telescope (ELT) will require some tens of thousands of correction modes. The resolution and sensitivity of the wavefront sensor (WFS) are key requirements for this science case. We present a new class of WFSs, the bi-orthogonal Foucault knife-edge sensors (or Bi–O edge), that is directly inspired by the Foucault knife-edge test. The idea consists of using a beam-splitter producing two foci, each of which is sensed by an edge with a direction orthogonal to the other focus.Aims. We describe two implementation concepts: The Bi–O edge sensor can be realised with a sharp edge and a tip-tilt modulation device (sharp Bi–O edge) or with a smooth gradual transmission over a grey edge (grey Bi–O edge). A comparison of the Bi–O edge concepts and the four-sided classical pyramid wavefront sensor (PWS) gives some important insights into the nature of the measurements.Methods. We analytically computed the photon noise error propagation, and we compared the results to end-to-end simulations of a closed-loop AO system.Results. Our analysis shows that the sensitivity gain of the Bi–O edge with respect to the PWS depends on the system configuration. The gain is a function of the number of control modes and the modulation angle. We found that for the sharp Bi–O edge, the gain in reduction of propagated photon noise variance approaches a theoretical factor of 2 for a large number of control modes and small modulation angle, meaning that the sharp Bi–O edge only needs half of the photons of the PWS to reach similar measurement accuracy. In contrast, the PWS is twice more sensitive than the Bi–O edge in the case of very low order correction and/or large modulation angles. Preliminary end-to-end simulations illustrate some of the results. The grey version of the Bi–O edge opens the door to advanced amplitude filtering, which replaces the need for a tip-tilt modulator while keeping the same dynamic range. We show that an additional factor of 2 in reduction of propagated photon noise variance can be obtained for high orders, such that the theoretical maximum gain of a factor of 4 in photon efficiency can be obtained. A diffractive Fourier model that accurately includes the effect of modulation and control modes shows that for the extreme AO (XAO) system configuration of the ELT, the overall gain will well exceed one magnitude in guide-star brightness when compared to the modulated PWS.Conclusions. We conclude that the Bi–O edge is an excellent candidate sensor for future very high order Adaptive Optics systems, in particular on GSMTs.

This paper proposes a novel navigation and optimization approach for mobile robots operating in intelligent manufacturing (IM) scenarios with unknown obstacles. First, we introduce a set of evaluation functions that simultaneously consider multiple metrics, including speed, security, sampling step, and obstacle avoidance ability, for scenarios with unknown dynamic obstacles. We then adopt a knee-guided multi-objective evolutionary algorithm capable of balancing parameter size and performance to optimize these conflicting objectives. Finally, we present a local navigation framework that integrates navigation and multi-objective optimization to enable obstacle avoidance in unstructured environments. The proposed algorithm is validated through simulations and practical scenarios, demonstrating its effectiveness in both static and dynamic scenarios. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The Human-cyber-physical System (HCPS) is the basic principle of the new generation of intelligent manufacturing (IM). It presents new requirements for the production method, and robot operate completely autonomously is expected to be the future solution for some complex tasks. However, traditional navigation methods often fail in these scenarios due to environmental changes and restricted workspaces. To address this challenge, this paper proposes a multi-objective optimization-based local navigation approach (MONA) that transforms the local navigation problem into a multi-objective optimization problem. The approach uses the knee-guided multi-objective evolutionary algorithm to optimize the control input for the mobile robot, ensuring safe and fast navigation in complex environments. The proposed approach has been validated through simulation and real scenario experiments, demonstrating its effectiveness.

Recent advances in decision-making algorithms used in mobile robotics require more advanced and adaptive control strategies. Model predictive control (MPC) is one of the prominent strategies to manage diverse kinds of complex dynamic systems. Despite their widespread adoption in industrial robotics owing to their structural simplicity and ease of implementation, proportional-integral-derivative (PID) controllers exhibit notable limitations in effectively addressing process variations and system constraints, particularly those arising from mechanical constraints on joint positions and velocities. As autonomous mobile robots (AMRs) have been increasingly deployed in various and demanding applications, the need for more advanced control algorithms has become critical. In this study, a novel hybrid control framework integrating MPC and PID strategies is proposed and experimentally validated on a real-world differential drive robot, aiming to enhance tracking accuracy and overall operational performance. The system model of the TurtleBot3 robot is identified using the System Identification Toolbox and validated through extensive motion tests on the real robot by using Robot Operating System 2. The proposed control scheme combines the predictive capabilities of MPC with the reactive nature of PID to facilitate improved management of system constraints, aiming to improve the performance of AMR in controlling both linear and angular velocities. Experimental results show that the hybrid MPC-PID controller exhibits better performance by reducing tracking errors while maintaining reliability and robustness characteristics over a conventional PID controller. These results demonstrate that the hybrid MPC-PID approach provides a more effective solution for dynamic control tasks in mobile robotic systems, particularly in scenarios requiring high accuracy and reliability.

Purpose: The aim of this study was development of a computer situational model of heat and power processes and transport operations for secondary steelmaking (SSM) to evaluate the effectiveness of the proposed SSM energy regimes and minimization the consumption of energy resources. Design/methodology/approach: For the solution of the tasks were used next methods: analytical and statistical methods of mathematical modeling; method of dynamic programming for the development of technological recommendations for energy modes on LF; Harel state charts to evaluate the effectiveness of the applied models. Findings: In order to provide rational energy regimes for SSM, it is necessary to introduce a new controlled parameter - the optimum time to start heating the melt at the ladle furnace unit (LF), which is determined by solving the dynamic programming task. The melt heating start time must be selected in such a way as to ensure that all the necessary technological operations are performed during metal processing in the LF, taking into account schedule constraints, and that the heating of the metal must be carried out with the maximum energy efficiency. Research limitations/implications: The main objective of the present study was to apply the mathematical modeling methods to ensure rational energy regimes of SSM. Practical implications: The developed situational model of technological operations for SSM will allow finding reserves to increase the productivity and quality of the process, and to evaluate the effectiveness of new technological solutions. Originality/value: To ensure an energy-efficient treatment of steel in LF, it is necessary: the time for starting the heating of the metal is chosen such that the energy efficiency of the LF, which depends on the thickness of the slag layer, is maximum at each stage; increase the power that is supplied to the heating of the melt by switching the voltage taps of the transformer as the thickness of the slag cover increases.

Multicontext team decision making under time stress is an extremely challenging issue faced by various real-world application domains. In this paper, we employ an experience-based cognitive agent architecture (called R-CAST) to address the informational challenges associated with military command and control (C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) decision-making teams, the performance of which can be significantly affected by dynamic context switching and tasking complexities. Using context switching frequency and task complexity as two factors, we conducted an experiment to evaluate whether the use of R-CAST agents as teammates and decision aids can benefit C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> decision-making teams. Members from a U.S. Army Reserve Officer Training Corps organization were randomly recruited as human participants. They were grouped into ten human-human teams, each composed of two participants, and ten human-agent teams, each composed of one participant and two R-CAST agents, as teammates and decision aids. The statistical inference of experimental results indicates that R-CAST agents can significantly improve the performance of C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> teams in multicontext decision making under varying time-stressed situations.

Decision Making in a Dynamic and Complex Task Environment

High-precision indoor positioning is the basis of factory intelligent management. However, the positioning accuracy will decrease because of the complex environment. This study proposes a multi-sensor fusion framework to fuse the data of Ultra Wide Band (UWB), inertial measurement unit (IMU), and odometer. First fuse the data from UWB and IMU by using EKF to obtain attitude, velocity, and position. And then fuse the speed and output with the odometer output using complementary filtering to increase accuracy. At the same time, the algorithm can output higher frequency positioning data. We evaluate the performance of the algorithm on mobile robots. Experimental data is from a 6-axis IMU and 5 UWB radio sensor devices. And the result shows that the position RMSE of our algorithm is 3.29 centimeters and our comprehensive localization algorithm can increase localization accuracy in complex environments compared with only UWB algorithm.

With the development of deep learning technology, object detection has been widely applied in various fields. However, in cross-dataset object detection, conventional deep learning models often face performance degradation issues. This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. Existing technologies still face performance bottlenecks when dealing with diverse scenarios. To address these issues, this study proposes a lightweight, cross-dataset enhanced object detection method for the agricultural domain based on YOLOv9, named Multi-Adapt Recognition-YOLOv9 (MAR-YOLOv9). The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively designed. A more streamlined and lightweight Main Neck structure has been introduced, along with innovative methods for feature extraction, up-sampling, and Concat connection. The hybrid connection strategy allows the model to flexibly utilize features from different levels. This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. At the same time, the model size was reduced by 9.3%, and the number of model layers was decreased, reducing computational costs and storage requirements. Additionally, MAR-YOLOv9 demonstrated significant advantages in detecting complex agricultural images, providing an efficient, lightweight, and adaptable solution for object detection tasks in the agricultural field. The curated data and code can be accessed at the following link: https://github.com/YangxuWangamI/MAR-YOLOv9.