New multiple target tracking strategy using domain knowledge and optimisation

This paper proposes an environment-dependent vehicle dynamic modeling approach considering interactions between the noisy control input of a dynamic model and the environment in order to make best use of domain knowledge. Based on this modeling, a new domain knowledge-aided moving horizon estimation (DMHE) method is proposed for ground moving target tracking. The proposed method incorporates different types of domain knowledge in the estimation process considering both environmental physical constraints and interaction behaviors between targets and the environment. Furthermore, in order to deal with a data association ambiguity problem of multiple-target tracking in a cluttered environment, the DMHE is combined with a multiple-hypothesis tracking structure. Numerical simulation results show that the proposed DMHE-based method and its extension could achieve better performance than traditional tracking methods which utilize no domain knowledge or simple physical constraint information only

New environmental dependent modelling with Gaussian particle filtering based implementation for ground vehicle tracking

This paper proposes a new domain knowledge aided Gaussian particle filtering based approach for the ground vehicle tracking application. Firstly, a new form of modelling is proposed to reflect the influences of different types of environmental domain knowledge on the vehicle dynamic: i) a non-Markov jump model is applied with multiple models while transition probabilities between models are environmental dependent ii) for a particular model, both the constraints and potential forces obtained from the surrounding environment have been applied to refine the vehicle state distribution. Based on the proposed modelling approach, a Gaussian particle filtering based method is developed to implement the related Bayesian inference for the target state estimation. Simulation studies from multiple Monte Carlo simulations confirm the advantages of the proposed method over traditional ones, from both the modelling and implementation aspects

Towards Human-like Walking with Biomechanical and Neuromuscular Control Features: Personalized Attachment Point Optimization Method of Cable-Driven Exoskeleton

The cable-driven exoskeleton can avoid joint misalignment, and is substantial alterations in the pattern of muscle synergy coordination, which arouse more attention in recent years to facilitate exercise for older adults and improve their overall quality of life. This study leverages principles from neuroscience and biomechanical analysis to select attachment points for cable-driven soft exoskeletons. By extracting key features of human movement, the objective is to develop a subject-specific design methodology that provides precise and personalized support in the attachment points optimization of cable-driven exoskeleton to achieve natural gait, energy efficiency, and muscle coordination controllable in the domain of human mobility and rehabilitation. To achieve this, the study first analyzes human walking experimental data and extracts biomechanical features. These features are then used to generate trajectories, allowing better natural movement under complete cable-driven exoskeleton control. Next, a genetic algorithm-based method is employed to minimize energy consumption and optimize the attachment points of the cable-driven system. This process identifies connections that are better suited for the human model, leading to improved efficiency and natural movement. By comparing the calculated elderly human model driven by exoskeleton with experimental subject in terms of joint angles, joint torques and muscle forces, the human model can successfully replicate subject movement and the cable output forces can mimic human muscle coordination. The optimized cable attachment points facilitate more natural and efficient collaboration between humans and the exoskeleton, making significant contributions to the field of assisting the elderly in rehabilitation

High-Quality Fiber Bragg Gratings Inscribed by Femtosecond Laser Point-by-Point Technology

We experimentally studied the inscription of fiber Bragg gratings by using femtosecond (fs) laser point-by-point (PbP) technology. The effects of the focusing geometry, grating order, laser energy and grating length on the spectral characteristics of the PbP FBG were investigated. After optimizing these parameters, a high-quality first-order PbP FBG with a reflectivity > 99.9% (i.e., Bragg resonance attenuation of 37.7 dB) and insertion loss (IL) of 0.03 dB was successfully created. Moreover, taking advantage of the excellent flexibility of the fs laser PbP technology, high-quality FBGs with various Bragg wavelengths ranging from 856 to 1902.6 nm were inscribed. Furthermore, wavelength-division-multiplexed (WDM) FBG arrays consisting of 10 FBGs were rapidly constructed. Additionally, a Fabry-Perot cavity was realized by using two high-quality FBGs, and its birefringence could be reduced from 3.04 × 10−5 to 1.77 × 10−6 by using a slit beam shaping-assisted femtosecond laser PbP technology. Therefore, such high-quality FBGs are promising to improve the performance of optical fiber sensors, lasers and communication devices

New environmental dependent modeling with Gaussian particle filtering based implementation for ground vehicle tracking

This paper proposes a new domain knowledge aided Gaussian particle filtering based approach for the ground vehicle tracking application. Firstly, a new form of modeling is proposed to reflect the influences of different types of environmental domain knowledge on the vehicle dynamic: i) a non-Markov jump model is applied with multiple models while transition probabilities between models are environmental dependent ii) for a particular model, both the constraints and potential forces obtained from the surrounding environment have been applied to refine the vehicle state distribution. Based on the proposed modeling approach, a Gaussian particle filtering based method is developed to implement the related Bayesian inference for the target state estimation. Simulation studies from multiple Monte Carlo simulations confirm the advantages of the proposed method over traditional ones, from both the modeling and implementation aspects

High-Temperature-Resistant Fiber Laser Vector Accelerometer Based on a Self-Compensated Multicore Fiber Bragg Grating

We propose and demonstrate a novel high-temperature-resistant vector accelerometer, consisting of a ring cavity laser and sensing probe (i.e., fiber Bragg gratings (FBGs)) inscribed in a seven-core fiber (SCF) by using the femtosecond laser direct writing technique. A ring cavity laser serves as a light source. Three FBGs in the outer cores of SCF, which are not aligned in a straight line, are employed to test the vibration. These three FBGs have 120° angular separation in the SCF, and hence, vibration orientation and acceleration can be measured simultaneously. Moreover, the FBG in the central core was used as a reflector in the ring cavity laser, benefiting to resist external interference factors, such as temperature and strain fluctuation. Such a proposed accelerometer exhibits a working frequency bandwidth ranging from 4 to 68 Hz, a maximum sensitivity of 54.2 mV/g, and the best azimuthal angle accuracy of 0.21° over a range of 0–360°. Furthermore, we investigated the effect of strain and temperature on the performance of this sensor. The signal-to-noise ratio (SNR) only exhibits a fluctuation of ~1 dB in the range (0, 2289 με) and (50 °C, 1050 °C). Hence, such a vector accelerometer can operate in harsh environments, such as in aerospace and a nuclear reactor

Data_Sheet_1_Towards Human-like Walking with Biomechanical and Neuromuscular Control Features: Personalized Attachment Point Optimization Method of Cable-Driven Exoskeleton.PDF

The cable-driven exoskeleton can avoid joint misalignment, and is substantial alterations in the pattern of muscle synergy coordination, which arouse more attention in recent years to facilitate exercise for older adults and improve their overall quality of life. This study leverages principles from neuroscience and biomechanical analysis to select attachment points for cable-driven soft exoskeletons. By extracting key features of human movement, the objective is to develop a subject-specific design methodology that provides precise and personalized support in the attachment points optimization of cable-driven exoskeleton to achieve natural gait, energy efficiency, and muscle coordination controllable in the domain of human mobility and rehabilitation. To achieve this, the study first analyzes human walking experimental data and extracts biomechanical features. These features are then used to generate trajectories, allowing better natural movement under complete cable-driven exoskeleton control. Next, a genetic algorithm-based method is employed to minimize energy consumption and optimize the attachment points of the cable-driven system. This process identifies connections that are better suited for the human model, leading to improved efficiency and natural movement. By comparing the calculated elderly human model driven by exoskeleton with experimental subject in terms of joint angles, joint torques and muscle forces, the human model can successfully replicate subject movement and the cable output forces can mimic human muscle coordination. The optimized cable attachment points facilitate more natural and efficient collaboration between humans and the exoskeleton, making significant contributions to the field of assisting the elderly in rehabilitation.</p