Resource-Efficient Cooperative Online Scalar Field Mapping via Distributed Sparse Gaussian Process Regression

Cooperative online scalar field mapping is an important task for multi-robot systems. Gaussian process regression is widely used to construct a map that represents spatial information with confidence intervals. However, it is difficult to handle cooperative online mapping tasks because of its high computation and communication costs. This letter proposes a resource-efficient cooperative online field mapping method via distributed sparse Gaussian process regression. A novel distributed online Gaussian process evaluation method is developed such that robots can cooperatively evaluate and find observations of sufficient global utility to reduce computation. The bounded errors of distributed aggregation results are guaranteed theoretically, and the performances of the proposed algorithms are validated by real online light field mapping experiments

Five-Tiered Route Planner for Multi-AUV Accessing Fixed Nodes in Uncertain Ocean Environments

This article introduces a five-tiered route planner for accessing multiple nodes with multiple autonomous underwater vehicles (AUVs) that enables efficient task completion in stochastic ocean environments. First, the pre-planning tier solves the single-AUV routing problem to find the optimal giant route (GR), estimates the number of required AUVs based on GR segmentation, and allocates nodes for each AUV to access. Second, the route planning tier plans individual routes for each AUV. During navigation, the path planning tier provides each AUV with physical paths between any two points, while the actuation tier is responsible for path tracking and obstacle avoidance. Finally, in the stochastic ocean environment, deviations from the initial plan may occur, thus, an auction-based coordination tier drives online task coordination among AUVs in a distributed manner. Simulation experiments are conducted in multiple different scenarios to test the performance of the proposed planner, and the promising results show that the proposed method reduces AUV usage by 7.5% compared with the existing methods. When using the same number of AUVs, the fleet equipped with the proposed planner achieves a 6.2% improvement in average task completion rate

Physics-informed Neural Network Combined with Characteristic-Based Split for Solving Navier-Stokes Equations

In this paper, physics-informed neural network (PINN) based on characteristic-based split (CBS) is proposed, which can be used to solve the time-dependent Navier-Stokes equations (N-S equations). In this method, The output parameters and corresponding losses are separated, so the weights between output parameters are not considered. Not all partial derivatives participate in gradient backpropagation, and the remaining terms will be reused.Therefore, compared with traditional PINN, this method is a rapid version. Here, labeled data, physical constraints and network outputs are regarded as priori information, and the residuals of the N-S equations are regarded as posteriori information. So this method can deal with both data-driven and data-free problems. As a result, it can solve the special form of compressible N-S equations -- -Shallow-Water equations, and incompressible N-S equations. As boundary conditions are known, this method only needs the flow field information at a certain time to restore the past and future flow field information. We solve the progress of a solitary wave onto a shelving beach and the dispersion of the hot water in the flow, which show this method's potential in the marine engineering. We also use incompressible equations with exact solutions to prove this method's correctness and universality. We find that PINN needs more strict boundary conditions to solve the N-S equation, because it has no computational boundary compared with the finite element method

CARE: Confidence-rich Autonomous Robot Exploration using Bayesian Kernel Inference and Optimization

In this paper, we consider improving the efficiency of information-based autonomous robot exploration in unknown and complex environments. We first utilize Gaussian process (GP) regression to learn a surrogate model to infer the confidence-rich mutual information (CRMI) of querying control actions, then adopt an objective function consisting of predicted CRMI values and prediction uncertainties to conduct Bayesian optimization (BO), i.e., GP-based BO (GPBO). The trade-off between the best action with the highest CRMI value (exploitation) and the action with high prediction variance (exploration) can be realized. To further improve the efficiency of GPBO, we propose a novel lightweight information gain inference method based on Bayesian kernel inference and optimization (BKIO), achieving an approximate logarithmic complexity without the need for training. BKIO can also infer the CRMI and generate the best action using BO with bounded cumulative regret, which ensures its comparable accuracy to GPBO with much higher efficiency. Extensive numerical and real-world experiments show the desired efficiency of our proposed methods without losing exploration performance in different unstructured, cluttered environments. We also provide our open-source implementation code at https://github.com/Shepherd-Gregory/BKIO-Exploration.Comment: Full version for the paper accepted by IEEE Robotics and Automation Letters (RA-L) 2023. arXiv admin note: text overlap with arXiv:2301.0052

Distributed Target Tracking with Fading Channels over Underwater Wireless Sensor Networks

This paper investigates the problem of distributed target tracking via underwater wireless sensor networks (UWSNs) with fading channels. The degradation of signal quality due to wireless channel fading can significantly impact network reliability and subsequently reduce the tracking accuracy. To address this issue, we propose a modified distributed unscented Kalman filter (DUKF) named DUKF-Fc, which takes into account the effects of measurement fluctuation and transmission failure induced by channel fading. The channel estimation error is also considered when designing the estimator and a sufficient condition is established to ensure the stochastic boundedness of the estimation error. The proposed filtering scheme is versatile and possesses wide applicability to numerous real-world scenarios, e.g., tracking a maneuvering underwater target with acoustic sensors. Simulation results demonstrate the effectiveness of the proposed filtering algorithm. In addition, considering the constraints of network energy resources, the issue of investigating a trade-off between tracking performance and energy consumption is discussed accordingly.Comment: 12 pages, 6 figures, 6 table

Comparative Evaluation of the Antioxidant Capacities, Organic Acids, and Volatiles of Papaya Juices Fermented by Lactobacillus acidophilus

Fermentation of foods by lactic acid bacteria is a useful way to improve the nutritional value of foods. In this study, the health-promoting effects of fermented papaya juices by two species, Lactobacillus acidophilus and Lactobacillus plantarum, were determined. Changes in pH, reducing sugar, organic acids, and volatile compounds were determined, and the vitamin C, total phenolic content, and flavonoid and antioxidant capacities during the fermentation process were investigated. Juices fermented by Lactobacillus acidophilus and Lactobacillus plantarum had similar changes in pH and reducing sugar content during the 48 h fermentation period. Large amounts of aroma-associated compounds and organic acids were produced, especially lactic acid, which increased significantly (p<0.05) (543.18 mg/100 mL and 571.29 mg/100 mL, resp.), improving the quality of the beverage. In contrast, the production of four antioxidant capacities in the fermented papaya juices showed different trends after 48 hours’ fermentation by two bacteria. Lactobacillus plantarum generated better antioxidant activities compared to Lactobacillus acidophilus after 48 h of fermentation. These results indicate that fermentation of papaya juice can improve its utilization and nutritional effect

InstructDET: Diversifying Referring Object Detection with Generalized Instructions

We propose InstructDET, a data-centric method for referring object detection (ROD) that localizes target objects based on user instructions. While deriving from referring expressions (REC), the instructions we leverage are greatly diversified to encompass common user intentions related to object detection. For one image, we produce tremendous instructions that refer to every single object and different combinations of multiple objects. Each instruction and its corresponding object bounding boxes (bbxs) constitute one training data pair. In order to encompass common detection expressions, we involve emerging vision-language model (VLM) and large language model (LLM) to generate instructions guided by text prompts and object bbxs, as the generalizations of foundation models are effective to produce human-like expressions (e.g., describing object property, category, and relationship). We name our constructed dataset as InDET. It contains images, bbxs and generalized instructions that are from foundation models. Our InDET is developed from existing REC datasets and object detection datasets, with the expanding potential that any image with object bbxs can be incorporated through using our InstructDET method. By using our InDET dataset, we show that a conventional ROD model surpasses existing methods on standard REC datasets and our InDET test set. Our data-centric method InstructDET, with automatic data expansion by leveraging foundation models, directs a promising field that ROD can be greatly diversified to execute common object detection instructions.Comment: 29 pages (include Appendix) Published in ICL

A snapshot of the transition from monogenetic volcanoes to composite volcanoes: Case study on the Wulanhada Volcanic Field (northern China)

The transition processes from monogenetic volcanoes to composite volcanoes are poorly understood. The Late Pleistocene to Holocene intraplate monogenetic Wulanhada Volcanic Field (WVF) in northern China provides a snapshot of such a transition. Here we present petrographic observations, mineral chemistry, bulk rock major and trace element data, thermobarometry, and a partial melting model for the WVF to evaluate the lithology and partial melting degree of the mantle source, the crystallization conditions, and pre-eruptive magmatic processes occurring within the magma plumbing system. The far-field effect of India-Eurasia collision resulted in a relatively high degree (10 %-20 %) of partial melting of a carbonate-bearing eclogite (~ 3 wt % carbonate; Gt/Cpx ≈ 2 : 8, where Gt denotes garnet and Cpx denotes clinopyroxene) followed by interaction with ambient peridotite. The primary melts ascended to the depth of the Moho (~ 33-36 km depth), crystallized olivine, clinopyroxene and plagioclase at the temperature of 1100-1160 °C with the melt water contents of 1.1 wt %- 2.3 wt %. Part of the primary melt interacted with the lithospheric mantle during ascent, resulting in an increase in the MgO contents and a decrease in the alkaline contents. The modified magma was subsequently directly emplaced into the middle crust (~ 23-26 km depth) and crystallized olivine, clinopyroxene and plagioclase at the temperature of 1100-1160 °C. The primary melts from the same mantle sources migrated upward to the twolevel magma reservoirs to form minerals with complex textures (including reverse and oscillatory zoning and sieve texture). Magma erupted along the NE-SW-striking basement fault and the NW-SE-striking Wulanhada- Gaowusu fault in response to the combined effects of regional tectonic stress and magma replenishment. The crustal magma reservoir in the WVF may represent a snapshot of the transition from monogenetic volcanoes to composite volcanoes. It is possible to form a composite volcano with large magma volumes and complex compositions if the magma is continuously supplied from the source and experiences assimilation and fractional crystallization processes in the magma plumbing system at crustal depth