Navigation Cost Research Articles

SRank: Guiding schema selection in NoSQL document stores

The rise of big data has led to a greater need for applications to change their schema frequently. NoSQL databases provide flexibility in organizing data and offer multiple choices for structuring and storing similar information. While schema flexibility speeds up initial development, choosing schemas wisely is crucial, as they significantly impact performance, affecting data redundancy, navigation cost, data access cost, and maintainability. This paper emphasizes the importance of schema design in NoSQL document stores. It proposes a model to analyze and evaluate different schema alternatives and suggest the best schema out of various schema alternatives. The model is divided into four phases. The model inputs the Entity-Relationship (ER) model and workload queries. In the Transformation Phase, the schema alternatives are initially developed for each ER model, and subsequently, a schema graph is generated for each alternative. Concurrently, workload queries undergo conversion into query graphs. In the Schema Evaluation phase, the Schema Rank (SRank) is calculated for each schema alternative using query metrics derived from the query graphs and path coverage generated from the schema graphs. Finally, in the Output phase, the schema with the highest SRank is recommended as the most suitable choice for the application. The paper includes a case study of a Hotel Reservation System (HRS) to demonstrate the application of the proposed model. It comprehensively evaluates various schema alternatives based on query response time, storage efficiency, scalability, throughput, and latency. The paper validates the SRank computation for schema selection in NoSQL databases through an extensive experimental study. The alignment of SRank values with each schema's performance metrics underscores this ranking system's effectiveness. The SRank simplifies the schema selection process, assisting users in making informed decisions by reducing the time, cost, and effort of identifying the optimal schema for NoSQL document stores.

Learning to Execute Timed-Temporal-Logic Navigation Tasks under Input Constraints in Obstacle-Cluttered Environments

This study focuses on addressing the problem of motion planning within workspaces cluttered with obstacles while considering temporal and input constraints. These specifications can encapsulate intricate high-level objectives involving both temporal and spatial constraints. The existing literature lacks the ability to fulfill time specifications while simultaneously managing input-saturation constraints. The proposed approach introduces a hybrid three-component control algorithm designed to learn the safe execution of a high-level specification expressed as a timed temporal logic formula across predefined regions of interest in the workspace. The first component encompasses a motion controller enabling secure navigation within the minimum allowable time interval dictated by input constraints, facilitating the abstraction of the robot’s motion as a timed transition system between regions of interest. The second component utilizes formal verification and convex optimization techniques to derive an optimal high-level timed plan over the mentioned transition system, ensuring adherence to the agent’s specification. However, the necessary navigation times and associated costs among regions are initially unknown. Consequently, the algorithm’s third component iteratively adjusts the transition system and computes new plans as the agent navigates, acquiring updated information about required time intervals and associated navigation costs. The effectiveness of the proposed scheme is demonstrated through both simulation and experimental studies.

Study on automated guided vehicle navigation method with external computer vision

Automated guided vehicle (AGV) navigation is extensively used in industrial manufacturing. Existing AGV navigation methods have high accuracy but usually require expensive positioning sensors. This paper proposes a novel method for AGV navigation based on external computer vision (NECV). No matter how many AGVs are in the workshop, the proposed NECV method uses only an external camera mounted on the top of the roof to detect and track AGVs, and all the AGVs don’t need to be equipped with any positioning sensors. Because there is no need to equip positioning sensors on AGVs, and also don’t need to arrange positioning signs, NECV significantly reduces the positioning cost of navigation. YOLOv8 was selected as the detector for NECV, and the training was completed using a prepared dataset. We improved the structure of the StrongSORT algorithm and used it as the tracker. The improved StrongSORT algorithm is the core of NECV. The imaging coordinates of the AGVs are detected by the detector, transformed into global coordinates through inverse perspective mapping, and passed to the master console. Experimental results indicated that the NECV detection deviation q of the AGV and the experimental accuracy metrics of the NECV after compensating q were considerably improved, close to those of the popular Quick Response (QR) code navigation method. Statistically, NECV can reduce the cost of AGV positioning detection by 90%.

Cost and Activity Analysis of Patient Navigation for Persons With HIV: Comparing Health Department and Health Clinic Delivered Interventions.

Housing and employment are key factors in the health and well-being of people with HIV (PWH). Patient navigation programs to improve housing and employment show success in achieving viral suppression. Replicating patient navigation interventions to improve population health is needed. Understanding costs associated with patient navigation is a key next step. Therefore, the purpose of this study is to describe the costs associated with delivering patient navigator interventions in two different organizations to improve housing and employment for PWH. We conducted a cost analysis of two models of patient navigation. Costs were collected from two sites' payroll, invoices, contracts, and receipts. Pre-implementation and implementation costs and utilization of service costs are presented. Potential reimbursement costs were calculated based on salaries from the Department of Labor. The health clinic's pre-implementation costs were higher ($169,133) than the health department's ($22,018). However, costs of patient navigation during the 2-year intervention were similar between health clinic and health department ($264,985 and $232,923, respectively). The health clinic reported more total time spent with clients (16,013.7 hours) than the health department (1,883.8 hours). The costs per additional person suppressed were $20,632 versus $37,810 for the health department and health clinic, respectively, which are lower than the average lifetime cost of HIV treatment. Replicability and scalability of a patient navigation intervention are possible in both health clinic and health department settings. Each site had specific costs, client needs, and other factors that required adaptations to successfully implement the intervention. Future programs should consider tailoring costs to site-specific factors to improve outcomes. Policymakers and public health officials should consider using these results to improve planning and investment in HIV treatment and prevention interventions.

Energetic cost of microswimmer navigation: The role of body shape

Energetic cost of microswimmer navigation: The role of body shape

Formation‐Aware Planning and Navigation with Corridor Shortest Path Maps

AbstractThe need to plan motions for agents with variable shape constraints such as under different formations appears in several virtual and real‐world applications of autonomous agents. In this work, we focus on planning and execution of formation‐aware paths for a group of agents traversing a cluttered environment. The proposed planning framework addresses the trade‐off between being able to enforce a preferable formation when traversing the corridors of the environment, versus accepting to switch to alternative formations requiring less clearance in order to utilize narrower corridors that can lead to a shorter overall path to the final destination. At the planning stage, this trade‐off is addressed with a multi‐layer graph annotated with per‐layer navigation costs and formation transition costs, where each layer represents one formation together with its specific clearance requirement. At the navigation stage, we introduce Corridor Shortest Path Maps (CSPMs), which produce a vector field for guiding agents along the solution corridor, ensuring unobstructed in‐formation navigation in cluttered environments, as well as group motion along lengthwise‐optimal paths in the solution corridor. We also present examples of how our multi‐layer planning framework can be applied to other types of multi‐modal planning problems.

Exploring the Economic Significance of Arctic Routes in the Supply Chain Field by Contrasting with Traditional Route

With global climate warming, the reduction of Arctic ice and the opening of shipping routes during the summer season, the Arctic route has gradually become an important choice in the field of supply chains as it connects Asia, Europe, and North America. It offers the advantages of reducing shipping time, lowering transportation costs, expanding trade opportunities, and diversifying supply chain routes. This article collected data from websites such as the Clarksons and the Shanghai Shipping Exchange to investigate the current number of voyages along the Arctic route and various factors that impact the cost of navigation. By comparing the navigation costs between the Arctic route and the traditional Suez Canal route, it was found that the Arctic region has higher voyage transportation costs compared to the Suez route, indicating that the Suez route still holds an economic advantage. However, choosing the Arctic route can shorten the delivery lead time and reduce certain supply chain costs.

Case Volume Justification of 3D-Navigated Spinal Procedures: A Cost-Benefit Analysis.

3D image-guidance platforms have transformed spinal surgery by enhancing visualization, increasing precision, and improving patient outcomes. However, with high procurement, operational, and maintenance costs relative to the standard of care, the benefits of acquiring these platforms must be thoroughly assessed. This study aims to develop a model that weighs the cost of a typical 3D navigation platform against its clinical benefits to determine the facility case volume required to justify its purchase. Using Medtronic's StealthStation and O-Arm as a market example, we calculated the break-even case volume by dividing the cost of the platform by the difference in gross margins between 3D navigation and the standard of care. Total gross margins earned from first-time and revision surgeries were calculated based on each payer's reimbursement rate and covered case volume, as well as each technology's revision rate. Values reported in literature and by Centers for Medicare and Medicaid Services databases were plugged into the model to calculate variables. At a 0% reimbursement rate from private payers for revision surgeries, an annual case volume of 158 spinal surgeries would be required to justify the per-year 3D navigation cost; at 100% private payer reimbursement, 352 surgeries would be required. Given these volumes, 61% of all US inpatient facilities cannot justify 3D navigation at 0% reimbursement, and 86% cannot justify it at 100% reimbursement. Accordingly, greater pricing flexibility, such as per-procedure models, is required for 3D navigation systems to standardize clinical outcomes across medical centers.

A novel path planning algorithm for ships in dynamic current environments

Ship meteorological path planning has received increasing attention, especially in the dynamic wind, wave, and current environments. Determining a safe and efficient voyage path is one of the most important issues for ships. In order to reduce the navigation costs in terms of distance, energy, and time under multiple constraints, a novel ship path planning method is proposed in a spatially-temporally variant environment, which can find the optimum paths under different task requirements. An improved A* algorithm in a dynamic current environment (A*-DCE) is proposed, which introduces the weights of distance, energy, and time to generate paths with significantly different costs. The attractive/repulsive field replaces the distance cost estimation of the conventional A* algorithm. The grid map is segmented based on velocity data to determine energy and time cost estimations. Under the varied spatial-temporal current environment in the Nw-European Shelf region, the B-spline curve is used to improve the smoothness and to reduce the yaw cost. In addition, the paths in the candidate set that conform to the Pareto front are obtained using the NSGA-II algorithm. The smallest distance path has the same distance-optimal capability as the path from the conventional A* algorithm. The energy and time cost of the remaining optimal paths are also reduced.

A multi-objective optimisation strategy for ice navigation under ship safety-following scenarios

Shipping activities in the Northern Sea Route (NSR) are increasing. However, due to the complex sea ice conditions, ships still need assistance of icebreakers when sailing through most Arctic waters. Icebreaker escort operations are important to ensure the safe passage of ships through ice-covered waters, but the speed of following ships will be limited, thus creating challenges for NSR sailing economics and emission reduction issues. Optimizing the sailing speed of following ships can reduce NSR ship emissions and improve navigation economy while safeguarding their sailing safety. In this paper, a green, economical and safe optimization approach under ship-following navigation has been developed by drawing on similarities with the car-following phenomenon. Then, three ship-following scenarios are designed for simulating the actual ice navigation conditions. Finally, a cost-eco-efficiency multi-objective optimisation model for ice navigation is established, and the performance of the proposed model is demonstrated by the navigation case of “Tian You”. The results show that the proposed model can effectively reduce navigation costs and control navigation emissions while ensuring navigation safety. This study also demonstrates that there is still room for a 20% improvement in the optimised speed and a 4.8% reduction in the ship-following distance compared to the actual situation.

Optimization of anchor position allocation considering efficiency and safety demand

With the increase of port throughput and the development of the trend of large-scale ships, the demand for ship anchoring is growing. The shortage of anchorage resources has become the bottleneck restricting the development of ports, especially in the busy inland waters. The anchor position assigned to the safety of ships enables the anchorage to be reasonably and fully utilized, which has become a key issue to promote the development of ports and ensure the safety of ships. This paper provides a dynamic multi-objective optimization model for anchor position allocation. The abovementioned model can optimize the position arrangement of ships in the anchorage, so that more ships can be accommodated in the anchorage while ensuring safety. To construct the model, two optimization indexes, namely anchorage utilization rate and comprehensive cost factor of anchorage navigation are proposed. A multi-objective optimization method based on genetic algorithm is used to solve the model. The multi-objective optimization method proposed in this paper is compared with that of the traditional method. Experimental results show that the proposed method can improve the anchorage utilization rate in 1%–8%, and more ships can be safely anchored in the anchorage under a reasonable location arrangement, reducing the navigation cost of ships in the anchorage. In addition, it is found that the performance of the proposed method is degraded in the initial stage of the experiment, but with the continuous progress of the anchor position allocation test, the comprehensive evaluation results are concentrated in the range of 0.71–0.73, and the performance of the optimization method is stable and reliable. Overall, the theory and method proposed in this paper can help ships to allocate reasonable anchoring positions, ensure safe berthing of ships, and effectively improve the utilization rate of anchorage, and provide practical technical reference for maritime management departments to improve the operation capacity of anchorage.

A Receding Horizon Navigation and Control System for Autonomous Merchant Ships: Reducing Fuel Costs and Carbon Emissions under the Premise of Safety

In order to solve the multi-objective planning and trajectory tracking control problem related to maritime autonomous surface ships (MASSs), a new design scheme for autonomous navigation is proposed in this paper, with a receding horizon navigation and control (RHNC) system that contains navigation and control modules. In the navigation module, we designed a superposition field gradient descent local search algorithm based on the cost field, emission field, and guidance field to navigate the MASS reference path, and in the control module, we designed a nonlinear controller that can handle multiple constraints based on the NMPC framework. Under the new scheme, the navigation module completes local path planning to reduce costs and emissions, the control module accomplishes accurate trajectory tracking and real-time collision avoidance, and the information is transmitted in both directions between the two modules to collaboratively complete the MASS navigation and control tasks. We conducted a simulation study of the navigation algorithm and controller and the autonomous navigation system using a Kriso Container ship (KCS). The simulation results demonstrate the effectiveness of the proposed cooperative design scheme in reducing navigation costs and emissions and avoiding autonomous collision avoidances.

Path planning for ships assisted by the icebreaker in ice-covered waters in the Northern Sea Route based on optimal control

This paper aims to develop an innovative model based on optimal control to optimize the shipping path of the fleet, including icebreakers and following ships in continuous time and space in the Northern Sea Route (NSR) waters, considering the influence of breakable and unbreakable ice. Path planning assumptions of ship navigation in ice-covered waters are summarized by considering navigation efficiency, navigation cost, icebreaking cost, and safety. The objective function of the model based on optimal control is formulated to convert the path planning problem into a multi-objective optimization problem. The cost functions based on path planning assumptions are specified in the objective function. Finally, dynamic programming and numerical simulation are used to solve the multi-objective optimization functions. Then, the optimal path in continuous time and space in ice-covered waters in the NSR is obtained. A case study of four scenarios, considering breakable and unbreakable sea ice conditions in the NSR, is conducted to verify the strength of the proposed model. The case study results show that the proposed model can optimize the path of ships in ice-covered waters. The method proposed in this research could guide ship fleet path planning for the bridge team and improve icebreaker management in the NSR.

Vision-Based Topological Mapping and Navigation With Self-Organizing Neural Networks.

Spatial mapping and navigation are critical cognitive functions of autonomous agents, enabling one to learn an internal representation of an environment and move through space with real-time sensory inputs, such as visual observations. Existing models for vision-based mapping and navigation, however, suffer from memory requirements that increase linearly with exploration duration and indirect path following behaviors. This article presents e -TM, a self-organizing neural network-based framework for incremental topological mapping and navigation. e -TM models the exploration trajectories explicitly as episodic memory, wherein salient landmarks are sequentially extracted as "events" from streaming observations. A memory consolidation procedure then performs a playback mechanism and transfers the embedded knowledge of the environmental layout into spatial memory, encoding topological relations between landmarks. Fusion adaptive resonance theory (ART) networks, as the building block of the two memory modules, can generalize multiple input patterns into memory templates and, therefore, provide a compact spatial representation and support the discovery of novel shortcuts through inferences. For navigation, e -TM applies a transfer learning paradigm to integrate human demonstrations into a pretrained locomotion network for smoother movements. Experimental results based on VizDoom, a simulated 3-D environment, have shown that, compared to semiparametric topological memory (SPTM), a state-of-the-art model, e -TM reduces the time costs of navigation significantly while learning much sparser topological graphs.

Combining Navigation and Manipulation Costs for Time-Efficient Robot Placement in Mobile Manipulation Tasks

Mobile manipulation tasks require a seamless integration of navigation and manipulation capabilities. Finding suitable robot placements to pick up and place objects in such tasks is crucial for time-efficient task execution. Sub-optimal robot placements result in infeasible solutions or require larger re-positioning of the mobile base to reach target objects, increasing the overall time to complete the task. In this work, we propose an approach that, given a set of objects, autonomously selects the optimal placements of a humanoid robot in conjunction with the best grasp candidate and corresponding arm. In contrast to previous approaches, our method considers both the navigation costs between consecutive robot placements and the manipulation costs to reduce the time needed to complete the task. We evaluate our method on a simulated table clearing task that requires the robot to move between pickup and discard locations and demonstrate the applicability in a real-world experiment on the humanoid robot ARMAR-6. In addition, we perform a run-time analysis and show that our approach can integrate sensory feedback to update the optimal placement in dynamic environments.

Height information aided 3D real‐time large‐scale underground user positioning

Due to the cost of inertial navigation and visual navigation equipment and lake of satellite navigation signals, they cannot be used in large-scale underground mining environment. To solve this problem, this study proposes a large-scale underground 3D real-time positioning method with seam height assistance. This method uses the ultra wide band positioning base station as the core and is combined with seam height information to build a factor graph confidence transfer model to realise 3D positioning. The simulation results show that the proposed real-time method is superior to the existing algorithms in positioning accuracy and can meet the needs of large-scale underground users.

Many-objective evolutionary algorithm based agricultural mobile robot route planning

Agricultural robot technology has experienced rapid development in the past ten years, and agricultural robots have been used to implement various complex agricultural tasks. In these processes, route planning is an important guarantee for reducing navigation distance and saving total turning angle. However, minimizing the cost of the entire navigation process on the premise of completing agricultural work is difficult. Many-objective Evolutionary Algorithm is used to solve the route planning problem of agricultural mobile robots under the premise of minimizing navigation cost. By scanning the radar map of the greenhouse, the path between all target points is calculated by using the probabilistic roadmap (PRM), and the route planning of the agricultural robot is carried out according to the sum of the path length and the path angle. To determine the best route for agricultural mobile robots, four algorithms are compared: Hypervolume Estimation Algorithm (HypE), Grid-Based Evolutionary Algorithm (GrEA), Knee Point-Driven Evolutionary Algorithm (KnEA), and Non-dominated sorting genetic algorithm (NSGA-III). The quality of the solutions was compared using C-Metric, and it could verify that HypE offers the best performance among four algorithms.

Establishing Opportunities and Limitations of Forecast Use in the Operational Management of Highly Constrained Multiobjective Water Systems

The potential utility of forecast information to improve reservoir control is well established, but physical, operational, and institutional constraints inhibit the full realization of these benefits. To help integrate forecast technology into operational water management, it is necessary to establish the opportunities and limitations of forecast use within the current system management framework, so that water managers and planners can identify in practice what constraints or aspects of control policy design most limit forecast utility. In this work, we explore the value of forecasts in the current outflow management plan of the Lake Ontario-St. Lawrence River basin, which is the first regulation policy of this system to use water supply forecasts to guide release decisions. We assess the value of forecasts for many-objective system management (e.g., upstream and downstream flood risk reduction, hydropower production, commercial navigation costs, recreational boating benefits, and wetland restoration), as a function of forecast accuracy, timing, and interactive effects, with system constraints and control rule structures. Results show that a 20%–30% reduction in forecast error over current statistical forecasts leads to observable improvements in most objectives, which grow significantly with additional forecast accuracy. Realizing these benefits is possible by increasing forecast accuracy, particularly in the springtime when tradeoffs between objectives are the greatest. However, we also identify one objective (wetland restoration) that declines with improved forecast skill, as well as operational constraints that limit forecast utility under the current outflow management plan. Overall, these results suggest that key aspects of the current control policy could be revised to better utilize forecast information, especially as forecasts improve over time.

Multiobjective path optimization for autonomous land levelling operations based on an improved MOEA/D-ACO

To improve the efficiency of the land levelling operation of a tractor-scraper system controlled by an autonomous guidance system which depends on the complete earthwork transportation and minimum navigation cost, it is crucial to optimize the travelling paths. This article presents an innovative method using an improved multiobjective algorithm to solve the path optimization problem for autonomous land levelling operations. First, an environment model was built based on collected field data, and a model decomposition method was proposed to decompose the field terrain model and realize complete land levelling. Then, a three-objective problem model (consisting of the travelling distance, steering angle and earthwork) was developed, and the Pareto set of the travel sequence was obtained using a Multiobjective Evolutionary Algorithm Using Decomposition and Further Mutation Ant Colony (MOEA/D-FMACO). Finally, the path optimization of five field cases based on three algorithms, the MOEA/D-FMACO, MOEA/D-ACO and NSGA-III, was simulated in MATLAB. The simulation results show that the MOEA/D-FMACO has better solution performance quality compared with the other two algorithms and shortens the execution time. In field experiments, a GNSS-based land leveller controlled by automatic steering and levelling systems tracked the planned paths based on the MOEA/D-FMACO and a previous method for a field in two periods. The field results verified that the land levelling effect based on the optimization paths of the MOEA/D-FMACO had a significant improvement over the previous method. The proposed method decreased the maximum elevation difference (ΔZ) by 10.5 cm and the flatness of the field (Sd) by 4.3 cm and increased the elevation distribution around the target elevation (η) by 11.15% compared to the previous method.

Optimal control of ship collision avoidance problem

The continuous increasing of maritime traffic amplified the severity of the collision risk issue in the maritime domain. Therefore, the calculus and optimization of ship’s navigation without collision risks have been known as a major challenge for the scientific researches’ community. Several solutions were proposed to enhance the maritime safety. The topic was covered as an optimal control problem with state constraints using Nonlinear Model Predictive Control in order to consider the nonlinearity of the ship motion. Other researches relied on calculating risks of collisions in ocean navigation by metaheuristic methods or by neural networks in order to cover multi-ship collision risk situation. In this paper, a detailed description of necessary elements used in the analysis of the maritime navigation without collision issue is presented including the ship motion, the International Regulations for Preventing Collisions at Sea COLREGs rules, and the navigation cost. An analytical study of optimal control based on Pontryagin’s Maximum Principle to avoid ship collision situation with more efficiency is proved and detailed. Simulation results that show the efficiency of the described method are calculated using MATLAB.