Dynamic Planning Research Articles

A Semantic Approach to Dynamic Path Planning for Fire Evacuation through BIM and IoT Data Integration

Fire evacuation path planning involves multiple data sources. In order to develop a dynamic planning, a comprehensive knowledge of the environment involving building information and fire development is required. This article presents a semantic approach that integrates Building Information Modeling (BIM) and Internet of Things (IoT) information to provide a data foundation for dynamic path planning. First, a fire evacuation (FE) ontology is introduced to fuse both knowledge and information relevant to dynamic path planning. Next, a dynamic knowledge graph that evolves according to the development of fire situation is instantiated based on the relevant FE ontology. Finally, to validate the feasibility of the semantic approach based on the ontology and knowledge graph, an example of application is conducted using a specific building as an example. This study provides a data foundation for more intelligent and precise decision-making in fire evacuation scenarios and offers a new approach for safety design and management in the field of construction.

Dynamic marine spatial planning for conservation and fisheries benefits

AbstractThe increasing global demand for marine resources raises concerns about sustainable resource management and biodiversity conservation. Spatial closures, such as marine protected areas, can be valuable tools for maintaining and restoring exploited populations. When these spatial closures adopt a dynamic nature being adapted to the changing environment, they can effectively account for factors such as shifting species distributions, which enhances their potential to achieve ecological and socio‐economic objectives. Here, we adapted a decision‐support tool (the software Marxan), typically used for selecting static and permanent areas, to produce management recommendations that integrate permanent and temporal closures to fisheries. Our aim was to compare the outputs of a static network of permanent no‐take reserves with four other dynamic scenarios, including permanent and temporal closures that account for seasonal variations in the populations of species. All scenarios prioritized sites for the conservation of one of the most valuable European fishing stocks, the Norway lobster (Nephrops norvegicus). Additionally, we considered 12 other commercially exploited species captured by the Norway lobster fishery. The assessed outputs included retained biomass, area extent, closure type (permanent and seasonal) and opportunity costs within each scenario. We observed that all dynamic scenarios required fewer management areas permanently closed than the static scenario. This resulted in a lower opportunity cost for fisheries but also a higher capacity for biodiversity conservation. Therefore, complementing permanent with temporal closures could enhance biodiversity conservation and fisheries management. The novel dynamic planning method presented here could be applicable to other species, ecosystems and socio‐economic contexts.

Optimizing radiation exposure in dynamic radioactive environments: Dynamic ACO-RRT* path planning algorithm

Abstract Radioactive environments are often dynamic for various reasons, such as the dismantling of nuclear facilities, leakage of radiation sources, and nuclear accidents. Path planning in a dynamic radioactive environment is one of the important issues in radiation protection for nuclear facility workers. In traditional dynamic optimal path planning, there are phenomena, in which the optimal path is not found within the allowable time or the quality of the path searched within the allowable time is low. To solve this problem, we propose a hybrid algorithm that combines meta-heuristic and sample-based algorithms, called dynamic ant colony optimization-rapidly exploring random tree star (ACO-RRT*). A low-level grid optimal path consisting of grid vertices is obtained by the ACO algorithm, and then it is further refined by the RRT* algorithm. At every instant of environmental change, the vertices of the grid optimal path are divided into vertices with and without samples, and new samples are generated only around vertices without samples. Sampling optimization is performed only around the grid optimal path using new samples and already existing samples. Simulations in a virtual radioactive environment showed that the proposed algorithm reduces computational costs and improves the quality of the optimal path compared to traditional algorithms.

Research on superstore vegetable replenishment decision-making based on time series and PSO particle swarm algorithm

Because vegetables are easy to wear and tear and not easy to store, supermarkets will replenish vegetables every day. In order to make reasonable replenishment and pricing decisions, this paper firstly correlates the total sales data with other data, and finds that the total sales data is negatively correlated with the average purchase price and average selling price, and positively correlated with the number of discounts, and then adopts the Feedforward Neural Network model, the Evenberg-Marquardt training algorithm, and the time series analysis prediction to predict the total daily replenishment and pricing policy of each vegetable in the coming week. Marquardt training algorithm, time series analysis and forecasting, predicting the total daily replenishment and pricing strategy of each vegetable in the coming week, and at the same time, adopting the method of dynamic planning, through the use of particle swarm algorithm of PSO to solve the total replenishment of each category and pricing strategy, and then iterated to get the total daily replenishment of each category of commodities and pricing strategy in the coming week.

THE HARMONIZING MINE PLANNING IN THE EAST KALIMANTAN COKING COAL MINE: THE CHALLENGES AND IMPROVEMENTS

The coking coal mine at the research site is in the Pamaluan formation East Kalimantan. The condition of the mine has many complexities and challenges, such as natural slope condition (30º-35º), narrow pit area and low out pit dump capacity limited by land compensation issue, overburden material, geological structure, coal with very thin layers (<1 m) and consist multiple products in a layer, small number of strips and large number of blocks and high SR pit and SR balance in short range. In mine planning at the research location, many improvements need to be made so that all aspects, starting with mine planning and operations, can run smoothly and profits can be as expected. Some of the improvements that have been made at the mine such as build mine planning concepts, build fleet allocation rules, structural and stratigraphical geology modelling and improve drill & blast plan, implementation dynamic mine planning and optimize mining digital product. By implementing these improvements, significant positive impacts have been achieved, such as Consistently maintain mine planning compliance (accuracy) >97%, Maintain fleet productivity performance in range of 95% - 110%, Consistently achieving production targets >97%, Able to maintain SR balance by achieving weekly coal plan with the range of 94 - 103%, Achieving blasting performance results with a value of >97% and Maintain work safety aspects with no accidents and loss time injury.

Path Planning for a Wheel-Foot Hybrid Parallel-Leg Walking Robot.

Mobile robots require the ability to plan collision-free paths. This paper introduces a wheel-foot hybrid parallel-leg walking robot based on the 6-Universal-Prismatic-Universal-Revolute and 3-Prismatic (6UPUR + 3P) parallel mechanism model. To enhance path planning efficiency and obstacle avoidance capabilities, an improved artificial potential field (IAPF) method is proposed. The IAPF functions are designed to address the collision problems and issues with goals being unreachable due to a nearby problem, local minima, and dynamic obstacle avoidance in path planning. Using this IAPF method, we conduct path planning and simulation analysis for the wheel-foot hybrid parallel-legged walking robot described in this paper, and compare it with the classic artificial potential field (APF) method. The results demonstrate that the IAPF method outperforms the classic APF method in handling obstacle-rich environments, effectively addresses collision problems, and the IAPF method helps to obtain goals previously unreachable due to nearby obstacles, local minima, and dynamic planning issues.

Ключевые направления и мировые практики повышения эффективности и надежности водоснабжения

The article reviews modern research and practical developments in the field of increasing the efficiency and reliability of water supply systems. The analysis covers both the micro level, which includes optimizing the opera-tion of individual components of the system, and the macro level, related to the management and optimization of the water supply system as a whole. Particular attention is paid to improving the efficiency of pumping units, leaks and pressure losses management, as well as water treatment processes optimization. At the macro level, the article presents a review of strategies for the optimal distribution of water between water intakes, a study of the zones of water intakes influence on the hydraulic network, dynamic pressure planning and power consump-tion management of pumping stations. The importance of monitoring and diagnostics systems for increasing the reliability of water supply systems is emphasized. Key areas of research and current problems are identified, and promising approaches to improving the management of urban water supply systems are proposed

Optimization-based dynamic motion planning and control for quadruped robots

Optimization-based dynamic motion planning and control for quadruped robots

Dynamic Path Planning Based on 3D Cloud Recognition for an Assistive Bathing Robot

Assistive bathing robots have become a popular point due to their metrics, such as a humanoid working approach in the solution of elder care. However, the abilities of dynamic recognition and path planning are the key to obtain the advantages. This paper proposes a novel approach to recognize and track the dynamical human back, and path planning on it via a 3D point cloud. Firstly, the human back geometric features are recognized through coarse-to-fine alignment. The Intrinsic Shape Signature (ISS) algorithm combined with the Fast Point Feature Histogram (FPFH) and the Sample Consensus Initial Alignment (SAC-IA) algorithm are adopted to complete the coarse alignment, and the Iterative Closest Point (ICP) algorithm is applied to the fine alignment to improve the accuracy of recognition. Then, the dynamic transformation matrix between the contiguous recognized back is deduced based on spatial motion between two adjacent recognized back point clouds. The path can be planned on the tracked human back. Finally, a set of testing experiments are conducted to verify the proposed algorithm. The results show that the running time is reduced by 66.18% and 96.29% compared with the other two common algorithms, respectively.

Abstract 1311: Planning for health equity in oncology trials: Expectations and feasibility

Abstract Achieving diverse representation in oncology studies is an ongoing challenge., Responding to calls to advance health equity in clinical trials, Congress amended the Food Drug and Cosmetic Act to require trial sponsors to submit a “diversity action plan” to the FDA with a study’s protocol. This requirement to plan for diverse trial recruitment drives a need to innovate approaches for site selection and trial recruitment that go beyond mirroring race-ethnicity distributions from the US Census or disease prevalence. We present a novel framework that nests clinical trials in a longitudinal patient registry to achieve diverse representation across multiple domains, evaluate site-selection and recruitment strategies, and examine health equity across the cancer patient journey. Implementing a registry to support longitudinal data collection (i.e., a cohort study), we establish a continuous view of participant characteristics including demographic and social determinants of health, treatment received, and relevant outcomes. Nesting clinical trials within the structure of the cohort, we can select sites and recruit for diverse representation to achieve equity goals. This framework involves a multi-layer analysis of external population statistics and internal reference registry to gauge the pool of eligible participants and calibrate the sampling strategy and recruitment expectations to the realities of clinical representation. Moreover, where clinical representation differs from population statistics, the registry offers an opportunity to report benchmarks for diversity and compare to site-level and study-level realized enrollment and feasibility. In conclusion, the path to health equity in oncology trials depends on a capacity to ask and answer the right questions in planning. What specific aspects of diversity are we aiming to capture? How do these aspects impact outcomes? We advocate for a dynamic planning process that continuously reevaluates goals and strategies in the face of enrollment data and evolving population demographics. By utilizing a registry with longitudinal follow-up, we can set forth clear expectations, transparently report on the progress of inclusion efforts, and candidly address feasibility challenges. We present a structured framework for trial sponsors to not only comply with the forthcoming requirement for diversity action plans, but ultimately to advance the goal of equitable cancer care through research that truly reflects the complex intersectional diversity of those impacted by the disease. Citation Format: Halcyon G. Skinner, Caroline Marra, April I. Alexandrow, Hamsa L. Subramaniam, Nathan C. Nussbaum, Joseph B. Franklin, Edgar P. Simard, Amy P. Abernethy. Planning for health equity in oncology trials: Expectations and feasibility [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1311.

Global Dynamic Path Planning of AGV Based on Fusion of Improved A* Algorithm and Dynamic Window Method.

Designed to meet the demands of AGV global optimal path planning and dynamic obstacle avoidance, this paper proposes a combination of an improved A* algorithm and dynamic window method fusion algorithm. Firstly, the heuristic function is dynamically weighted to reduce the search scope and improve the planning efficiency; secondly, a path-optimization method is introduced to eliminate redundant nodes and redundant turning points in the path; thirdly, combined with the improved A* algorithm and dynamic window method, the local dynamic obstacle avoidance in the global optimal path is realized. Finally, the effectiveness of the proposed method is verified by simulation experiments. According to the results of simulation analysis, the path-planning time of the improved A* algorithm is 26.3% shorter than the traditional A* algorithm, the search scope is 57.9% less, the path length is 7.2% shorter, the number of path nodes is 85.7% less, and the number of turning points is 71.4% less. The fusion algorithm can evade moving obstacles and unknown static obstacles in different map environments in real time along the global optimal path.

Dynamic Path Planning of Intelligent Warehouse Robot Based on Improved A* Algorithm

Dynamic Path Planning of Intelligent Warehouse Robot Based on Improved A* Algorithm

Dynamic Planning of Optimally Safe Lane-change Trajectory for Autonomous Driving on Multi-lane Highways Using a Fuzzy Logic–based Collision Estimator

The collision avoidance system in an autonomous vehicle, intended to address traffic safety issues, has a crucial function called collision estimation. It accomplishes this by identifying potential dangers and notifying the drivers in advance or by using autonomous control to navigate safely. In this work, a novel approach is proposed for generating and selecting a lane-change trajectory for the vehicle in a driving scenario where two vehicles are simultaneously executing lane-change processes on highways and approaching the same target lane. Moreover, a novel fuzzy logic estimator based on time-to-collision (TTC) and time-to-gap (TTG) is designed to estimate the collision risk. In the collision-avoidance process, the proposed estimator is utilized to determine the risk of a collision with polynomial function-based generation of possible lane-change trajectories. The safest lane-change trajectory is then provided to the motion controller so it can navigate the vehicle safely through such a challenging lane-change scenario. This work also investigates Stanley and Pure Pursuit controllers to follow the optimized trajectory. The simulation experiment results demonstrate that the proposed approach for dynamic trajectory generation during the lane-change process can successfully handle this type of challenging situation and prevent a potential collision. Experimental results also indicate that monitoring the movement of the nearby lane-changing vehicle is crucial for safe lane-change execution and that the proposed approach successfully handles the challenging situation, preventing potential collision.

Anatomical cup implantation assisted with dynamic 3D planning improves functional outcomes in primary total hip arthroplasty: A retrospective study.

Anatomical cup implantation is a promising approach in primary total hip arthroplasty (THA) and improves functional outcomes. We aimed to evaluate the cup position and functional outcomes in primary THA with preoperative dynamic 3D planning. We retrospectively reviewed 54 hips in 48 patients who underwent primary THA with anatomical cup implantation (mean follow-up time: 52months). Cup positions were evaluated based on patient-specific morphology, the acetabular fossa and the combined anteversion test. Functional outcomes were assessed after THA. The paired-sample t-test was performed for surgical and contralateral native hips among 42 patients who underwent unilateral THA. Two hips suffered intraoperative trochanteric fracture, but no hip dislocations occurred. No patients reported groin or thigh pain, and all patients were capable of deep squatting and one-leg standing. The mean Harris hip score, WOMAC score, and physical SF-36 score were 94.46 ± 6.16, 10.41 ± 3.62, and 95.19 ± 8.74, respectively. Except for acetabular offset, THA restored biomechanics to those of contralateral native hip, including cup anteversion, abduction, femoral offset and acetabular height (P> 0.05). This study provided evidence for the application of anatomical cup implantation assisted with dynamic 3D planning in primary THA, which restored morphology and improved functional outcomes.

Dynamic path planning via Dueling Double Deep Q-Network (D3QN) with prioritized experience replay

Path planning is a key requirement for mobile robots employed for different tasks such as rescue or transport missions. Conventional methods such as A* or Dijkstra to tackle path planning problem need a premise map of the robot's environment. Nowadays, dynamic path planning is a popular research topic, which drives mobile robots without prior static requirements. Deep reinforcement learning (DRL), which is another popular research area, is being harnessed to solve dynamic path planning problem by the researchers. In this study, Deep Q-Networks, which is a subdomain of DRL are opted to solve dynamic path planning problem. We first employ well known techniques Double Deep Q-Networks (D2QN) and Dueling Double Deep Q-Networks (D3QN) to train a model which can drive a mobile robot in environments with static and dynamic obstacles within 3 different configurations. Then we propose D3QN with Prioritized Experience Replay (PER) extension in order to further optimize the DRL model. We created a test bed to measure the performance of the DRL models against 99 randomly generated goal locations. According to our experiments, D3QN-PER method performs better than D2QN and D3QN in terms of path length and travel time to the goal without any collisions. Robot Operating System and Gazebo simulation environment is utilized to realize the training and testing environments, thus, the trained DRL models can be deployed to any ROS compatible robot seamlessly.

Dynamic Affect-Based Motion Planning of a Humanoid Robot for Human-Robot Collaborative Assembly in Manufacturing

The objective was to investigate the impacts of the robot’s dynamic affective expressions in task-related scenarios on human–robot collaboration (HRC) and performance in human–robot collaborative assembly tasks in flexible manufacturing. A human–robot hybrid cell was developed to facilitate a human co-worker and a robot to collaborate to assemble a few parts into a final product. The collaborative robot was a humanoid manufacturing robot with the ability to display its affective states due to changes in task scenarios on its face. The assembly task was divided into several subtasks, and based on an optimization strategy, the subtasks were optimally allocated to the human and the robot. A computational model of the robot’s affective states was derived inspired by that of humans following the biomimetic approach, and an affect-based motion planning strategy for the robot was proposed to enable the robot to adjust its motions and behaviors with task situations and communicate (inform) the situations to the human co-worker through affective expressions. The HRC and the assembly performance for the affect-based motion planning were experimentally evaluated based on a comprehensive evaluation scheme and were compared with two alternative conditions: (i) motion planning that did not display affective states, and (ii) motion planning that displayed text messages instead of displaying affective states to communicate the situations to the human co-worker. The results clearly showed that the dynamic affect-based motion planning produced significantly better HRC and assembly performance than that produced by motion planning associated with the display of no affective states or text messages. The results encouraged employing manufacturing robots with dynamic affective expressions to collaborate with humans in flexible assembly in manufacturing to improve HRC and assembly performance.

Optimal Reactive Power Planning Under Wind Power Uncertainties with Techno-Commercial Assessment

Power systems have numerous issues arising from increased demand and advancements in technology. Among these challenges, the most major ones are the planning of reactive power and the enhancement of voltage stability. Previous studies focused on improving the voltage stability and lesser power losses with installation of renewable sources. Cost effective planning lacks the consideration of service provider’s benefits of buying the renewable power. This work is focused to reduce the cost on consumers with multiple reactive planning objectives like voltage stability and power losses. FACT device and Weibull modeled wind power plant are installed and sensitive installation location is identified by PTDF. Thus, formulated nonlinear multi-objective techno-economic problem is solved by a novel enhanced pareto front optimization algorithm which is uses the strength of a hypergraph network. The hypergraph network is modeled to solve the complex connections and locate the best solution in the search space. The results demonstrate that the proposed novel multi-objective hypergraph particle swarm optimization (MOHGPSO) effectively improves the voltage stability index by up to 30.10% when compared to MOPSO. The power loss and total cost decreased by 31.70% and 7.43%, respectively.

An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival

Sustainable concepts for on-demand transportation, such as ridesharing or ridehailing, require advanced technologies and novel dynamic planning and prediction methods. In this paper, we consider the prediction of taxi trip durations, focusing on the problem of the estimated time of arrival (ETA). ETA can be used to compute and compare alternative taxi schedules and to provide information to drivers and passengers. To solve the underlying hard computational problem with high precision, machine learning (ML) models for ETA are the state of the art. However, these models are mostly black box neural networks. Hence, the resulting predictions are difficult to explain to users. To address this problem, the contributions of this paper are threefold. First, we propose a novel stacked two-level ensemble model combining multiple ETA models; we show that the stacked model outperforms state-of-the-art ML models. However, the complex ensemble architecture makes the resulting predictions less transparent. To alleviate this, we investigate explainable artificial intelligence (XAI) methods for explaining the first- and second-level models of the ensemble. Third, we consider and compare different ways of combining first-level and second-level explanations. This novel concept enables us to explain stacked ensembles for regression tasks. The experimental evaluation indicates that the considered ETA models correctly learn the importance of those input features driving the prediction.

Dynamic planning of office building elevator scheduling

Every morning rush hour is the most crowded time for elevators in office buildings, and many office workers will be late because they can't wait for the elevator. How to reasonably call the existing elevator resources has become a problem we need to pay attention to. In this paper, we will discuss the modeling of elevator scheduling optimization problem for office buildings. In this paper, the background and significance of the research is firstly explained, and then the elevator group linkage control system is briefly understood and analyzed, and the evaluation indexes are determined according to its multi-objective and diversified characteristics, and then the group control method focusing on making the elevator run time the shortest is adopted, and the principle of the ratio of elevator running period/total running time equal to the number of passengers transported/total passengers transported by the elevator in one cycle is followed to compare several common elevator running methods, and the results are summarized in the following table. Common elevator operation methods are compared, the most effective way to arrive at, and finally use dynamic programming to solve the partition point, to establish the optimal scheme of elevator scheduling for the office building division. Since it is not possible to expand the elevator in the office building, reducing the number of stops of each elevator to shorten the time required for the elevator to carry passengers becomes the most feasible method. After comparing the common elevator operation methods, it can be found that zoning can shorten the final average arrival time of passengers more than no zoning. That is, the elevators in an office building are divided into different zones, and each zone has a designated elevator serving a designated floor segment. In order to solve the floor segment allocation problem, the shortest path algorithm is used in this paper. The paper concludes with a summary and future outlook of the resulting optimization model.

Dynamic planning of edge sensing terminals in distribution network supporting distributed resources observable and controllable

With the advancement of low-carbon distribution networks, the heightened stochasticity introduced by a multitude of renewable energy sources in the power grid has significantly augmented the regulatory challenges faced by the power grid. Dispatching distributed resources emerges as an effective solution to this issue. However, these resources often lack observability and controllability, hindering their participation in power regulation services. To establish a reliable interaction between distributed resources and power grids, the deployment of numerous edge sensing terminals becomes essential, albeit incurring high costs. In light of this, our paper proposes a dynamic network planning method for edge sensing terminals based on node differentiation and resource observability criteria, aiming to facilitate real-time and dependable observation of distributed resources. Initially, the node weight, a metric to gauge the disparity among nodes, is computed, considering communication quality deviation, resource development synergy, and the distribution of distributed resources. Subsequently, an optimal configuration method is introduced, accounting for the terminal’s reliability under faults. Lastly, a method for dynamic terminal networking planning is presented, gradually reducing the depth of unobservable resources. An enhanced genetic algorithm is employed to address this challenge. This method was validated using an IEEE 33 node system and a 91 node actual system, demonstrating significant effectiveness in reducing terminal configuration costs.