Planning Methods Research Articles

A trajectory planning method for robotic arms based on improved dynamic motion primitives

Purpose Traditional robot arm trajectory planning methods have problems such as insufficient generalization performance and low adaptability. This paper aims to propose a method to plan the robot arm’s trajectory using the trajectory learning and generalization characteristics of dynamic motion primitives (DMPs). Design/methodology/approach This study aligns multiple demonstration motion primitives using dynamic time warping; use the Gaussian mixture model and Gaussian mixture regression methods to obtain the ideal primitive trajectory actions. By establishing a system model that improves DMPs, the parameters of the nonlinear function are learned based on the ideal primitive trajectory actions of the robotic arm, and the robotic arm motion trajectory is reproduced and generalized. Findings Experiments have proven that the robot arm motion trajectory learned by the method proposed in this article can not only learn to generalize and demonstrate the movement trend of the primitive trajectory, but also can better generate ideal motion trajectories and avoid obstacles when there are obstacles. The maximum Euclidean distance between the generated trajectory and the demonstration primitive trajectory is reduced by 29.9%, and the average Euclidean distance is reduced by 54.2%. This illustrates the feasibility of this method for robot arm trajectory planning. Originality/value It provides a new method for the trajectory planning of robotic arms in unstructured environments while improving the adaptability and generalization performance of robotic arms in trajectory planning.

Material Planning Analysis with Material Requirement Planning Method in High-rise Building Projects

Planning material supplies well is very important in implementing construction projects to ensure smoothness and efficiency. Smoothness includes thorough planning, including ensuring materials are met as planned, while efficiency aims to reduce time and cost. In this study, material planning will be carried out using the Material Requirement planning (MRP) method, using three different lot measurement techniques, namely Lot for Lot (LFL), Economic Order Quantity (EOQ), and Period Order Quantity (POQ). The MRP method is carried out to accurately calculate the amount of material needed to avoid excessive material inventory levels. After the MRP method, the most optimal number of orders and the total cost required for each lot measurement technique are obtained. The results of this study show that the LFL technique produces the optimum number of orders for gelam wood, plywood, reinforcement, bendrat wire, and ready mix concrete. Meanwhile, for materials such as lubricating oil, meranti wood, randu wood, and nails, it is optimal to order using the POQ technique.

Spiral complete coverage path planning based on conformal slit mapping in multi-connected domains

The generation of smoother and shorter spiral complete coverage paths in multi-connected domains is a crucial research topic in path planning for robotic cavity machining and other related fields. Traditional methods for spiral path planning in multi-connected domains typically incorporate a subregion division procedure that leads to excessive subregion bridging, requiring longer, more sharply turning, and unevenly spaced spirals to achieve complete coverage. To address this issue, this paper proposes a novel spiral complete coverage path planning method using conformal slit mapping. It takes advantage of the fact that conformal slit mapping can transform multi-connected domains into regular disks or annuluses without the need for subregion division. Firstly, a slit mapping calculation technique is proposed for segmented cubic spline boundaries with corners. Secondly, a spiral path spacing control method is developed based on the maximum inscribed circle radius between adjacent conformal slit mapping iso-parameters. Thirdly, the spiral coverage path is derived by offsetting iso-parameters. Numerical experiments indicate that our method shares a comparable order-of-magnitude in computation time with the traditional PDE-based spiral complete coverage path method, but it excels in optimizing total path length, smoothness, and spacing consistency. Finally, we performed experiments on cavity milling and dry runs to compare the new method with the traditional PDE-based method in terms of machining duration and steering impact, respectively. The comparison reveals that, with both algorithms achieving complete coverage, the new algorithm reduces machining time and steering impact by 12.34% and 22.78%, respectively, compared with the traditional PDE-based method.

Cooperative control method of multiple spacecraft formation based on graphtheory

Spacecraft cluster is a new way of multi-vehicle cooperation and a major problem in the current space distribution system. A large number of core technologies of spacecraft cluster technology need to be simulated, analyzed and verified on the ground. On this basis, this article proposes the construction of a spacecraft system model and a dynamic model, and designs a path planning method that combines the spacecraft system model with the a* algorithm. And use the Morphin search tree algorithm to improve the A-star algorithm, and then simulate and analyze the spacecraft formation control scheme. The simulation results of trajectory planning show that after running the model for 21 ms, the improved A* algorithm achieved the expected error. Its MAE value is 10 - 4, achieving good path planning effect. The research plan can effectively achieve control of spacecraft formation and meet the needs of collaborative tasks and navigation route planning.

Activities of the oral health teams in primary health care: a time-motion study

BackgroundEfficient planning of the oral health workforce in Primary Health Care (PHC) is paramount to ensure equitable community access to services. This requires a meticulous examination of the population’s needs, strategic distribution of oral health professionals, and effective human resource management. In this context, the average time spent on care to meet the needs of users/families/communities is the central variable in healthcare professional workforce planning methods. However, many time measures are solely based on professional judgment or experience.ObjectiveCalculate the average time parameters for the activities carried out by the oral health team in primary health care.MethodThis is a descriptive observational study using the time-motion method carried out in five Primary Health Care Units in the city of São Paulo, SP, Brazil. Direct and continuous observation of oral health team members occurred for 40 h spread over five days of a typical work week.ResultsA total of 696.05 h of observation were conducted with 12 Dentists, three Oral Health Assistants, and five Oral Health Technicians. The Dentists’ main activity was consultation with an average duration of 24.39 min, which took up 42.36% of their working time, followed by documentation with 12.15%. Oral Health Assistants spent 31.57% of their time on infection control, while Oral Health Technicians spent 22.37% on documentation.ConclusionThe study establishes time standards for the activities performed by the dental care team and provides support for the application of workforce planning methods that allow for review and optimization of the work process and public policies.

An Aerial Robotic Missing-Person Search in Urban Settings—A Probabilistic Approach

Autonomous robotic teams have been proposed for a variety of lost-person searches in wilderness and urban settings. In the latter scenarios, for missing persons, the application of such teams, however, is more challenging than it would be in the wilderness. This paper, specifically, examines the application of an autonomous team of unmanned aerial vehicles (UAVs) to perform a sparse, mobile-target search in an urban setting. A novel multi-UAV search-trajectory planning method, which relies on the prediction of the missing-person’s motion, given a known map of the search environment, is the primary focus. The proposed method incorporates periodic updates of the estimates of where the lost/missing person may be, allowing for intelligent re-coverage of previously searched areas. Additional significant contributions of this work include a behavior-based motion-prediction method for missing persons and a novel non-parametric estimator for iso-probability-based (missing-person-location) curves. Simulated experiments are presented to illustrate the effectiveness of the proposed search-planning method, demonstrating higher rates of missing-person detection and in shorter times compared to other methods.

A new sample-size planning approach for person-specific VAR(1) studies: Predictive accuracy analysis.

Researchers increasingly study short-term dynamic processes that evolve within single individuals using N = 1 studies. The processes of interest are typically captured by fitting a VAR(1) model to the resulting data. A crucial question is how to perform sample-size planning and thus decide on the number of measurement occasions that are needed. The most popular approach is to perform a power analysis, which focuses on detecting the effects of interest. We argue that performing sample-size planning based on out-of-sample predictive accuracy yields additional important information regarding potential overfitting of the model. Predictive accuracy quantifies how well the estimated VAR(1) model will allow predicting unseen data from the same individual. We propose a new simulation-based sample-size planning method called predictive accuracy analysis (PAA), and an associated Shiny app. This approach makes use of a novel predictive accuracy metric that accounts for the multivariate nature of the prediction problem. We showcase how the values of the different VAR(1) model parameters impact power and predictive accuracy-based sample-size recommendations using simulated data sets and real data applications. The range of recommended sample sizes is smaller for predictive accuracy analysis than for power analysis.

Intelligent Ship Scheduling and Path Planning Method for Maritime Emergency Rescue

Intelligent ship navigation scheduling and planning is of great significance for ensuring the safety of maritime production and life and promoting the development of the marine economy. In this paper, an intelligent ship scheduling and path planning method is proposed for a practical application scenario wherein the emergency rescue center receives rescue messages and dispatches emergency rescue ships to the incident area for rescue. Firstly, the large-scale sailing route of the task ship is pre-planned in the voyage planning stage by using the improved A* algorithm. Secondly, the full-coverage path planning algorithm is used to plan the ship’s search route in the regional search stage by updating the ship’s navigation route in real time. In order to verify the effectiveness of the proposed algorithm, comparative experiments were carried out with the conventional algorithm in the two operation stages of rushing to the incident sea area and regional search and rescue. The experimental results show that the proposed algorithm can adapt to emergency search and rescue tasks in the complex setting of the sea area and can effectively improve the efficiency of the operation, ensure the safety of the operation process, and provide a more intelligent and efficient solution for the planning of maritime emergency rescue tasks.

Study of medicinal plants used in ethnoveterinary medical system in riverine areas of Punjab, Pakistan

BackgroundThe use of medicinal plants to treat various veterinary illnesses has been practiced for millennia in many civilizations. Punjab is home to a diverse ethnic community, the majority of whom work in dairy farming, agriculture, and allied professions and have indigenous practices of treating animal illnesses using native flora. This study was designed to (1) document and preserve information about the applications of medicinal plant species in ethnoveterinary remedies among inhabitants of Punjab, Pakistan, and (2) identify popular plants for disease treatment by quantitative analysis of the obtained data and to assess the pharmacological relevance of these species.MethodsTo collect data from informants (N = 279), questionnaires and semi-structured interviews were used. The ethnoveterinary data were analyzed using principal component analysis, relative frequency citation, fidelity level, relative popularity level, and rank order priority.ResultsA total of 114 plant species utilized in the ethnoveterinary medicinal system were found, which were divided into 56 families and used to treat 16 different illnesses. The Poaceae family, with 16 species, was the most common in the region. The most commonly employed growth form in herbal preparation was herb (49%). The most used part in ethnoveterinary remedies was leaves (35%), while powder was the most commonly used way for preparing ethnoveterinary remedies (51 applications). According to principal component analysis, the most typically used species in the research region were grasses. Five grasses (Arundo donax, Desmostachya bipinnata, Eleusine indica, Hordeum vulgare, and Pennisetum glaucum) showed a 100% FL value when used to treat diuretics, helminthiasis, digestive problems, fever, cough, worm infestation, indigestion, galactagogue, oral infections, and genital prolapse. The maximum value of disease cured level (DCL%) was recorded at 87.6% for endo- and ecto-parasitic ailments in the study area.ConclusionThis study demonstrates that medicinal plants play an important part in satisfying farmers’ animal healthcare demands, making it a feasible practice. The study also provides a wealth of knowledge regarding ethnoveterinary methods for further planning and application, providing an option for farmers who cannot afford allopathic therapy.

3D-AMM: a 3D artificial moment method for path planning of manipulator in multiple obstacles scenario

Purpose In response to the issue of traditional algorithms often falling into local minima or failing to find feasible solutions in manipulator path planning. The purpose of this paper is to propose a 3D artificial moment method (3D-AMM) for obstacle avoidance for the robotic arm's end-effector. Design/methodology/approach A new method for constructing temporary attractive points in 3D has been introduced using the vector triple product approach, which generates the attractive moments that attract the end-effector to move toward it. Second, distance weight factorization and spatial projection methods are introduced to improve the solution of repulsive moments in multiobstacle scenarios. Third, a novel motion vector-solving mechanism is proposed to provide nonzero velocity for the end-effector to solve the problem of limiting the solution of the motion vector to a fixed coordinate plane due to dimensionality constraints. Findings A comparative analysis was conducted between the proposed algorithm and the existing methods, the improved artificial potential field method and the rapidly-random tree method under identical simulation conditions. The results indicate that the 3D-AMM method successfully plans paths with smoother trajectories and reduces the path length by 20.03% to 36.9%. Additionally, the experimental comparison outcomes affirm the feasibility and effectiveness of this method for obstacle avoidance in industrial scenarios. Originality/value This paper proposes a 3D-AMM algorithm for manipulator path planning in Cartesian space with multiple obstacles. This method effectively solves the problem of the artificial potential field method easily falling into local minimum points and the low path planning success rate of the rapidly-exploring random tree method.

Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

PurposeThe present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.Design/methodology/approachThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.FindingsThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.Research limitations/implicationsThe rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.Originality/valueThe originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

Research on Integrated Mission Planning and Design Method for China’s Manned Lunar Exploration

Research on Integrated Mission Planning and Design Method for China’s Manned Lunar Exploration

Calculation method for novel upright CT scanner isodose curves.

A computational method based on Monte-Carlo calculations is presented and used to calculate isodose curves for a new upright and tilting CT scanner useful for radiation protection purposes. The TOPAS code platform with imported CAD files for key components was used to construct a calculation space for the scanner. A sphere of water acts as the patient would by creating scatter out of the bore. Maximum intensity dose maps are calculated for various possible tilt angles to make sure radiation protection for site planning uses the maximum possible dose everywhere. The resulting maximum intensity isodose lines are more rounded than ones for just a single tilt angle and so closer to isotropic. These maximum intensity curves are closer to the isotropic assumption used in CTDI or DLP based methods of site planning and radiation protection. The isodose lines are similar to those of a standard CT scanner, just tilted upwards. There is more metal above the beam that lessens the dose above versus below isocenter. Aside from the orientation, this upright scanner is very similar to a typical CT scanner, and nothing different for shielding needs to be done for this new upright tilting CT scanner, because an isotropic scatter source is often assumed for any CT scanner.

Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set.

We present a freely available data set of surgical case mixes and surgery process duration distributions based on processed data from the German Operating Room Benchmarking initiative. This initiative collects surgical process data from over 320 German, Austrian, and Swiss hospitals. The data exhibits high levels of quantity, quality, standardization, and multi-dimensionality, making it especially valuable for operating room planning in Operations Research. We consider detailed steps of the perioperative process and group the data with respect to the hospital's level of care, the surgery specialty, and the type of surgery patient. We compare case mixes for different subgroups and conclude that they differ significantly, demonstrating that it is necessary to test operating room planning methods in different settings, e.g., using data sets like ours. Further, we discuss limitations and future research directions. Finally, we encourage the extension and foundation of new operating room benchmarking initiatives and their usage for operating room planning.

Assessing the dynamic social values of the ‘deep city’: An integrated methodology combining online and offline approaches

This monograph presents findings from original research on urban heritage transformations and advances existing scholarship on three grounds: (1) it offers tested combinations of methods to capture the social values of heritage; (2) it distils the complex, diverse social values generated by urban heritage and revealed by the use of these methods; and (3) it discusses the implications and potential applications of these methods for urban planning. Cities are multi-layered deposits of tangible historic features and intangible meanings, memories, practices and associated values. These dense socio-material assemblages have been conceptualised as the ‘deep city’, a concept that recognises dynamic relationships between past, present and future, whilst simultaneously repositioning heritage at the heart of sustainable transformation. However, methods for understanding people’s relationships with urban heritage are mostly applied piecemeal in urban planning and heritage management. Here, we introduce research involving a suite of social and digital research methods, which can be deployed rapidly in online and offline spaces to examine the social values generated by urban heritage. Three in-depth case studies, in Edinburgh, London, and Florence, reveal how these values are involved in urban place-making. Failure to take them into account in development and regeneration projects can result in fragmentation and/or marginalisation of communities and their place attachments. The research has important implications for urban planning, offering methods and tools for working with communities to create more socially sustainable urban futures.

Optimization method for renewable energy investment planning in the electricity carbon coupling market environment considering economy

China is developing rapidly in renewable energy development, electricity market reform, and carbon market construction, using market mechanisms for efficient planning and investment. The electricity and carbon markets are interrelated for market entities. This context has led to a dual-level optimization model for renewable energy investment planning, considering cost recovery and new power system goals. The model includes deriving electricity carbon coupling quotations for conventional units based on dynamic carbon emission characteristics and proposing an electricity spot market mechanism influenced by carbon quota trading. The model’s upper level maximizes renewable energy investment capacity, determining its size and location, while the lower level simulates electricity spot and voluntary emission reduction market trading to guide investment decisions. The model transformed into a mixed integer linear programming problem for optimal solutions was validated numerically, examining various factors affecting optimal renewable energy investment capacity.

Shopping trip recommendations: A novel deep learning–enhanced global planning approach

Brick-and-mortar shopping malls are embracing Artificial Intelligence (AI) technology and recommender systems to enhance the shopping experience and boost mall revenue. Echoing this trend, we formulate a new shopping trip recommendation problem, which aims to recommend a shopping trip (i.e., a list of stores) that matches customer preferences and has appropriate trip lengths. To solve this problem, we develop a novel deep learning–enhanced global planning (DeepGP) approach featuring three methodological novelties. First, we introduce a new shopping intensity term based on deep neural networks to capture the variation of trip lengths specific to different shopping contexts. Second, we innovatively formulate the learning and optimization objectives in a consistent form by balancing the shopping choice likelihood and the shopping intensity likelihood, thus resolving the inconsistency issue encountered by prior global planning methods. Third, to overcome the computational challenge caused by the nonlinear shopping intensity term, we design a new exact and efficient solution technique based on piecewise linear transformations. Using a real-world offline shopping dataset, we empirically demonstrate the superior performances of our approach compared to representative benchmarks in offering more accurate and relevant shopping trip recommendations. Through a simulation, we show the capacity of our approach to attract and balance customer traffic in practical deployments. Overall, our research highlights the efficacy of combining shopping choices and shopping intensity in a consistent learning and optimization framework for offline shopping trip recommendations.

Perceptions and Surgical Approaches of Periodontists and Residency Students for Bone Augmentation Procedures

Aim: Bone deficiency due to various systemic and periodontal diseases, trauma, and tumors remains a major challenge for osseointegration in implant therapies. To provide implant survival, sufficient bone volume is mandatory. The purpose of the present study is to assess the techniques used by periodontists in implant applications, treatment plans, and solutions to the complications they experience. Material and Methods: 126 periodontists and/or residency students participated in this study. A questionnaire consisting of 20 questions was used. The knowledge and treatment methods about the technique, materials, and planning they use in bone regeneration and the management of complications were questioned. Descriptive statistics and the Pearson Chi-square test were used to analyze the data. Results: All participants needed bone regeneration in implant cases. The most used material for regeneration by the participants was xenograft, with 43.5%. The most preferred application in bone regeneration is guided bone regeneration (GBR) using the collagen membrane, with a rate of 78.3%. The most common complication was membrane exposure. In order to prevent complications, 77.3% of the participants performed adequate soft tissue release, while 63.6% controlled periodontitis. The most commonly used treatment option for complications was the use of postoperative antibiotics/antiseptics. 82.6% of the participants received support in the management of complications. Conclusion: In our study, it was observed that periodontists frequently chose different planning methods and treatment options when performing bone reconstruction/regeneration. It will be beneficial for periodontists to include the latest treatment models applied in the current literature in periodontology residency education.

Impact of Covid-19 Pandemic upon Iraqi women’s Commitment to Family Planning at Primary Health Care Centers in Baghdad City

Objectives: To evaluate the impact of COVID-19 upon Iraqi women's commitment to family planning before and during COVID-19 pandemic Methods: A descriptive design, correlational approach was conducted from November /2021 to July / 2022. The setting of the study including two primary health care centers in Baghdad City. A non-probability 'purposive sample' consists of (200) women at reproductive age (16-49) years old. Descriptive and correlational statistics were used for data analysis. Results: The results of the study shows that regarding commitment to use of family planning methods among women before and during COVID19 pandemic; the “Use family planning methods” among (94.5%) before-COVID19 and (83%) during COVID19. there is high significant relationship between commitment to use of family planning before-COVID19 and during COVID19 at p-value= .001 respectively. Conclusion: The pandemic has affected women’s commitment to family planning methods, as well as its impact on the type of method that used. Despite the pandemic family planning is very important to women’s reproductive health so, it is necessary to improve family planning information, services, and access.

A study on real-time path planning method for skin machining based on frame feature extraction and chronological feature optimization

Skin, as the crucial component of aircraft, should be milled along the edges of the frame to ensure the mounting accuracy. Traditional skin milling uses visual measurement and offline planning to generate a machining path that corresponds to the overall shape of the frame edge. To enhance adaptability and processing efficiency, an online planning method is proposed. However, it imposes greater demands on the computational efficiency, accuracy, and stability of the algorithm. In this paper, a real-time path planning method based on frame feature extraction and chronological feature optimization is proposed for the milling of attached frame-skin. The method uses a line laser to scan the surface features of both skin and frame structures, facilitating real-time extraction of feature structures. Periodic calculations of position and posture are conducted based on the time series, and a non-fitting method is used to generate a smooth and high-precision machining trajectory. The efficacy in achieving real-time path planning for the milling module is verified by experiments on prototypes of aircraft skin. The algorithm exhibits a duration of less than 8 ms in a run cycle, while maintaining accuracy within ±0.5mm.