Task Planning Research Articles

Robust tracking of deformable anatomical structures with severe occlusions using deformable geometrical primitives

Background and objective:Surgical robotics tends to develop cognitive control architectures to provide certain degree of autonomy to improve patient safety and surgery outcomes, while decreasing the required surgeons’ cognitive load dedicated to low level decisions. Cognition needs workspace perception, which is an essential step towards automatic decision-making and task planning capabilities. Robust and accurate detection and tracking in minimally invasive surgery suffers from limited visibility, occlusions, anatomy deformations and camera movements. Method:This paper develops a robust methodology to detect and track anatomical structures in real time to be used in automatic control of robotic systems and augmented reality. The work focuses on the experimental validation in highly challenging surgery: fetoscopic repair of Open Spina Bifida. The proposed method is based on two sequential steps: first, selection of relevant points (contour) using a Convolutional Neural Network and, second, reconstruction of the anatomical shape by means of deformable geometric primitives. Results:The methodology performance was validated with different scenarios. Synthetic scenario tests, designed for extreme validation conditions, demonstrate the safety margin offered by the methodology with respect to the nominal conditions during surgery. Real scenario experiments have demonstrated the validity of the method in terms of accuracy, robustness and computational efficiency. Conclusions:This paper presents a robust anatomical structure detection in present of abrupt camera movements, severe occlusions and deformations. Even though the paper focuses on a case study, Open Spina Bifida, the methodology is applicable in all anatomies which contours can be approximated by geometric primitives. The methodology is designed to provide effective inputs to cognitive robotic control and augmented reality systems that require accurate tracking of sensitive anatomies.

Review of metrics to assess resilience capacities and actions for supply chain resilience

Efficiency and profitability are the main drivers of globalization and have led to long and complex supply chains. Recent disturbances such as COVID-19 or the Suez Canal obstruction caused severe supply disruptions and thereby unveiled the vulnerability of global trade. Resilient supply chains are characterized by the capacity to absorb, adapt to, and restore after disruptions. Building upon the established concept of the ‘resilience curve’, this article explores the interplay between resilience capacities, metrics, and actions in the state-of-the-art literature. We first analyze and harmonize the terminology used to describe capacities as well as metrics for quantifying resilience. This results in a set of 17 resilience metrics that describe all characteristics of the resilience curve and can be used as a tool to assess the resilience of a supply chain. Subsequently, we propose how these metrics can be applied to quantify the effect of resilience actions. Finally, we analyze which actions are proposed in the literature and classify those actions according to their relation to traditional supply chain planning tasks. Practitioners such as supply chain decision-makers can implement these actions to strengthen the absorptive, adaptive, and restorative capacities and are provided with mathematical formulations to quantify the strengthening effect of actions. Academic research can, inter alia, integrate the metrics into multi-criteria optimization models for decision-making and explore the interplay between economic efficiency, environmental sustainability, and resilience.

Improving Students' Mathematical Critical Thinking Abilities Using Join Learning Groups Techniques

Joint Learning Groups (JLG) are groupings of students where one group of students works with activities that are interrelated with other groups, resulting in a set of interlinked activities. Task Planning Groups (TPG) are grouped based on the task plan that the teacher will give. This research aims to determine the increase in students' mathematical critical thinking skills after different treatments in the final test. The experimental research method aims to determine the increase in students' critical mathematical thinking skills using the Join Learning Groups technique and the results of students' critical mathematical thinking using the Task Planning Groups technique regarding Systems of Linear Equations in Two Variables. Research at SMPN 11 Jambi City, which was the sample for class VIII experimental group VIIIJ control group VIIIK. Experiments were treated with the JLG technique, and controls were treated with the TPG technique. From the results of this research hypothesis research using the t-test and SPSS.25, with tcount = 3.08 – 1.667, the JLG Technique further improves critical thinking skills with the TPG Technique in class VIII SMPN 11 Jambi City. The results of students' critical mathematical thinking using the JLG technique were 83.33, and the TPG technique was 75.03, better than the results of students' critical mathematical thinking using the TPG technique. In this research, it is recommended that teachers pay more attention to techniques that can improve student learning outcomes.

Multi-informant Assessment of Organizational Skills: Psychometric Characteristics of the Children’s Organizational Skills Scale (COSS)

Organization, time management, and planning (OTMP) skills are behavioral manifestations of executive functioning linked to academic outcomes. Interventions to improve OTMP skills have shown favorable outcomes. The Children’s Organizational Skills Scale parent and teacher forms (COSS-P, COSS-T) are widely used for assessing OTMP skills, but there is limited research on this scale. This study examined the factor structure and construct validity of the COSS in a non-clinical and clinical sample of students in grades 2–8 (Study 1; n = 1138 COSS-T; 1155 COSS-P), and teacher-referred students with OTMP deficits in grades 3–5 (Study 2; n = 184). In Study 1, a bifactor model with a general factor and three subfactors demonstrated good fit for the COSS-P (RMSEA = .057) and COSS-T (RMSEA = .052). This model was replicated in Study 2. The general factor on both versions demonstrated an acceptable to high level of internal reliability. An acceptable degree of reliability was also identified for subfactor 1 (Task Planning). The COSS was shown to be effective in differentiating children with ADHD from a comparison group drawn primarily from the non-clinical sample (AUC = .84 COSS-P; .85 COSS-T). Sex differences of medium magnitude were identified on the total score of both COSS versions. Correlations between the COSS-P and COSS-T were variable across samples. The findings provide strong evidence for using the total score on the COSS-P and COSS-T as an index of overall OTMP skills deficits. Given variable cross-informant correlations on the COSS, multi-informant assessment of OTMP skills is strongly indicated.

A multi-channel spatial-temporal transformer model for traffic flow forecasting

Traffic flow forecasting is a crucial task in transportation management and planning. The main challenges for traffic flow forecasting are that (1) as the length of prediction time increases, the accuracy of prediction will decrease; (2) the predicted results greatly rely on the extraction of temporal and spatial dependencies from the road networks. To overcome the challenges mentioned above, we propose a multi-channel spatial-temporal transformer model for traffic flow forecasting, which improves the accuracy of the prediction by fusing results from different channels of traffic data. Our approach leverages graph convolutional network to extract spatial features from each channel while using a transformer-based architecture to capture temporal dependencies across channels. We introduce an adaptive adjacency matrix to overcome limitations in feature extraction from fixed topological structures. Experimental results on six real-world datasets demonstrate that introducing a multi-channel mechanism into the temporal model enhances performance and our proposed model outperforms state-of-the-art models in terms of accuracy.

Data-Driven Decision Support Tool Co-Development with a Primary Health Care Practice Based Learning Network

Background The Alliance for Healthier Communities is a learning health system that supports Community Health Centres (CHCs) across Ontario, Canada to provide team-based primary health care to people who otherwise experience barriers to care. This case study describes the ongoing process and lessons learned from the first Alliance for Healthier Communities’ Practice Based Learning Network (PBLN) data-driven decision support tool co-development project. Methods We employ an iterative approach to problem identification and methods development for the decision support tool, moving between discussion sessions and case studies with CHC electronic health record (EHR) data. We summarize our work to date in terms of six stages: population-level descriptive-exploratory study, PBLN team engagement, decision support tool problem selection, sandbox case study 1: individual-level risk predictions, sandbox case study 2: population-level planning predictions, project recap and next steps decision. Results The population-level study provided an initial point of engagement to consider how clients are (not) represented in EHR data and to inform problem selection and methodological decisions thereafter. We identified three initial meaningful types of decision support, with target application areas: risk prediction/screening, triaging specialized program referrals, and identifying care access needs. Based on feasibility and expected impact, we started with the goal to support earlier identification of mental health decline after diabetes diagnosis. As discussions deepened around clinical use cases associated with example prediction task set ups, the target problem evolved towards supporting the upstream task of organizational planning and advocacy for adequate mental health care service capacity to meet incoming needs. Conclusions This case study contributes towards a tool to support diabetes and mental health care, as well as lays groundwork for future CHC EHR-based decision support tool initiatives. We share lessons learned and reflections from our process that other primary health care organizations may use to inform their own co-development initiatives.

Investigation of the effect of object size on accuracy of human localisation in images acquired from unmanned aerial vehicles

The use of unmanned aerial vehicles is gaining wide popularity in various areas of research and information acquisition. More and more often, unmanned aerial vehicles are used to obtain various types of images of the Earth’s surface for its study. In particular, such data can be used in law enforcement, localization of crowds, etc. Typically, such systems operate independently of humans and provide information about objects in an automatic mode, with humans working only under the control of the aircraft. One of the main components of such systems is a neural network for localization and classification of objects, the parameters of which determine both the accuracy of the system as a whole and the design of the aircraft for shooting. In particular, the accuracy of the neural network determines the profitability of such a system, because if the accuracy is insufficient, the use of such systems will not make sense. Therefore, the main subject of research in this paper is a neural network for object localization, in particular YOLO v5, and its accuracy parameters on images obtained from unmanned aerial vehicles. The main focus of this paper is on the parameters of the neural network and the study of its metrics, which are important parameters of a trained neural network. Another important parameter for the further use of a neural network is its training parameters, as well as the data used for training. This study also pays attention to the details of the training process. The main goal of this study is to train a neural network on a selected dataset and to study the accuracy metrics of the trained neural network. The main goal of this study is to determine the dependence of localization accuracy on the area of the object, which will allow for more detailed development of unmanned systems with automatic object detection, as well as to assess the profitability of using such systems in task planning. On the basis of the data obtained, conclusions were drawn about the dependence of localization accuracy on the area of an object in images from unmanned aerial vehicles. These data can serve as a reference for unmanned aerial vehicle developers, particularly when selecting photo modules or planning the system architecture.

Development of Modified ECC-Based Secured FoG-Assisted Healthcare Data Management System in IoT-Enabled WSN

The Internet of Things (IoT) consists of a massive count of connected devices with different sensing support, particularly in medical health constraints sensing. In these summaries, secure communication and data collection to centralized servers is rather difficult for preventing the occurrence of diverse attacks for illegal data access. To tackle these problems, this task plans to design and implement the FoG-based secure data management system in IoT-WSN using adaptive encryption and decryption processes. As the FoG server is away from the corresponding aggregator node, it is transmitted via the other nodes using the proposed modified Elliptic Curve Cryptography (ECC)-based encryption process. For secured data transmission the designed method undergoes two main stages as message receiving stage, and the message-extraction stage. The hybridized meta-heuristic algorithm termed Jaya-Galactic Swarm Optimization (J-GSO) is used for modifying the ECC with an optimized key. From the simulation findings, the performance of the suggested scheme at the data size of three bytes is 3.08%, 3.22%, 3.65%, and 3.33% better than the MFO-m-ECC, PSO-m-ECC, GSO-m-ECC, and JA-m-ECC at the ECC curve variation of “secp192r1”. Experimental results reveal the superiority of the designed method when tested with baseline schemes regarding time complexity, space complexity, and cost function.

Multi-objective comprehensive container scheduling and resource allocation for container cloud using tuna swarm optimization algorithm

The paper addresses the optimization challenges in cloud resource task execution within the container paradigm, introducing the Multi-Objective Comprehensive Container Scheduling and Resource Allocation (MOCCSRA) scheme. It aims to enhance cost-effectiveness and efficiency by utilizing the Tuna Swarm Optimization (TSO) technique to optimize task planning and resource allocation. This novel approach considers various objectives for task scheduling optimization, including energy efficiency, compliance with service level agreements (SLAs), and quality of service (QoS) metrics like CPU utilization, memory usage, data transmission time, container-VM correlation, and container grouping. Resource allocation decisions are guided by the VM cost and task completion period factors. MOCCSRA distinguishes itself by tackling the multi-objective optimization challenge for task scheduling and resource allocation, producing non-dominated Pareto-optimal solutions. It effectively identifies optimal tasks and matches them with the most suitable VMs for deploying containers, thereby streamlining the overall task execution process. Through comprehensive simulations, the results demonstrate MOCCSRA’s superiority over traditional container scheduling methods, showcasing reductions in resource imbalance and notable enhancements in response times. This research introduces an innovative and practical solution that notably advances the optimization field for cloud-based container systems, meeting the increasing demand for efficient resource utilization and enhanced performance in cloud computing environments.

AN ANALYSIS OF THE CURRICULUM “FOREIGN LANGUAGE FOR PROFESSIONAL PURPOSES” IN THE CONTEXT OF EDUCATIONAL PROGRAMME “STATE BORDER SECURITY” OF MASTER’S DEGREE

Peculiarities and objectives of the Educational programme “State Border Security” for the second (Master’s) level of higher education have been analysed. Theoretical content of the subject area, methods, techniques and technologies which include general and specific methods of military science, information and communication technologies and techniques, methods and techniques for ensuring the security of the state border have been studied. Tools and equipment, teaching and training consisting of problem-oriented and contextual learning, activity and competence-oriented technologies, practical performance of training and combat tasks, practical training and daily activities have been specified.Assessment that is divided into forms of control and assessment of educational achievements were investigated.We studied program competences (Integral competence, General competencies and Special (professional, subject) competencies) and Program learning outcomes. Educational programme components were specified, namely Curriculum “Foreign language for professional purposes” was described with the aim to show how students’ professional communicative competence in English, which involves the study of such aspects of language as speaking, reading, translating, listening and writing is forming.Content of the discipline, planned educational activities, forms (methods) of study were investigated, including Bloom’s taxonomy levels (knowledge, comprehending, application, analysis, synthesis and assessment).Five-stage lesson plan (introduction, input, practice, production and conclusion) and credit tasks were described.

Research on construction and task planning of police equipment support system based on background of anti-terrorism operation

Research on construction and task planning of police equipment support system based on background of anti-terrorism operation

PLACEMENT PREDICTION SYSTEM USING MACHINE LEARNING

Engineering students are not sure what they want to study after graduation. Students are confused by the many options offered by universities such as postgraduate admissions, and factors such as salaries and different jobs worsen the situation. There is no reliable platform that allows students to predict outcomes from the beginning of engineering and take action to bridge the gap and create a better future. Students studying in engineering faculties need to know where they stand compared to others and what kind of placement they will get. Training and workshops are available when students enter their final year, but these are not useful for students to plan their future studies. Student placement is one of the most important goals of the school. Schools work hard to accommodate students. The aim is to predict the current year's student placement by analyzing the data students have collected from previous years. Prediction of student performance is an important part of the study because today all student development is directly related to the student's success in tests and activities. Therefore, there are many situations where it is necessary to predict student performance, for example, identifying students who are not performing well and taking steps to improve them. There is no platform for girls to review their current work and highlight their strengths. . Currently, there are platforms that are not trained on real and complete data and cannot learn from error prediction, which reduces accuracy in the long run. Our goal is to create one. To ensure the results are acceptable, the model will be trained on real data and a large number of positive and negative results will be obtained. The model proposes an algorithm to estimate the model. Information is collected by the school where the prediction will be made, using the necessary information before the process or by examining the historical data of the previous year's students, and the placement of the current students is predicted and helps to improve the placement percentage of the students. institute. Keywords: task planning, task prediction, data decision making, machine learning algorithms, prediction prediction, student prediction, good study, student learning results, pre-process data, learning results.

Synchronous planning of initial and target positions of redundant space manipulator based on deterministic configuration tree

Planning the operating position of redundant space manipulators (RSMs) can effectively improve the performance of manipulators while improving the quality of on-orbit servicing (OOS) is of great significance. For RSM, OOS with synchronous planning of initial and target positions (ITPs) has many combinations of positions or configurations, making it difficult to apply existing methods directly. To address this problem, this study proposes a method for synchronous planning ITPs of RSMs based on a deterministic configuration tree (DCT). First, the planning area is divided into grids, with the global performance analyzed utilizing self-motion manifolds. Then, a DCT is constructed based on the analysis results of global performance. Finally, a position planning strategy of pre-planning, rewiring, and regrowth is proposed to achieve synchronized planning of the ITP of the RSM on the DCT. This method is conducted alternately in operational space and joint space. The gridding and regrowth process in operational space avoids the considerable calculation required by gridding in high-dimensional joint space and improves the efficiency of position planning. In joint space pre-planning and rewiring, the ITP is optimized with the constraint of motion planning results while position planning is realized. The simulation experiment results of the 3-degree-of-freedom (DOF) planar RSM, 4-DOF RSM, and 7-DOF RSM indicate that the proposed method reduces time consumption by 34.81%, 18.15%, and 16.26% compared to conventional methods. Furthermore, energy consumption is reduced by 30.68%, 16.93%, and 15.64%, respectively. A typical ITPs synchronous planning task is also simulated, and the results also show that this planning method can effectively optimize the ITPs and reduce energy consumption.

Repeated context-specific actions disrupt feedforward adjustments in motor commands in younger and older adults.

The flexibility of the motor system to adjust a planned action before or during the execution of the movement in response to sensory information is critical for preventing errors in motor control. As individuals age, this function declines, leading to an increased incidence of motor errors. Although sensory processing and cognitive decline are known contributors to this impairment, here, we test the hypothesis that repetition of context-specific planned actions interferes with the adjustment of feedforward motor commands. Younger and older participants were instructed to grasp and lift a T-shaped object with a concealed, off-sided center of mass and minimize its roll through anticipatory force control, relying predominantly on predictive model-driven planning (i.e., sensorimotor memories) developed through repeated lifts. We selectively manipulate the number of trial repeats with the center of mass on one side before switching it to the other side of the T-shaped object. The results showed that increasing the number of repetitions improved performance in manipulating an object with a given center of mass but led to increased errors when the object's center of mass was switched. This deleterious effect of repetition on feedforward motor adjustment was observed in younger and older adults. Critically, we show these effects on an internal model-driven motor planning task that relies predominantly on sensorimotor memory, with no differences in sensory inputs from the repetition manipulation. The findings indicate that feedforward motor adjustments are hampered by repetitive stereotyped planning and execution of motor behavior.NEW & NOTEWORTHY Adjusting planned actions in response to sensory stimuli degrades with age contributing to increased incidence of errors ranging from clumsy spills to catastrophic falls. Multiple factors likely contribute to age-related motor inflexibility, including sensory- and cognition-supporting system declines. Here, we present compelling evidence for repetition to disrupt feedforward adjusting of motor commands in younger and older adults, which suggests increases in stereotypy as a deleterious potentiator of motor control errors.

Застосування ArcGIS для оптимізації військового планування

The purpose of the article is to substantiate the advantages of using this software in comparison with a paper map. Theoretical justification of practical software studies. In modern conditions of military conflict, where strategic management and decision-making determine the effectiveness of actions on the battlefield, the use of advanced technologies becomes an integral part of the functioning of the Armed Forces. One of the key areas of innovation in this context is the use of geographic information systems (GIS), which provide unit commanders with the means to obtain, analyze, and use spatial information. This article is intended to review the practical experience of using GIS in the Armed Forces, particularly focusing on important aspects related to unit management. The basis of our research is the experience and use of the ArcGIS geoinformation system, which was used in the military. The main advantages and capabilities of this system from the point of view of the unit commander are considered, as well as how these technologies can improve leadership and interaction on the battlefield. Method. Methods of analysis of key system functions, including ArcMap, ArcScene, and other modules, were used to understand their potential for unit management. A comparison with paper maps was made: a comparative analysis of the effectiveness of the implementation of regulations on paper topographic maps and in ArcGIS software. Methods of systematization and generalization of scientific information: the advantages of 3D Visualization are highlighted, the study of ArcScene capabilities to improve understanding of the terrain in a three-dimensional format, details and advantages of using special tools for military analysis and management. The results. As a result of the study of the use of the ArcGIS geoinformation system by the unit commander, it was established that the implementation of ArcGIS significantly accelerates a number of actions related to the deployment of artillery, the time required to hit an unplanned target is reduced. Tactical planning is improved: the use of Military Tools for ArcGIS allows the commander to effectively manage and control the actions of his units and the enemy, the creation of tactical symbols and artificial maps simplifies the planning of tasks and responding to unexpected circumstances. The use of 3D Visualization in ArcScene allows you to get a three-dimensional overview of the area, improving the perception of the commander of the unit of the situation. Built-in functions such as centering and zooming in on the target make it easy to monitor objects in real time. The obtained results indicate the great potential and advantages of using the ArcGIS geographic information system in military management, which can lead to an increase in the efficiency and success of military operations. The scientific novelty is that although the ArcGIS software is in service with many NATO countries and is being implemented in the Armed Forces of Ukraine, it is determined that only this program should be used in the Armed Forces of Ukraine for digital maps and direct research that would determine the advantages of this product and analyze there are no particularities of application. The main focus of the research is on the features of ArcGIS application. The components of ArcGIS were identified, their analysis and prospects in the military sector were carried out. Practical meaning. As a result of the study of the use of the geographic information system ArcGIS by the commander of the unit, a significant potential for improving operational management and decision-making in the conditions of military operations was revealed. The use of ArcGIS allows you to speed up actions, improve tactical planning, provide three-dimensional visualization of the terrain and optimize geodetic calculations. The obtained results confirm the importance and necessity of using modern geoinformation tools in the military leadership to ensure the efficiency and success of military operations.

ARCHITECTURE OF RESIDENTIAL BUILDINGS IN ASTANA 30-s-50s XX CENTURY

The study of the architecture of residential buildings is of great scientific interest, and the topic of housing always remains one of the most relevant in the development of society. The article analyzes the features of the formation of the architecture of residential buildings in Astana in the period from 1936 to 1954, which developed under the influence of socio-economic factors, political decisions, architectural and urban planning tasks of the first half of the 20th century. The study involves the use of a method of analysis, study and field survey of residential buildings in Astana of the 1930s-1950s. The article is based on documents collected by the author in 2014-2015 from the funds of the National Archive of the Republic of Kazakhstan, the State Archive of the city of Astana, the archive of the real estate center in the city of Astana, as well as materials from field surveys and photographic recordings of residential buildings. The theoretical foundation for the study was the works of Kazakh scientists: B.A. Glaudinova, M.G. Seydalina, A.S. Karpykova, T.K. Basenova, B.U. Kuspangalieva, K.I. Samoilova, K.G. Tokzhumanova, etc. As a result of the study, three periods of formation and development of the architecture of residential buildings in Astana were identified: pre-war (1936-1940s); war period (1941-1945); post-war period (1946-1953). Within the established periods, the characteristic features of the architecture of residential buildings in the city are determined: urban planning solutions for residential development, typological methods of housing construction and architectural style.

Automated mission planning for aerial large‐scale power plant thermal inspection

AbstractClose‐range aerial inspection of large‐scale industrial facilities such as power plants is complex. It is characterized by a few areas suitable for safe landing, many obstacles, complicated radio communication, and so forth. The paper presented a new offline approach for automated mission planning in such conditions. It is based on novel concepts of ‐shaped flight profile and safe altitude map. These concepts reduce the mission planning task to a multiple traveling salesmen problem on a two‐dimensional map, which is solved using rapidly exploring random tree and genetic algorithms. A computational fluid dynamics‐based local turbulence map concept was proposed to avoid turbulence zones during inspection flights. As an advantage, this map can be generated at least 6.8× faster than the known approaches. Our new multicopter flight time prediction model provides an error of less than 10% in most cases and significantly outperforms those implemented in commercial mission planning software. All the proposed solutions were verified during a 2400‐MW power plant inspection. Our algorithms managed to plan to visit all the inspection points in six routes, approximately 15 min long each. This mission set has a comparable overall duration but better safety and even battery usage compared with the routes planned by a qualified operator. Moreover, the designed aerial inspection system provides higher quality images than manually from the ground using a thermal camera with two times higher sensitivity and equipped with more advanced optics. To our knowledge, this research is the first to report the successful implementation of autonomous unmanned aerial vehicle‐based power equipment diagnostics on a large‐scale thermal power plant.

Path Planning Method for Manipulators Based on Improved Twin Delayed Deep Deterministic Policy Gradient and RRT*

This paper proposes a path planning framework that combines the experience replay mechanism from deep reinforcement learning (DRL) and rapidly exploring random tree star (RRT*), employing the DRL-RRT* as the path planning method for the manipulator. The iteration of the RRT* is conducted independently in path planning, resulting in a tortuous path and making it challenging to find an optimal path. The setting of reward functions in policy learning based on DRL is very complex and has poor universality, making it difficult to complete the task in complex path planning. Aiming at the insufficient exploration of the current deterministic policy gradient DRL algorithm twin delayed deep deterministic policy gradient (TD3), a stochastic policy was combined with TD3, and the performance was verified on the simulation platform. Furthermore, the improved TD3 was integrated with RRT* for performance analysis in two-dimensional (2D) and three-dimensional (3D) path planning environments. Finally, a six-degree-of-freedom manipulator was used to conduct simulation and experimental research on the manipulator.

Research on Robot Path Planning Technology Combining Ant Algorithm and DWA Algorithm

Path planning is an important problem in the field of robotics, the main purpose of which is to plan the best path to avoid obstacles during the robot's action, so as to realize the robot's autonomous action. However, the traditional path planning algorithm often cannot deal with the situation of multiple dynamic obstacles, which makes the application of the robot in the actual scene is greatly limited. To solve this problem, a path planning algorithm based on ant colony algorithm and dynamic window method is proposed in this paper. This fusion algorithm can not only obtain the global optimal path by using the global known information, but also cope with the unknown dynamic obstacles to avoid collision. It can better complete the path planning task in the actual situation with superior performance, improve the safety of the robot during the execution of the task, and ensure the subsequent execution efficiency.

Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models for AI Planning

This is the first work to look at the application of large language models (LLMs) for the purpose of model space edits in automated planning tasks. To set the stage for this union, we explore two different flavors of model space problems that have been studied in the AI planning literature and explore the effect of an LLM on those tasks. We empirically demonstrate how the performance of an LLM contrasts with combinatorial search (CS) – an approach that has been traditionally used to solve model space tasks in planning, both with the LLM in the role of a standalone model space reasoner as well as in the role of a statistical signal in concert with the CS approach as part of a two-stage process. Our experiments show promising results suggesting further forays of LLMs into the exciting world of model space reasoning for planning tasks in the future.