Planning Module Research Articles

The Validation of Diabetes Mellitus Discharge Planning Module Incorporated into the Summary of Diabetes Self-Care Activity

Background: In Indonesia, several hospitals have designed various forms of discharge planning in line with the guidelines provided by the Indonesian Endocrinology Association. These initiatives were implemented to enhance the quality of healthcare service. Despite the efforts made by the government, the rate of non-compliance (9%) and readmission (20%) has continued to increase. This indicates that there is a need to reevaluate the existing discharge planning module. Therefore, this study aims to validate the contents of the diabetes mellitus discharge planning module, which was incorporated into the summary of diabetes self-care activity. The effectiveness of the intervention in improving the compliance of patients with self-care activity was also evaluated. Materials and Methods: This study used a mixed methodological approach, which combined an evaluation method and an experimental quantitative design. The content validity of the module used was carried out using professional judgment, involving competent experts in the bahteramas hospital in southeast Sulawesi, Indonesia. The sample population consisted of sixty-five randomly selected respondents, who participated in the validation of the summary of Diabetes Self-Care Activities (SDSCA) questionnaire, which had three phases, namely pre-test, during, fieldwork, and post-test. Results: The content validity of the module was tested using Aiken’s v, and the results ranged from 0.82 to 0.88 for each session, indicating that it was valid. Furthermore, the internal consistency (Cronbach’s alpha) obtained in this study was acceptable, with values ranging from 0.60 to 0.92. Conclusions: The results showed that the diabetes mellitus discharge planning module could be used to improve self-care among patients.

Dynamic Obstacle Avoidance Strategy for High-Speed Vehicles Via Constrained Model Predictive Control and Improved Artificial Potential Field

Abstract The paper proposes an automatic planning and control strategy to keep the automatic driving high-speed vehicles collision-free based on the improved artificial potential field (APF) and constrained model predictive control (MPC). First, the road potential field satisfying the constraints of vehicle safe driving is constructed according to the driving characteristics of vehicles and road boundary conditions. Focusing on the influence of the speed and direction of the obstacle vehicle on the potential field, the dynamic obstacle vehicle potential field is established. Secondly, the road potential field and the dynamic obstacle vehicle potential field are incorporated into the objective function of the path planning module to establish a real-time path planner. Thirdly, the yaw stability constraints of the vehicle are established, which are added to the QP solver and updated in real-time according to the current vehicle states, so as to establish the constrained MPC controller. In the end, the safety of this planning and control strategy for obstacle avoidance overtaking and the effectiveness of constrained MPC in improving vehicle stability are verified by comparative simulation analysis in multi-obstacle vehicles scenarios.

Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory

AbstractThe advancements in intelligent manufacturing have made high‐precision trajectory tracking technology crucial for improving the efficiency and safety of in‐factory cargo transportation. This study addresses the limitations of current forklift navigation systems in trajectory control accuracy and stability by proposing the Enhanced Stability and Safety Model Predictive Control (ESS‐MPC) method. This approach includes a multi‐constraint strategy for improved stability and safety. The kinematic model for a single front steering‐wheel forklift vehicle is constructed with all known state quantities, including the steering angle, resulting in a more accurate model description and trajectory prediction. To ensure vehicle safety, the spatial safety boundary obtained from the trajectory planning module is established as a hard constraint for ESS‐MPC tracking. The optimisation constraints are also updated with the key kinematic and dynamic parameters of the forklift. The ESS‐MPC method improved the position and pose accuracy and stability by 57.93%, 37.83%, and 57.51%, respectively, as demonstrated through experimental validation using simulation and real‐world environments. This study provides significant support for the development of autonomous navigation systems for industrial forklifts.

Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model

Tower cranes are the most used equipment in construction projects, and the path planning of tower crane operations directly affects the safety performance of construction projects. Traditional tower crane operations rely on only the driving experience and manual path planning of crane operators. Poor judgement and bad path planning may increase safety risks and even cause severe construction safety accidents. To reduce safety risks in construction tower crane operations, this research proposes a dynamic path planning model for tower crane operations based on computer vision technology and dynamic path planning algorithms. The proposed model consists of three modules: first, a path information collection module preprocessing the video data to capture relevant operational path information; second, a path safety risk evaluation module employing You Only Look Once version 8 (YOLOv8) instance segmentation to identify potential risk factors along the operational path, e.g., potential drop zones and the positions of nearby workers; and finally, a path planning module utilizing an improved Dynamic Window Approach for tower cranes (TC-DWA) to avoid risky areas and optimize the operational path for enhanced safety. A prototype based on the theoretical model was constructed and tested on actual construction projects. Through experimental scenarios, it was found that each tower crane operation poses safety risks to 3–4 workers on average, and the proposed prototype can significantly reduce the safety risks of dropped loads from tower crane operations affecting ground workers and important equipment. A comparison between the proposed model and other regular algorithms was also conducted, and the results show that compared with traditional RRT and APF algorithms, the proposed model reduces the average maximum collision times by 50. This research provides a theoretical model and a preliminary prototype to provide dynamic path planning and reduce safety risks in tower crane operations. Future research will be conducted from the aspects of multiple device monitoring and system optimization to increase the analysis speed and accuracy, as well as on human–computer interactions between tower crane operators and the path planning guidance model.

Differentiable Integrated Motion Prediction and Planning With Learnable Cost Function for Autonomous Driving.

Predicting the future states of surrounding traffic participants and planning a safe, smooth, and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs). There are two major issues with the current autonomous driving system: the prediction module is often separated from the planning module, and the cost function for planning is hard to specify and tune. To tackle these issues, we propose a differentiable integrated prediction and planning (DIPP) framework that can also learn the cost function from data. Specifically, our framework uses a differentiable nonlinear optimizer as the motion planner, which takes as input the predicted trajectories of surrounding agents given by the neural network and optimizes the trajectory for the AV, enabling all operations to be differentiable, including the cost function weights. The proposed framework is trained on a large-scale real-world driving dataset to imitate human driving trajectories in the entire driving scene and validated in both open-loop and closed-loop manners. The open-loop testing results reveal that the proposed method outperforms the baseline methods across a variety of metrics and delivers planning-centric prediction results, allowing the planning module to output trajectories close to those of human drivers. In closed-loop testing, the proposed method outperforms various baseline methods, showing the ability to handle complex urban driving scenarios and robustness against the distributional shift. Importantly, we find that joint training of planning and prediction modules achieves better performance than planning with a separate trained prediction module in both open-loop and closed-loop tests. Moreover, the ablation study indicates that the learnable components in the framework are essential to ensure planning stability and performance. Code and Supplementary Videos are available at https://mczhi.github.io/DIPP/.

A Globally Guided Dual-Arm Reactive Motion Controller for Coordinated Self-Handover in a Confined Domestic Environment.

Future humanoid robots will be widely deployed in our daily lives. Motion planning and control in an unstructured, confined, and human-centered environment utilizing dexterity and a cooperative ability of dual-arm robots is still an open issue. We propose a globally guided dual-arm reactive motion controller (GGDRC) that combines the strengths of global planning and reactive methods. In this framework, a global planner module with a prospective task horizon provides feasible guidance in a Cartesian space, and a local reactive controller module addresses real-time collision avoidance and coordinated task constraints through the exploitation of dual-arm redundancy. GGDRC extends the start-of-the-art optimization-based reactive method for motion-restricted dynamic scenarios requiring dual-arm cooperation. We design a pick-handover-place task to compare the performances of these two methods. Results demonstrate that GGDRC exhibits accurate collision avoidance precision (5 mm) and a high success rate (84.5%).

Deep reinforcement learning augmented decision model for intelligent driving cars

In view of the fact that the state machine decision model cannot effectively handle the rich contextual information and the influence of uncertain factors in ice and snow environments, a deep reinforcement learning agent based on the deep Q network algorithm (DQN) was constructed. The motion planner was used to augment the agent, and the rule-based decision planning module and the deep reinforcement learning model were integrated to establish the DQN-planner model, thereby improving the convergence speed and driving ability of the reinforcement learning agent. Finally, based on the CARLA simulation platform, a comparative experiment was conducted on the driving ability of the DQN model and the DQN-planner model on low-adhesion ice and snow roads, and the training process and verification results were analyzed respectively.

Energy-Aware Hierarchical Control of Joint Velocities

Nowadays, robots are applied in dynamic environments. For a robust operation, the motion planning module must consider other tasks besides reaching a specified pose: (self) collision avoidance, joint limit avoidance, keeping an advantageous configuration, etc. Each task demands different joint control commands, which may counteract each other. We present a hierarchical control that, depending on the robot and environment state, determines online a suitable priority among those tasks. Thereby, the control command of a lower-prioritized task never hinders the control command of a higher-prioritized task. We ensure smooth control signals also during priority rearrangement. Our hierarchical control computes reference joint velocities. However, the underlying concepts of hierarchical control differ when using joint accelerations or joint torques as control signals instead. So, as a further contribution, we provide a comprehensive discussion on how joint velocity control, joint acceleration control, and joint torque control differ in hierarchical task control. We validate our formulation in an experiment on hardware.

A real time data-driven dynamic glasius bionic neural network path planning algorithm for polar under-ice feature scanning by “Xinghai 1000” AUV

Using Autonomous Underwater Vehicle (AUV) to survey polar under-ice feature is crucial for studying polar marine environment and global climate change. Compared with seafloor terrain scanning, it faces several challenges, such as uncertain under-ice environment, lack of reliable positioning methods, and stringent constraints on recovery location. Thus, taking “Xinghai 1000” as analysis object, this paper proposes a real time data-driven dynamic glasius bionic neural network path planning algorithm (RDGPP), which consists of an online bi-level path planning module. In global planning module, dynamically hierarchical strategy is used to realize task area layering. On this basis, global coverage path is planned by combining neuron activity value updated which utilize real-time environmental information obtained by FLS sonar with multivariant strategy. Moreover, due to AUV recovery mode limitation, return strategy has been devised in multivariant strategy to ensure AUV automatic return to launch point, thereby reducing energy usage. In local planning module, Flagged bit and GBNN-driven A-star algorithm (FGA) is introduced to minimize deadlock escape time for AUV. Simulation experiments verify RDGPP algorithm advancement in key indicators such as turning times, running time, etc., and effectiveness is verified by “Xinghai 1000” under-ice experiment.

Development of a digital module for planning and installation of the audio system subject in audio video engineering skills competency

Development of a digital module for planning and installation of the audio system subject in audio video engineering skills competency

Generative design for complex floorplans in high-rise residential buildings: A Monte Carlo tree search-based self-organizing multi-agent system (MCTS-MAS) solution

This research aims to develop an innovative generative design algorithm for complex floorplan layout designs in high-rise residential buildings. Unlike conventional methods, which treat such a task as an optimization problem, we approach it as an adaption problem to be solved by combining a self-organized multi-agent system (MAS) and Monte Carlo tree search (MCTS) algorithm. The approach involves two steps. The first is design constraints pre-processing, which comprises (a) initial user input, and (b) pre-processing for initial setup. The second is building plan generation, involving (a) perception, (b) planning, and (c) action modules. Multiple illustrative examples are explored to verify the proposed generative design algorithm in terms of robustness, efficiency, and scale. We discover that the algorithm can handle complex combinations of input constraints (e.g., number of rooms, room areas, adjacency connections, and building boundaries) and generate plausible floorplans in a robust, efficient way that can be scaled up to bigger, more complex floorplan generative design problems. With proper adaptation, the algorithm can potentially be applied to other building types, such as office or hospital buildings.

Competing with autonomous model vehicles: a software stack for driving in smart city environments

This article introduces an open-source software stack designed for autonomous 1:10 scale model vehicles. Initially developed for the Bosch Future Mobility Challenge (BFMC) student competition, this versatile software stack is applicable to a variety of autonomous driving competitions. The stack comprises perception, planning, and control modules, each essential for precise and reliable scene understanding in complex environments such as a miniature smart city in the context of BFMC. Given the limited computing power of model vehicles and the necessity for low-latency real-time applications, the stack is implemented in C++, employs YOLO Version 5 s for environmental perception, and leverages the state-of-the-art Robot Operating System (ROS) for inter-process communication. We believe that this article and the accompanying open-source software will be a valuable resource for future teams participating in autonomous driving student competitions. Our work can serve as a foundational tool for novice teams and a reference for more experienced participants. The code and data are publicly available on GitHub.

Probabilistic Path Planning for UAVs in Forest Fire Monitoring: Enhancing Patrol Efficiency through Risk Assessment

Forest fire is a significant global natural disaster, and unmanned aerial vehicles (UAVs) have gained attention in wildfire prevention for their efficient and flexible monitoring capabilities. Proper UAV patrol path planning can enhance fire-monitoring accuracy and response speed. This paper proposes a probabilistic path planning (PPP) module that plans UAV patrol paths by combining real-time fire occurrence probabilities at different points. Initially, a forest fire risk logistic regression model is established to compute the fire probabilities at different patrol points. Subsequently, a patrol point filter is applied to remove points with low fire probabilities. Finally, combining fire probabilities with distances between patrol points, a dynamic programming (DP) algorithm is employed to generate an optimal UAV patrol route. Compared with conventional approaches, the experimental results demonstrate that the PPP module effectively improves the timeliness of fire monitoring and containment, and the introduction of DP, considering that the fire probabilities and the patrol point filter both contribute positively to the experimental outcomes. Different combinations of patrol point coordinates and their fire probabilities are further studied to summarize the applicability of this method, contributing to UAV applications in forest fire monitoring and prevention.

Multi-institutional investigation into the robustness of intra-cranial multi-target stereotactic radiosurgery plans to patient setup errors

PurposeThis study aimed to analyse correlations between planning factors including plan geometry and plan complexity with robustness to patient setup errors. MethodsMultiple-target brain stereotactic radiosurgery (SRS) plans were obtained through the Trans-Tasman Radiation Oncology Group (TROG) international treatment planning challenge (2018). The challenge dataset consisted of five intra-cranial targets with a 20 Gy prescription. Setup error was simulated using an in-house tool. Dose to targets was assessed via dose covering 99 % (D99 %) of gross tumour volume (GTV) and 98 % of planning target volume (PTV). Dose to organs at risk was assessed using volume of normal brain receiving 12 Gy and maximum dose covering 0.03 cc of brainstem. Plan complexity was assessed via edge metric, modulation complexity score, mean multi-leaf collimator (MLC) gap, mean MLC speed and plan modulation. ResultsEven for small (0.5 mm/°) errors, GTV D99 % was reduced by up to 20 %. The strongest correlation was found between lower complexity plans (larger mean MLC gap and lower edge metric) and higher robustness to setup error. Lower complexity plans had 1 %–20 % fewer targets/scenarios with GTV D99 % falling below the specified tolerance threshold. These complexity metrics correlated with 100 % isodose volume sphericity and dose conformity, though similar conformity was achievable with a range of complexities. ConclusionsA higher level of importance should be directed towards plan complexity when considering plan robustness. It is recommended when planning multi-target SRS, larger MLC gaps and lower MLC aperture irregularity be considered during plan optimisation due to higher robustness should patient positioning errors occur.

A novel query execution time prediction approach based on operator iterate-aware of the execution plan on the graph database

Query execution time prediction is essential for database query optimization tasks, such as query scheduling, progress monitoring, and resource allocation. In the query execution time prediction tasks, the query plan is often used as the modeling object of a prediction model. Although the learning-based prediction models have been proposed to capture plan features, there are two limitations need to be considered more. First, the parent–child dependencies between plan operators can be captured, but the operator’s branch independence cannot be distinguished. Second, each operator’s output row is its following operator input, but the data iterate transfer operations between operators are ignored. In this study, we propose a graph query execution time prediction model containing a plan module, a query module, a plan-query module, and a prediction module to improve prediction effectiveness. Specifically, the plan module is used to capture the data iterate transfer operations and distinguish independent of branch operators; the query module is used to learn features of query terms that have an influence on the composition of operators; the plan-query interaction module is used to learn the logical correlations of plan and query. The experiment on datasets proves the effectiveness of the operator iterate-aware and query-plan interaction method in our proposed graph query execution prediction model.

The impact of plan complexity on calculation and measurement-based pre-treatment verifications for sliding-window intensity-modulated radiotherapy

Background and purposeIn sliding-window intensity-modulated radiotherapy, increased plan modulation often leads to increased plan complexities and dose uncertainties. Dose calculation and/or measurement checks are usually adopted for pre-treatment verification. This study aims to evaluate the relationship among plan complexities, calculated doses and measured doses. Materials and methodsA total of 53 plan complexity metrics (PCMs) were selected, emphasizing small field characteristics and leaf speed/acceleration. Doses were retrieved from two beam-matched treatment devices. The intended dose was computed employing the Anisotropic Analytical Algorithm and validated through Monte Carlo (MC) and Collapsed Cone Convolution (CCC) algorithms. To measure the delivered dose, 3D diode arrays of various geometries, encompassing helical, cross, and oblique cross shapes, were utilized. Their interrelation was assessed via Spearman correlation analysis and principal component linear regression (PCR). ResultsThe correlation coefficients between calculation-based (CQA) and measurement-based verification quality assurance (MQA) were below 0.53. Most PCMs showed higher correlation rpcm-QA with CQA (max: 0.84) than MQA (max: 0.65). The proportion of rpcm-QA ≥ 0.5 was the largest in the pelvis compared to head-and-neck and chest-and-abdomen, and the highest rpcm-QA occurred at 1 %/1mm. Some modulation indices for the MLC speed and acceleration were significantly correlated with CQA and MQA. PCR’s determination coefficients (R2) indicated PCMs had higher accuracy in predicting CQA (max: 0.75) than MQA (max: 0.42). ConclusionsCQA and MQA demonstrated a weak correlation. Compared to MQA, CQA exhibited a stronger correlation with PCMs. Certain PCMs related to MLC movement effectively indicated variations in both quality assurances.

Sparing the Hippocampus in Prophylactic Cranial Irradiation Using Three Different Linear Accelerators: A Comparative Study.

Hippocampus protection, as an organ at risk in brain radiotherapy, might protect patients' quality of life. Prophylactic cranial irradiation (PCI) has been used traditionally in small cell lung cancer (SCLC) patients as it increases survival. This study aimed to discover the contributing parameters for a successful PCI with simultaneous protection of the hippocampus by using three different treatment machines. For this purpose, treatment plans were generated for 45 SCLC patients using three half-arcs in three linear accelerators (LINACs; Elekta Infinity, Synergy, and Axesse; Elekta Ltd, Stockholm, Sweden) with different radiation field sizes and multileaf collimator (MLC) leaf thickness characteristics. The prescribed dose was 25 Gy in 10 fractions. Thresholds for the hippocampus were calculated based on the Radiation Therapy Oncology Group 0933 dose constraints. The planning and treatment system templates were common to all three LINACs. Plan evaluation was based on the dosimetric target coverage by the 95% isodose, the maximum dose of the plan, the conformity index (CI), the degree of plan modulation (MOD), and the patient-specific quality assurance (QA) pass rate. The mean target coverage was highest for Infinity (97.3%), followed by Axesse (96.6%) and Synergy (95.5%). The mean maximum dose was higher for Synergy (27.5 Gy), followed by Infinity (27.0 Gy) and Axesse (26.9 Gy). Axesse plans had the highest CI (0.93), followed by Infinity (0.91) and Synergy (0.88). Plan MOD was lower for Synergy (2.88) compared with Infinity (3.07) and Axesse (3.69). Finally, patient-specific QA was successful in all Infinity plans, in all but one Synergy plan, and in 17/45 Axesse plans, as was expected from the field size in that treatment unit. Based on overall performance, the most favorable combination of target coverage, hippocampus sparing, and plan deliverability was obtained with the LINAC, which has the largest field opening and thinnest MLC leaves.

6-DOF UAV Path planning and tracking control for obstacle avoidance: a deep learning-based integrated approach

Path planning is crucial for achieving autonomous and safe flight of fixed-wing unmanned aerial vehicles (UAVs). The existing methods generally cannot simultaneously balance real-time performance, near optimality, and feasibility in three-dimensional obstacle environments. To this end, this paper proposes a deep learning-based integrated path planning and tracking control framework for obstacle avoidance. First, a fundamental framework, including an interfered fluid dynamical system (IFDS)-based path planning module and a backstepping control-based path tracking module, is established. Then, to satisfy high real-time requirements, a long short-term memory (LSTM)-based fast online decision-making network matched with the IFDS is developed. Extensive training samples are generated by the fundamental framework, and receding horizon control is employed to optimize these samples so that the LSTM can provide near-optimal coefficients for the IFDS. Furthermore, the dynamic characteristics of an actual UAV system and the performance of the tracking controller are fully considered during sample generation, enabling that the paths planned by the LSTM-assisted IFDS are highly feasible. Finally, the results of numerical simulations and hardware-in-the-loop experiments show that our method can plan near-optimal obstacle-free paths with high feasibility in real-time.

Synthesis and Verification of Mission Plans for Multiple Autonomous Agents under Complex Road Conditions

Mission planning for multi-agent autonomous systems aims to generate feasible and optimal mission plans that satisfy given requirements. In this article, we propose a tool-supported mission-planning methodology that combines (i) a path-planning algorithm for synthesizing path plans that are safe in environments with complex road conditions, and (ii) a task-scheduling method for synthesizing task plans that schedule the tasks in the right and fastest order, taking into account the planned paths. The task-scheduling method is based on model checking, which provides means of automatically generating task execution orders that satisfy the requirements and ensure the correctness and efficiency of the plans by construction. We implement our approach in a tool named MALTA, which offers a user-friendly GUI for configuring mission requirements, a module for path planning, an integration with the model checker UPPAAL, and functions for automatic generation of formal models, and parsing of the execution traces of models. Experiments with the tool demonstrate its applicability and performance in various configurations of an industrial case study of an autonomous quarry. We also show the adaptability of our tool by employing it in a special case of an industrial case study.

A domain-independent agent architecture for adaptive operation in evolving open worlds

1Angry Birds Competition. Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world. We propose HYDRA, a framework for designing model-based agents operating in mixed discrete-continuous worlds that can autonomously detect when the environment has evolved from its canonical setup, understand how it has evolved, and adapt the agents' models to perform effectively. HYDRA is based upon PDDL+, a rich modeling language for planning in mixed, discrete-continuous environments. It augments the planning module with visual reasoning, task selection, and action execution modules for closed-loop interaction with complex environments. HYDRA implements a novel meta-reasoning process that enables the agent to monitor its own behavior from a variety of aspects. The process employs a diverse set of computational methods to maintain expectations about the agent's own behavior in an environment. Divergences from those expectations are useful in detecting when the environment has evolved and identifying opportunities to adapt the underlying models. HYDRA builds upon ideas from diagnosis and repair and uses a heuristics-guided search over model changes such that they become competent in novel conditions. The HYDRA framework has been used to implement novelty-aware agents for three diverse domains - CartPole++ (a higher dimension variant of a classic control problem), Science Birds (an IJCAI competition problem1), and PogoStick (a specific problem domain in Minecraft). We report empirical observations from these domains to demonstrate the efficacy of various components in the novelty meta-reasoning process.