Sequence Planning Research Articles

Station-Keeping Control of Stratospheric Balloons Based on Simultaneous Optimistic Optimization in Dynamic Wind

Stratospheric balloons serve as cost-effective platforms for wireless communication. However, these platforms encounter challenges stemming from their underactuation in the horizontal plane. Consequently, controllers must continually identify favorable wind conditions to optimize station-keeping performance while managing energy consumption. This study presents a receding horizon controller based on wind and balloon models. Two neural networks, PredRNN and ResNet, are utilized for short-term wind field forecast. Additionally, an online receding horizon controller, based on simultaneous optimistic optimization (SOO), is developed for action sequence planning and adapted to accommodate various constraints, which is especially suitable due to its gradient-free nature, high efficiency, and effectiveness in black-box function optimization. A reward function is formulated to balance power consumption and station-keeping performance. Simulations conducted across diverse positions and dates demonstrate the superior performance of the proposed method compared with traditional greedy and A* algorithms.

Benefits derived from WASP (a Grasshopper plugin) to Design for Disassembly Principles in the Built Environment

Abstract Design for Disassembly (DfD, henceforth) is a way to improve the effectiveness of the disassembly process in built environments, and it mainly contains ten principles. Examples of the principles include deconstruction techniques; selected materials; reachable connections; utilizing connections with fewer bolts, screws, and nails; the straightforwardness of the structure and design; and versatility. On the other hand, the WASP plugin for Grasshopper (on Rhinocheros©) is a toolkit for discrete computational design to simulate the aggregation and assembly possibilities in the building system. This paper explores the integration of the WASP plugin within the context of DfD through three criteria: the aggregation processes of part geometries, connection locations, and their orientation toward the building elements. Design with discrete elements, which can be defined as shape grammars for building structure, is the primary approach for the joining behavior and detaching behavior of elements discussed in this paper. Also, this type of component-based design can optimize the geometry and data-driven generation of the structure. The design process in this way can be disassembly sequence planning and knowledge-based design by applying the design constraints. To illustrate the integration, research is conducted through WASP simulation in designing a traditional Iranian timber pavilion-inspired structure with a 3×3m footprint in a roof structure. The fusion of WASP and DfD represents a promising avenue for advancing circular design principles and achieving more sustainable, resource-efficient buildings. It is concluded from this study that WASP makes the aggregation simulation and assembly possibilities of the vertical and horizontal components in a building more accessible.

Using the Cartwheel CME to Predict Off-limb Observations of CMEs for New and Upcoming UV and EUV Spectrometers

Coronal mass ejections (CMEs) expel multithermal, magnetized plasma from the Sun, and when directed toward Earth, can cause extensive damage to space and ground-based electronics. To better understand the triggering, acceleration, and evolution of CMEs, it is critical to study CME plasma properties close to the Sun. High-resolution ultraviolet and extreme ultraviolet (UV-EUV) spectroscopy can give the most detailed plasma diagnostics of CMEs in the low solar corona. Unfortunately, very few spectrally resolved observations of CMEs in the low solar corona exist. However, with the recent launch of the Spectral Imaging of the Coronal Environment on board Solar Orbiter and the upcoming missions, including the EUV High-Throughput Solar Telescope (EUVST) on Solar-C and the Multi-slit Solar Explorer (MUSE), we will have the opportunity to obtain unprecedented, spectrally resolved CME observations. Using the only full EUV spectral observation of a CME by the Hinode/EUV Imaging Spectrometer, we predict the spectra that SPICE, EUVST, and MUSE are expected to observe during an off-limb CME eruption to investigate the diagnostic capabilities of each instrument. Finally, we provide a list of density-sensitive and temperature-sensitive ratios for CME plasma diagnostics along with the expected spectral atlas for each instrument to facilitate observing sequence planning.

A rule-driven method for disassembly sequence real-time planning of intelligent mixed-flow disassembly line

Intelligent mixed-flow disassembly serves an important role in enhancing the flexible operation capability of the disassembly line for multi-variety and small-batch used electromechanical products, and in forming the scale benefit of recycling. However, in mixed-flow disassembly lines, pre-planning the disassembly sequence for products is typically unfeasible and can lead to “over-disassembly” or “under-disassembly”, resulting in economic losses. This is attributed to significant variations in disassembly features, such as internal structural damage and subassembly recycling income, even within the same type of used electromechanical product. These differences contribute to considerable uncertainties in disassembly sequence planning. To this end, a method is proposed to leverage dynamic disassembly feature information for mining disassembly rules, efficiently driving disassembly sequences real-time planning, and realizing intelligent optimization of product disassembly profit on mixed-flow disassembly line. First, a multi-matrix collaboration based dynamic representing method of product disassembly feature information is proposed. It aims to eliminates the influence of uncertain information on the disassembly sequence planning by recording real-time disassembly feature information, such as disassembly operation time and subassembly recycling income. On this basis, a rule-driven disassembly sequences real-time planning model is built. This model realizes disassembly sequence real-time planning based on offline mined rules using the updated disassembly feature information in matrices. Finally, an improved gene expression programming-based disassembly rule offline mining algorithm is proposed, which incorporates a mutation probability mapping function to improve the efficiency and effectiveness of disassembly rule mining. A case study containing two products with differences in structure and function are used to confirm the effectiveness of the proposed method.

AR/VR assisted integrated framework of autonomous disassembly system for industrial products

An immersive Augmented Reality (AR) technology enhances productivity and flexibility in manufacturing by providing real-time instructions within workers’ field of view, aiding in complex tasks. According to cognitive load and attention-based theories, AR guidance can reduce cognitive strain, enhancing task performance. The affordance of AR in industrial processes like assembly or disassembly for repair/maintenance has been exploited broadly. However, the prevailing studies on automated user interfaces for AR instruction development, specifically virtual scene creation and target generation for object mapping rendering, still need to be explored. Notably, production industries currently rely on outsourcing skilled professionals to create AR overlaying instructions using manufacturer input. However, the exactness of the disassembly repository is substantial to avoid defects in AR guidance particulars. This underscores the significance of generalized automated AR instruction development and emphasizes the demands for further research. In this work, the autonomous AR-integrated framework is developed for the disassembly context, empowering manufacturers to acquire desired outcomes across various modules. These modules include the validation of the manual user disassembly sequence repository during the prefatory phase of AR instruction creation and the provision of Disassembly Sequence Planning (DSP) input. Additionally, the framework supports the simulation/rendering of disassembly efforts in a virtual platform and the generation of rendering virtual display scenes for appropriate AR-guided object mapping. Furthermore, it enables the generation of essential model targets for registering the AR scenes. Subsequently, the extracted repositories (virtual scenes and model targets) are input into the Unity AR tool to create AR mapping guidance for disassembly operations. The performance of the autonomous disassembly module is evaluated through three case studies with different AR visualizations. Furthermore, the potential of the proposed framework is exploited by extracting feedback from experts regarding flexibility, compatibility, completion time, and cognitive level.

Solving a Stochastic Multi-Objective Sequence Dependence Disassembly Sequence Planning Problem with an Innovative Bees Algorithm

As the number of end-of-life products multiplies, the issue of their efficient disassembly has become a critical problem that urgently needs addressing. The field of disassembly sequence planning has consequently attracted considerable attention. In the actual disassembly process, the complex structures of end-of-life products can lead to significant delays due to the interference between different tasks. Overlooking this can result in inefficiencies and a waste of resources. Therefore, it is particularly important to study the sequence-dependent disassembly sequence planning problem. Additionally, disassembly activities are inherently fraught with uncertainties, and neglecting these can further impact the effectiveness of disassembly. This study is the first to analyze the sequence-dependent disassembly sequence planning problem in an uncertain environment. It utilizes a stochastic programming approach to address these uncertainties. Furthermore, a mixed-integer optimization model is constructed to minimize the disassembly time and energy consumption simultaneously. Recognizing the complexity of the problem, this study introduces an innovative bees algorithm, which has proven its effectiveness by showing a superior performance compared to other state-of-the-art algorithms in various test cases. This research offers innovative solutions for the efficient disassembly of end-of-life products and holds significant implications for advancing sustainable development and the recycling of resources.

The impact of Playdough games on the development of oral motor skills in preschool children with childhood apraxia of speech

The relevant challenges are the use of innovative approaches that conserve both the speech therapist’s and the child’s resources while addressing multiple goals in speech therapy. The authors of this article have adopted such an approach to develop oral motor skills, which are crucial for speech development in preschoolers, particularly those with childhood apraxia of speech. This study aimed to evaluate the effectiveness of an author-developed therapy using Playdough games for preschool children with childhood apraxia of speech. A controlled design with repeated measures was employed. The primary diagnosis of CAS was established based on clinical assessment. A comparative evaluation of two groups (control and experimental) was conducted using a developed methodology for assessing the level of manual and oral motor skills in preschool children before and after the author-developed therapy using Playdough games. Researchers discovered that the systematic use of their author-developed Playdough games positively impacted the motor skills of children with CAS. Children in the experimental group, who participated in Playdough therapy, demonstrated significant improvements in both manual and oral motor skills, increased precision and focus in movement execution, and enhanced accuracy and speed of articulatory movements. There was a reduction in delays and incorrect movement sequences during finger movement tasks. Children required less additional time and significantly less assistance from the speech therapist, indicating a positive impact of using Playdough in therapy. The following skills improved: transitions between articulatory positions, which helped children avoid coarticulation difficulties; planning of movement sequences; and smooth transitions between sounds and syllables. The practical significance of this study lies in the effectiveness of using Playdough games for significantly improving oral motor skills, movement precision, and concentration in children with childhood apraxia of speech, thereby reducing the need for additional speech therapy support

A novel fine-grained assembly sequence planning method based on knowledge graph and deep reinforcement learning

In the assembly sequence planning (ASP) of aviation products, recalibration of components or sufficient space to assemble subsequent components are critical factors for ensuring product quality. To address this need, a fine-grained ASP (FASP) is defined to take assembly operations as units to plan sequences. Lots of operations have complex sequence constraints that are attended unequally in the FASP. A method based on knowledge graph (KG) and deep reinforcement learning is proposed to plan assembly operations. Firstly, continuous and discrete procedures are defined, and a quantitative characterization method is presented to deduce complex constraints objectively. Then, a dynamic KG is designed to establish and update the information model mainly composed of constraints. Finally, a labeled degree centrality algorithm (LDCA) considers edge labels to minimize the number of assembly tool changes and assembly direction changes for sequences. An improved deep Q-network (IDQN) introduces a convolutional layer to extract local features of technical requirements for planning procedures more efficiently. A helicopter structure assembly is used to verify the effectiveness of the proposed method. The improved algorithms have better performance in solving speed, sequence quality, and convergence speed than ordinary ASP methods, respectively. The fine-grained assembly sequence is more reasonable and feasible by comparing it with the ordinary sequence.

Parallel Disassembly Sequence Planning Using a Discrete Whale Optimization Algorithm for Equipment Maintenance in Hydropower Station

In a hydropower station, equipment needs maintenance to ensure safe, stable, and efficient operation. And the essence of equipment maintenance is a disassembly sequence planning problem. However, the complexity arises from the vast number of components in a hydropower station, leading to a significant proliferation of potential combinations, which poses considerable challenges when devising optimal solutions for the maintenance process. Consequently, to improve maintenance efficiency and decrease maintenance time, a discrete whale optimization algorithm (DWOA) is proposed in this paper to achieve excellent parallel disassembly sequence planning (PDSP). To begin, composite nodes are added into the constraint relationship graph based on the characteristics of hydropower equipment, and disassembly time is chosen as the optimization objective. Subsequently, the DWOA is proposed to solve the PDSP problem by integrating the precedence preservative crossover mechanism, heuristic mutation mechanism, and repetitive pairwise exchange operator. Meanwhile, the hierarchical combination method is used to swiftly generate the initial population. To verify the viability of the proposed algorithm, a classic genetic algorithm (GA), simplified teaching–learning-based optimization (STLBO), and self-adaptive simplified swarm optimization (SSO) were employed for comparison in three maintenance projects. The experimental results and comparative analysis revealed that the proposed PDSP with DWOA achieved a reduced disassembly time of only 19.96 min in Experiment 3. Additionally, the values for standard deviation, average disassembly time, and the rate of minimum disassembly time were 0.3282, 20.31, and 71%, respectively, demonstrating its superior performance compared to the other algorithms. Furthermore, the method proposed in this paper addresses the inefficiencies in dismantling processes in hydropower stations and enhances visual representation for maintenance training by integrating Unity3D with intelligent algorithms.

Research on complex products assembly sequence planning and its application based on PSO-GA

Abstract A PSO-GA-based method is proposed to improve the efficiency of complex product assembly sequence planning. We minimize total assembly time by optimizing the assembly sequence, considering the number of tool changes and assembly direction changes as evaluation criteria. A genetic algorithm (GA) is introduced into particle swarm optimization (PSO) to solve and verify assembly sequence planning. Finally, the effectiveness and rationality of the hybrid algorithm are verified by taking a certain type of gear pump as an example.

Intervention to improve care delivery and patient self-management for people with primary liver cancer who have socioeconomic disadvantages.

e16232 Background: The 4R Oncology Model (4R = Right information/care/patient/time) supports patient self-management (Trosman, JCOOP 2021) and guideline-based care delivery (Liu, JCOOP 2022). 4R Care Sequence plans provide a care checklist and a graphical depiction of care timing and sequence. Here we report the impact of the 4R Care Sequence plans on care delivery and patient self-management among patients with primary liver cancer with socioeconomic disadvantages. Methods: 4R plans were provided to patients with liver cancer Jan – Dec ’23 (4R cohort). Patient-reported data were collected via surveys. Analyses compared the 4R cohort (N = 42) to a control cohort of patients who received care pre-4R, Jan - Dec ’22 (N = 52). Statistical analysis used Fisher’s exact and t-tests. Results: Survey response rates: control cohort 61% (52/85), 4R cohort 58% (42/73). Comparing control to 4R cohort respectively, patients exhibited the following characteristics: male (63%, 67%), White (67%, 74%), Black (25%, 19%), an average age (64.1, 63.9), and the majority had annual income less than $30,000 (62%, 67%). 4R significantly improved care delivery and patient self-management in all metrics vs control (Table). Patient rating of provider satisfaction on a 10-point scale was lower in the control cohort compared to the 4R cohort (8.4, 9.8, p < 0.0001), data not shown. The significant improvement across metrics was observed regardless of age, race, sex. Among patients who made < $30k annually, referral rates to a dentist before treatment increased significantly between control and 4R cohorts (19%,79%, p < 0.0001). Among patients who made > $30k annually, dental referral rates did not change between the control and 4R cohorts (70%, 71%, p = 1.00). Income was not a factor for other metrics. Conclusions: The 4R intervention using Care Sequence plans for patients with primary liver cancer facilitated improvements in care delivery and patient self-management regardless of age, race, sex or income. Our results indicate that 4R benefits all patients, including those with socioeconomic disadvantages. [Table: see text]

Information and support needs of young Black women with early breast cancer to enable self-management and informed decisions during breast cancer treatment.

e12587 Background: Black women with breast cancer are more likely to experience care delays and worse outcomes than White women (Emerson, Cancer ’20). Patient self-management contributes to more timely cancer care delivery and improved outcomes. We interviewed young black women with breast cancer to explore information needs related to their self-management interests and obtain feedback on the independent use of self-management tools developed within the 4R Oncology model. 4R (Right Info/Care/Patient/Time) is an approach to facilitate patient self-management using a Care Sequence plan- a care checklist with a visual depiction of timing and sequence of care, as well as a side effect self-care tool. These tools are typically provided in the clinic, but we aimed to explore if they could be used independently by patients. Methods: The interviewee cohort (N=20) was recruited from the Tigerlily Foundation ANGEL (Advocate Now to Grow, Empower and Lead) patient program. The cohort included Black women, average age 36 (21 to 46), stage I-III breast cancer. The framework approach of qualitative research was used. Results: All women reported information needs not addressed by their providers. Mental wellness and social support information were lacking for 70% (“I never worked with a social worker or anyone to support my mental health”). Most women (65%) wanted more information about breast cancer surgery options and post-surgery drains, bras, pain, and tingling (“before I had my surgery, my doctor didn't tell me all the options I had”). 40% mentioned a lack of reconstructive option information, including how radiation affects implants, “risk of encapsulation” and the long-term impact of having implants “changing them out every 10 years.” A slight majority (55%) desired nutrition and integrative medicine information. About one-third (35%) of women mentioned considerable skin issues during radiation treatment (“I needed skin care, for people with darker skin”). When reviewing a Care Sequence and a side-effect self-care tool, 95% reported they would use them independently if their clinic did not provide them ("I just got general information about breast cancer, I would have preferred a step-by-step process like this"; “This describes what services each role provides, and links to more support”). Most (95%) would have used the side-effect tool ("My chemo education was overwhelming, just a bunch of info thrown at me all at once, having a resource to look at would have been helpful"). Conclusions: In interviews with young Black women with breast cancer we found several information needs and a preference for tools to support self-management during treatment. These indicate considerable improvements are needed in this area. Using the input from the interviews, we are enhancing the 4R Oncology tools to better support the needs of Black patients with breast cancer.

Research on Assembly Sequence Planning of Hybrid Power Transmission Device Based on Improved Genetic Algorithm

Research on Assembly Sequence Planning of Hybrid Power Transmission Device Based on Improved Genetic Algorithm

Embodied AI for dexterity-capable construction Robots: DEXBOT framework

The construction industry is integrating robots into critical tasks at an accelerated pace, with the aim of enhancing efficiency, safety, and productivity. However, construction tasks requiring dexterity remain a challenge due to the need for precise movements, accurate perception, real-time decision-making, and a comprehensive understanding of the environment. To address these challenges, the introduction of embodied artificial intelligence (AI) represents a significant shift in robotic capabilities to enhance their alignment with the broader spectrum of construction settings. Rooted in cognitive science, embodied AI emphasizes the integration of an agent’s physical form into its computational intelligence processes. It resembles how humans develop motor skills by interacting with physical world. This paper introduces DEXBOT, an exploratory framework for designing construction robots capable of high dexterity using embodied AI principles that mimics human strategies in complex tasks. The framework outlines six key perspectives for solving high-dexterity tasks with embodied AI: scene understanding, localization and motion planning, position-based control, force-based control, sequence planning, and correction decision-making. By presenting preliminary test cases for each perspective, the paper emphasizes the role of embodied AI in advancing dexterity level of construction robots. The DEXBOT framework is expected to encourage interdisciplinary collaboration of designing capable construction robots in the future.

Criticality-based planning of prototype sequences

AbstractThe understanding of prototyping has changed in recent years to an approach that accompanies the product development process. This paper examines whether classic approaches from product development are also suitable for planning prototyping sequences. The stepwise process-oriented and the problem-oriented approach are discussed. A criticality assessment is proposed as a metric for the prioritization of the functional areas and a procedure is derived from this. The procedure is illustrated using an example. The result is discussed and future steps are suggested.

Automating the assembly planning process to enable design for assembly using reinforcement learning

AbstractThis paper introduces a new concept for the automation of the assembly planning process, to enable Design for Assembly (DfA). The approach involves the application of reinforcement learning (RL) to assembly sequence planning (ASP) based on a 3D-CAD model. The ASP algorithm determines assembly sequences through assembly by disassembly. The assembly sequence is then used for the generation of subassemblies by considering the product contact information. The approach aims to support the creation of the manufacturing bill of materials (MBOM) by automating the assembly planning process.

Sequence planning for on-orbit robotic assembly based on symbiotic organisms search with diversification strategy

The demands for efficiency and stability in space missions have necessitated the development of sequence planning schemes for on-orbit robotic assembly of space structures. A sequence planning method is proposed in this work based on robotic kinematic analysis and Symbiotic Organisms Search (SOS) algorithm with a diversification strategy. With feasibility as the constraint and assembly cost as the objective function, the assembly sequence planning is regarded as an optimization problem. The assembly relationship constraint is depicted as the dependency between assembly steps represented by a directed acyclic graph. An abstract modeling method based on robotic kinematics is proposed to construct geometric feasibility constraint, accommodating the interference stemming from the multi-degree-of-freedom motion of the robotic system. A diversification strategy is exploited to address the issue of local optima arising from solving the optimal sequence based on SOS. A cost prediction method using previously calculated populations as samples is proposed to reduce the reliance on robotic dynamic analysis for cost calculation. Finally, simulations are carried out to validate the practicability of the proposed method.

Multiobjective Human–Robot Collaborative Disassembly Sequence Planning: Considering the Properties of Components

Multiobjective Human–Robot Collaborative Disassembly Sequence Planning: Considering the Properties of Components

Man-in-the-Loop Control and Mission Planning for Unmanned Underwater Vehicles

UUVs (unmanned underwater vehicles) perform tasks in the marine environment under direction from a commander through a mother ship control system. In cases where communication is available, a UUV task re-planning system was designed to ensure task completion despite uncertain events faced by UUVs. First, the XML language standardizes the expression of UUV task elements. Second, considering the time sequence and spatial path planning requirements of human-supervised UUV control tasks, time sequence planning based on a genetic algorithm and spatial path planning based on an improved genetic algorithm were designed to plan near-optimal approximate spatial paths for control tasks. Third, uncertainties encountered during UUV task execution were classified so that the commander could adjust according to the situation or invoke the control task re-planning algorithm to re-plan. Finally, a simulation platform was built using the QT development environment to simulate human-supervised UUV control task planning and re-planning, verifying the algorithm’s design effectiveness.

An Assembly Sequence Planning Method Based on Multiple Optimal Solutions Genetic Algorithm

Assembly sequence planning (ASP) is an indispensable and important step in the intelligent assembly process, and aims to solve the optimal assembly sequence with the shortest assembly time as its optimization goal. This paper focuses on modular cabin construction for large cruise ships, tackling the complexities and challenges of part assembly during the process, based on real engineering problems. It introduces the multiple optimal solutions genetic algorithm (MOSGA). The MOSGA analyzes product constraints and establishes a mathematical model. Firstly, the traditional genetic algorithm (GA) is improved in the case of falling into the local optimum when facing complex problems, so that it can jump out of the local optimum under the condition of satisfying the processing constraints and achieve the global search effect. Secondly, the problem whereby the traditional search algorithm converges to the unique optimal solution is solved, and multiple unique optimal solutions that are more suitable for the actual assembly problem are solved. Thirdly, for a variety of restrictions and emergencies that may occur during the assembly process, the assembly sequence flexible planning (ASFP) method is introduced so that each assembly can be flexibly adjusted. Finally, an example is used to verify the feasibility and effectiveness of the method. This method improves the assembly efficiency and the diversity of assembly sequence selection, and can flexibly adjust the assembly sequence, which has important guiding significance for the ASP problem.