Sequential Planning Research Articles

Collision Avoidance Path Planning for Automated Vehicles Using Prediction Information and Artificial Potential Field

With the advancement of autonomous driving systems, the need for effective emergency avoidance path planning has become increasingly important. To enhance safety, the predicted paths of surrounding vehicles anticipate risks and incorporate them into avoidance strategies, enabling more efficient and stable driving. Although the artificial potential field (APF) method is commonly employed for path planning due to its simplicity and effectiveness, it can suffer from the local minimum problem when using gradient descent, causing the vehicle to become stuck before reaching the target. To address this issue and improve the efficiency and stability of path planning, this study proposes integrating prediction data into the APF and optimizing the control points of the quintic Bézier curve using sequential quadratic planning. The validity of the proposed method was confirmed through simulation using IPG CarMaker 12.0.1 and MATLAB/Simulink 2022b.

Changes in the sensory regions of the brain in patients with multiple sclerosis after complex neurorehabilitation according to resting functional magnetic resonance imaging

BACKGROUND: Multiple sclerosis is the one of leading causes of non-traumatic disability in young adult patients. An in-depth understanding of the processes of neuroplasticity underlying rehabilitation measures will ensure full and effective recovery of patients with this disease. AIM: To evaluate changes in the brain connectome in patients with multiple sclerosis in response to complex rehabilitation. MATERIALS AND METHODS: A prospective cohort study included 20 patients with relapsing-remitting multiple sclerosis (EDSS 1.5–6.5) in remission. All patients underwent comprehensive inpatient neurorehabilitation in a volume corresponding to individual rehabilitation needs for 5 weeks. To assess changes in the connectome, resting-state functional magnetic resonance imaging (rs-fMRI) was performed at three points: before the start of rehabilitation, immediately after its completion, and one month after discharge from the hospital. Statistical analysis is carried out using the CONN 7 (based on MathLab). Clinical neurological examination included examination using functional tests, passing questionnaires, and determining scores on the EDSS scale before and after rehabilitation. RESULTS: A total of 20 patients were examined, 13 of them at three control points. According to rs-fMRI data, clusters of decreased connectivity were identified between the left parahippocampal gyrus and the lateral cortex of the right occipital lobe, and between the right parahippocampal gyrus and the precuneus (p-FWE, p-FDR of cluster size and mass 0.05). Clusters of increased connectivity were determined between the left inferior temporal gyrus and the lateral occipital cortex of the left hemisphere, between the left middle temporal gyrus and the right frontal field, between the pole of the left temporal lobe and the lateral cortex of the left hemisphere (p-FWE, p-FDR of cluster size and mass 0.05). Other clusters of sufficient size demonstrated borderline statistical significance (individual adjusted p values for cluster size and mass exceeded 0.05). CONCLUSION: The identified changes indicate a functional reorganization of brain structures responsible for the perception of complex visual information, the functioning of executive control systems, as well as the implementation of memory and sequential action planning.

HYBRID PLANNING A SIMPLE TECHNIQUE FOR CRANIOSPINAL IRRADIATION (CSI)

Purpose: A simple planning technique for craniospinal irradiation using Eclipse treatment planning system. Material and methods: In RT treatment planning, base plan optimization feature is used in sequential RT planning by anticipating the dose delivered to organ at risk (OAR) & planning target volume (PTV) in the base plan. In hybrid planning technique, the whole PTV is divided into two parts, Brain PTV & Spine PTV. Spine PTV is overlapped minimum 2-3 cm over the Brain PTV at their junction while creating structures for RT planning. Brain PTV is planned with conventional RT technique & the Spine PTV planned with rapid arc technique (VMAT). In the plan optimization process of spine PTV, first base plan is selected then the optimization parameter is set accordingly as per total prescribed dose of 36 Gy for both the PTVs. The base plan is the dose calculated plan which is incorporated in the rapid arc plan optimization hence the pre-existed doses of Brain PTV & OAR help to understand the plannerfor achieving desired planning objectives during Spine PTV, RT plan optimization. Finally for the dose calculation of Spine PTV RT plan, planner has to copy fields of Brain PTV & paste it in Spine PTV plan and need to calculate dose for these copied fields. The final plan contains a dose calculated of Brain PTV fields and Spine PTV fields termed as hybrid plan. Result: Reduction in the total number of monitor units is observed for the hybrid plan compared to single VMAT plan, which is statistically significant (p<0.05). Conclusion: Present study introduced the hybrid planning technique which can exploit benefits of conventional and modern techniques.

A differentiable dynamic modeling approach to integrated motion planning and actuator physical design for mobile manipulators

AbstractThis paper investigates the differentiable dynamic modeling of mobile manipulators to facilitate efficient motion planning and physical design of actuators, where the actuator design is parameterized by physically meaningful motor geometry parameters. The proposed differentiable modeling comprises two major components. First, the dynamic model of the mobile manipulator is derived, which differs from the state‐of‐the‐art in two aspects: (1) the model parameters, including magnetic flux, link mass, inertia, and center‐of‐mass, are represented as analytical functions of actuator design parameters; (2) the dynamic coupling between the base and the manipulator is captured. Second, the state and control constraints, such as maximum angular velocity and torque capacity, are established as analytical functions of actuator design parameters. This paper further showcases two typical use cases of the proposed differentiable modeling work: integrated locomotion and manipulation planning; simultaneous actuator design and motion planning. Numerical experiments demonstrate the effectiveness of differentiable modeling. That is, for motion planning, it can effectively reduce computation time as well as result in shorter task completion time and lower energy consumption, compared with an established sequential motion planning approach. Furthermore, actuator design and motion planning can be jointly optimized toward higher performance.

Strategic workforce and project planning for engineering automotive production systems: tackling the transition to electric vehicles

Car manufacturers are electrifying their product portfolios as a reaction to restrictions imposed on car emissions. This leads to additional product introductions of battery electric vehicles to the existing portfolio of internal combustion engine vehicles. For each introduction, car manufacturers must engineer the corresponding production systems. The engineering of these production systems is executed as projects that consist of designing, building, installing, and testing, requiring different types of engineers. Car manufacturers deploy an engineering workforce and outsource work to engineering service providers. In practice, workforce planning, project planning, and outsourcing are performed sequentially using spreadsheets. However, portfolio electrification increases the complexity of planning, calling for more advanced methods. We formulate an integrated workforce and project planning problem (mixed-integer programme) to minimise outsourcing and perform an experimental study using realistic data representing different prevalent strategies for portfolio electrification. Our approach significantly reduces outsourcing and increases workforce utilisation compared to current industry practice for a wide range of workforce sizes. Furthermore, we investigate the benefit of partially aligning the current practice with our integrated approach. Optimising outsourcing alone while maintaining the sequential planning process reaps 70% to 80% of the potential benefits, and integrating workforce or project planning achieves up to 90%.

Equity-Transformer: Solving NP-Hard Min-Max Routing Problems as Sequential Generation with Equity Context

Min-max routing problems aim to minimize the maximum tour length among multiple agents as they collaboratively visit all cities, i.e., the completion time. These problems include impactful real-world applications but are known as NP-hard. Existing methods are facing challenges, particularly in large-scale problems that require the coordination of numerous agents to cover thousands of cities. This paper proposes Equity-Transformer to solve large-scale min-max routing problems. First, we model min-max routing problems into sequential planning, reducing the complexity and enabling the use of a powerful Transformer architecture. Second, we propose key inductive biases that ensure equitable workload distribution among agents. The effectiveness of Equity-Transformer is demonstrated through its superior performance in two representative min-max routing tasks: the min-max multi-agent traveling salesman problem (min-max mTSP) and the min-max multi-agent pick-up and delivery problem (min-max mPDP). Notably, our method achieves significant reductions of runtime, approximately 335 times, and cost values of about 53% compared to a competitive heuristic (LKH3) in the case of 100 vehicles with 1,000 cities of mTSP. We provide reproducible source code: https://github.com/kaist-silab/equity-transformer.

A multi-stage anticipated surprise model with dynamic expectation for economic decision-making

There are many modeling works that aim to explain people’s behaviors that violate classical economic theories. However, these models often do not take into full account the multi-stage nature of real-life problems and people’s tendency in solving complicated problems sequentially. In this work, we propose a descriptive decision-making model for multi-stage problems with perceived post-decision information. In the model, decisions are chosen based on an entity which we call the ‘anticipated surprise’. The reference point is determined by the expected value of the possible outcomes, which we assume to be dynamically changing during the mental simulation of a sequence of events. We illustrate how our formalism can help us understand prominent economic paradoxes and gambling behaviors that involve multi-stage or sequential planning. We also discuss how neuroscience findings, like prediction error signals and introspective neuronal replay, as well as psychological theories like affective forecasting, are related to the features in our model. This provides hints for future experiments to investigate the role of these entities in decision-making.

A human-cyber-physical system enabled sequential disassembly planning approach for a human-robot collaboration cell in Industry 5.0

Industry 5.0 characterized by human-centricity and sustainability has sparked a new wave of the industrial revolution. Human-cyber-physical system is the cornerstone of human-centric manufacturing while remanufacturing has gained considerable attention in fostering sustainability. Disassembly planning is a vital but challenging task in remanufacturing. In order to accommodate the frequent changes resulting from mass personalization, a human-robot collaboration cell is valued for its ability to achieve flexible disassembly. However, recent studies treated disassembly sequence and task allocation as independent aspects. And tasks are allocated by predefined metrics and thresholds without considering the ergonomics of operators. This paper proposes a human-cyber-physical framework for the human-robot collaboration cell by illustrating human-in-the-loop and human-on-the-loop paradigms. A multi-objective sequential disassembly planning model is presented by simultaneously considering disassembly sequence and task allocation. The task complexity and operator ergonomics are evaluated based on cloud models to handle the fuzziness and randomness in human cognition. Moreover, an improved multi-objective hybrid grey wolf optimization algorithm is proposed to obtain Pareto optimal disassembly plans. A case study on the disassembly planning of a control box is presented to illustrate the feasibility and practicability of the proposed approach. The results underscore the superiority of the proposed approach in handling rigorous constraints inherent in the sequential disassembly planning model, which acquires more convergent, diverse, and uniform disassembly plans along the Pareto front.

Sequential action-based dynamic decision-support model for urban ecological planning

Decision-makers often encounter the challenge of efficient long-term ecological planning to consider economic and environmental trade-offs. This challenge becomes particularly complex while addressing dynamic ecological responses to ongoing urban development. Thus, we developed a sequential action-based dynamic decision-making model to simulate sequential action mitigation measures over time, while considering the dynamic ecological responses that emerge with urban development. We used a multi-objective optimization algorithm to identify optimal plans that maximize landscape connectivity and edge density, while minimizing the implementation costs per action. We focused on utilizing ecological corridors and habitat creation as mitigation measures from 1980s to 2010s. The results show that the initial low-investment strategy adopted in 1980s and sequential planning led to significantly improved ecological benefits beyond the 2000s when compared to the single-action planning approach implemented in 2010s. In contrast, an initial high-cost investment in the 1980s gradual declined the ecological benefits starting from 1995. We have demonstrated that adopting sequential planning provides valuable insights into effective urban ecological planning, especially when analyzing time-series data. This sequential approach enables decision-makers to make informed choices, thereby ultimately facilitating sustainable urban and regional planning.

Minimum Cost Flow-Based Integrated Model for Electric Vehicle and Crew Scheduling

Vehicle and crew scheduling is vital in public transit planning. Conventionally, the issues are handled sequentially as the vehicle scheduling problem (VSP) and crew scheduling problem (CSP). However, integrating these planning steps offers additional flexibility, resulting in improved efficiency compared with sequential planning. Given the ever-growing market share of electric buses, this paper introduces a new model for integrated electric VSP and CSP, called EVCSPM. This model employs the minimum cost flow formulations for electric VSP, set partitioning for CSP, and linking constraints. Due to the nonlinear integer property of EVCSPM, we propose a method that hybrids a matching-based heuristic and integer linear programming solver, GUROBI. The numerical results demonstrate the efficiency of our methodology, and the integrated model outperforms the sequential model in real-life scenarios.

Dynamics between warehouse operations and vehicle routing

When scheduling the distribution of ordered items from a warehouse to customers, the transportation planning is generally done first and serves as input for planning warehouse operations. Such a sequential approach can lead to substantial inefficiencies when the customer deliveries are restricted by time windows, and the warehouse has limited resources available (both order pickers and space in the staging area). This paper studies the trade‐offs between warehouse operations and transportation planning. The goal is to understand the impact of three specific managerial interventions: adopting an integrated planning approach, expanding the available staging space, and expanding the delivery time windows. To this end, we propose a mathematical model for a general vehicle routing problem that incorporates order batching, order picker scheduling, staging, and vehicle loading. We introduce a novel idea to express the picking time of an order batch as a function of the batch size and develop a metaheuristic to solve this integrated problem. Furthermore, we develop exact algorithms to provide optimal solutions for the individual warehouse and transportation problems in a sequential planning approach. Managerial insights are distilled from case studies in two warehouses, one for ambient products and the other for refrigerated products, of a leading grocery retailer in the Netherlands. Our results show that integrated planning outperforms the other managerial interventions and generates cost savings between 9% and 11%. Savings are generally realized by executing larger order batch sizes to be picked in the warehouses at the expense of additional routing cost (around 2–3%). The second intervention in the form of time window expansions of only 15 minutes for customer deliveries can lead to cost savings between 4% and 6%, which results from a reduction in both transportation and warehousing cost. Expanding the capacity of the staging area is only meaningful when the staging space is highly utilized and only results in cost savings for the warehouse operations.

Apple recognition and picking sequence planning for harvesting robot in a complex environment

In order to improve the efficiency of robots picking apples in challenging orchard environments, a method for precisely detecting apples and planning the picking sequence is proposed. Firstly, the EfficientFormer network serves as the foundation for YOLOV5, which uses the EF-YOLOV5s network to locate apples in difficult situations. Meanwhile, the soft non-maximum suppression algorithm is adopted to achieve accurate identification of overlapping apples. Secondly, the adjacently identified apples are automatically divided into different picking clusters by the improved density-based spatial clustering of applications with noise. Finally, the order of apple harvest is determined to guide the robot to complete the rapid picking, according to the weight of the Gauss distance weight combined with the significance level. In the experiment, the average precision of this method is 98.84%, which is 4.3% higher than that of YOLOV5s. Meanwhile, the average picking success rate and picking time are 94.8% and 2.86 seconds, respectively. Compared with sequential and random planning, the picking success rate of the proposed method is increased by 6.8% and 13.1%, respectively. The research proves that this method can accurately detect apples in complex environments and improve picking efficiency, which can provide technical support for harvesting robots.

Apple recognition and picking sequence planning for harvesting robot in the complex environment

In order to improve the efficiency of robots picking apples in challenging orchard environments, a method for precisely detecting apples and planning the picking sequence is proposed. Firstly, the EfficientFormer network serves as the foundation for YOLOV5, which uses the EF-YOLOV5s network to locate apples in difficult situations. Meanwhile, the Soft Non-Maximum Suppression (NMS) algorithm is adopted to achieve accurate identification of overlapping apples. Secondly, the adjacently identified apples are automatically divided into different picking clusters by the improved density-based spatial clustering of applications with noise (DBSCAN). Finally, the order of apple harvest is determined to guide the robot to complete the rapid picking, according to the weight of the Gauss distance weight combined with the significance level. In the experiment, the average precision of this method is 98.84%, which is 4.3% higher than that of YOLOV5s. Meanwhile, the average picking success rate and picking time are 94.8% and 2.86 seconds, respectively. Compared with sequential and random planning, the picking success rate of the proposed method is increased by 6.8% and 13.1%, respectively. The research proves that this method can accurately detect apples in complex environments and improve picking efficiency, which can provide technical support for harvesting robots.

On Joint Optimization of UAV-Assisted Covert Communication Systems with NOMA for Hydropower Internet of Things

The intelligent terminals deployed in hydropower IoT can quickly sense the status of hydropower equipment, thus improving the efficiency of system control and operation. However, communication security between the base station and intelligent terminals challenges the IoT hydropower plant. In this paper, we propose a UAV-assisted covert communication system (CCS), where a UAV acts as the base station to provide communication service to ground terminals monitored by malicious users. To improve access effectiveness, we adopt non-orthogonal multiple access (NOMA) for intelligent terminals to access the hydropower IoT. Since two devices can synchronously access the communication system with the NOMA scheme, we select one terminal to receive covert messages and the other to interfere with the malicious users to detect confidential communications. To maximize the covert rate, we formulate the optimization problem that jointly optimizes the transmit power, the altitude of the UAV, and trajectory under the constraints of covertness and the finite length of the transmission message block. Additionally, we transform the optimization problem into a geometric planning one, which is solved by a developed sequential geometric planning (SGP) approximation algorithm. Simulation results show the proposed algorithm can improve the covert rate compared to the traditional methods.

A Systematic Review on Prosthodontic Rehabilitation of Hemimandibulectomy Defects.

Restoration of hemimandibulectomy defects following tumour extirpation to restore oral function is a herculean task for practitioners. Prosthetic treatment alternatives available for rehabilitation of acquired hemimandibulectomy defects according to mandibular reconstruction type and extent (Cantor-Curtis classification) are unclear. This systematic review aims to assess the spectrum of prosthodontic rehabilitation approaches with regard to reconstruction type and extent of mandibular surgical defects.The databases incorporated for literature search were Google Scholar and Medline (PubMed). Relevant search terms for hemimandibulectomyand reconstruction with prosthetic rehabilitation were used. Two reviewers independently assessed the articles using eligibility criteria; published case reports and case series in the English language anddepicting prosthodontic treatment modality of patients greater than 15 yearswere included. A total of 202 records were identified from the database search of which 19 duplicates were removed. The remaining articles were assessed for eligibility, and 55 articles (comprising58 cases) were finally included in the study. This review revealed various prosthetic alternatives ranging from guide flange, twin occlusion, palatal ramp, conventional to hybrid partial and complete dentures to implant-supported prosthesis including a few innovative prosthetic approaches. This systematic review provides a plethora of prosthodontic rehabilitation approaches according to the extent of hemimandibular surgical defect and type of reconstruction. This will facilitate practitioners and prosthodontists in sequential treatment planning and management of hemimandibulectomy cases in their routine practice.

Sequential Bayesian Planning for Accelerated Degradation Tests Considering Sensor Degradation

Most classical accelerated degradation test (ADT) planning models implicitly overlook the errors when measuring the degradation levels of the test units. However, the sensor measurement errors are inevitable and the magnitude of the errors may have a trend to increase over time due to sensor degradation. As a consequence improperly overlooking the sensor degradation in ADT planning could result in a test plan with unsatisfactory performance. This article addresses this issue by proposing a sequential ADT planning model that factors in sensor degradation. The system degradation level is periodically measured, based on which we dynamically adjust the stress level during ADT. We adopt a Bayesian framework that periodically updates the posterior distribution of model parameters considering the sensor degradation. An approximate Bayesian computation algorithm is developed to circumvent the difficulty of directly evaluating the complicated likelihood function in our problem. Numerical studies on a gas turbine reveal that our sequential model outperforms several traditional ADT designs that overlook the sensor degradation.

Distributed spot welding task allocation and sequential planning for multi-station multi-robot coordinate assembly processes

Distributed spot welding task allocation and sequential planning for multi-station multi-robot coordinate assembly processes

Cognitive stimulation of executive functions through computational thinking

The healthy development of cognitive functions, including executive functions, has been shown to depend mainly on the experiences and learning opportunities of people, especially during childhood. Over the past few years, researchers have been studying the impacts of diverse types of interventions on children’s cognitive development in which computational thinking programs are a recent field. This pilot study evaluated the effect of computational thinking training based on the “Programming for Children” program on the executive functions of children aged 10 and 11 years: working memory, inhibition, and planning (N = 30). The results showed that children in the experimental group improved on tests of visuospatial working memory, cognitive inhibition, and sequential planning compared with the control group. However, tests of verbal working memory, memory strategy, and visual spatial planning did not show any observed changes. Although this was an exploratory study, and its findings should be interpreted cautiously due to the small sample size, the findings support the relevance and feasibility of conducting similar larger studies with larger samples.

Multi‐stage stochastic long‐term planning of grid‐connected hydrogen‐based energy system based on improved SDDIP

AbstractHydrogen‐based energy systems (HESs) have shown great potential to promote the process of decarbonization. Conventional studies mainly focus on the sizing and operation of HESs in a determined static situation, and the dynamic planning model of HESs considering large‐scale uncertain scenarios of future developments should be considered. This paper proposes a multi‐stage stochastic programming (MSP) long‐term planning model to find the optimal sequential planning results of the grid‐connected HES. The planning model considers the long‐term uncertainties of the investment cost decrease and the load increase. Additionally, the short‐term uncertainties of renewable energies are also considered to obtain robust results in each stage. The improved stochastic dual dynamic integer programming (SDDIP) is then employed to solve the MSP long‐term planning model with consideration of the realized uncertainties. Specifically, the sequential planning order is developed to improve the efficiency of the SDDIP. Numerical case studies are constructed to show the convergence process of the improved SDDIP and the planning results of the HES. Moreover, the improved SDDIP shows greater efficiency compared with the traditional SDDIP and the method which solves the model directly.

SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment

Navigating robots through large-scale environments while avoiding dynamic obstacles is a crucial challenge in robotics. This study proposes an improved deep deterministic policy gradient (DDPG) path planning algorithm incorporating sequential linear path planning (SLP) to address this challenge. This research aims to enhance the stability and efficiency of traditional DDPG algorithms by utilizing the strengths of SLP and achieving a better balance between stability and real-time performance. Our algorithm generates a series of sub-goals using SLP, based on a quick calculation of the robot's driving path, and then uses DDPG to follow these sub-goals for path planning. The experimental results demonstrate that the proposed SLP-enhanced DDPG path planning algorithm outperforms traditional DDPG algorithms by effectively navigating the robot through large-scale dynamic environments while avoiding obstacles. Specifically, the proposed algorithm improves the success rate by 12.33% compared to the traditional DDPG algorithm and 29.67% compared to the A*+DDPG algorithm in navigating the robot to the goal while avoiding obstacles.