Dynamic Planning Research Articles

Evolutionary computation for unmanned aerial vehicle path planning: a survey

Unmanned aerial vehicle (UAV) path planning aims to find the optimal flight path from the start point to the destination point for each aerial vehicle. With the rapid development of UAV technology, UAVs are required to tackle missions in increasingly complex environments. Consequently, UAV path planning encounters more challenges, causing traditional deterministic algorithms to struggle to find the optimal path within a certain time. Evolutionary computation (EC) is a series of nature-inspired methodologies and algorithms, which have shown effectiveness and efficiency in solving many complex optimization problems in real-world applications. Recently, EC algorithms have been effectively applied in UAV path planning and have shown encouraging performance in obtaining high-quality solutions. Therefore, it is crucial to review the related research experience and literature in the field of using EC for UAV path planning. This paper presents a comprehensive survey to showcase the existing studies on EC in UAV path planning, especially in complex environments. The paper first proposes a novel taxonomy to categorize the relevant studies into three different categories according to the complex environmental properties of the application scenarios. These environmental properties include complex search space, complex time control, and complex optimization objectives. Then, the EC algorithms for UAV path planning in these complex environments are further systematically surveyed as constrained search space and large-scale search space in complex search space, dynamic UAV path planning and multi-UAV concurrent path planning in complex time control, and expensive objective and multiple objectives in complex optimization objectives. Finally, some potential future research directions for applying EC algorithms to UAV path planning are presented and discussed.

A framework for integrated resource planning in surgical clinics

The problem under study is based on the challenges faced by the Orthopaedic Clinic at St. Olav’s Hospital in Trondheim, Norway. Variations in demand and supply cause fluctuating waiting lists, and it is challenging to level the activities between the clinic’s two units, the outpatient clinic and the operating theater, to obtain short waiting times for all activities. Based on these challenges, we describe and present a planning problem referred to as the Long-term Master Scheduling Problem (LMSP), where the objective is to construct an integrated Long-term Master Schedule (LMS) that facilitates short waiting times in both units. The LMS can be separated into two schedules, one cyclic high-level schedule, and one non-cyclic low-level schedule. The demand for outpatient clinic consultations and surgeries is stochastic, as are the waiting lists. To account for this, we propose a planning framework consisting of an optimization model to solve the LMSP, and a two-level planning procedure. In the planning procedure, we first solve the LMSP to construct the LMS for the upcoming planning horizon. Then, to adjust to the fluctuating waiting lists, we periodically refine the low-level schedule by solving a constrained LMSP. We also develop a simulation-based evaluation procedure to evaluate the planning framework in a real-life setting and use this to investigate different planning strategies. We find that imposing flexible, dynamic and agile planning strategies improve waiting time outcomes and patient throughput. Furthermore, combining the strategies yields additive improvements.

Research on collaborative management technology of deep multi-coal seam mining and pre-reclamation in coal-grain composite area

Subterranean coal mining results in the occurrence of surface subsidence, soil degradation, and damage to agricultural lands, which is especially severe in regions with high groundwater levels. The collaborative reclamation scheme combining surface pre-reclamation and underground mining layout optimization can effectively control the problem of damage to farmlands. Hence, it is imperative to study the collaborative management technology. The research area for this study is Jiulishan Mine, located in coal-grain composite areas with a high groundwater table. Initially, a combined UAV and USV measuring system was utilized to acquire spatial data of the proposed reclamation area. Furthermore, we examined the evolution features of the subsidence ponding related to various mining methods using FLAC3D numerical modeling. Ultimately, the ArcGIS spatial analysis module was utilized to model and calculate the reclamation volume and rate for each plan. Our findings indicate that: (1) The extent of damage caused by shallow coal seam mining includes an area of 50.67 hm2 for farmland damaged area (FDA), 31.49 hm2 for seasonal subsidence ponding (SSP), and 23.51 hm2 for perennial subsidence ponding (PSP). Additionally, the boundary subsidence values for each damaged area are 0.8 m, 1.6 m, and 2.8 m, respectively. (2) Through the optimization of deep coal seam mining designs, the spatial configuration of the subsidence basin is successfully altered, creating the necessary condition for the development of a dynamic pre-reclamation plan. (3) The reclamation rate of the optimized pre-reclamation (PR) scheme is significantly higher, ranging from 40 % to 44 %, compared to the reclamation rate of 24 % of the traditional reclamation (TR). The choice of a suitable land reclamation and ecological restoration strategy was determined by optimizing mining plans and considering the characteristics of the subsidence area. This offers empirical evidence and specialized assistance for the ecological rehabilitation of coal-grain composite areas with high groundwater levels.

Online dual robot–human collaboration trajectory generation by convex optimization

For dynamic collision-free trajectory planning in dual-robot and human collaborative tasks, this paper develops an online dual-robot Mutual Collision Avoidance (MCA) scheme based on convex optimization. A novel convex optimization formulation model, named Disciplined Convex programming by Shifting reference paths (DCS), is proposed for solving the single-robot trajectory optimization problem. Furthermore, a new dual-robot trajectory convex optimization algorithm is presented for online adjustment of the dual-robot trajectories according to the collaborative task priority. The overall pipeline, named DCS-MCA, generates collision-free and time-optimal dual-robot trajectories, while prioritizing the task accessibility of the high-priority robot. Simulation experiments demonstrate that DCS exhibits comparable performance to the current state-of-the-art single-robot motion planner, while the DCS-MCA outperforms common algorithms by up to 30% in time optimality for dual-robot collaborative tasks. The feasibility and dynamic performance of the proposed approach are further validated in a real collaborative cell, illustrating its suitability for collaborative dual-robot tasks in moderately dynamic environments.

Multi-level traffic-responsive tilt camera surveillance through predictive correlated online learning

In urban traffic management, the primary challenge of dynamically and efficiently monitoring traffic conditions is compounded by the insufficient utilization of thousands of surveillance cameras along the intelligent transportation system. This paper introduces the multi-level Traffic-responsive Tilt Camera surveillance system (TTC-X), a novel framework designed for dynamic and efficient monitoring and management of traffic in urban networks. By leveraging widely deployed pan–tilt-cameras (PTCs), TTC-X overcomes the limitations of a fixed field of view in traditional surveillance systems by providing mobilized and 360-degree coverage. The innovation of TTC-X lies in the integration of advanced machine learning modules, including a detector–predictor–controller structure, with a novel Predictive Correlated Online Learning (PiCOL) methodology and the Spatial–Temporal Graph Predictor (STGP) for real-time traffic estimation and PTC control. The TTC-X is tested and evaluated under three experimental scenarios (e.g., maximum traffic flow capture, dynamic route planning, traffic state estimation) based on a simulation environment calibrated using real-world traffic data in Brooklyn, New York. The experimental results showed that TTC-X captured over 60% total number of vehicles at the network level, dynamically adjusted its route recommendation in reaction to unexpected full-lane closure events, and reconstructed link-level traffic states with best MAE less than 1.25 vehicle/hour. Demonstrating scalability, cost-efficiency, and adaptability, TTC-X emerges as a powerful solution for urban traffic management in both cyber–physical and real-world environments.

Research and application of the flatness target curve discrete dynamic programming based on two-dimensional decision making

The flatness target curve (FTC) is a fundamental process model in cold rolling flatness control, which directly affects the quality control effect of the strip. The accurate and efficient setting of the flatness target curve is the key to ensuring continuous rolling and is a common problem faced by various production companies. This paper establishes a discrete dynamic planning (DDP) setting method for FTC with two-dimensional decision-making. To quantitatively analyze the roles of the coefficients in the FTC, the preprocessing process is established using standardized processing methods and statistical analysis methods. The flatness target curve discrete dynamic programming (FTC-DDP) is based on Bellman optimization theory with a dual structure of local and system decision-making. The decision-making functions are established based on the effect of each coefficient on FTC geometric characteristics. The concepts of “curvature factor” and “wedge factor” are innovatively proposed to obtain the decision-making value functions. The regression analysis establishes the solution equations of even number coefficients, odd number coefficients, and gain coefficients, respectively. Roll bending and tilting roll analyze the FTC’s ability to correct flatness defects. Meanwhile, utilizing a 1450 mm five-stand six-roll cold rolling mill, it is verified that FTC-DDP can quickly and accurately set the FTC, which provides an effective solution for obtaining high-precision and high-quality cold-rolled strips. Application results show that FTC-DDP can reduce the difficulty of setting up FTC by more than 70 %, reduce the setup time to milliseconds, and improve the setup accuracy by more than 50 %.

Selection and Promotion of Rural Revitalization Path Mode Incorporating Fuzzy Dynamic Planning

Rural revitalization refers to efforts aimed at renewing and invigorating rural communities to enhance their economic, social, and environmental well-being. To promote the appropriate development in rural revitalization it is necessary to select and promote the appropriate scheme with effective planning, this paper presented effective rural revitalization strategies with the advanced methodology of Genetic Ant Swarm Fuzzy (GAsF), to assess and optimize interventions across various dimensions. The constructed GAsF model implementation of the genetic algorithm-based ant swarm optimization the key features related to the revitalization are estimated. The estimated features are categorized with the fuzzy dynamic planning model for the classification of the program in rural revitalization with the path model implementation in the fuzzy dynamic planning system. Through the computed features classification is performed for the estimation of the features in the programs. Results stated that strategies such as the "Ecotourism Development Strategy" and the "Community Empowerment Program" as particularly promising, demonstrating strong performance in economic, social, and environmental dimensions. Simulation analysis demonstrated that the propose GAsF model achieved the classification accuracy of 0.98 with significant green development values. Through the fuzzy dynamic modelling the GAsF optimize the best fitness value for the ecotourism development value of 0.93.

Adaptive virtual team planning and coordination: a mathematical programming approach

Purpose The rise of remote work increasingly requires organizations to coordinate a single large, consolidated talent pool into ad-hoc, short-term project teams on demand. This problem involves many simultaneous considerations including project revenues and rejection costs, conflicting projects and roles, worker assignment costs, worker utilization preferences and limits, worker reassignment costs, and arbitrary role start and end times. Moreover, plans must be continuously updated in response to changing circumstances. This paper addresses the problem of dynamic virtual team planning and coordination. Design/methodology/approach We show this problem is NP-hard and provide a dynamic mixed integer linear programming (MILP) formulation for both optimal initial plan generation as well as continuous plan adjustment and re-optimization. We utilized a factorial experiment design to generate benchmark problems spanning a wide range of characteristics and conducted extensive computational experimentation using a common MILP solver. Findings Exactly optimal solutions to large, realistically sized problems were consistently obtained in short amounts of time. All observed solution times were sufficient to support the operational decision-making requirements of real-world virtual team coordination, demonstrating the viability of this approach. Practical implications The approach developed in this research can enable organizations to optimally coordinate virtual teams on a large scale and continually adjust plans in response to changing circumstances, all in an automated manner. Originality/value This paper addresses a new and complex problem of increasing importance to organizations due to the rise in remote work. We provide a problem formulation and exact approach for optimally solving both the planning and re-planning aspects of this problem.

A Sustainable Dynamic Capacity Estimation Method Based on Bike-Sharing E-Fences

Increasing urban traffic congestion and environmental pollution have led to the embrace of bike-sharing for its low-carbon convenience. This study enhances the operational efficiency and environmental benefits of bike-sharing systems by optimizing electronic fences (e-fences). Using bike-sharing order data from Shenzhen, China, a data-driven multi-objective optimization approach is proposed to design the sustainable dynamic capacity of e-fences. A dynamic planning model, solved with an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), adjusts e-fence capacities to match fluctuating user demand, optimizing resource utilization. The results show that an initial placement of 20 bicycles per e-fence provided a balance between cost efficiency and user convenience, with the enterprise cost being approximately 76,000 CNY and an extra walking distance for users of 15.1 m. The optimal number of e-fence sites was determined to be 40 based on the solution algorithm constructed in the study. These sites are strategically located in high-demand areas, such as residential zones, commercial districts, educational institutions, subway stations, and parks. This strategic placement enhances urban mobility and reduces disorderly parking.

Dynamic path planning of autonomous bulldozers using activity-value-optimised bio-inspired neural networks and adaptive cell decomposition

Autonomous bulldozers encounter critical challenges in dynamic path planning in complex construction environments such as dynamic obstacles and narrow passages. Bio-inspired neural networks (BINN) are commonly employed for real-time path planning in dynamic environments. However, the algorithm is not feasible enough to calculate the positive input of neighbour cells, thus resulting in unreasonable distribution of activity values in the obstacle-dense regions. Additionally, the use of regular grids results in excessive computation. These drawbacks cause the algorithm to plan irrational paths and reduce computational efficiency. This study proposed an activity-value-optimised BINN and adaptive cell decomposition for the dynamic path planning of autonomous bulldozers. First, an adaptive cell decomposition method was proposed to model a highly intricate and constantly evolving environment, resulting in a reduction in the computational workload of the BINN. Second, the calculation of the activity value is optimised by enhancing the shunt equation of the BINN, which considers both obstacles and grid levels. This improvement enabled more reasonable distribution results. The smoothness of dynamic path planning is refined by incorporating the turning radius, which can be more effectively integrated with local path planning. Compared with the original BINN algorithm and other baseline algorithms, a practical large-scale hydropower project application demonstrated that the proposed method can effectively reduce the number of computational iterations, enrich the travel direction, and improve path quality while ensuring dynamic obstacle avoidance.

From few large to many small investments: lessons for adaptive irrigation development in an uncertain world

Conventional approaches to irrigation development involve large lumpsum investments in big infrastructure that cannot adapt to changing climate and socio-economic conditions. There is an urgent need for alternative ways of investing in smallholder irrigation in Sub-Saharan Africa (SSA) that are adaptive and avoid capital lock-in. Adaptive Investment Pathways (AdIP), inspired by the Dynamic Adaptive Policy Pathways (DAPP) concept, proposes stepwise investments to support smallholder irrigation development. AdIP builds resilience to future shocks through dynamic and flexible investment plans instead of investing in single static solutions. To develop an empirical grounding for operationalizing AdIP, we draw lessons from three case studies representing different stages of irrigation development along shallow sand river aquifers in Kenya and Zimbabwe. We retrospectively analyse the nature of investments at farm and landscape scales, and the type of risks and opportunities that farmers respond to. We find that in face of risks, farmers diversify their livelihoods, make small investments incrementally especially in response to opportunities and risks created by external triggers, and pause or reorient activity when they reach saturation points, i.e., biophysical or socio-political limits to their development objective, here irrigation development. Governments and external agencies can support smallholder irrigation development in SSA through targeted landscape scale investments that address saturation points faced by smallholders. This requires a robust participatory monitoring framework to identify and respond to saturation points, and a re-thinking of financing mechanisms which do not measure progress against a fixed schedule of investments, but instead measure continuous progress towards the development objective.

Interval many‐objective dynamic charging planning in wireless rechargeable sensor networks

SummaryCharging path planning in wireless sensor networks (WSNs) refers to designing an efficient charging path for sensor nodes in the network. However, most charging schemes mainly consider the planning of charging paths and pay little attention to the impact of uncertainties, such as road conditions and environment on the planning of charging paths, as well as ignoring the charging problem of new nodes in need of charging. Road conditions and the environment directly affect the energy consumption of wireless charging vehicles (WCVs) during traveling. To address the aforementioned challenges, this article proposes an interval many‐objective charging path scheme model, the WCV consumption is an uncertain value, it will change according to the environment, and road conditions, so we represent it as an interval parameter with upper and lower bounds. An interval high‐dimensional multi‐objective model with target energy consumption, path distance, number of dead nodes, and communication delay is constructed. Second, to implement this model, an interval SPEA2 algorithm (I‐SPEA2) that introduces an environmental response mechanism is proposed. I‐SPEA2 treats individual target interval values as ranges of values on a two‐dimensional coordinate axis, forming a quadrilateral, calculates individual size probabilities based on the area to determine the dominant relationship, and combines fixed distance and interval overlap to eliminate redundant individuals. The simulation results show that the interval dynamic model is effective in prolonging the lifecycle of WSN as well as the proposed algorithm reduces the mortality rate of the nodes by 15%, 28%, 13%, 16%, and 21% compared with other algorithms.

Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability.

Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures. By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability. The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation. The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific. This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

A Review of Autonomous Berthing Technology for Ships

Autonomous berthing technology is a crucial engineering control problem within the ship intelligence system, encompassing a series of complex operations and technologies. Firstly, this paper analyses the research on autonomous berthing technology from a bibliometric point of view in order to obtain an overview of its past and present development and to outline the importance of this technology. Secondly, a literature review is conducted on each of the four aspects of autonomous berthing technology, namely sensing technology, berthing type, control method, and evaluation method, which can help to quickly understand the main aspects of this technology. Thirdly, the ship-assisting technologies needed to achieve autonomous berthing are discussed and analysed from six aspects: dynamic collision avoidance, path planning, path tracking, heading control, tug assistance, and shore-based systems. Finally, the challenges faced by the ship autonomous berthing technology on the way of development are summarised, and future development is projected. This paper aims to provide a more comprehensive perspective for analysing and researching ship autonomous berthing technology.

Optimal time reuse strategy-based dynamic multi-AGV path planning method

The window strategy, known for its flexibility and efficiency, is extensively used in dynamic path planning. To further enhance the performance of the Automated Guided Vehicles (AGVs) sorting system, the two processes of AGV movement and path planning can be executed concurrently based on the window strategy. Nonetheless, difficulties in matching the computing time of the planning server with the moving time of AGVs may cause delays or reduced path optimality. To address the problem, this paper proposes an optimal time reuse strategy. The proposed solution controls computing time by managing path length for each planning instance, ensuring alignment with the moving time of AGVs to maximize path optimality and avoid delays. To achieve this, two aspects need to be considered. Firstly, on a systemic level, we control the entry rate of AGVs by adjusting the replanning period, thus avoiding congestion caused by excessive AGVs and maintaining high system efficiency. Secondly, we reversely control the computing time by adjusting the path length that needs to be planned for each single planning, so that it matches the moving time of AGVs. Simulation results show that our method outperforms existing top-performing methods, achieving task completion rates 1.64, 1.57, and 1.12 times faster across various map sizes. This indicates its effectiveness in synchronizing planning and movement times. The method contributes significantly to dynamic path planning methodologies, offering a novel approach to time management in AGV systems.

Bulk water services delivery challenges in the Dr. Ruth Segomotsi Mompati district municipality in South Africa's North West Province

AbstractThis study thoroughly examines the difficulties in providing bulk water services in Dr. Ruth Segomotsi Mompati district municipality in the North West Province, with the end goal of giving comprehensive suggestions for operational and strategic improvements. The study adopted a qualitative research approach, employing semistructured interviews for gathering data from officials of various municipalities responsible for overseeing infrastructure within the geographical confines of the municipalities they serve. The findings identified themes indicative of the focus areas contributing to the bulk water services delivery challenges. These emphasized the importance of fostering a collaborative work environment, thus promoting organizational learning, and implementing effective change management to elevate service delivery standards. It identifies the necessity for heightened operational efficiency and advocates for a cultural shift within municipal operations. Strategic planning, marked by the creation and regular refinement of master plans, is highlighted alongside the implementation of robust project management practices. The study emphasizes the critical need for synchronizing municipal planning processes with statutory requirements and the changing demands of the local community. The research advocates for a flexible and responsive approach to master planning, which is indispensable for adapting to shifts in administrative frameworks and community needs. The study recommends the development of a dynamic master plan for water services, one that is regularly reviewed and updated to reflect the evolving legislative landscape and community priorities. It also suggests that municipal authorities should ensure that the planning process remains inclusive and responsive to the residents' needs. Furthermore, training and capacity‐building initiatives for municipal officials could be instrumental in enhancing their ability to adapt to these changing demands and legislative environments.

A self-adaptive agent for flexible posture planning in robotic milling system

In recent years, industrial robots are widely used in the milling processes of the fundamental components such as aeronautic parts, aerospace cabins and blades on the marine propellers of the worships, because of their flexible structure, large operating space, and robust capacity of rapid reconfiguration. Along with the robotic machining process, multiple types of geometrical and physical constraints display the time-varying characteristics, which are difficult to be considered simultaneously by the traditional static planning methods on the robotic milling posture. Therefore, a deep reinforcement learning (DRL) enhanced virtual repulsive potential field flexible model is proposed for the robotic milling posture dynamic planning. The preliminary planning of static milling posture is realized based on the fixed setting of the virtual modal parameters in the posture planning model, which is treated as a theory preparation for training the deep reinforcement learning agent. To develop the static planning model, multiple constraints are established by considering the performances of geometric, kinematic and machining precision simultaneously. Furthermore, a reward function is constructed for the dynamic agent, comprehensively combining the long and short reward benefits in nonlinear correlation with virtual repulsive forces. By taking the virtual modal parameters as the action space of the dynamic agent, a DRL enhanced agent VRPF-SAC is created and trained under the dynamic milling environment from virtual repulsive planning field. Four tasks of large size robotic milling simulations and experiments are designed and developed to validate the effectiveness of the flexible planning model. In comparison with the static model, the flexible planning model displays the excellent global effects covering the kinematic, and precision performance, with 65.78 %, 72.04 %, and 70.25 % reduction in velocity, acceleration, and jerk of robotic joints, and ensuring the effective precision performance from −0.365 to 0.283 mm of large size workpiece with large curvature changes. The proposed flexible posture planning architecture can provide a basic for robotic dynamic milling of core parts with large size, narrow space and sharp curvatures of the aeronautic parts, aerospace cabins and worship propellers.

Freeway merging trajectory prediction for automated vehicles using naturalistic driving data

Due to differences in speed and the complex interaction between merging and through-lane vehicles at freeway merging sections, crashes involving both human drivers and automated vehicles (AVs) persist. To assist AVs to predict the intentions of surrounding vehicles for further dynamic motion planning, researchers have focused on developing trajectory prediction algorithms. Few studies, however, have developed merging trajectory prediction models using naturalistic driving data in China, making it urgent to put it on the agenda for AVs’ safety and efficiency at freeway merging sections. Based on merging periods extracted from the Shanghai Naturalistic Driving Study (SH-NDS), this study compares merging behavior on freeways with through-lane speed limits of 80 km/h, 100 km/h, and 120 km/h using analysis of variance (ANOVA). Merging trajectory prediction algorithms for these three speed limit cases are trained and tested using backpropagation neural network (BPNN) and long short-term memory neural network (LSTM NN) approaches. Results show 1) significant differences among the three cases in all merging behavior variables except for longitudinal gap; and that 2) the BPNN algorithm for merging trajectory prediction demonstrates superior performance compared to the LSTM NN. Two major contributions to the safe operation of AVs are provided: 1) the developed algorithms can be integrated into AV systems to accurately predict real-time desired trajectories of nearby merging vehicles in uncongested traffic conditions, and assist ongoing motion planning strategies for AVs; and 2) the algorithms can be incorporated in simulation tests for AV safety evaluation involving freeway merging sections.

CDM-MPC: An Integrated Dynamic Planning and Control Framework for Bipedal Robots Jumping

CDM-MPC: An Integrated Dynamic Planning and Control Framework for Bipedal Robots Jumping

Intelligent planning of fire evacuation routes in buildings based on improved adaptive ant colony algorithm

Intelligent planning of fire evacuation routes is an important guarantee for rapid emergency response. Ant colony optimization, as an intelligent bionic algorithm, has notable advantages in route planning. However, traditional ant colony optimization corresponds to a low convergence rate, is easily caught in local optimal solution, and regards route length as the only constraint. To resolve these problems, an improved adaptive ant colony optimization (IAACO) algorithm was proposed in this study. Risk, energy consumption, and route length were taken as key factors to improve the heuristic function, optimize the pheromone update function, and establish multi-objective constraints, The standards for fire evacuation better align with practical requirements. Meanwhile, the adaptive pheromone volatile coefficient was introduced to balance convergence and global searching ability. In addition, the hazard range on the grid map was visualized. The results indicate that under various complex obstacle grid maps, the path inferiority of IAACO is reduced by 61.7% and 58.4%, 43.6% and 36.7%, and 41.6% and 67.7% compared to ACO and IACO, respectively; under the condition of multiple exits, the inferiority is reduced by 63.8% and 54.6%; under the condition of multiple fire sources, the inferiority is reduced by 40.1% and 34.6%; compared with other algorithms, IAACO shows the lowest path inferiority index, 26.6. IAACO is applicable to both the dynamic planning of fire evacuation routes and the evacuation simulation software, Pathfinder, and it performs better than the built-in algorithms of Pathfinder. Facts have proved that the IAACO algorithm significantly improves the safety level of evacuation compared to traditional evacuation methods and other optimization algorithms.