Adaptive Path Research Articles

Adaptive Dynamic Model-Based Path Following Controller Design for an Unmanned Surface Vessel

Abstract The effective design of a path following controller for Unmanned Surface Vessels (USVs) under uncertain influences induced by various factors such as environmental disturbances is a challenging task. In this study, we propose to ful?lling this task through taking benefits from an online parameter identi?cation technique, the discrete-time sliding mode control (DSMC) method, and the improved line of sight (LOS) algorithm. The Particle Swarm Optimization algorithm (PSO) was adopted to provide initial settings for the straightforward online identification method, i.e., the Forgetting Factor Recursive Least Square method (FFRLS). In order to handle the time-varying sideslip angle of a ship that exists in reality due to environmental disturbances, a multi-model course control scheme is proposed to improve the control performance. For this control scheme, a flexible selection mechanism in-between a heading angle or a course angle tracking controller based on the DSMC method is designed. A solution to ?xing the tracking deviation problem of the LOS guidance law is investigated for which the gradient descent method is introduced. A series of experiments are carried out at sea with a USV called Orca to verify and validate the proposed hybrid path following approach. The results showed that tracking errors mainly induced by environmental disturbances existed but the maximum magnitude among them was small enough and remaining within the acceptable range especially from the marine engineering point of view. These results, to a high degree, validated the robustness and precision of the proposed controller.

Learning-based methods for adaptive informative path planning

Adaptive informative path planning (AIPP) is important to many robotics applications, enabling mobile robots to efficiently collect useful data about initially unknown environments. In addition, learning-based methods are increasingly used in robotics to enhance adaptability, versatility, and robustness across diverse and complex tasks. Our survey explores research on applying robotic learning to AIPP, bridging the gap between these two research fields. We begin by providing a unified mathematical problem definition for general AIPP problems. Next, we establish two complementary taxonomies of current work from the perspectives of (i) learning algorithms and (ii) robotic applications. We explore synergies, recent trends, and highlight the benefits of learning-based methods in AIPP frameworks. Finally, we discuss key challenges and promising future directions to enable more generally applicable and robust robotic data-gathering systems through learning. We provide a comprehensive catalog of papers reviewed in our survey, including publicly available repositories, to facilitate future studies in the field.

Adaptive Lifelong Multi-Agent Path Finding With Multiple Priorities

Adaptive Lifelong Multi-Agent Path Finding With Multiple Priorities

Adaptive path tracking control for autonomous vehicles with sideslip effects and unknown input delays

This paper presents an adaptive path-tracking control of autonomous ground vehicles with sideslip effects and time-varying input delays. To handle the problem of unknown time-varying input delays, the vehicle kinematic model is transformed into a novel chain model, and a delay-based auxiliary integral term is introduced for the estimation of the unknown terms caused by time-varying delays. To avoid modeling errors caused by existing linear approximation methods, a new adaptive path-tracking control strategy is proposed, such that the optimal tracking performance is achieved by appropriately adjusting certain design parameters. In addition, the inherent complexity explosion issue is avoided with the aid of a boundary estimation strategy. All signals of the closed-loop systems are guaranteed to be bounded. Simulation results illustrate the effectiveness of the method proposed.

Multi-orientation adaptive slice and path generation based on support constraint and relationship matrix

PurposeThe step effect and support structure generated by the manufacturing process of fused deposition molding parts increase the consumables cost and decrease the printing quality. Multiorientation printing helps improve the surface quality of parts and reduce support, but path interference exists between the printing layer and the layers printed. The purpose of this study is to design printing paths between different submodels to avoid interference when build orientation changed.Design/methodology/approachConsidering support constraint, build orientation sequence is designed for submodels decomposed by model topology. The minimum printing angle between printing layers is analyzed. Initial path through the oriented bounding box is planned and slice interference relationship is then detected according to the projection topology mapping. Based on the relationship matrix of multiorientation slice, feasible path is calculated by directed graph (DG). Final printing path is determined under support constraint and checked by minimum printing angle. The simulation model of the robotic arm is established to verify the accessibility of printing path under the constraint of support and slice.FindingsThe proposed method can reduce support structure, decrease volume error and effectively solve the interference problem of the printing path for multiorientation slice.Originality/valueThe method based on projection topology mapping greatly improves the efficiency of interference detection. A feasible path calculated through DGs ensures the effectiveness of the printing path with the constraint of support and slice.

A novel method for solving the multi-commodity flow problem on evolving networks

The minimum cost multi-commodity flow (MMCF) problem on evolving networks is the latest challenge to solving the min-cost network flow problem of multi-commodity flowing from multi-source and multi-sink on an evolving network. Previous researches primarily focus on the solution of MMCF problem within static networks. However, researchers ignored the MMCF problem within networks where the structure dynamically changes over time. Evolving networks have a dynamically changing and unpredictable network structure. The existing methods must recalculate when the network structure changes, leading to high computational complexity. This manuscript proposes a dynamic graph computing model for adaptive dynamic path selection and optimization. First, the model selects the path with the least cost for each commodity, assuming the route can transport a specified number of such commodities to find the optimal path faster. Second, we design path selection and flow update strategies for each node in the graph to adapt to changes in the graph structure. At the same time, we can prevent multiple commodity conflicts from exceeding the marginal capacity limit. Finally, the optimal path of each commodity is dynamically pruned based on the original optical path to obtain the tree structure and determine the absolute path to meet the specified transportation quantity of each commodity. Performance analysis results show that our method can efficiently cope with the dynamic changes of the network structure, which provides ideas for solving the multi-commodity network flow problem in dynamic networks.

Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search

Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively optimized by destroying and repairing a fixed number of parts, i.e., the neighborhood of the solution, using randomized destroy heuristics and prioritized planning. Despite their recent success in various MAPF instances, current LNS-based approaches lack exploration and flexibility due to greedy optimization with a fixed neighborhood size which can lead to low-quality solutions in general. So far, these limitations have been addressed with extensive prior effort in tuning or offline machine learning beyond actual planning. In this paper, we focus on online learning in LNS and propose Bandit-based Adaptive LArge Neighborhood search Combined with Exploration (BALANCE). BALANCE uses a bi-level multi-armed bandit scheme to adapt the selection of destroy heuristics and neighborhood sizes on the fly during search. We evaluate BALANCE on multiple maps from the MAPF benchmark set and empirically demonstrate performance improvements of at least 50% compared to state-of-the-art anytime MAPF in large-scale scenarios. We find that Thompson Sampling performs particularly well compared to alternative multi-armed bandit algorithms.

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.

Autonomous navigation and adaptive path planning in dynamic greenhouse environments utilizing improved LeGO‐LOAM and OpenPlanner algorithms

AbstractThe autonomous navigation of greenhouse robots depends on precise mapping, accurate localization information and a robust path planning strategy. However, the complex agricultural environment introduces significant challenges to robot perception and path planning. In this study, a hardware system designed exclusively for greenhouse agricultural environments is presented, employing multi‐sensor fusion to diminish the interference of complex environmental conditions. Furthermore, a robust autonomous navigation framework based on the improved lightweight and ground optimized lidar odometry and mapping (LeGO‐LOAM) and OpenPlanner has been proposed. In the perception phase, a relocalization module is integrated into the LeGO‐LOAM framework. Comprising two key steps—map matching and filtering optimization, it ensures a more precise pose relocalization. During the path planning process, ground structure and plant density are considered in our Enhanced OpenPlanner. Additionally, a hysteresis strategy is introduced to enhance the stability of system state transitions. The performance of the navigation system in this paper was evaluated in several complex greenhouse environments. The integration of the relocalization module significantly decreases the absolute pose error (APE) in the perception process, resulting in more accurate pose estimation and relocalization information. In our experiments, the APE was reduced by at least 24.42%. Moreover, our enhanced OpenPlanner exhibits the capability to plan safer trajectories and achieve more stable state transitions in the experiments. The results underscore the safety and robustness of our proposed approach, highlighting its promising application prospects in autonomous navigation for agricultural robots.

A Herd-Foraging-Based Approach to Adaptive Coverage Path Planning in Dual Environments.

Coverage path planning (CPP) is essential for robotic tasks, such as environmental monitoring and terrain surveying, which require covering all surface areas of interest. As the pioneering approach to CPP, inspired by the concept of predation risk in predator-prey relations, the predator-prey CPP (PPCPP) has the benefit of adaptively covering arbitrary bent 2-D manifolds and can handle unexpected changes in an environment, such as the sudden introduction of dynamic obstacles. However, it can only work in bounded environment and cannot handle tasks in unbounded one, e.g., search and rescue tasks where the search boundary is unknown. Sometimes, robots are required to handle both bounded and unbounded environments, i.e., dual environments, such as capturing criminals in a city. Once encountering a building, the robot enters it to cover the bounded environment, then continues to cover the unbounded one when leaving the building. Therefore, the capability of swarm robots for the coverage tasks both in bounded and unbounded environments is important. In nature, herbivores live in groups to find more food and reduce the risk of predation. Especially the juvenile ones prefer to forage near the herd to protect themselves. Inspired by the foraging behavior of animals in a herd, this article proposes an online adaptive CPP approach that enables swarm robots to handle both bounded and unbounded environments without knowing the environmental information in advance, called dual-environmental herd-foraging-based CPP (DH-CPP). It's performance is evaluated in dual environments with stationary and dynamic obstacles of different shapes and quantity, and compared with three state-of-the-art approaches. Simulation results demonstrate that it is highly effective to handle dual environments.

Path planning of complicated hierarchical thin-wall structures using multi-material additive manufacturing technology

Multilayer thin-wall structures have demonstrated significant application potential in wearable devices, pressure vessels, and aerospace industries, with additive manufacturing (AM) poised to further unlock their capabilities. Although path planning, a crucial aspect of AM, has been extensively studied for homogeneous structures, research on path planning for heterogeneous structures remains limited. This study introduces a novel path planning algorithm, termed CPCNHTS, for generating continuous paths in complex non-rotating bodies with hierarchical thin-walled structures. CPCNHTS encompasses adaptive slicing, path offset, and robotic postprocessing techniques. The adaptive slicing method is employed to enhance the slicing model’s accuracy through volume error control. Moreover, the path offset method is designed to derive the printing path using a parallel curve of the inner contour. Identification of the inner contour is based on the curvatures and areas of single and double contours, respectively. The robotic postprocessing method is employed to convert the printing path into executable codes for multimaterial additive equipment. As a compelling application of the CPCNHTS algorithm, a limb prosthetic socket was successfully fabricated, highlighting the remarkable potential of this approach within the wearable devices domain.

Unmanned aerial vehicle–human collaboration route planning for intelligent infrastructure inspection

AbstractMotivated by the strengths of unmanned aerial vehicle (UAV), the UAV–human collaboration route planning (UHCRP) for intelligent infrastructure inspection is a problem worthy of discussion to help reduce human costs and minimize the risk of noninspected infrastructures under limited resources. To facilitate UHCRP, this paper proposes a novel deep reinforcement learning (DRL)‐based approach to well handle multi‐source uncertain features and constraints at a fast speed. To begin with, UHCRP is mathematically described and reformulated as a dual interdependent deep reinforcement learning (diDRL) framework to reflect real‐world scenarios. Afterward, a novel policy network named the attention‐based deep neural network (A‐DNN) is introduced to learn the route planning decisions for the combinatorial optimization problem. In particular, A‐DNN is made up of an encoder and a dual decoder for UAV and human inspection, where the multi‐head attention mechanism is incorporated to generate richer representations for model performance improvement. Performance of the proposed dual multi‐head attention model (DAM) has been tested in simulations and a real‐world case study regarding wind farm inspection. Results indicate that DAM under the sampling decoding strategy can deliver a high‐quality path plan and show better generalizability for larger scale problem sizes compared to single‐head attention model (SAM), multi‐head attention model (AM), and two baseline models, namely OR‐Tools and genetic algorithm. Moreover, DAM trained by randomly generated data can be directly employed to solve the practical problem with standardization of inputs. Overall, DRL integrates decision‐making for inspection method selection and inspected infrastructure selection, providing adaptive and intelligent inspection path planning for UAV and human in complex and dynamic engineering environments.

Research on the improvement path of grassroots social governance innovation performance in China--Qualitative comparative analysis based on 35 cases.

With the rapid development of China's economy and society, the innovation of grassroots social governance has become increasingly important. This paper constructs 35 grassroots social governance innovation samples. Using the TOE theoretical framework and a fuzzy set qualitative comparative analysis (fsQCA), this paper analyzes the joint effects and interactive relationships of multiple factors on grassroots social governance innovation performance from three dimensions: technology, organization, and environment. The research reveals that internal environmental openness is a necessary condition for achieving high innovation performance in grassroots social governance, and proposes four grouping models that affect the performance of grassroots social governance innovation. This paper explores the inner logic of grassroots social governance innovation from a histological perspective, and on this basis proposes an adaptive path to enhance the performance of grassroots social governance innovation.

Active noise control modeling for an electric bus driving position and its effect on sound quality

Current electric bus has become an important public transport for people in China, and its interior sound quality directly affects the driver’s physical and mental health for a long working time and the comprehensive performance of the vehicle. Therefore, it is an inevitable and urgent trend to research electric bus interior sound quality. Aiming at the problem of subjective uncomfortable auditory sensation caused by different speeds, the noise signals near the driver’s ears were collected under two constant speed conditions. On the basis of principle analysis of feed-forward adaptive control system, the primary and secondary path transfer functions were presented using Simulink module in MATLAB to construct three active noise control models for an electric bus based on least mean square (LMS), normalized LMS (NLMS) and filtered-x LMS (FxLMS). Numerical simulation results indicate that the established control models are convergent, and four acoustic parameters are effectively reduced after controls to achieve the effect of improving the sound quality.

Adaptive Weight Model Predictive Path Integral Control for Multiconstrained Missile Guidance

This paper aims to propose a new computational guidance method for missiles that can satisfy multiple practical constraints, such as impact angle and time, seeker’s field-of-view, and acceleration constraints. The proposed method is based on the model predictive path integral (MPPI) with a novel adaptive weight scheme. MPPI control is a numerical optimization approach that solves optimal control problems using a stochastic process. In this approach, the optimal control inputs are repeatedly updated to minimize the cost functions of sampled state trajectories generated by propagating the system model with a noise input. However, in the conventional MPPI architecture, the cost functions for constraints are typically formulated using fixed weight values, and it is also challenging to handle terminal constraints. Finding appropriate weight values for each cost function and the terminal constraints requires a lot of effort, making the conventional MPPI approach inadequate for solving optimal control problems with multiple constraints and terminal constraints, such as multiconstrained guidance problems. To solve this problem, we propose a new method called adaptive weight MPPI control. The cost weights are automatically adjusted using the estimated states from the sampled state trajectories. This proposed MPPI architecture allows us to solve multiconstrained guidance problems without carefully tuning the weight values. Moreover, the proposed method can be easily applied to various multiconstrained guidance problems without significant configuration changes in guidance algorithms. Numerical simulations are performed for various engagement conditions to verify the effectiveness and feasibility of the proposed guidance algorithm in this study.

Effectiveness of Clinical Management of COVID-19 Based on Structured Clinical Knowledge and Process Paths.

Between 2017 and 2019, a clinical process support system based on structured clinical knowledge (Team Compass with the Patient Condition Adaptive Path System; TC-PCAPS) was developed, and implemented in hospitals. In 2020, the COVID-19 clinical management system (COVID-19-CMS) was developed. In this study, the effectiveness of implementing both systems was analyzed. The analysis covered hospitals N, T, and B, where TC-PCAPS implementation started in 2019, 2020, and 2022, respectively. Data for the period from 2018 to 2022 were collected and compared. Hospitals N and T implemented TC-PCAPS in the first year and the COVID-19-CMS in the following year. The nurse turnover rates of these hospitals were lower than those of the prefectures in which they were located. There was a trend towards a gradual reduction in nurse turnover. In contrast, hospital B, which had only just started to introduce these systems, saw a gradual increase in nurse turnover. The data collected from these three hospitals suggested that this systematic approach has the potential to reduce nurse turnover, in addition to the previously reported ability of TC-PCAPS to reduce nurse overtime. In Japan, there is a need to respond to future pandemics and reform the work styles of physicians and nurses. The abovementioned systematic approach has great potential for contributing to both of these aims.

Efficient 3D real-time adaptive AUV sampling of a river plume front

The coastal environment faces multiple challenges due to climate change and human activities. Sustainable marine resource management necessitates knowledge, and development of efficient ocean sampling approaches is increasingly important for understanding the ocean processes. Currents, winds, and freshwater runoff make ocean variables such as salinity very heterogeneous, and standard statistical models can be unreasonable for describing such complex environments. We employ a class of Gaussian Markov random fields that learns complex spatial dependencies and variability from numerical ocean model data. The suggested model further benefits from fast computations using sparse matrices, and this facilitates real-time model updating and adaptive sampling routines on an autonomous underwater vehicle. To justify our approach, we compare its performance in a simulation experiment with a similar approach using a more standard statistical model. We show that our suggested modeling framework outperforms the current state of the art for modeling such spatial fields. Then, the approach is tested in a field experiment using two autonomous underwater vehicles for characterizing the three-dimensional fresh-/saltwater front in the sea outside Trondheim, Norway. One vehicle is running an adaptive path planning algorithm while the other runs a preprogrammed path. The objective of adaptive sampling is to reduce the variance of the excursion set to classify freshwater and more saline fjord water masses. Results show that the adaptive strategy conducts effective sampling of the frontal region of the river plume.

Adaptive polishing path optimization for free-form uniform polishing based on footprint evolution

Adaptive polishing path optimization for free-form uniform polishing based on footprint evolution

An Adaptive Learning Path Optimization Model for Advanced English Learners

Abstract The advent of the big data era has triggered a major change in teaching methods, and personalized learning based on adaptive learning platforms with big data analysis technology has become a new paradigm of education technology. Firstly, the basic information characteristics of learners are described, and the learner characteristic model is constructed with cognitive level, learning style, learning emotion, and learning ability. At the same time, the framework construction of the learning path method is completed based on the knowledge map. On this basis, the adaptive learning path system improved based on a genetic algorithm was used to characterize the higher-order English learners, and then the clustering algorithm was used to analyze the characteristics of the higher-order English learners by clustering analysis. Eight clustering variables were selected to divide the learners into four categories. The first category of the “excellent learners” scored very high in the eight elements, indicating that the learners had a very high score in the adaptive learning path. The first category, “excellent learners”, scored high on all eight elements, indicating that the learners have strong learning abilities in the adaptive learning path. In order to verify the effect of the learning path on students’ academic performance, a pre-and post-test was conducted, and the difference in the mean values reflected that the learning path system has a significant effect on the improvement of performance. The learners’ perception of the system’s usefulness and ease of use resulted in F-values of 9.539 and 5.856, respectively, and the Sig. The values are 0.005 and 0.016, which are less than 0.05, and there is a significant difference. It indicates that the optimized adaptive learning path is highly recommended for use among higher-order English learners.

Development of a Critical Edge-Based Adaptive Toolpath Strategy to Improve Geometrical Accuracy of Incrementally Formed Titanium Implants

Incremental sheet forming has become increasingly popular due to its advantages over traditional metal forming processes, including flexibility, low setup cost, and enhanced formability. It holds great potential for manufacturing asymmetrical components, particularly biomedical implants like cranio-facial implants. However, a significant drawback of this technique is a lack of geometrical accuracy in the produced parts. This research aims to enhance the geometric accuracy of a patient-specific frontal cranial implant by introducing a novel tool path strategy called the adaptive tool path strategy (ATP). The ATP dynamically adjusts step sizes by selectively removing slices based on local curvature requirements. The proposed strategy begins with slicing a CAD model at an extremely fine step depth (0.01 mm) to generate the initial dataset. Subsequently, slices are strategically eliminated to conform to desired curvature specifications. The manufacturing process utilizes commercially pure titanium grade 2 alloy, which is incrementally formed into the desired implant shape using both the traditional constant step depth method and the new adaptive tool path strategy. The produced parts are then scanned using high resolution 3D scanners and a detailed comparison is made between the final geometries and the design geometry. The assessment includes evaluating the forming depth, analysing bending due to spring back, and measuring geometric deviations. Furthermore, numerical simulations are conducted to assess the estimation capabilities of finite element analysis in predicting the forming process. The results demonstrate that the ATP tool path strategy significantly reduces spring back, indicated by a nearly 30 % decrease in the angle of bend along the critical edge. Moreover, the root mean square deviation is reduced by 11 %. Importantly, the experimental results aligned with the patterns observed in the numerical simulations, validating the findings obtained through the study.