
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39251697
71636
10.1038/s41598-024-71636-4
Article
A framework of insole blanking robot based on adaptive edge detection and FSPS-BIT* path planning
Tang Rui 1
Guo Shirong shirong.guo@monash.edu

2
Wang Kunfu 1
Lin Hongdi 1
Huang Lujin 1
Mou Gang MouGangMail@163.com

1
1 https://ror.org/011xvna82 grid.411604.6 0000 0001 0130 6528 College of Advanced Manufacturing, Fuzhou University, Jinjiang, 362200 China
2 https://ror.org/02bfwt286 grid.1002.3 0000 0004 1936 7857 Faculty of Engineering, Monash University, Wellington Road, Clayton, VIC 3800 Australia
6 9 2024
6 9 2024
2024
14 2079118 3 2024
29 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Insole blanking production technology plays a vital role in contemporary machining and manufacturing industries. Existing insole blanking production models have limitations because most robots are required to accurately position the workpiece to a predetermined location, and special auxiliary equipment is usually required to ensure the precise positioning of the robot. In this paper, we present an adaptive blanking robotic system for different lighting environments, which consists of an industrial robot arm, an RGB-D camera configuration, and a customized insole blanking table and mold. We introduce an innovative edge detection framework that utilizes color features and morphological parameters optimized through particle swarm optimization (PSO) techniques to Adaptive recognition of insole edge contours. A path planning framework based on FSPS-BIT* is also introduced, which integrates the BIT* algorithm with the FSPS algorithm for efficient path planning of the robotic arm.

Keywords

Path planning
Robotic system
Adaptive edge detection
BIT*
Insole blanking
Subject terms

Mechanical engineering
Electrical and electronic engineering
501100008859 Fuzhou University (FZU) 29359 issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Under the circumstances of modern automatic manufacturing, blanking technology is vital1, especially in labor-intensive industries such as footwear. Conventional blanking methods are limited by the need for precise workpiece placement and dependence on specific environmental settings, and therefore must be frequently adjusted to changes in production parameters. Autonomous robots with the ability to adapt to the surrounding environment have gradually become an important direction in the development of robotics research2,3. The adaptive algorithm enhances stability by dynamically optimizing parameters to ensure consistent performance across varying conditions and image qualities4,5. At the same time, the feeding robotic arm and the blanking robotic arm in the blanking system need to cooperate collaboratively , In multi arm collaboration, each robotic arm needs to integrate environmental information obtained by itself and other robots, and then plan its motion accordingly6,7. For example, assembly8; Quality inspection, precision such as machining polishing, polishing, etc9; material handling such as packaging, stacking, etc10; Material removal such as grinding, deburring, milling11; welding12 and more. Scholars, both domestic and foreign, have investigated both path planning and edge detection, and developed the integration of a robotic arm for dynamic task execution with a depth camera-based autonomous vision localization system13,14. The industrial robot based on PLC design proposed by Xu Hongqing et al. can well realize industrial automation, but it cannot meet the requirements of adaptive flexible production15. Recently, there are some advanced technologies that combine Luximon’s proposed image processing technology16 with robots to generate trajectories for cutting leather16,17. In addition, we can also find shoe gluing systems based on vision technology, using shape reconstruction, and by a robotic end-effector18 or a robot working with a human during the footwear packaging process19, but there is still no complete robot design for the task of shoe liner blanking.

Path planning algorithms are crucial in motion planning, it has evolved from the basic Rapidly Expanding Random Tree (RRT) to RRT*20 proposed by Karamians et al. which continuously constructs the rooted search tree by iterative sampling, reduces the length of paths while locally optimizing the search tree, possesses asymptotic optimality, but suffers from the problem of slower convergence speed. In order to solve this problem, Gammell et al. proposed Informed-RRT*21, which introduces a subset of elliptic sampling and considers path length information. It is able to quickly reduce the search space and thus accelerate the convergence process of the solution. In addition to the basic algorithm, more and more scholars have proposed different variants based on the informed-RRT* algorithm, such as Shiarlis et al. proposed RLT* (Rapidly Exploring Random Trees)22, and Mashayekhi proposed the Informed RRT*-Connect23 algorithm in a more complex forms that aim to optimize path length and computational efficiency. To overcome problems such as unstable performance, further advances include the Fast Marching Tree (FMT*)24 algorithm proposed by L. Janson, et al. which utilizes pre-generated sample points for dynamic programming and improves the convergence of the optimal solution. Innovative techniques such as the Batch Information Tree (BIT*)25,26 algorithm proposed by Gammell, et al. combine large-scale sampling with efficient graph search techniques to achieve optimal path planning in continuous space. To satisfy the need for path simplification, the feed-forward simplified path smoothing (FSPS) algorithm27 reduces path nodes while maintaining path integrity, which significantly improves computational efficiency in automatic navigation and robot path planning. For insole blanking robot system application scenarios, these advanced path planning algorithms can be attempted to be applied to improve its overall efficiency and performance.

In the field of image processing, edge detection, as a fundamental and critical task, has experienced significant progress from traditional methods to modern techniques. Traditional edge detection techniques28, such as gradient-based Sobel, Prewitt and Canny operators, detect edges by calculating the image gradient magnitude, with the advantage of simplicity and computational efficiency, but are more sensitive to noise. And methods based on second-order derivatives29,30, such as the Laplacian and Marr–Hildreth operators, can capture edge variations more accurately, but may increase false detections. In recent years, the development of deep learning techniques, especially the application of convolutional neural networks, has greatly improved the performance of edge detection in complex environments31. In addition, in simple application scenarios, such as the insole mold edge detection in this paper, the combination of traditional methods and optimization algorithms (e.g., Particle Swarm Algorithm (PSO) and Gray Wolf Optimization Algorithm (GWO)) can effectively improve the performance without the need to train on huge datasets while maintaining high efficiency30,32. Meanwhile, the PSO algorithm can improve adaptability and stability by sharing information between particles and dynamically adjusting search strategies., For example Radha et al. improved the stability of PSO algorithm using Level Set Method (IFLSM)33. Chen Cheng et al. proposed a new elite strategy to improve the comprehensive particle swarm optimization (PSO) method for finding the optimal segmentation threshold of the objective function34. Swarup Kr Ghost et al. proposed a PSO algorithm based on fuzzy systems for processing retinal images in different environments35. Future trends in edge detection technology include the fusion of traditional methods with deep learning, the enhancement of algorithmic interpretability, and the strengthening of real-time processing capabilities, all of which will lead to a wider range of application possibilities for image processing30.

Thus, in the context of the robotics framework, the integration of these different technologies and approaches opens up new possibilities for increasing automation and efficiency in manufacturing and other industrial sectors. This integration is particularly important in the context of advanced manufacturing systems, where robotics and intelligent algorithms play an increasingly important role. The application of these technologies to specific use cases (e.g., insole manufacturing) demonstrates their potential to significantly enhance processes.

In this paper, we propose a new framework for intelligent manufacturing systems, which seamlessly integrates an adaptive edge detection framework for insole molds based on color features and PSO optimized shape parameters, solving the problem of adaptive edge detection for insoles in different environments. Simultaneously constructing a collaborative robot operation and path planning framework based on the combination of BIT * algorithm and FSPS algorithm, solving the problem of efficient operation of blanking robots in narrow environments. Our main contributions to work are as follows.Aiming at the customized image processing algorithm for the insole manufacturing industry, a contour detection algorithm was developed based on PSO and color features, which can improve production efficiency and product quality.

Development of an enhanced path planning algorithm that merges the Batch Informed Trees (BIT*) algorithm with FSPS for efficient and precise navigation in complex environments.

Implementation and validation of an adaptive collaborative robot system designed to adjust dynamically to varying production environments and task requirements, demonstrating significant adaptability and effectiveness in practical applications.

Our method

Overall system overview

As shown in Fig. 1. Initially, a robotic arm II is employed to retrieve the insole from an automatic loader and subsequently transport it to the blanking table. Positioned above the table, a D435 depth camera captures the contour details of the insole. Utilizing an edge detection algorithm based on color features and PSO-optimized morphological parameters, the camera accurately locates the insole, transmitting the corresponding 3D coordinates of its localization to robotic arm I. Equipped with a blanking mold at its extremity, robotic arm I precisely maneuvers the blanking molds to the designated position on the upper surface of the insole upon receiving the pertinent information from the camera. Regarding path planning, we have devised a planning scheme based on an enhanced variant of the Forward sequential path simplification—Batch Informed Trees (FSPS-BIT*) algorithm. This strategy effectively enables obstacle avoidance and enhances the blanking efficiency during system operation. Upon completion of the blanking task by robotic arm I, a pickup signal is relayed to robotic arm II to efficiently conclude the operation.Fig. 1 Block diagram of the system as a whole.

Hardware system design

The developed blanking robot system is shown in Fig. 2a. The main goal of the hardware system design is to build a completely independent modular insole blanking platform, which supports automatic loading and visual positioning blanking, and solves the problems of high labor intensity of manually placing blanking molds and inaccurate positioning of molds at the edge of insole. In order to facilitate the blanking and loading of the insole, the whole system is built on the basis of a mobile platform, and the insole can be flexibly picked up and put down. It consists of four main modules: a robotic arm module, a depth camera module, a blanking table and an automatic insole loading mechanism.Fig. 2 Insole blanking systems: (a) Blanking total system and function of each part; (b) Main components of the blanking system.

The main components of the blanking robot system are shown in the Fig. 2b, including the visual perception component, the dual six-degree-of-freedom robotic arms, and the engine. Visual localization and robotic arm path planning are described in detail in the following section. The system control platform consists of a high-performance industrial computer and a workstation from which the user can monitor and control the robot system. The industrial computer is equipped with an Intel Xeon E2176G processor, 64GB of RAM and an NVIDIA GeForce RTX 2080 Ti graphics processor. This computer hosts all software algorithms and communication connections to all components.

Edge detection of insole mold based on color features and PSO optimized morphological parameters

In order to efficiently extract the information parameters of insole molds from the images captured by the depth camera, we propose an image processing algorithm based on color features and PSO-optimized morphological parameters. The input of the algorithm is the original RGB image captured by the camera, while the output is the coordinates and angles of the minimum outer rectangle of the insole mold. During the implementation of the algorithm, the first step is image preprocessing, which involves reading the image and scaling it to reduce the amount of computation and increase the processing efficiency, while maintaining the original aspect ratio of the image. Immediately after that, the algorithm converts the image from the RGB color gamut to the HSV color gamut in order to extract color features more efficiently36. After the color gamut conversion is complete, the algorithm defines a specific blue range and creates a mask based on this range to mark regions in the original image that match this blue range. Subsequently, in order to reduce image noise and enhance key features, the algorithm applies median filtering and expansion morphological operations to the mask. In the proposed image processing algorithm, we use PSO algorithm to adaptively optimize the parameters of filtering and morphological operations37,38 The objective function of the algorithm is constructed based on two main aspects: first, the quantification of the noise elimination effect, which uses the mean square error (MSE)39 to measure the difference between the original image matrix A and the processed image matrix B; and second, the quantification of the difference between the areas of the two largest regions in the processed image (since the insole mold region will be finally presented as the two regions with the larger area), in order to assess the accuracy of the region selection. The objective function is of the form:1 F(A,B)=λ·Fnoise(A,B)+(1-λ)·Farea(R1,R2)

where λ is a weighting coefficient used to balance the importance of noise elimination effect and region selection criteria. The optimal solution of this objective function is searched by PSO algorithm, and the best feature parameters of each image can be obtained.

Thereafter, the algorithm uses Gaussian fuzzy and Canny edge detection methods to recognize the edges in the image, which lays the foundation for the next contour detection. In the contour detection stage, the algorithm analyzes the objects in the image and calculates the area of each contour to filter out the contours that meet the preset criteria for subsequent processing. For each selected contour, the algorithm calculates its minimum outer rectangle and draws the contour on this basis. Eventually, the algorithm outputs the coordinates and angles of the minimum outer rectangle of the insole mold based on these contours to complete the entire image processing process. The block diagram of the algorithm flow and the images at each stage of the processing are shown in Fig. 3.Fig. 3 The block diagram of the algorithm flow (a) and the images at each stage of the processing (b).

Developed BIT* based motion planning program

Fundamentals of the BIT*

The block diagram of the algorithm is shown in Fig. 4. BIT*25,26 is a path planning algorithm that balances the features of graph-based search algorithms and sampling-based algorithms. It approximates the environment by maintaining a Random Geometric Graph (RGG)40,41, using the parameter r to delineate the neighboring nodes at the time the RGG graph is built, and with multiple batches of sampling, the RGG graph will become denser and more specific in its approximation of the environment. BIT* orders the edges and nodes according to an estimated cost that is consists of the current cost in the RGG graph and a heuristic for the goal. BIT* utilizes an edge queue and a node queue to guide the search. If the best node in the node queue outperforms the best edge in the edge queue, this node is expanded. Conversely consider whether the best edge in the edge queue can improve the current search tree, if it can then check whether this edge will generate a collision, if there is no collision then add it to the current search tree.Fig. 4 BIT* algorithm flowchart.

If a better solution is found in this iteration, BIT* will utilize the method in Informed RRT*4 to improve the planning efficiency of the next batch by using an elliptical sampling approach instead of global uniform sampling, constraining the sample space within the elliptical boundaries determined by the current solution. If the best edge in the edge queue does not improve the current solution, BIT* will sample again to start a new batch. And r is updated by the number of samples thus constructing a denser RGG. Where η ≥ 1 is a tuning parameter20.2 r=2η1+1n1nλxf^ζn1nlogqq1n

FSPS-BIT*

The conventional path planning algorithm of BIT* shows excellent performance in the narrow environment of high-dimensional space and can generate a collision-free path well. However, it still has the disadvantage of meandering paths. To address the above problems, this paper proposes a FSPS-BIT* algorithm to optimize the path.

We utilize a rejection sampling strategy for sampling and use Forward sequential path simplification27 in conjunction with the BIT* algorithm to achieve the purpose of path simplification. The flow of the algorithm is as follows.Algorithm 1 BIT* xstart∈Xfree,xgoal∈Xgoal.

Algorithm 2 FSPS(E).

The set of all points attempted to be sampled in the Algorithm 1 line 7 “sampling” function in Algorithm 1 is denoted by Xrand. We use a rejection of sampling, thus facilitating the direct calculation of the bases of Xrand and Xsample. Here:3 Xsample|x∈Xrand∩Xfree,cXroot,X+cXgoal,X<Cbest

4 λXf≈XsampleXrand×Vspace.

In Algorithm 2, the algorithm first uses the starting point of the original path as the initial node of the simplified path. Then, the original path is processed iteratively through a while loop and a nested for loop. In the inner loop, the cost function is used to determine whether there is an obstacle between two nodes, and if there is no obstacle, the farthest subsequent node is connected to the simplified path. If a node cannot be directly connected to the subsequent farthest node, the algorithm will continue to traverse the original path until all nodes are processed.

This simplified method can effectively reduce the number of redundant path points. This approach also reduces collision detection for long localized paths (e.g., linear paths from a start point to a goal point). As the distance of the path increases, the frequency of each collision detection increases, so avoiding collision detection for long paths can significantly improve the efficiency of the algorithm.

Experiments

In order to verify the feasibility and superiority of the improved BIT*, two groups of experimental tests are designed. In the first group, the linked collision detection of the robot arm is not considered, and the robot arm is regarded as a point in space, and RRT*, FMT*, informed-RRT* and the original BIT* algorithms are selected to conduct comparative experiments in 2D and 3D environments. In the second group, the linked collision detection of manipulators is considered, and the FSPS-BIT* improved algorithm is simulated and tested in a simulated real blanking scenario. To reduce the computational complexity, the experiments use rectangular and square objects as obstacles.

Random sampling algorithm comparison experiment 2D and 3D scene simulation

The 2D experiment sets up four control groups RRT*, FMT*, Informed RRT*, BIT* and experimental group FSPS-BIT*, which are mainly compared in terms of path search speed and path cost, and the planning results are visualized. The experimental results are shown in Fig. 5a, Results are based on a 50 mm × 30 mm obstacle map environment with the start point set to (12,2) and the end point set to (40,23), where the time taken by each algorithm to find the illustrated path is recorded, as well as the cost of that path in terms of Euclidean distance. In the 3D experiments, the visualization of a set of data is shown in Fig. 5b. We normalized the map and set the starting point as (0.8, 0.3, 0.5); the end point as (0.1, 0.6, 0.4); the two control groups of the experiments were Informed RRT* and BIT*, and in order to ensure the reliability and stability of the experiments, each group of experiments was conducted for 100 times, and the average of each group of experiments was taken as a comparison of the data values and recorded in.Fig. 5 Random sampling algorithm comparison experiment 2D (a) and 3D (b) scene simulation.

It can be seen that the basic BIT* algorithm outperforms the first three algorithms in terms of performance, convergence speed, number of nodes, and area of the search sampling region when compared to RRT*, FMT*, and Informed RRT*. However, this paper proposes FSPS-BIT* algorithm based on BIT* algorithm, which is better than BIT*, and this algorithm can make the tree growth more inspiring and speed up the path finding. Generating shorter paths and fewer nodes reduces redundant computations and also reduces vertex-to-vertex bending. As shown in Fig. 5b, it can be seen that FSPS-BIT* compares favorably with Informed RRT* and BIT* in terms of path cost and time cost in 3D space, as well as in terms of smoothness of the planned paths, which proves the feasibility of FSPS-BIT* in 3D space in this paper.

Robotic arm real scene path planning simulation experiments

As shown in Fig. 6a. In order to verify the feasibility of the algorithm, this paper builds an actual blanking scene on 2022MATLABb software. Since the range of motion of robotic arm 1, which is responsible for visually locating the mold, is relatively small, we replace it with a rectangular obstacle, and at the same time, we simulate the camera during the blanking process, as well as the location of the work table and the work shown in Fig. 6b, we use the improved algorithm to plan the execution of the robotic arm from the start point to the target point, and the result proves that the FSPS-BIT* algorithm can make the UR5 robotic arm complete the blanking work well.Fig. 6 Real scene simulation experiment: (a) realistic scene simulation; (b) planning paths.

Systematic clinical trials

In order to verify the insole blanking accuracy of the blanking system, this article constructed a robotic arm visual positioning system, as shown in Fig. 7. The system consists of a UR5 robotic arm, a designed fixture, and a D435 depth camera, etc. The system is designed with a 6-degree-of-freedom fixture at the wrist of the UR5 robotic arm as the end-effector. The camera was mounted directly above the table to obtain good RGB-D information and the RGB color map was fed into a pre-trained network for visual localization to obtain the coordinates and angle of the smallest external rectangle of the output insole mold. The camera is calibrated to correct for lens aberrations and to calculate the intrinsic performance of the camera. IKc and extrinsic parameters. CTw. The average reprojection error after camera calibration is 0.15 pixels. For safety reasons, the UR5 robot end-effector speed is limited to 250 mm/s.Fig. 7 Physical building platform and experimental results.

As shown in the Fig. 7. In the actual experiment, in order to evaluate the blanking accuracy of the insole, according to the accuracy requirements of the shoe factory during on-site inspections and taking into account the accuracy limitations of the D435 camera, the midpoint of the groove center line was used as the standard and the positioning error was set to 1mm. Accuracy requirements. The laser is used to hit the midpoint of the center line of the insole, use the real sense SDK to obtain the three-dimensional coordinates of the laser point as the actual coordinates, and compare the results with the visual positioning results. The results were compared with the visual positioning results to verify the positioning accuracy of the insoles in various lighting environments. The results of the experiment are shown in the Table 1.Table 1 Results of the experiment.

Environment	x-axis mean error (mm)	y-axis mean error (mm)	Maximum error (x) (mm)	Maximum error (y) (mm)	
Over light	0.6	0.4	0.8	0.7	
Normal	0.3	0.2	0.4	0.5	
Darker	0.4	0.4	0.6	0.5	

Experimental data indicate optimal algorithmic function under standard lighting, with average errors on the x and y axes measuring at 0.3 mm and 0.2 mm, respectively, staying well within the 1mm tolerance mandated by industrial standards. These findings confirm the algorithm’s sensitivity to illumination variances while underscoring its capacity to uphold accuracy through adaptive optimization, even in less than ideal lighting conditions. This attests to the robustness of method and its suitability for industrial application in insole precision placement. The demonstrated precision and adaptability further promise to enhance production efficiency and elevate product quality, attributing to the system’s innovative integration of advanced technologies.

Conclusion

This study describes the development and field evaluation of an innovative automated insole blanking processing system that employs advanced software algorithms to complement the hardware functionality. For dealing with the complex problem of insole edge delineation, an image processing method is applied that relies on PSO for color feature extraction and adaptive feature parameter selection. To solve the problem of robotic arm path planning in a narrow system space, the application is extended to a 3D environment by improving BIT* and integrating it with the FSPS algorithm to simplify the paths, which further enhances the system’s operational capability. Field evaluations proved that insole positioning errors were consistently within tight tolerances specified by industry standards under a variety of environmental conditions, confirming the system’s accuracy and feasibility for industrial deployment.

Acknowledgements

The Student Research Train Program from Fuzhou University and the full PhD scholarship program from Monash University is gratefully acknowledged.

Author contributions

R.T.: Writing—review and editing, project administration, methodology, conceptualization. S.G.: Writing—review and editing, project administration, writing—original draft, methodology, conceptualization. K.W.: Methodology, writing—review and editing. H.L.: Resources, methodology, conceptualization. L.H.: Resources, methodology, conceptualization. G.M.: Writing—review and editing, supervision, project administration, methodology, conceptualization.

Funding

Funding was provided by Fuzhou University (FZU) (Grant No. 29359).

Data availability

All data included in this study are available upon request by contact with the corresponding author.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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