A shape sensing method and system for a rope-driven continuum robot

Abstract

The embodiment of the specification discloses a shape sensing method and system of a rope-driven continuum robot, at least two flexible sensors are fixed on a flexible skeleton in a spiral manner, the method comprises the following steps: establishing a general shape sensing model based on a constant curvature assumption; for any joint module: driving the joint module to a test shape, based on the general shape sensing model, solving the actual fixed position of the flexible sensor by using a numerical iteration method; driving the joint module to a working shape, based on the general shape sensing model and the actual fixed position of the flexible sensor, solving the output value of the flexible skeleton shape parameter by using a numerical iteration method; determining the robot shape based on the output value of the flexible skeleton shape parameter corresponding to each joint module. The application does not need to consider the fixing accuracy when fixing the flexible sensor, the model is established and calibrated, the change of the robot shape is accurately sensed, and the problems of high sensor installation accuracy and poor sensing reliability in the prior art are solved.

Description

Technical Field

[0001] Several embodiments of this specification relate to the field of rope-driven continuum robot technology, and more specifically to the optimization of shape perception methods for rope-driven continuum robots. Background Technology

[0002] Rope-driven continuum robots are a type of flexible robot driven by lightweight ropes. They achieve continuous deformation capabilities through modular design and are typically composed of multiple joint modules connected in series. Each joint module includes a base platform, a moving platform, a flexible skeleton 1, and multiple driving ropes. The flexible skeleton 1 serves as a passive support structure and has the characteristics of low bending stiffness, high tensile stiffness, and high torsional stiffness, enabling the robot to adapt to complex environments and achieve highly compliant motion.

[0003] Currently, shape perception methods for rope-driven continuum robots mainly rely on multiple flexible sensors 2 mounted on the surface of the flexible skeleton 1. (See attached image) Figure 1 , Figure 2 As shown, for a joint module with two degrees of freedom of bending motion, a flexible sensor 2 is typically placed on the flexible skeleton 1 in the bendable direction, with the length direction of the sensor parallel to the axis of the flexible skeleton 1. This arrangement simplifies the flexible sensor 2 to a straight line along the skeleton axis or an arc when bending. By measuring the deformation of the flexible sensor 2, shape parameters are calculated, thereby achieving indirect perception of the robot's shape.

[0004] However, the scheme of setting up the flexible sensor 2 in the parallel direction has extremely high requirements for the accuracy of the sensor's fixed position. The actual fixed position of the sensor needs to be completely consistent with the ideal fixed position. Once the actual installation position of the sensor is offset, especially when the fixed angle is offset, the sensor cannot be simplified into a straight line, which will lead to a significant decrease in the accuracy of the shape perception model and limit the reliability of the rope-driven continuum robot in fine operation scenarios. Summary of the Invention

[0005] This specification provides a shape perception method and system for a rope-driven continuum robot. This solution eliminates the need to consider the fixing accuracy when fixing the flexible sensor. By setting at least two helically arranged flexible sensors in each joint module and establishing and calibrating a general shape perception model for the helically arranged flexible sensors, the robot's shape changes can be accurately perceived. This solves the problems of high sensor installation accuracy requirements and poor perception reliability in existing shape perception methods.

[0006] The technical solution is as follows:

[0007] In a first aspect, the embodiments of this specification provide a shape perception method for a rope-driven continuum robot. The robot includes multiple joint modules connected in series. Each joint module includes a cylindrical flexible skeleton and at least two flexible sensors, and each flexible sensor is fixed to the side of the flexible skeleton in a spiral manner.

[0008] The shape-aware method includes the following steps:

[0009] Based on the constant curvature assumption, a general shape perception model is established to describe the mapping relationship between the shape parameters of the flexible skeleton and the length change of the flexible sensor at any fixed position on the flexible skeleton.

[0010] For any joint module:

[0011] The joint module is driven to the test shape corresponding to the determined flexible skeleton shape parameters. Based on the general shape perception model, the actual fixed position of each flexible sensor in the joint module is solved by numerical iteration method.

[0012] Drive the joint module to the working shape corresponding to the shape parameters of the flexible skeleton to be determined. Based on the general shape perception model and the actual fixed position of each flexible sensor in the joint module, the output value of the shape parameters of the flexible skeleton in the joint module is solved by numerical iteration method.

[0013] The robot shape is determined based on the output values ​​of the flexible skeleton shape parameters corresponding to each joint module.

[0014] As a preferred embodiment, the general shape-sensing model, based on the constant curvature assumption, describes the mapping relationship between the shape parameters of the flexible skeleton and the length change of the flexible sensor at any fixed position on the flexible skeleton, including:

[0015] Define a set of positioning parameters in the joint module that describe the fixed position of the flexible sensor relative to the flexible skeleton;

[0016] Based on the constant curvature assumption, a general shape perception model is established using the positioning parameter set of the flexible sensor and the structural parameters of the flexible skeleton.

[0017] As a preferred embodiment, the positioning parameter set describing the fixed position of the flexible sensor relative to the flexible skeleton in the defined joint module includes:

[0018] A coordinate system is established with the center of the bottom circle of the flexible skeleton in the vertical state as the origin. The Z-axis of the coordinate system coincides with the neutral axis of the flexible skeleton, and the X-axis of the coordinate system is used as the reference direction.

[0019] Starting from the intersection of the X-axis of the coordinate system and the side of the flexible skeleton, the side of the flexible skeleton is cut open and flattened into a two-dimensional plane along the direction parallel to the Z-axis of the coordinate system.

[0020] The first positioning parameter is defined as the intercept between the length direction of the flexible sensor and the vertical axis of the two-dimensional plane. The second positioning parameter is defined as the angle between the length direction of the flexible sensor and the horizontal axis of the two-dimensional plane. The third positioning parameter is defined as the angle between the projection point of any endpoint of the flexible sensor on the bottom surface of the flexible skeleton and the X-axis of the coordinate system. The fourth positioning parameter is defined as the central angle between the projection points of the two endpoints of the flexible sensor on the bottom surface of the flexible skeleton.

[0021] As a preferred embodiment, the step of using a numerical iteration method based on a general shape-sensing model to solve for the actual fixed position corresponding to each flexible sensor in the joint module includes:

[0022] Obtain the first Jacobian matrix of the general shape perception model. The first Jacobian matrix represents the local sensitivity of the length change of all flexible sensors in any joint module to the positioning parameter set.

[0023] Based on the general shape perception model and the first Jacobian matrix, a numerical iterative method is used to solve for the calibration value of the positioning parameter group corresponding to each flexible sensor in the joint module with the length change deviation of each flexible sensor in the joint module as the convergence target. The length change deviation is the difference between the actual length change obtained from the output value of the flexible sensor and the theoretical length change obtained from the general shape perception model.

[0024] As a preferred embodiment, the method based on a general shape-sensing model and a first Jacobian matrix, employing a numerical iteration method with the length variation deviation of each flexible sensor in the joint module as the convergence target, solves for the calibration values ​​of the positioning parameter set corresponding to each flexible sensor in the joint module, including:

[0025] The initial given values ​​of the determined flexible skeleton shape parameters corresponding to the test shape and the positioning parameter group corresponding to each flexible sensor in the joint module are used as inputs to the general shape perception model to obtain the theoretical length change corresponding to each flexible sensor in the joint module.

[0026] The actual length change of each flexible sensor in the joint module is obtained based on the output value of each flexible sensor in the joint module.

[0027] The convergence of the numerical iteration method is determined by the difference between the theoretical and actual length changes of each flexible sensor in the joint module.

[0028] If convergence is not achieved, based on the first Jacobian matrix and the difference between the theoretical and actual length changes of each flexible sensor in the joint module, update the given values ​​of the positioning parameter group corresponding to each flexible sensor in the joint module for the next iteration, and re-input the general shape perception model for the next iteration.

[0029] If convergence is achieved, the current given value of the positioning parameter group corresponding to each flexible sensor in the joint module is used as the calibration value.

[0030] As a preferred embodiment, driving the joint module to the working shape corresponding to the shape parameters of the flexible skeleton to be determined, based on a general shape perception model and the actual fixed position of each flexible sensor in the joint module, uses a numerical iteration method to solve for the output value of the shape parameters of the flexible skeleton in the joint module, including:

[0031] Obtain the second Jacobian matrix of the general shape-aware model. The second Jacobian matrix represents the local sensitivity of the length change of all flexible sensors in any joint module to the shape parameters of the flexible skeleton.

[0032] Based on the general shape perception model, the second Jacobian matrix, and the calibration values ​​of the positioning parameter group corresponding to each flexible sensor in the joint module, the numerical iteration method is used to solve for the output value of the flexible skeleton shape parameter in the joint module with the length change deviation of each flexible sensor in the joint module as the convergence target.

[0033] As a preferred embodiment, the step of using a numerical iteration method based on a general shape-sensing model, a second Jacobian matrix, and the calibration values ​​of the positioning parameter sets corresponding to each flexible sensor in the joint module, and taking the length variation deviation of each flexible sensor in the joint module as the convergence target, to solve for the output values ​​of the flexible skeleton shape parameters in the joint module includes:

[0034] The calibration values ​​of the positioning parameter group corresponding to each flexible sensor in the joint module and the initial given values ​​of the flexible skeleton shape parameters are used as inputs to the general shape perception model to obtain the theoretical length change corresponding to each flexible sensor in the joint module.

[0035] The actual length change of each flexible sensor in the joint module is obtained based on the output value of each flexible sensor in the joint module.

[0036] The convergence of the numerical iteration method is determined by the difference between the theoretical and actual length changes of each flexible sensor in the joint module.

[0037] If convergence is not achieved, based on the second Jacobian matrix and the difference between the theoretical and actual length changes corresponding to each flexible sensor in the joint module, update the given values ​​of the flexible skeleton shape parameters in the joint module for the next iteration, and re-input the general shape perception model for the next iteration.

[0038] If convergence is achieved, the current given value of the flexible skeleton shape parameter in the joint module will be used as the output value.

[0039] As a preferred embodiment, the shape-aware method further includes:

[0040] Obtain the range of change of the shape parameters of the flexible skeleton and the structural parameter data of the flexible skeleton for each joint module;

[0041] For any joint module:

[0042] Based on the structural parameter data of the flexible skeleton in the joint module, the particle swarm optimization algorithm is used to maximize the range of change of the shape parameters of the flexible skeleton as the optimization objective, and to obtain the recommended fixed position value of each flexible sensor in the joint module.

[0043] The flexible sensors in the joint module are fixed based on the recommended fixed position values ​​corresponding to each flexible sensor in the joint module.

[0044] As a preferred embodiment, the flexible sensor is a stretchable capacitive sensor.

[0045] Secondly, this specification provides a shape perception system for a rope-driven continuum robot, which applies the shape perception method described in the first aspect of the above embodiments;

[0046] The shape sensing system includes a position calibration unit and a shape sensing unit;

[0047] The position calibration unit, for any joint module, drives the joint module to the test shape corresponding to the determined flexible skeleton shape parameters, and uses a numerical iteration method to solve the actual fixed position of each flexible sensor in the joint module based on the constructed general shape perception model.

[0048] The shape sensing unit, for any joint module, drives the joint module to the working shape corresponding to the shape parameters of the flexible skeleton to be determined. Based on the general shape sensing model and the actual fixed position of each flexible sensor in the joint module, it uses a numerical iteration method to solve for the output value of the shape parameters of the flexible skeleton in the joint module; it also determines the robot shape based on the output value of the shape parameters of the flexible skeleton corresponding to each joint module.

[0049] Thirdly, embodiments of this specification provide an electronic device, including a processor and a memory; the processor is connected to the memory; the memory is used to store executable program code; the processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to perform the steps described in the first aspect of the above embodiments.

[0050] Fourthly, embodiments of this specification provide a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the steps described in the first aspect of the above embodiments.

[0051] The beneficial effects of the technical solutions provided in some embodiments of this specification include at least the following:

[0052] The flexible sensors in this invention employ a helical arrangement. By establishing a universal geometric model based on the assumption of constant curvature, the physical relationship between sensor deformation and skeleton bending is described more accurately in principle, fundamentally overcoming the inherent geometric simplification errors of models corresponding to traditional parallel arrangements. Simultaneously, an adaptive calibration process for the sensor's fixed position is introduced, automatically compensating for deviations introduced during installation and fixing, ensuring a high degree of consistency between the theoretical model and the physical entity, thereby achieving stable and high-precision robot shape reconstruction.

[0053] This solution significantly improves the system's engineering practicality and cost-effectiveness. It reduces the stringent requirements for sensor installation processes, allowing for fixation within certain installation tolerances, followed by algorithmic calibration to ensure final accuracy. This drastically reduces assembly difficulty and production costs. Simultaneously, this solution requires only a minimum of two flexible sensors to achieve complete shape sensing of a two-degree-of-freedom joint, ensuring performance while reducing the number of sensors, system complexity, and hardware costs.

[0054] This solution possesses excellent versatility and real-time performance. The established universal sensing model is independent of specific installation locations and can adapt to different sensor layout designs by changing parameters, demonstrating strong scalability and adaptability. The numerical iterative algorithm used for real-time solution converges rapidly, meeting the real-time control and feedback requirements of continuum robots. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of how a flexible sensor is set up in a vertical state of a flexible skeleton in a traditional shape sensing method (the driving rope is not shown).

[0057] Figure 2 This is a schematic diagram of how a flexible sensor is set up in a flexible skeleton under bending conditions in a traditional shape sensing method (the driving rope is not shown).

[0058] Figure 3 This is a flowchart illustrating a shape perception method for a rope-driven continuum robot provided in the embodiments of this specification;

[0059] Figure 4 This is a three-dimensional geometric reference diagram of the flexible frame in a vertical state when defining the positioning parameter group of the flexible sensor provided in the embodiments of this specification, and also shows the spiral fixing method of the flexible sensor;

[0060] Figure 5 This is a planar geometric reference diagram of the side of the flexible skeleton when defining the positioning parameter group of the flexible sensor as provided in the embodiments of this specification;

[0061] Figure 6 This is a planar geometric reference diagram of the bottom surface of the flexible skeleton when defining the positioning parameter group of the flexible sensor as provided in the embodiments of this specification;

[0062] Figure 7 This is a schematic diagram of the geometric model of the flexible skeleton provided in the embodiments of this specification in a vertical state;

[0063] Figure 8 This is a schematic diagram of the geometric model of the flexible skeleton provided in the embodiments of this specification in a bent state;

[0064] Figure 9 This is a flowchart of the calibration of the general shape-aware model provided in the embodiments of this specification;

[0065] Figure 10 This is a schematic diagram of the structure of a shape perception system for a rope-driven continuum robot provided in the embodiments of this specification;

[0066] Figure 11 This is a schematic diagram of the structure of an electronic device provided in the embodiments of this specification. Detailed Implementation

[0067] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings.

[0068] The terms "first," "second," "third," etc., in the description, claims, and accompanying drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0069] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this specification. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0070] The rope-driven continuum robot consists of multiple two-degree-of-freedom rope-driven joint modules connected in series. Each joint module comprises a base platform, a moving platform, a flexible skeleton, and four driving ropes. Figure 1 , Figure 2 As shown. In traditional shape sensing methods, four parallel flexible sensors 2 are typically used, with the length direction of each sensor parallel to the axis of the flexible skeleton 1. When establishing a joint module sensing model based on this parallel sensor arrangement, the sensor is simplified to a straight line along the skeleton axis. When the flexible skeleton 1 undergoes bending deformation, the sensor is simplified to an arc. This sensor arrangement has good robustness, can reduce the noise influence of sensors by utilizing the difference between the signals of two relatively arranged sensors, and simplifies the complexity of the shape sensing model by relying on the parallel relationship. However, the fixed position of each sensor must be very accurate, and the actual fixed position needs to be completely consistent with the theoretical fixed position. Even a slight error will greatly reduce the output accuracy of the shape sensing model. In reality, there will inevitably be operational errors when attaching and fixing the sensors, which cannot be completely avoided, and there is a lack of effective self-calibration methods. Moreover, this shape sensing model also has serious limitations in terms of universality for flexible skeletons 1 of different sizes. In addition, sensors arranged parallel to the axis of the flexible skeleton 1 can only effectively sense bending in their own plane. If the bending direction is perpendicular to the plane, their strain response will be very weak, leading to a decrease in sensing sensitivity. Therefore, this application is proposed.

[0071] A shape perception method for a rope-driven continuum robot.

[0072] The robot comprises multiple articulated joint modules, each including a cylindrical flexible skeleton 1 and at least two flexible sensors 2, with each flexible sensor 2 screwed onto the side of the flexible skeleton 1; see reference. Figure 3 As shown, Figure 3 This specification provides a flowchart illustrating a shape perception method for a rope-driven continuum robot according to an embodiment, which may include at least the following steps:

[0073] Step 102: Based on the constant curvature assumption, establish a general shape perception model that describes the mapping relationship between the shape parameters of the flexible skeleton 1 and the length change of the flexible sensor 2 at any fixed position on the flexible skeleton 1.

[0074] For any joint module:

[0075] Step 104: Drive the joint module to the test shape corresponding to the determined shape parameters of the flexible skeleton 1. Based on the general shape perception model, use the numerical iteration method to solve the actual fixed position of each flexible sensor 2 in the joint module.

[0076] Step 106: Drive the joint module to the working shape corresponding to the shape parameters of the flexible skeleton 1 to be determined. Based on the general shape perception model and the actual fixed position of each flexible sensor 2 in the joint module, use the numerical iteration method to solve the output value of the shape parameters of the flexible skeleton 1 in the joint module.

[0077] Step 108: Determine the robot shape based on the output values ​​of the shape parameters of the flexible skeleton 1 corresponding to each joint module.

[0078] Explanatoryly, this embodiment first adjusts the arrangement of the flexible sensors 2 in each joint module of the robot. Instead of using the traditional parallel arrangement of multiple sensors, they are fixed to the side of the cylindrical flexible frame 1 in a spiral manner. Refer to the attached diagram. Figure 4 This arrangement forms the physical basis for all subsequent technical steps; the spirally arranged flexible sensor 2 itself defines a three-dimensional spatial curve. Regardless of the direction of bending, some fibers will always be in the tensile zone and some in the compressive zone, thus making it naturally sensitive to bending in any direction without any blind spots. This allows the flexible sensor 2 to sense richer and more differentiated strain information when the flexible skeleton 1 bends, making high-precision sensing possible.

[0079] It should be noted that multiple flexible sensors 2 can be spirally wound and fixed onto the flexible skeleton 1 along its axial direction, without the need for precise positioning of the sensors 2. The spiral angle of each flexible sensor 2 is not specifically limited and can be flexibly adjusted according to the actual deformation requirements and sensing accuracy requirements of the flexible skeleton 1. The core constraints are: the flexible sensors 2 must remain continuous and non-overlapping during the spiral winding process, and reasonable spacing must be reserved between each sensor 2 to avoid intersections or overlaps. This ensures that each sensor can independently and completely capture the deformation information of its corresponding area on the flexible skeleton 1 without interference. This fully utilizes the spatial coverage advantage of the spiral arrangement while ensuring the independence and accuracy of the measurement data.

[0080] Explained, as the passive support of the rope-driven continuum robot, the flexible skeleton 1 possesses characteristics of low bending stiffness, high tensile stiffness, and high torsional stiffness. Based on these stiffness characteristics, the bending shape of the flexible skeleton 1 can be equivalently regarded as a segment of a circular arc with constant curvature, described by shape parameters, i.e., the joint variables of the joint modules. The shape parameter φ is represented by the bending angle θ and the bending direction angle α. The bending angle θ describes the degree of bending of each rope-driven continuum joint module, and the bending direction angle α describes the bending direction of each rope-driven continuum joint module. The theoretical basis of the shape perception method in this embodiment is a general shape perception model established based on the constant curvature assumption. The constant curvature assumption is a reasonable simplification of the bending shape of the flexible skeleton 1, that is, assuming that when the flexible skeleton 1 bends, it presents a segment of a circular arc with constant curvature. This greatly simplifies the geometric relationship while still highly approximating the actual deformation. The mathematical model corresponding to the general shape perception model is a nonlinear equation, which describes the mapping relationship between shape parameters (such as the bending angle and direction of the flexible skeleton 1) and the length change of any flexible sensor 2 with a specific fixed position. The versatility of this model lies in the fact that it does not depend on a specific sensor location, but is a function framework that can be adapted to different installation parameters.

[0081] After establishing the general shape-aware model, it is applied to each joint module. First, the actual fixed positions of each flexible sensor 2 under the test shape are adaptively calibrated using the shape-aware model to obtain accurate values. Under a known shape (test shape), the joint module is driven to the corresponding posture. At this point, the shape parameters are known inputs, and the length change of the flexible sensor 2 is the measured output. Through a numerical iterative method, the solution is reversed; the target is no longer the shape, but rather the actual fixed position of each sensor. Therefore, when fixing the flexible sensor 2, there is no need to worry about the deviation between the actual fixed position and the preset fixed position. The accurate fixed position data of the flexible sensor 2 can be determined through adaptive calibration, allowing the general shape-aware model to adapt to each joint module.

[0082] After calibration, the actual fixed position after calibration is input into the shape-aware model, enabling real-time shape perception of the working shape of the joint module with an unknown shape. The numerical iteration method is applied again, this time with the input being the real-time length change of the flexible sensor 2, and the solution objective becoming the shape parameters of the flexible skeleton 1 in the current joint module. Specifically, the real-time signal output values ​​of multiple flexible sensors 2 are read and converted into length changes (current length of flexible sensor 2 - initial length) as actual measured values. The real-time signal output value of flexible sensor 2 is proportional to its length. These actual measured values ​​are substituted into the shape-aware model after fixed-position calibration. The model quickly calculates the shape parameters of the current flexible skeleton 1 using the numerical iteration method, thus determining the shape of the joint module.

[0083] Finally, by integrating the shape parameters of the flexible skeleton 1 solved by each serial joint module into spatial pose, the three-dimensional shape of the entire continuum robot can be reconstructed.

[0084] To illustrate, the spiral arrangement of the flexible sensor 2 makes the mapping relationship between the length change of the flexible sensor 2 and the shape parameter values ​​of the robot more complex. Therefore, the numerical iteration method is chosen to solve for the actual fixed position of the sensor and the shape parameters of the flexible skeleton 1.

[0085] This method, through a spiral arrangement combined with a universal shape perception model based on the constant curvature assumption, provides a general, stable, and highly accurate shape perception solution for rope-driven continuum robots. It innovatively employs a two-stage process of "first calibrating system parameters, then sensing the working state," achieving high-precision, interference-resistant shape measurement under bending deformation, while simultaneously reducing robot assembly costs and operational complexity.

[0086] In one embodiment of this specification, the flexible sensor 2 is a stretchable capacitive sensor.

[0087] Explanatory, stretchable capacitive sensors use flexible fabric as the sensor's matrix material, embedding highly sensitive magnetoelectric functional materials into the flexible fabric to achieve high-precision sensing functionality.

[0088] In one embodiment of this specification, based on the constant curvature assumption, a general shape-sensing model is established to describe the mapping relationship between the shape parameters of the flexible skeleton 1 and the length change of the flexible sensor 2 at any fixed position on the flexible skeleton 1, including:

[0089] Define a set of positioning parameters in the joint module that describe the fixed position of the flexible sensor 2 relative to the flexible skeleton 1;

[0090] Based on the constant curvature assumption, a general shape perception model is established using the positioning parameter set of flexible sensor 2 and the structural parameters of flexible skeleton 1.

[0091] For explanatory purposes, to provide a precise and parameterized geometric framework for subsequent mathematical modeling, it is necessary to first define a set of positioning parameters describing the fixed position of the flexible sensor 2. Compared to the parallel arrangement scheme, describing the fixed position of the helically arranged flexible sensor 2 on the flexible frame 1 is more difficult and requires more parameters. Ignoring the width of the flexible sensor 2, we consider it as a helix, and abstract the complex three-dimensional helical winding path of the flexible sensor 2 on the surface of the flexible frame 1 into a finite set of geometric parameters with clear physical meaning (e.g., helix angle, starting position, etc.). This definition process transforms the ever-changing actual installation and fixing positions into a unified mathematical language.

[0092] Illustratively, the structural parameters of the cylindrical flexible skeleton 1 itself include its length L and base radius r in the vertical state. By combining the defined positioning parameter set of the flexible sensor 2 with the structural parameters of the flexible skeleton 1, and through rigorous spatial geometric derivation, a precise mathematical relationship can be established between the length change of the flexible sensor 2 and the shape parameters of the flexible skeleton 1, i.e., a general shape perception model.

[0093] In one embodiment of this specification, a set of positioning parameters describing the fixed position of the flexible sensor 2 relative to the flexible skeleton 1 in the joint module is defined, including:

[0094] A coordinate system is established with the center of the bottom circle of the flexible skeleton 1 in the vertical state as the origin. The Z-axis of the coordinate system coincides with the neutral axis of the flexible skeleton 1, and the X-axis direction of the coordinate system is used as the reference direction.

[0095] Starting from the intersection of the X-axis of the coordinate system and the side of the flexible skeleton 1, cut open the side of the flexible skeleton 1 along the direction parallel to the Z-axis of the coordinate system and flatten it into a two-dimensional plane;

[0096] The first positioning parameter is defined as the intercept of the length direction of the flexible sensor 2 with the vertical axis of the two-dimensional plane. The second positioning parameter is defined as the angle between the length direction of the flexible sensor 2 and the horizontal axis of the two-dimensional plane. The third positioning parameter is defined as the angle between the projection point of any endpoint of the flexible sensor 2 on the bottom surface of the flexible skeleton 1 and the X-axis of the coordinate system. The fourth positioning parameter is defined as the central angle between the projection points of the two endpoints of the flexible sensor 2 on the bottom surface of the flexible skeleton 1.

[0097] Explained, when the flexible skeleton 1 is in its initial state, i.e., without bending deformation, its surface shape is cylindrical. After bending motion, its surface shape can be considered as part of a torus. As the flexible skeleton 1 bends and deforms, the position of the fixed point of the flexible sensor 2 changes, and the change in distance between the two fixed points relative to the initial length is the change in length. (See Appendix) Figure 4 , Figure 5 and Figure 6 , Figure 4 , Figure 5 , Figure 6 This is the geometric reference diagram used when defining the positioning parameter group of the flexible sensor 2 as provided in the embodiments of this specification. The two endpoints of the flexible sensor 2 are denoted as S1 and S2, respectively, and any point on the flexible sensor 2 is denoted as point S. A coordinate system {B} is established at the center of the bottom plane of the flexible skeleton 1. To ensure that the shape description of the flexible skeleton 1 is consistent with that of the joint module, the coordinate axis directions are consistent with the coordinate axis directions of the base coordinate system of the joint module, and the Z-axis of the coordinate system coincides with the neutral axis of the flexible skeleton 1. Coordinate axes The intersection point with the surface of the flexible skeleton 1 is denoted as point A, and the point corresponding to point A on the top plane of the flexible skeleton 1 is denoted as point B. The surface of the flexible skeleton 1 is unfolded along the straight line AB into a two-dimensional plane ABB´A´. A rectangular coordinate system is established at point A on the two-dimensional plane ABB´A´. At this time, the flexible sensor 2 is a straight line S1S2 on the plane ABB´A´. The angle between the flexible sensor 2 and the x-axis of the rectangular coordinate system is denoted as λ, which is the second positioning parameter, and the intercept on the y-axis is denoted as b, which is the first positioning parameter. The spiral formed by the flexible sensor 2 is then projected onto the bottom plane of the flexible skeleton 1. The projection point of point S1 is denoted as S1´, and the projection point of point S2 is denoted as S2´. The resulting projected arc is denoted as... The projection point of point S is denoted as S'. Vector with coordinate axes The included angle is t. , where t0 is a vector with coordinate axes The included angle, i.e., the third positioning parameter, where ∆t is a vector. with vector The angle between the two points is the fourth positioning parameter. The first positioning parameter b and the third positioning parameter t0 determine the fixed point position of one end of the flexible sensor 2 on the flexible skeleton 1. The second positioning parameter λ is used to define the fixed angle of the sensor, i.e., the helix angle. The fourth positioning parameter ∆t is used to define the tensile length of the sensor, i.e., the number of turns of the helix. Four geometric parameters are used to describe the fixed position of the flexible sensor 2 on the surface of the flexible skeleton 1, i.e., the positioning parameter set. .

[0098] Illustratively, the neutral axis of the flexible skeleton 1 refers to the axis on which the material fibers at all points are neither stretched nor compressed when the flexible skeleton 1 undergoes bending deformation, and whose length remains consistent with that of the skeleton in its original straight state, serving as a geometric reference.

[0099] Further, refer to the appendix. Figure 7 and Figure 8 , Figure 7 , Figure 8This is a schematic diagram of the geometric model of the flexible skeleton 1 provided in the embodiments of this specification in the vertical and bent states, respectively. A local coordinate system {F} is defined on the neutral axis of the flexible skeleton 1, with the origin at point F. When the flexible skeleton 1 does not undergo bending deformation, as shown... Figure 7 The coordinate axes of coordinate system {F} are aligned with the coordinate axes of coordinate system {B}. Point S describes the coordinates of point S in coordinate system {F}. Coordinates in coordinate system {B} They can be represented as:

[0100] ,

[0101] .

[0102] After the flexible skeleton 1 undergoes bending deformation, as Figure 8 The neutral axis of the skeleton is equivalent to a circular arc with constant curvature, and the coordinate system {F} is... The N1 axis points in the tangent direction to the neutral axis, and a coordinate system {N} is defined with its origin at point F. The N1 axis points in the direction of the center of curvature at point M, and the N3 axis points in the direction of the tangent to the neutral axis. The axes are aligned. At this point, point S describes its coordinates in coordinate system {N}. It can be represented as:

[0103] .

[0104] The N1, N2, and N3 axes, described by unit direction vectors in coordinate system {B}, are expressed as follows:

[0105] ,

[0106] ,

[0107] .

[0108] Therefore, the direction matrix N of the coordinate system {N} can be represented as .

[0109] Based on the assumption of a circular arc with constant curvature, point F describes the coordinates in coordinate system {B}. The expression is:

[0110]

[0111] in, Let be the radius of curvature of the flexible skeleton 1. For lines FM and O b The included angle of M.

[0112] Point S describes the coordinates in coordinate system {B}. for:

[0113]

[0114] .

[0115] Based on geometric relationships, the initial length of flexible sensor 2 The expression is .

[0116] Taking the differential with respect to variable t yields:

[0117]

[0118]

[0119]

[0120] .

[0121] in, .

[0122] Therefore, the change in length of the flexible sensor 2 It can be calculated that:

[0123]

[0124] .

[0125] In one embodiment of this specification, based on a general shape-sensing model, a numerical iterative method is used to solve for the actual fixed position corresponding to each flexible sensor 2 in the joint module, including:

[0126] Obtain the first Jacobian matrix of the general shape perception model. The first Jacobian matrix represents the local sensitivity of the length change of all flexible sensors 2 in any joint module to the positioning parameter set.

[0127] Based on the general shape perception model and the first Jacobian matrix, the numerical iteration method is used to solve for the calibration value of the positioning parameter group corresponding to each flexible sensor 2 in the joint module with the length change deviation of each flexible sensor 2 in the joint module as the convergence target. The length change deviation is the difference between the actual length change obtained from the output value of the flexible sensor 2 and the theoretical length change obtained from the general shape perception model.

[0128] Explanatoryly, due to errors in the actual fixed position of the flexible sensor 2 during the experiment, a deviation occurs between its theoretical and actual length changes, reducing the accuracy of the shape-sensing model. Therefore, it is necessary to calibrate the actual fixed position of the sensor. Given the shape parameters of the flexible skeleton 1, a numerical iterative method is used to solve for the positioning parameter set corresponding to each flexible sensor 2. Therefore, it is necessary to solve for the first Jacobian matrix between the change in sensor length and the positioning parameter set corresponding to each flexible sensor 2. By differentiating the second positioning parameter λ, the third positioning parameter t0, and the fourth positioning parameter ∆t respectively, we can obtain:

[0129] ,

[0130] ,

[0131] .

[0132] in, .

[0133] Assuming the number of flexible sensors 2 fixed on the flexible skeleton 1 is m, the first Jacobian matrix between the length change of the flexible sensor 2 and the positioning parameter set corresponding to each flexible sensor 2 is... It can be defined as:

[0134]

[0135] in, This represents the change in length of the i-th flexible sensor 2.

[0136] When the flexible skeleton 1 undergoes bending deformation, the actual length of each flexible sensor 2 after tensile deformation... The initial length of the flexible sensor 2 before bending deformation can be obtained from its output capacitance value. And calculate the difference between the two. The deviation of length change was obtained. The numerical iterative method is used to continuously update the positioning parameter set corresponding to each flexible sensor 2 until... (The norm of the length change deviation) converges to within the allowable error range, and the calibration values ​​of the positioning parameter group corresponding to each flexible sensor 2 are solved.

[0137] It is important to note that while the sensing accuracy problem caused by the offset of the flexible sensor 2's installation position can be solved by calculating the Jacobian matrix between the length change of the parallel-arranged flexible sensor 2 and the fixed position parameters in the traditional method, the calibration effect can only be achieved using the general shape sensing model derived in this scheme. The Jacobian matrix between the length change of the parallel-arranged flexible sensor 2 and the fixed position parameters in the traditional method is obtained by differentiating the analytical expression of the length change of the flexible sensor 2. This analytical expression is based on the assumption that the four flexible sensors are perfectly parallel and is obtained using geometric relationships. When installation deviations occur, the Jacobian matrix becomes inaccurate, thus failing to achieve the calibration effect.

[0138] In one embodiment of this specification, based on a general shape-sensing model and a first Jacobian matrix, a numerical iterative method is used with the length variation deviation of each flexible sensor 2 in the joint module as the convergence target to solve for the calibration values ​​of the positioning parameter set corresponding to each flexible sensor 2 in the joint module, including:

[0139] The given initial values ​​of the determined shape parameters of the flexible skeleton 1 corresponding to the test shape and the positioning parameter group corresponding to each flexible sensor 2 in the joint module are used as inputs to the general shape perception model to obtain the theoretical length change corresponding to each flexible sensor 2 in the joint module.

[0140] The actual length change of each flexible sensor 2 in the joint module is obtained based on the output value of each flexible sensor 2 in the joint module.

[0141] The convergence of the numerical iteration method is determined based on the difference between the theoretical length change and the actual length change of each flexible sensor 2 in the joint module.

[0142] If convergence is not achieved, based on the first Jacobian matrix and the difference between the theoretical and actual length changes of each flexible sensor 2 in the joint module, update the given values ​​of the positioning parameter group corresponding to each flexible sensor 2 in the joint module for the next iteration, and re-input the general shape perception model for the next iteration.

[0143] If convergence is achieved, the current given value of the positioning parameter group corresponding to each flexible sensor 2 in the joint module is used as the calibration value.

[0144] Explanatory, combined with appendix Figure 9 , Figure 9 This is a flowchart illustrating the calibration of a general shape-sensing model provided in the embodiments of this specification. Given the determined shape parameters of the flexible skeleton 1 corresponding to the test shape, and initial given values ​​for the positioning parameter sets corresponding to each flexible sensor 2 in the joint module. The first iteration begins. Calculation... The result is compared with a first preset value representing the allowable error range. If the result is greater than this value, the result has not converged. The result is then determined according to the first Jacobian matrix. Calculate the deviation of the positioning parameter set of flexible sensor 2:

[0145]

[0146] in, for The pseudo-inverse of the matrix is ​​primarily intended to enable the solution of linear equations even when the matrix is ​​not invertible or is not a square matrix. When only two flexible sensors 2 are mounted on the flexible skeleton 1... That is, a square array.

[0147] Simultaneously update the given values ​​of the positioning parameter set for the next iteration:

[0148]

[0149] Where i (i=1,…,k) represents the iteration number. Until… If the value is less than the first preset value representing the allowable error range, convergence is complete, and the latest given value of the positioning parameter set is used as the calibration value, ending the iteration. Alternatively, iteration can end after reaching a given maximum number of iterations, using the given value of the positioning parameter set corresponding to the last iteration as the calibration value.

[0150] Illustratively, the initial setpoints for the positioning parameter sets corresponding to each flexible sensor 2 are the target values ​​when they are fixed.

[0151] In one embodiment of this specification, the joint module is driven to a working shape corresponding to the shape parameters of the flexible skeleton 1 to be determined. Based on a general shape-sensing model and the actual fixed position corresponding to each flexible sensor 2 in the joint module, the output value of the shape parameters of the flexible skeleton 1 in the joint module is solved using a numerical iteration method, including:

[0152] Obtain the second Jacobian matrix of the general shape-aware model. The second Jacobian matrix represents the local sensitivity of the length change of all flexible sensors 2 in any joint module to the shape parameters of the flexible skeleton 1.

[0153] Based on the general shape perception model, the second Jacobian matrix, and the calibration values ​​of the positioning parameter group corresponding to each flexible sensor 2 in the joint module, the numerical iteration method is used to solve the output value of the shape parameter of the flexible skeleton 1 in the joint module with the length change deviation of each flexible sensor 2 in the joint module as the convergence target.

[0154] Explanatoryly, given the length change of the flexible sensor, the shape parameters α and θ of the joint module are solved using a numerical iterative method. Therefore, it is necessary to solve for the sensor length change and shape parameters. The second Jacobian matrix between Taking the derivatives with respect to the shape parameters α and θ respectively, we get:

[0155] ,

[0156] .

[0157] in, .

[0158] Assuming the number of flexible sensors 2 fixed on the flexible skeleton 1 is m, the second Jacobian matrix between the length change of the flexible sensor 2 and its shape parameters is... transpose It can be defined as:

[0159]

[0160] in, This represents the change in length of the i-th flexible sensor 2.

[0161] When the flexible skeleton 1 undergoes bending deformation, the actual length of each flexible sensor 2 after tensile deformation... The initial length of the flexible sensor 2 before bending deformation can be obtained from its output capacitance value. And calculate the difference between the two. The deviation of length change was obtained. The numerical iterative method is used to continuously update the positioning parameter set until... (The norm of the length change deviation) converges to within the allowable error range, and the calibration values ​​of the positioning parameter group corresponding to each flexible sensor 2 are solved.

[0162] In one embodiment of this specification, based on a general shape-aware model, a second Jacobian matrix, and the calibration values ​​of the positioning parameter sets corresponding to each flexible sensor 2 in the joint module, a numerical iterative method is used with the length variation deviation of each flexible sensor 2 in the joint module as the convergence target to solve for the output values ​​of the shape parameters of the flexible skeleton 1 in the joint module, including:

[0163] The calibration values ​​of the positioning parameter group corresponding to each flexible sensor 2 in the joint module and the initial given values ​​of the shape parameters of the flexible skeleton 1 are used as inputs to the general shape perception model to obtain the theoretical length change corresponding to each flexible sensor 2 in the joint module.

[0164] The actual length change of each flexible sensor 2 in the joint module is obtained based on the output value of each flexible sensor 2 in the joint module.

[0165] The convergence of the numerical iteration method is determined based on the difference between the theoretical length change and the actual length change of each flexible sensor 2 in the joint module.

[0166] If convergence is not achieved, based on the second Jacobian matrix and the difference between the theoretical and actual length changes of each flexible sensor 2 in the joint module, update the given values ​​of the shape parameters of the flexible skeleton 1 in the joint module for the next iteration, and re-input the general shape perception model for the next iteration.

[0167] If convergence is achieved, the current given value of the shape parameter of the flexible skeleton 1 in the joint module will be used as the output value.

[0168] Explanatory, similar to the calibration process, provides calibration values ​​for the positioning parameter set corresponding to each flexible sensor 2, and initial set values ​​for the shape parameters of the flexible skeleton 1. The first iteration begins. Calculation... The result is compared with a second preset value representing the allowable error range. If the result is greater than this value, the result has not converged. The second Jacobian matrix is ​​then used to determine the outcome. Calculate the deviation of the positioning parameter set of flexible sensor 2:

[0169]

[0170] in, for The false rebellion.

[0171] Simultaneously update the given values ​​of the positioning parameter set for the next iteration:

[0172]

[0173] Where i (i=1,…,k) represents the iteration number. Until… If the value is less than the second preset value representing the allowable error range, convergence is complete, and the latest given value of the shape parameter is used as the output value, ending the iteration. Similarly, iteration can also end after reaching a given maximum number of iterations, using the given value of the shape parameter corresponding to the last iteration as the output value.

[0174] For illustrative purposes, the initial given values ​​for the shape parameters of the flexible skeleton 1 can be the solution from the previous moment or a rough shape parameter value calculated based on the length of the drive rope; if the system has just started, the vertical state or a safe intermediate value is used as the initial value.

[0175] In one embodiment of this specification, the shape-aware method further includes:

[0176] Obtain the variation range of the shape parameters of the flexible skeleton 1 corresponding to each joint module and the structural parameter data of the flexible skeleton 1;

[0177] For any joint module:

[0178] Based on the structural parameter data of the flexible skeleton 1 in the joint module, the particle swarm optimization algorithm is used to maximize the range of shape parameter variation of the flexible skeleton 1 as the optimization objective, and to obtain the recommended fixed position value of each flexible sensor 2 in the joint module.

[0179] Each flexible sensor 2 in the joint module is fixed based on the recommended fixed position value corresponding to each flexible sensor 2 in the joint module.

[0180] Explained, while the model allows for the selection of fixed positions within a certain range, different combinations of positioning parameters result in varying sensitivities to changes in shape parameters. To achieve optimal sensing performance, the selection of fixed positions needs optimization. There exists a set or range of optimal values ​​that enable the shape-sensing model to exhibit the highest sensitivity and accuracy within the expected curvature range. This embodiment represents a crucial pre-optimization design step in the shape-sensing method. Its core objective is to ensure, through algorithmic optimization, that the recommended fixed position value with optimal sensing performance is determined before the sensor is physically fixed, thereby improving the effectiveness and robustness of the entire shape-sensing system.

[0181] Illustratively, given the variation range of the shape parameters of the flexible skeleton 1 corresponding to each joint module and the structural parameter data of the flexible skeleton 1 (length L and base radius r in the vertical state), these data define the optimization boundary conditions and target scenario. Since different fixed positions of the flexible sensor 2 result in varying sensitivities to skeleton bending deformation, a particle swarm optimization algorithm is introduced. The optimization objective is to maximize the variation range of the shape parameters of the flexible skeleton 1. That is, the algorithm searches through all possible combinations of sensor positioning parameters to find a set of parameters that, when the flexible sensor 2 is installed based on these parameters, still produces a significant and measurable length change when the skeleton bends to its theoretical limit, thus avoiding blind spots or areas of insufficient sensitivity. Finally, the recommended fixed position values ​​obtained from the optimization calculation are used as guidance for the physical fixing of the flexible sensor 2.

[0182] By leveraging known structural and motion constraints, optimization algorithms are used to proactively determine the ideal sensor placement parameters, thereby laying the optimal sensing foundation for the entire shape sensing system at the hardware level.

[0183] Working principle:

[0184] Based on the joint variable range of each joint module, stretchable capacitive sensors with characteristics such as good stability, high tensile strength, high resolution, and strong anti-interference ability are selected. Two flexible sensors 2 are fixed at arbitrary positions on the surface of the flexible skeleton 1, both in a pre-stretched state. The shape of the flexible sensor 2 is simplified to a helix. Based on the assumption of constant curvature, a general shape perception model is established, namely the mapping relationship between the length change of the flexible sensor 2 and the shape parameters. Since there is an error in the actual fixed position of the flexible sensor 2, a numerical iteration method is used to calibrate the shape perception model to improve its accuracy.

[0185] The shape sensing method based on multiple flexible sensors 2 proposed in this invention can achieve accurate measurement of the shape of the flexible skeleton 1 under large bending deformation. The output signal of the flexible sensor 2 is not easily affected by external environmental interference, such as light and temperature, and has the advantages of good stability and high accuracy.

[0186] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0187] A shape perception system for a rope-driven continuum robot.

[0188] like Figure 10 As shown, Figure 10 A schematic diagram of the shape perception system of a rope-driven continuum robot provided in an embodiment of this specification is shown.

[0189] The shape sensing system 100 applies the shape sensing method in the above embodiments and includes a position calibration unit 1001 and a shape sensing unit 1002.

[0190] The position calibration unit 1001 drives any joint module to the test shape of the corresponding determined shape parameters of the flexible skeleton 1. Based on the constructed general shape perception model, it uses a numerical iteration method to solve the actual fixed position of each flexible sensor 2 in the joint module.

[0191] The shape sensing unit 1002 drives any joint module to the working shape corresponding to the shape parameters of the flexible skeleton 1 to be determined. Based on the general shape sensing model and the actual fixed position of each flexible sensor 2 in the joint module, it uses a numerical iteration method to solve the output value of the shape parameters of the flexible skeleton 1 in the joint module. It also determines the robot shape based on the output value of the shape parameters of the flexible skeleton 1 corresponding to each joint module.

[0192] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the shape sensing system embodiments are basically similar to the shape sensing method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the shape sensing method embodiments.

[0193] Please see Figure 11 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this specification.

[0194] like Figure 11 As shown, the electronic device 110 may include at least one processor 1101, at least one network interface 1104, a user interface 1103, a memory 1105, and at least one communication bus 1102.

[0195] The communication bus 1102 can be used to realize the connection and communication of the above components.

[0196] The user interface 1103 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0197] The network interface 1104 may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0198] The processor 1101 may include one or more processing cores. The processor 1101 connects to various parts within the electronic device 110 using various interfaces and lines. It performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1105, and by calling data stored in the memory 1105. Optionally, the processor 1101 may be implemented using at least one hardware form selected from DSP, FPGA, and PLC. The processor 1101 may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 1101.

[0199] The memory 1105 may include RAM or ROM. Optionally, the memory 1105 may include a non-transitory computer-readable medium. The memory 1105 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 1105 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1105 may also be at least one storage device located remotely from the aforementioned processor 1101. As a computer storage medium, the memory 1105 may include an operating system, a network communication module, a user interface module, and a shape-aware application. The processor 1101 may be used to call the shape-aware application stored in the memory 1105 and execute the steps of the shape-aware method mentioned in the foregoing embodiments.

[0200] This specification also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps in the above-described shape sensing method embodiments. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0201] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this specification is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0202] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and its implementation can be combined arbitrarily.

[0203] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Any modifications and improvements made by those skilled in the art to the technical solutions of this specification without departing from the spirit of this specification should fall within the protection scope defined by the claims of this specification.

Claims

1. A shape perception method for a rope-driven continuum robot, the robot comprising multiple series-connected joint modules, characterized in that... Each joint module includes a cylindrical flexible skeleton and at least two flexible sensors, with each flexible sensor fixed to the side of the flexible skeleton in a spiral manner; the shape sensing method includes the following steps: Based on the constant curvature assumption, a general shape perception model is established to describe the mapping relationship between the shape parameters of the flexible skeleton and the length change of the flexible sensor at any fixed position on the flexible skeleton. For any joint module: The joint module is driven to the test shape corresponding to the determined flexible skeleton shape parameters. Based on the general shape perception model, the actual fixed position of each flexible sensor in the joint module is solved by numerical iteration method. Drive the joint module to the working shape corresponding to the shape parameters of the flexible skeleton to be determined. Based on the general shape perception model and the actual fixed position of each flexible sensor in the joint module, the output value of the shape parameters of the flexible skeleton in the joint module is solved by numerical iteration method. The robot shape is determined based on the output values ​​of the flexible skeleton shape parameters corresponding to each joint module.

2. The shape perception method for a rope-driven continuum robot according to claim 1, characterized in that, The general shape-sensing model, based on the constant curvature assumption, describes the mapping relationship between the shape parameters of the flexible skeleton and the length change of the flexible sensor at any fixed position on the flexible skeleton, including: Define a set of positioning parameters in the joint module that describe the fixed position of the flexible sensor relative to the flexible skeleton; Based on the constant curvature assumption, a general shape perception model is established using the positioning parameter set of the flexible sensor and the structural parameters of the flexible skeleton.

3. The shape perception method for a rope-driven continuum robot according to claim 2, characterized in that, The defined joint module describes the set of positioning parameters that define the fixed position of the flexible sensor relative to the flexible skeleton, including: A coordinate system is established with the center of the bottom circle of the flexible skeleton in the vertical state as the origin. The Z-axis of the coordinate system coincides with the neutral axis of the flexible skeleton, and the X-axis of the coordinate system is used as the reference direction. Starting from the intersection of the X-axis of the coordinate system and the side of the flexible skeleton, the side of the flexible skeleton is cut open and flattened into a two-dimensional plane along the direction parallel to the Z-axis of the coordinate system. The first positioning parameter is defined as the intercept between the length direction of the flexible sensor and the vertical axis of the two-dimensional plane. The second positioning parameter is defined as the angle between the length direction of the flexible sensor and the horizontal axis of the two-dimensional plane. The third positioning parameter is defined as the angle between the projection point of any endpoint of the flexible sensor on the bottom surface of the flexible skeleton and the X-axis of the coordinate system. The fourth positioning parameter is defined as the central angle between the projection points of the two endpoints of the flexible sensor on the bottom surface of the flexible skeleton.

4. The shape perception method for a rope-driven continuum robot according to claim 2, characterized in that, The method of using a numerical iterative method based on a general shape-sensing model to solve for the actual fixed position of each flexible sensor in the joint module includes: Obtain the first Jacobian matrix of the general shape perception model. The first Jacobian matrix represents the local sensitivity of the length change of all flexible sensors in any joint module to the positioning parameter set. Based on the general shape perception model and the first Jacobian matrix, a numerical iterative method is used to solve for the calibration value of the positioning parameter group corresponding to each flexible sensor in the joint module with the length change deviation of each flexible sensor in the joint module as the convergence target. The length change deviation is the difference between the actual length change obtained from the output value of the flexible sensor and the theoretical length change obtained from the general shape perception model.

5. The shape perception method for a rope-driven continuum robot according to claim 4, characterized in that, Based on the general shape-sensing model and the first Jacobian matrix, a numerical iterative method is used with the length variation deviation of each flexible sensor in the joint module as the convergence target to solve for the calibration values ​​of the positioning parameter set corresponding to each flexible sensor in the joint module, including: The initial given values ​​of the determined flexible skeleton shape parameters corresponding to the test shape and the positioning parameter group corresponding to each flexible sensor in the joint module are used as inputs to the general shape perception model to obtain the theoretical length change corresponding to each flexible sensor in the joint module. The actual length change of each flexible sensor in the joint module is obtained based on the output value of each flexible sensor in the joint module. The convergence of the numerical iteration method is determined by the difference between the theoretical and actual length changes of each flexible sensor in the joint module. If convergence is not achieved, based on the first Jacobian matrix and the difference between the theoretical and actual length changes of each flexible sensor in the joint module, update the given values ​​of the positioning parameter group corresponding to each flexible sensor in the joint module for the next iteration, and re-input the general shape perception model for the next iteration. If convergence is achieved, the current given value of the positioning parameter group corresponding to each flexible sensor in the joint module is used as the calibration value.

6. The shape perception method for a rope-driven continuum robot according to claim 4, characterized in that, The process of driving the joint module to the working shape corresponding to the shape parameters of the flexible skeleton to be determined involves, based on a general shape perception model and the actual fixed position of each flexible sensor in the joint module, using a numerical iterative method to solve for the output value of the flexible skeleton shape parameters in the joint module, including: Obtain the second Jacobian matrix of the general shape-aware model. The second Jacobian matrix represents the local sensitivity of the length change of all flexible sensors in any joint module to the shape parameters of the flexible skeleton. Based on the general shape perception model, the second Jacobian matrix, and the calibration values ​​of the positioning parameter group corresponding to each flexible sensor in the joint module, the numerical iteration method is used to solve for the output value of the flexible skeleton shape parameter in the joint module with the length change deviation of each flexible sensor in the joint module as the convergence target.

7. The shape perception method for a rope-driven continuum robot according to claim 6, characterized in that, The method, based on the general shape perception model, the second Jacobian matrix, and the calibration values ​​of the positioning parameter sets corresponding to each flexible sensor in the joint module, employs a numerical iterative method with the length variation deviation of each flexible sensor in the joint module as the convergence target to solve for the output values ​​of the flexible skeleton shape parameters in the joint module, including: The calibration values ​​of the positioning parameter group corresponding to each flexible sensor in the joint module and the initial given values ​​of the flexible skeleton shape parameters are used as inputs to the general shape perception model to obtain the theoretical length change corresponding to each flexible sensor in the joint module. The actual length change of each flexible sensor in the joint module is obtained based on the output value of each flexible sensor in the joint module. The convergence of the numerical iteration method is determined by the difference between the theoretical and actual length changes of each flexible sensor in the joint module. If convergence is not achieved, based on the second Jacobian matrix and the difference between the theoretical and actual length changes corresponding to each flexible sensor in the joint module, update the given values ​​of the flexible skeleton shape parameters in the joint module for the next iteration, and re-input the general shape perception model for the next iteration. If convergence is achieved, the current given value of the flexible skeleton shape parameter in the joint module will be used as the output value.

8. The shape perception method for a rope-driven continuum robot according to claim 1, characterized in that, Also includes: Obtain the range of change of the shape parameters of the flexible skeleton corresponding to each joint module and the structural parameter data of the flexible skeleton; For any joint module: Based on the structural parameter data of the flexible skeleton in the joint module, the particle swarm optimization algorithm is used to maximize the range of change of the shape parameters of the flexible skeleton as the optimization objective, and to obtain the recommended fixed position value of each flexible sensor in the joint module. The flexible sensors in the joint module are fixed based on the recommended fixed position values ​​corresponding to each flexible sensor in the joint module.

9. A shape perception method for a rope-driven continuum robot according to any one of claims 1-8, characterized in that, The flexible sensor is a stretchable capacitive sensor.

10. A shape perception system for a rope-driven continuum robot, characterized in that, A shape perception method for a rope-driven continuum robot according to any one of claims 1-9, wherein the shape perception system includes a position calibration unit and a shape perception unit; The position calibration unit, for any joint module, drives the joint module to the test shape corresponding to the determined flexible skeleton shape parameters, and uses a numerical iteration method to solve the actual fixed position of each flexible sensor in the joint module based on the constructed general shape perception model. The shape sensing unit drives any joint module to the working shape corresponding to the shape parameter of the flexible skeleton to be determined. Based on the general shape sensing model and the actual fixed position of each flexible sensor in the joint module, the numerical iteration method is used to solve the output value of the shape parameter of the flexible skeleton in the joint module. The robot shape is also determined based on the output values ​​of the flexible skeleton shape parameters corresponding to each joint module.

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