the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Kinematics and control of a cabledriven snakelike manipulator for underwater application
Fufeng Xue
Zhimin Fan
In view of the large volume, complex structure, and poor performance of traditional underwater manipulators in some complicated underwater scenarios, a cabledriven snakelike manipulator (CDSLM) is proposed. In this paper, the kinematics model of the proposed CDSLM is firstly established, which can be decomposed into three parts: motor–cable kinematics, cable–joint kinematics, and joint–end kinematics. A tipfollowing algorithm is then presented to weave through the confined and hazardous spaces along a defined path with high efficiency. The main merit of the algorithm is that only the terminal section variables need to be calculated and recorded, which solves the problem of expensive computational cost for the inverse kinematics of snakelike manipulators. Finally, evaluation indexes of the pathfollowing performance are proposed to evaluate the effect of the tipfollowing algorithm. Simulations of the pathtracking performance are carried out using MATLAB. The results demonstrate that the average computation time is about 1.6 ms, with a deviation of less than 0.8 mm from the desired path, and the stability and effectiveness of the tipfollowing algorithm are verified.
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Humankind has turned its attention to the vast ocean due to the region's abundant mineral and biological resources (Brito et al., 2019; Liu et al., 2018). Underwater manipulators play an irreplaceable role in the development of marine resources. To date, most underwater manipulators have been rigidlink structures with a small number of degrees of freedom (DOFs) (Barbieri et al., 2018) and have not performed well in confined spaces. With the development of deepsea equipment, marine structures, such as deepsea laboratories, deepsea space stations, and deepsea cities, have emerged. Their operation, construction, and maintenance will inevitably involve some complicated underwater environments, such as deep cavities, pipe networks, and other confined and hazardous spaces. Therefore, there is an urgent need for a lightweight underwater manipulator with superior dexterity and good obstacle avoidance capabilities in the complex underwater environment.
Due to their high dexterity and strong adaptability to environment, hyperredundant manipulators have great advantages with respect to obstacle avoidance. This had made them tremendously fascinating in numerous research fields, and they have already been used in practice in fields such as inspection, maintenance, assemblage, repair, and so on, in nuclear reactors (Wu et al., 2015), aircraft (Dong et al., 2017), minimally invasive surgery (Cianchetti et al., 2014; Kai and Simaan, 2010; Li et al., 2017), and other fields (Tang et al., 2019a; Xu et al., 2020). Generally, hyperredundant manipulators can be grossly classified into two types: (1) discrete manipulators with multiple rigid links (Tang et al., 2017) and (2) continuum manipulators with multiple segmented elastic backbones (Hu et al., 2010). The kinematics modeling for continuum manipulators is based on the piecewise constant curvature assumption (Tang et al., 2019; Yan et al., 2018), and the effects of gravity and external loads are ignored. Hence, it is hard to accurately depict the real position of a continuum manipulator in some cases. Compared with a continuum manipulator, a discrete manipulator with multiple rigid links has a stronger payload capacity and motional accuracy (Cao et al., 2012), and the elastic materials are easily affected by various factors, such as high pressure and corrosion, which is not conducive to operations in marine environments. Therefore, the focus of this paper is a cabledriven snakelike manipulator (CDSLM) composed of multiple rigid links and joints. Due to the large number of DOFs, small size, and the fact that it does not require a complex sealing structure, the snakelike manipulator has strong potential for use in complex marine applications.
In contrast to traditional underwater manipulators that have the actuators installed directly at the joints, a cabledriven mechanism permits the actuators to be mounted remotely from the joints; therefore, the weight of the manipulator is effectively reduced. To date, most existing cabledriven manipulators have mainly been for land applications (Tang et al., 2019) and usage in the underwater environment has been rare. Recently, Li et al. (2019) presented a novel underwater cabledriven manipulator for autonomous underwater vehicles, but the structure did not break away from the shackles of traditional manipulators and could not perform well in confined spaces. Therefore, a lightweight CDSLM with superior dexterity and large maneuverability will inevitably become the focus of research on manipulators in complicated underwater environments.
Many efforts have been made to improve the obstacle avoidance ability of hyperredundant robots. Conkur et al. (2003) explored how to follow the planar line using hyperredundant manipulators, and Tanaka et al. (2015) presented an approximate pathtracking control method for a wheeled snake robot. However, these studies were mainly applicable to the planar path. Palmer et al. (2014) presented a tipfollowing method that used the sequential quadratic programming optimization approach and created the objective function to minimize the deviation from the desired path, but they still had the problem of a huge computational cost. Andersson et al. (2008) used the bisection search method to find the location of the points along the curve, but the efficiency of this method is affected by the initial values. Tang et al. (2019b) proposed a twolevel motionplanning method to improve the tracking accuracy and motion smoothness of a cabledriven snake robot; however, the study did not give a specific implementation in confined spaces. Bulut and Conkur (2021) presented a realtime pathplanning algorithm for hyperredundant manipulators, and their paper focused on the instrument's maneuvering capabilities.
Based on the idea of discretization, a tipfollowing algorithm is proposed in this work. The main advantage is that only the terminal section variables of the CDSLM need to be solved, which addresses the computational cost problem for the inverse kinematics of hyperredundant manipulators. Evaluation indexes are proposed to assess the effects of the tipfollowing algorithm: these are the tracking error and response time. Simulations are analyzed in MATLAB, and the results show that the algorithm can follow the given path effectively.
2.1 Overall design of the CDSLM robot system
In this work, a novel CDSLM is designed to perform specific tasks in some complicated underwater scenarios. It is composed of seven sections with 2DOFs, and each section includes two joint discs and a link, as depicted in Fig. 1. Considering the working environment of the CDSLM, the 6061T6 aluminum alloy is used in joints and links. No driving motors are installed at the joints, and the links can be designed as a permeable structure so that the water resistance under the same size condition will be further reduced, thereby weakening the coupling effect between the manipulator and the underwater vehicle (Chang et al., 2020).
Unlike the traditional underwater manipulators that have the actuators mounted directly on the joints, the cables are used to transmit mechanical power from the motors to joints. Usually, a cable is driven by a servomotor (Peng et al., 2021). However, this structure requires too many motors, which leads to an oversized drive device and high energy consumption. In this paper, the gearrack structure is adopted, and each drive unit controls two cables simultaneously, which minimizes the number of motors. The drive unit is depicted in Fig. 2.
Due to the structure of the CDSLM, a mutual coupling exists between the cables. To counter the coupling effect caused by joints, Xu et al. (2018) and Hu et al. (2010) compensated for the changes in cable length using the movement of the corresponding motors. However, this increases the control difficulty and the computation requirements, thereby affecting the realtime performance of the CDSLM. The cable in this study adopts a structure similar to the brake cable on bicycles. The brake cable on bicycles consists of a protective sheath and an internal transmission cable with relative motion, which is a typical cable–sheath transmission system. The cable length does not change with the shape of the sheath. Based on this advantage, the cable–sheath mechanism is widely used for longdistance power transmission.
The CDSLM robot system consists of a snakelike manipulator, an actuation system, an end effector, and a feedin mechanism, as shown in Fig. 3.
2.2 Multilevel mapping relationships of the CDSLM
For a CDSLM with n sections, the multilevel mapping relationships among the motors, cables, joints, and end effector can be described as shown in Fig. 4, which can be decomposed into motor–cable kinematics, cable–joint kinematics, and joint–end kinematics.
2.3 Motor–cable kinematics
Four cables are used to control the 2 DOFs of motion in each section. The two cables with an interval angle of 180^{∘} are controlled by a servomotor. The relationship between the motor angle and the cable length is as follows:
where D=18 mm and I=84 are the pitch circle diameter of the drive gear and the reduction ratio of the reducer, respectively; Δl and Δθ_{i} denote the variations in the cable length and motor angle, respectively.
2.4 Cable–joint kinematics
To describe the relationship between the cable lengths and the joint variables, a geometric model of a single joint is created, as shown in Fig. 5. The two red dotted lines denote the state of the cables when the joint is not rotating; the two solid black lines indicate the state of the cables after the joint has rotated; h is the joint distance; L_{1}, L_{2} represents the length of the cables after rotation; r is the distance from the cable to the center of the joint; θ is the joint rotation angle;
The relationship between length variable and the joint angle is as follows:
According to the mechanical design, the following relationship exists:
2.5 Joint–end kinematics
In order to establish the mapping relationship from the joint space to the operation space, the Denavit–Hartenberg (D–H) method is utilized. The coordinate system of each section is defined as shown in Fig. 6, and the corresponding D–H parameters of the CDSLM are listed in Table 1. The CDSLM consisted of seven joints and eight key points (joint center K_{1}–K_{7} and the terminal point K_{8}).
According to the D–H rules, the homogeneous transformation matrix between the frame {i+1} and {i} can be denoted as follows:
For hyperredundant manipulators, the tipfollowing algorithm can perform well in spaceconstrained environments. The terminal section to enter the work environment is regarded as the base until the feedin mechanism has advanced enough to consider a new section. The joint variables of the terminal section are calculated and recorded after the feedin mechanism advances one step, and the joint variables of the subsequent sections can be obtained directly from the recorded data when they reach the same position. Like the snake game, each section of the snake takes up the position of the previous one as it chases the food with its head. Therefore, the key issue of the tipfollowing algorithm is to calculate and record the joint variables of the terminal section when the feedin mechanism progresses one step. The tipfollowing algorithm has three main parts: path planning, the yardstick method, and the inverse resolution.
3.1 Path planning
Path planning refers to choosing an effective path with the least cost and making it collisionfree from the start point to the target point in confined spaces. In this work, path planning is not a focal point, and we pay more attention to the tipfollowing algorithm with a welldefined environment. The equal arc length method is used to generate discrete points of the desired path curve; the discrete points are ordered, and the first point from the beginning is P_{1}.
3.2 The yardstick method
The key points of the CDSLM should be matched with the discrete points on the desired path. Assuming that the length of a single section is L and the step value of the actuation system is s, the number of steps required to consider a new section is shown as follows:
The yardstick method is proposed to match the key point K_{8} and the discrete point P_{i,j} ($i=\mathrm{1},\mathrm{2},\mathrm{3}\mathrm{\dots},n$; $j=\mathrm{1},\mathrm{2},\mathrm{3}\mathrm{\dots},w$). In the initial state, the terminal point K_{8} coincides with start point P_{1}, and the positions of the key points K_{1}–K_{8} are known. An example is given to explain the yardstick method.
As shown in Fig. 7, when the feedin mechanism advances a step from the initial state, it must find a discrete point that meets the length constraint $\Vert {K}_{\mathrm{7}}{K}_{\mathrm{8}}\Vert =L$ (i.e., it makes a circle with L as the radius and the key point K_{7} as the center) to search for discrete point P_{1,1}. Taking discrete point P_{1,1} as the center and the length L as the radius, the system then finds the discrete point P_{2,1}. Subsequently, ${P}_{\mathrm{3},\mathrm{1}},{P}_{\mathrm{4},\mathrm{1}},\mathrm{\dots}{P}_{i,\mathrm{1}}$ can be found in the same way. The feedin mechanism then advances a step. When the key point K_{8} coincides with point P_{2,1}, the position of K_{7} coincides with P_{1,1}, and the joint variables of section S_{6} are the same as for terminal section S_{7} at this position. The pseudocode of the yardstick method is presented in Fig. 8.
The distance between the discrete points found by the yardstick method may not exactly equal L due to the discretization. In this paper, an interpolation method is proposed to reduce the tracking error.
As shown in Fig. 9, P_{m−1}, P_{m}, and P_{m+1} are three candidate points (m is the discrete point number), and the lengths of K_{7}P_{m−1} and K_{7}P_{m} are d_{m−1} and d_{m}, respectively. It can be seen from Fig. 9 that P_{m} is the best point for the length constraint found by the yardstick method. If the length constraint is performed strictly, point A is the point that satisfies the constraint of $\Vert {K}_{\mathrm{7}}A\Vert =L$. However, the real position of point A cannot be found due to the discretization.
Points A, B, and C are on the line P_{m−1}P_{m}, which satisfies the constraint of $\Vert {K}_{\mathrm{7}}B\Vert =L$. The position of point B cannot be obtained directly, so we approximately replace it with point C. According to the geometric relationship, Eq. (8) can be obtained, and the position of C can be denoted using Eq. (9).
Point C obtained by Eq. (9) is a little different from point B. To simplify the calculation, point C is considered to meet the accuracy requirement.
3.3 Inverse resolution
The task of solving inverse kinematics for a CDSLM is quite challenging due to its hyperredundancy. This section discusses how to calculate the joint variables based on the positions of the key points obtained in Sect. 3.2. With the kinematics transformation matrix, the relationship between the x axes of sections S_{k+1} and S_{k} can be obtained as follows:
${x}_{\mathrm{2}k\mathrm{1}}^{(\mathrm{2}k\mathrm{1})}=[\mathrm{0}\phantom{\rule{0.33em}{0ex}}\mathrm{0}\phantom{\rule{0.33em}{0ex}}\mathrm{1}{]}^{\prime}$, and ${x}_{\mathrm{2}k+\mathrm{1}}^{(\mathrm{2}k\mathrm{1})}=[a\phantom{\rule{0.33em}{0ex}}b\phantom{\rule{0.33em}{0ex}}c{]}^{\prime}$. Thus, the joint angles of the terminal section can be denoted as follows:
The inverse kinematics for the CDSLM is transformed into the inverse kinematics of the terminal section S_{n} with 2 DOFs. The decomposition process is carried out as follows. When the terminal section S_{n} follows the first segment of the path, the joint variables of the first (n−1) sections are zero. For each step of the feedin mechanism, the joint variables of S_{n} are calculated and recorded. When the subsequent sections reach the same position, these data can be given to the sections from the database directly. When S_{n} follows the second segment of the path, the joint variables of S_{n} are calculated and recorded. S_{n−1} follows the first segment of the path simultaneously and replicates the joint variables of S_{n} at this location. This method only solves the joint variables of the terminal section S_{n}, which solves the huge calculation requirement issue of the inverse kinematics of snakelike manipulators. A flowchart of the tipfollowing algorithm is shown in Fig. 10.
Simulations of the tipfollowing algorithm are performed on a standard commercial desktop running an i56500 (3.2 GHz) processor to verify the effectiveness of the proposed algorithm. Figures 11 and 12 show the CDSLM following the defined planar and space paths, respectively.
The tracking error and response time are proposed to evaluate the performance of the tipfollowing algorithm. Without losing generality, this study tracks a spiral path, as shown in Fig. 13. The length of each section is 150 mm, and the step value is 6 mm. There are 125 steps in total, and each step is 160 ms. The space path can be expressed as follows:
where Curve_x, Curve_y, and Curve_z represent the respective x, y, and z coordinates of points on the curve, and t is the curve parameter.
4.1 Response time
The response time refers to the time required to find the discrete points according to the yardstick method and to calculate the joint variables of the CDSLM, which are used to measure the realtime performance of the tipfollowing algorithm. To minimize the calculation time, one must ensure that this algorithm only performs the necessary calculations. The tipfollowing algorithm only calculates the joint variables of the terminal section and ignores the parameters of the sections that do not enter the working environment. This is beneficial and greatly reduces the computation time. For a snakelike manipulator with 14 DOFs, the computation time spent finding the discrete points that correspond to the key points using the optimization method (Palmer et al., 2014), the bisection method (Niu et al., 2014), and yardstick method are listed in Table 2.
4.2 Tracking error
The tracking error represents the distance between the terminal point K_{8} and the defined path. For each step, only the joint variables of the terminal section need to be solved. Therefore, the actual position of the terminal point K_{8} is calculated by forward kinematics with the joint variables obtained by Eq. (11). Finally, the distance between the key point K_{8} and the defined path is calculated.
The position of the key point K_{8} is shown in Fig. 14. The length changes of cables for the terminal section are shown in Fig. 15. Figure 16 shows the detailed error.
It can be seen from Fig. 16 that the tracking error increases with time. This article considers that the error reaches its maximum at the termination time. These results indicate that the interpolation method can effectively reduce the tracking error: the maximum error with interpolation is less than 0.8 mm, and the mean error is less than 1 % of the total length of the manipulator. The rootmeansquare error (RMSE) of the terminal point with and without interpolation are shown in Table 3, and the results show that the RMSE with interpolation is reduced by about 52 %.
In this paper, a novel cabledriven snakelike manipulator (CDSLM) with large maneuverability and superior dexterity is designed. This CDSLM has strong potential for some complicated underwater applications.
The multilevel mapping relationships between the control space, drive space, joint space, and operation space are clearly described, and the corresponding kinematics equations are derived and solved by combining numerical and analytical approaches.
A tipfollowing algorithm is proposed to improve the efficiency of the CDSLM's motion in complicated underwater scenarios. The algorithm composes three steps: path generation, the yardstick method, and inverse kinematics. The yardstick method is presented in order to match the key point K_{8} and the discrete points. Meanwhile, an interpolation method is proposed to reduce the errors caused by the discretization. For a CDSLM robot with seven sections, the average calculation time is 1.6 ms, with a mean error of less than 1 %. Thus, this method can be used for realtime control of the CDSLM robot.
All data included in this study can be requested from the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/ms134952022supplement.
FX conceived the original ideas and drafted the manuscript. ZF supervised the project and reviewed the writing.
The contact author has declared that neither they nor their coauthor has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The authors acknowledge the financial supported by the National Natural Science Foundation of China.
This research has been supported by the National Natural Science Foundation of China (grant no. 52075279).
This paper was edited by Mohamed Amine Laribi and reviewed by Petr Chalupa and one anonymous referee.
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