Three-degree-of-freedom snake robot control method and robot with variable telescopic structure

Abstract

The application discloses a three-degree-of-freedom snake robot control method and robot with a variable telescopic structure, adopts a modular design snake robot structure, each module includes pitch, yaw and joint, the joint structure is designed to adopt the telescopic joint associated with the rotary joint; the rotary joint as a whole adopts a serpentine curve Serpenoid curve to move, the telescopic joint different gait is matched with the rotary joint; the telescopic joint is combined with the serpentine curve, a genetic algorithm is used to optimize the snake robot route, and route deviation is solved; the proximity sensor of the head of the snake robot detects the obstacle, automatically switches the gait, calculates the height of the obstacle and the lifting angle of the snake robot, and designs a multi-modal control system; parameters can be adjusted according to the terrain characteristics, a gait strategy is generated, the motion performance of the snake robot is changed, a gait switching effect in complex terrain is realized, and the flexibility and safety of the motion of the snake robot are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robots, in particular to a three-degree-of-freedom snake robot control method and robot with variable telescopic structure. BACKGROUND

[0002] With the rapid development of automation and intelligent technology, snake robots have received increasing attention due to their flexible movement capabilities and wide application potential. As a multi-degree-of-freedom bionic robot, snake robots play an important role in human public life and military needs. The mechanical structure of snake robots is divided into rigid, flexible, and flexible-rigid combinations. The connection methods include parallel connection, orthogonal connection, universal joint connection, and P-R connection. Various structures of snake robots are formed by interlacing each other.

[0003] However, existing snake robots have many technical problems in motion control. First, traditional snake robots can only adopt a single mode during movement, such as peristalsis, serpentine, or lateral movement, and cannot coordinate multiple movement modes at the same time. This limitation results in insufficient adaptability and flexibility of the robot in complex environments, making it difficult to effectively deal with variable terrains and obstacles, reducing its efficiency and effectiveness in practical applications. Second, existing technologies perform poorly in terms of snake robot obstacle crossing ability. Most snake robots rely on additional devices such as active wheels to help climb over step-type obstacles, increasing mechanical complexity and energy consumption. At the same time, these traditional control methods fail to fully utilize the structural advantages of the robot, resulting in low action efficiency and even safety hazards such as falling when dealing with obstacles of different heights. With the continuous development of information technology related to robots and artificial intelligence, the ability to quickly act and accurately reach a position is a trend for snake robots. SUMMARY

[0004] The present application provides a three-degree-of-freedom snake robot control method and robot with variable telescopic structure, based on a genetic algorithm control method, which optimizes the design of snake robots with variable telescopic structure, solving the technical problems of single movement mode and insufficient obstacle crossing ability in existing technologies. By setting an adaptive function, the application optimizes the movement trajectory of the robot in different modes of the telescopic joint in real time, thereby achieving multi-modal motion control and enhancing the flexibility and adaptability of the robot. At the same time, combined with virtual robot technology, the application records the running trajectory in different modes through online programming, enabling the robot to automatically adjust the movement strategy according to the terrain characteristics, improving the overall running efficiency and achieving precise, safe, and efficient control of snake robots in complex environments.

[0005] The present application provides a three-degree-of-freedom snake robot control method with variable telescopic structure, comprising:

[0006] Step S1, a modular design serpentine robot structure is adopted, each module comprising a pitch, yaw and telescopic joint, the joint structure being designed to adopt a telescopic joint associated with a rotary joint;

[0007] Step S2, the road surface is divided into three types: flat ground, obstacle road surface and step type obstacle according to the road surface state; the movement mode, distance transformation and rhythm transformation of the telescopic joint are analyzed, and the telescopic joint gait is divided into six modes: single standing wave gait, multiple standing wave gait, escape gait, obstacle crossing gait, acceleration and deceleration gait and stop gait;

[0008] Step S3, the rotary joint as a whole adopts a meandering curve for motion control, and the telescopic joint gait is different under different motion modes;

[0009] Step S4, the telescopic joint is combined with the meandering curve, and a genetic algorithm is used to optimize the overall trajectory of the serpentine robot to solve the route deviation caused by the vertex problem of the change of joint angle;

[0010] Step S5, the proximity distance sensor of the head of the serpentine robot detects the obstacle, automatically switches the gait, calculates the height of the obstacle, calculates the lifting angle of the serpentine robot, and designs a multi-mode control system.

[0011] Preferably, the telescopic joint associated with the rotary joint in step S1 is specifically: when the angle of the rotary joint is within ±0.5 degrees, the control system triggers the corresponding telescopic joint to extend or retract, forming a telescopic control mode based on the angle of the joint.

[0012] Preferably, the number of standing waves of the telescopic joint gait in step S2 is:

[0013] m∈[(n+2) / (h+2), (n+h) / (h+2)]

[0014] Wherein, the number of standing waves m and the number of joint units n satisfy m<n / 2; the interval h between multiple standing waves is set to satisfy h<n.

[0015] Preferably, step S4 is specifically:

[0016] Step S41, initialize individuals and populations, and generate a certain number of random trajectories;

[0017] Step S42, set the fitness function; the fitness function is used to evaluate the quality of each motion trajectory, and the specific calculation method can be based on the deviation between the current trajectory and the ideal trajectory. Yc is the y coordinate of the current trajectory, Yi is the y coordinate of the ideal trajectory, and n is the number of points of the trajectory;

[0018] Step S43, selection operation; using tournament selection, 3 individuals are randomly selected from the population for comparison each time, and the individual with the highest fitness is selected as the parent;

[0019] Step S44, crossover operation; single-point crossover is adopted, a crossover point is randomly selected, and the crossover probability is set to 80%;

[0020] Step S45, mutation operation; the mutation rate is set to 3%, and 3% of the individuals are randomly selected in each generation for small-scale adjustment to introduce new genes;

[0021] Step S46, setting the number of iterations; 200 generations are set;

[0022] Step S47, setting the termination condition; if the fitness does not significantly improve in 20 consecutive generations, the algorithm is stopped.

[0023] Preferably, the step S5 obstacle height calculation formula is:

[0024] H = h1 + h2 = (l + d) tan theta + h2

[0025] Wherein, h1 is the height of a single module of the snake robot; h2 is the height between a single module of the snake robot and the obstacle; l is the length of a single module of the snake robot; d is the distance between a single module of the snake robot and the obstacle; theta is the lifting angle of a single module of the snake robot.

[0026] Preferably, the step S5 snake robot lifting angle calculation formula is:

[0027] (1) When the obstacle height is less than or equal to the length of a single module of the snake robot, the robot can be placed on the obstacle by lifting two joints, and the lifting angle satisfies theta = arcsin (H / l);

[0028] (2) When the obstacle height is greater than or equal to the length of a single module of the snake robot, the robot needs to lift more than or equal to three joints to be placed on the obstacle, and the lifting angle satisfies n is the number of joints to be lifted.

[0029] The application also provides a three-degree-of-freedom snake robot with a variable telescopic structure, comprising a plurality of robot single modules, a telescopic structure, and a single passive wheel, wherein the connection mode between the robot single modules includes parallel connection, orthogonal connection, universal joint connection, and P-R connection.

[0030] The one or more technical solutions provided in the embodiments of the application have at least the following technical effects or advantages:

[0031] 1、Due to the adoption of the serpentine curve (Serpenoid curve) proposed by Hirose team to generate gait motion. After in-depth analysis of the serpentine motion characteristics of biological snakes, Hirose team simplified the motion curve and obtained a sine wave function to control the serpentine motion of the snake robot. Unlike the comparative literature, the present application fits the serpentine curve as a sine wave control function and directly uses it for robot motion control, greatly reducing the complexity of the mathematical model, making the control process more concise, and having better controllability and adjustment flexibility in the motion direction.

[0032] 2、The present application adopts genetic algorithm to optimize the initial angle a of the serpentine curve and the wave number kn in the serpentine motion, which directly determines the serpentine motion trajectory of the snake robot. During the optimization process, the system records the distance of the robot offset from the x-axis in the simulation environment, and feeds back the offset distance to the genetic algorithm as the fitness standard, continuously adjusts a and kn to reduce the offset distance. This method avoids complex feedback control system, directly takes the offset distance as the optimization target, and makes the motion trajectory more consistent with the ideal path. At the same time, since the optimization parameters are concentrated in the serpentine curve control, the control model is simplified, and the optimization efficiency is higher.

[0033] 3、The present application realizes scene-based multi-gait mode switching, and the switching process relies on real-time detection feedback of the camera and sensor. Specifically, the present application designs an obstacle crossing behavior, which automatically switches the gait when the distance sensor detects an obstacle, to adapt to the shape and distance of the obstacle. Through monitoring of environmental information, the sensor feedback triggers automatic switching during robot motion, enhancing the adaptability to multi-gait environment.

[0034] 4、The present application adopts a telescopic joint associated with a rotary joint in the joint structure design. When the angle of the rotary joint is within ±0.5 degrees, the control system will trigger the corresponding telescopic joint to extend and retract, forming a telescopic control mode based on the angle of the joint. This structure design refers to the prototype of a servo electric cylinder, which has functions such as adjusting the telescopic force and telescopic amount. Unlike the comparative literature, the telescopic joint in the present application can respond to the angle change of the rotary joint, forming a coordination relationship and improving the flexible control effect of the robot during motion, especially in scenarios such as obstacle avoidance and climbing. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 The flow chart of the snake robot control method in Embodiment One of the present application;

[0036] Figure 2 The multi-modal control schematic diagram in Embodiment One of the present application;

[0037] Figure 3A single wave straight line gait schematic diagram in the embodiment one of the present application;

[0038] Figure 4 A multi-wave straight line gait schematic diagram in the embodiment one of the present application;

[0039] Figure 5 A combination of telescopic joint and meandering curve schematic diagram in the embodiment one of the present application;

[0040] Figure 6 A joint flattening processing schematic diagram in the embodiment one of the present application;

[0041] Figure 7 An ideal curve iteration schematic diagram in the embodiment one of the present application;

[0042] Figure 8 A route offset and correction schematic diagram in the embodiment one of the present application;

[0043] Figure 9 A snake robot obstacle detection schematic diagram in the embodiment one of the present application;

[0044] Figure 10 A snake robot obstacle detection head-up action schematic diagram in the embodiment one of the present application;

[0045] Figure 11 A snake robot obstacle height calculation schematic diagram in the embodiment one of the present application;

[0046] Figure 12 A snake robot structure schematic diagram in the embodiment two of the present application. DETAILED DESCRIPTION

[0047] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in combination with the drawings of the specification and specific embodiments.

[0048] A three-degree-of-freedom snake robot control method with a variable telescopic structure, as shown in Figure 1 , includes the following steps: S1, a modular design snake robot structure is adopted, each module includes pitch, yaw and telescopic joint, and the telescopic joint associated with the rotary joint is adopted in the joint structure design. When the angle of the rotary joint is within ±0.5 degrees, the corresponding telescopic joint is triggered to be telescoped by the control system, forming a telescopic control mode based on the joint angle. The physiological structure of biological snakes and earthworms is referred to in the design of the snake robot body, so as to ensure that the structure body of the robot has good walking ability, load-bearing capacity and three-dimensional space movement ability.

[0049] S2, as shown in Figure 2 , the road surface is divided into three types: flat ground, obstacle road surface and step type obstacle according to the road surface state; as shown in Figure 3 ,Figure 4 As shown, the movement mode, distance transformation, and rhythm transformation of the telescopic joint are analyzed, and the telescopic joint gait is divided into six modes: single standing wave gait, multiple standing wave gait, escape gait, obstacle crossing gait, acceleration and deceleration gait, and stop gait. The relationship between the number of standing waves of the telescopic joint and the body speed is as follows:

[0050] m∈[(n+2) / (h+2), (n+h) / (h+2)]

[0051] Where m is the number of standing waves and n is the number of joint units, satisfying m < n / 2; the interval h between multiple standing waves is set to satisfy h < n; when the robot enters the motion cycle, the average speed of the multiple standing wave motion is n / (h+2) times compared to the single standing wave motion.

[0052] Step S3, as shown in Figure 5 Step S3, the overall rotation joint adopts a serpentine curve for motion control, and the telescopic joint gait is different under different motion modes.

[0053] Serpenoid serpentine curve is a mathematical model used to simulate the movement of a snake or a snake-shaped robot. The shape of this curve is similar to a sine wave, but it is characterized by simulating the twisting motion of a snake's body as it moves forward on a plane. The shape of the Serpenoid curve can be described by parametric equations that involve sine and cosine functions to simulate the body's motion in the lateral and longitudinal directions. Each part of the snake will oscillate at different phases and frequencies, and the overall effect is a continuous curve that advances and periodically swings left and right. The specific formula is as follows:

[0054]

[0055] δσ=αcosβσ+cσ (2)

[0056] Where α is the amplitude of the curve, β is the number of cycles of the waveform within a unit length, and c is the offset angle of the waveform. By adjusting the parameters of the Serpenoid curve, the propagation amplitude, frequency, and offset angle of the serpentine curve can be flexibly changed, thereby adjusting the propagation direction of the curve. In order to more intuitively see how the Serpenoid curve controls the snake-shaped robot, the above curve is deformed and rearranged, and the simplified curve formula is as follows:

[0057]

[0058] Where βi(S) is the rotation angle of rotation joint i; α is the initial angle of the rotation joint, i.e. the amplitude of oscillation; Kn is the number of S waves in the serpentine process, π is the circular constant, s is the total arc length, L is the total length of the snake-shaped robot, and N is the number of modules.

[0059] A serpentine curve is used to generate gait motion, and the serpentine curve is fitted into a sinusoidal wave control function, which is directly used to control the robot motion. This greatly reduces the complexity of the mathematical model, makes the control process simpler, and has better controllability and adjustment flexibility in the direction of motion.

[0060] The system records the distance the robot deviates from the x-axis in the simulation environment, and feeds the offset back to the genetic algorithm as a fitness criterion, continuously adjusting α and kn to reduce the offset.

[0061] The target trajectory is a pre-set straight path, and the offset can be measured by calculating the Euclidean distance between the robot's current position and the target trajectory: The smaller the offset, the higher the fitness, so the fitness function can be defined as: fitness = 1 / (1 + total offset). The inverse form is used here to ensure that the smaller the offset, the closer the fitness is to 1, and the optimization goal is to minimize the offset. Randomly generate the initial population, and each individual contains a set of initial values ​​​​of α and Kn. The population size P can be set to dozens to hundreds of individuals. Individuals with higher fitness are selected to enter the next generation. Common methods include roulette selection and tournament selection. Select parent individuals P1 = [α1, Kn1] and P2 = [α2, Kn2]. After crossover, the offspring individuals are obtained: C = [α1, Kn2] or C = [α2, Kn1]. Add a small random perturbation to a control parameter (such as α or Kn) to avoid falling into a local optimum. α new =α old +N(0,δ) where N(0,σ) is Gaussian noise with mean 0 and standard deviation σ. In each generation, the simulation is run using the current individual's control parameters (i.e., the current values ​​of α and Kn): the robot's offset from the predetermined path is recorded.

[0062] The smaller the offset, the higher the fitness, and the genetic algorithm will iterate in the direction of reducing the offset. Each individual's offset is fed back to the genetic algorithm as fitness, ensuring that individuals with smaller offsets are selected. The algorithm stops when the fitness reaches a preset threshold or when the number of iterations reaches the maximum. This results in a set of optimized parameters, α and Kn.

[0063] Step S4, in step S4, the telescopic joint is combined with the serpentine curve, and the overall trajectory of the snake-like robot is optimized using a genetic algorithm to solve the route deviation caused by the vertex problem of joint angle change.

[0064] like Figure 6 As shown in , the joint angles are flattened by genetic algorithm, as shown in Figure 7The steps can be divided into selection, crossover, mutation, and fusion, which is one generation. Set the fitness value function and select the appropriate range of curves. The curves that meet the conditions are selected again and fused with the ideal copy. This cycle continues.

[0065] The specific operation is as follows:

[0066] Step S41, initialize individuals and populations, and generate a certain number of random trajectories.

[0067] Each individual represents a motion trajectory, which is composed of a series of time point corresponding x and y coordinates. When initializing the population, a certain number of random trajectories can be generated, which should cover the potential motion space to ensure diversity, and is set to 50 to ensure diversity in the case of limited computing resources.

[0068] Step S42, set the fitness function.

[0069] The fitness function is used to evaluate the quality of each motion trajectory. The specific calculation method can be based on the deviation between the current trajectory and the ideal trajectory. Yc is the y coordinate of the current trajectory, Yi is the y coordinate of the ideal trajectory, and n is the number of points. Using negative values makes it better (smaller deviation) to have higher fitness.

[0070] Step S43, selection operation.

[0071] Use tournament selection, randomly select 3 individuals from the population each time for comparison, and select the individual with the highest fitness as the parent.

[0072] Step S44, crossover operation.

[0073] Single-point crossover is used, and a crossover point is randomly selected. The crossover probability is set to 80%, which means that 80% of the parents will undergo crossover operation.

[0074] Step S45, mutation operation.

[0075] Mutation operation is used to introduce new genes and increase the diversity of the population. The mutation rate is set to 3%, and 3% of the individuals are randomly selected in each generation for small adjustments to introduce new genes.

[0076] Step S46, set the number of iterations.

[0077] Set to 200 generations, which is enough to optimize the path without excessive consumption of computing resources.

[0078] Step S47, set the termination condition

[0079] If the fitness does not significantly improve for 20 consecutive generations, stop the algorithm.

[0080] As Figure 8 shown, the red line is the route deviation caused by the vertex phenomenon, the blue line is the result after correction by the genetic algorithm, and the red straight line is the ideal curve middle line.

[0081] Step S5, the proximity ranging sensor of the snake robot head detects obstacles and automatically switches the gait;

[0082] As Figure 9 , Figure 10 shown, if there are obstacles and reach the specified range, switch to straight-line gait, the robot head is lifted, the proximity ranging sensor continues to work, and when the detection distance suddenly changes, stop monitoring and record the pitch joint angle, as Figure 11 shown, the height of the obstacle can be calculated at this time.

[0083] Obstacle height: H = h1 + h2 = (l + d) tan theta + h2

[0084] Where h1 is the height of a single module of the snake robot; h2 is the height between a single module of the snake robot and the obstacle; l is the length of a single module of the snake robot; d is the distance between a single module of the snake robot and the obstacle; theta is the lifting angle of a single module of the snake robot.

[0085] Case 1: when the obstacle height is less than or equal to the length of a single module of the snake robot, the robot only needs to lift two joints to climb on the obstacle, and the lifting angle satisfies theta = arcsin (H / l).

[0086] Case 2: when the obstacle height is greater than or equal to the length of a single module of the snake robot, the robot needs to lift more than or equal to three joints to climb on the obstacle, and the lifting angle satisfies n is the number of lifted joints.

[0087] Based on the above two cases, we can get that the obstacle climbing lifting angle of the robot has a great relationship with the length of its own module, the longer the module, the smaller the required lifting angle, and the energy consumption is also smaller. Therefore, for the same lifting angle, the snake robot with variable telescopic joints can climb over higher obstacles, and the energy supply for climbing is only supplied by other telescopic joints that do not perform climbing.

[0088] Example two

[0089] A three-degree-of-freedom snake robot with a variable telescopic structure, as Figure 12 shown, includes a plurality of robot single modules, a telescopic structure, and a single passive wheel, the connection between the robot single modules has parallel connection, orthogonal connection, universal joint connection, and P-R connection.

[0090] The embodiments of the specific implementation are the preferred embodiments of the present application, and do not limit the protection scope of the present application, so: any equivalent changes made according to the structure, shape, principle of the present application should be covered within the protection scope of the present application. Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application. Obviously, those skilled in the art can make various modifications and changes to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and changes of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and changes.

Claims

1. A control method for a three-degree-of-freedom snake-like robot with a variable telescopic structure, characterized in that: Including: Step S1: The structure of the snake robot is designed modularly. Each module includes pitching, yawing, and telescopic joints. In the joint structure design, a telescopic joint associated with a rotary joint is adopted. Step S2: According to the road surface conditions, the road surface is divided into three types: flat ground, obstacle road surface, and step - type obstacle. Analyze the movement mode, distance transformation, and rhythm transformation of the telescopic joint, and then divide the gait of the telescopic joint into six modes: single - standing - wave gait, multi - standing - wave gait,脱困 gait (it seems there is a misspelling here, maybe "escape gait"), obstacle - crossing gait, acceleration - deceleration gait, and stop gait. Step S3: The rotary joint as a whole is controlled to move along a meandering curve. The meandering curve is fitted into a sine - wave control function for the movement control of the snake robot. Record the distance of the robot offset from the x - axis, and use this offset as the fitness criterion to feedback to the genetic algorithm. Continuously adjust the swing amplitude of the curve and the number of S - waves during the meandering process to reduce the offset. When the fitness reaches the preset threshold or the number of iterations reaches the maximum value, the algorithm stops, and a set of optimized swing amplitudes of the adjusted curve and the number of S - waves during the meandering process are obtained. The gait of the telescopic joint is different under different movement modes. Step S4: Combine the telescopic joint with the meandering curve to solve the route - offset problem caused by the vertex problem of joint - angle change. Step S41: Initialize individuals and populations, and generate a certain number of random trajectories. Step S42: Set the fitness function. The fitness function is used to evaluate the quality of each movement trajectory, and the specific calculation method is based on the deviation degree between the current trajectory and the ideal trajectory. Step S43: Selection operation. Use tournament selection. Each time, randomly select 3 individuals from the population for comparison, and select the individual with the highest fitness as the parent. Step S44: Crossover operation. Adopt single - point crossover, randomly select a crossover point, and set the crossover probability to 80%. Step S45: Mutation operation. Set the mutation rate to 3%, and randomly select 3% of the individuals in each generation for small - amplitude adjustment to introduce new genes. Step S46: Set the number of iterations. Set it to 200 generations. Step S47: Set the termination condition. If the fitness does not increase significantly within 20 consecutive generations, stop the algorithm. Step S5: The proximity ranging sensor at the head of the snake robot detects obstacles, automatically switches the gait, calculates the height of the obstacle, calculates the lifting angle of the snake robot, and designs a multi - modal control system.

2. The snake-like robot control method according to claim 1, wherein: The telescopic joint associated with the rotary joint in step S1 is specifically: when the angle of the rotary joint is within the range of ±0.5 degrees, the control system will trigger the corresponding telescopic joint to expand and contract, forming a telescopic control mode based on the joint angle.

3. The snake-like robot control method according to claim 1, wherein: The number of standing - waves in the gait of the telescopic joint in step S2 is: ， Among them, the number of standing - waves m and the number of joint units n satisfy m < n / 2; set the interval between multiple standing - waves as h, and satisfy h < n.

4. The snake-like robot control method according to claim 1, wherein: The calculation formula for the height of the obstacle in step S5 is: ， in, is the height of a single module of the snake robot; is the height between the single module of the snake robot and the obstacle; is the length of a single module of the snake robot; is the distance between the single module of the snake robot and the obstacle; is the lifting angle of a single module of the snake-like robot.

5. The snake-like robot control method according to claim 1, wherein: The calculation formula for the lifting angle of the snake robot in step S5 is: (1) When the obstacle height is less than or equal to the length of a single module of the snake robot, the robot can lift two joints to place on the obstacle, and the lifting angle meets the requirements. ; (2) When the obstacle height is ≥ the length of a single module of the snake robot, the robot needs to lift more than or equal to three joints to place it on the obstacle, and the lifting angle must meet , n is the number of lifted joints.

6. A snake-like robot using the snake-like robot control method according to any one of claims 1 to 5, characterized in that: The invention comprises a plurality of snake-like robot single modules, a retractable structure and a unidirectional passive wheel. The connection modes between the robot single modules include parallel connection, orthogonal connection, universal joint connection and PR connection.

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