Six-axis robot movement method for XR device assembly

Abstract

The present application relates to the technical field of robot path planning and motion control, and particularly relates to a six-axis manipulator moving method for XR device assembly. The method comprises: discretizing the workspace of the six-axis manipulator, obtaining the nominal maximum angular velocity and the maximum rated output torque of the joint, performing path search by using an improved ant colony algorithm, calculating the local motion time heuristic function value of the path segment when the ant selects the path segment between adjacent nodes, calculating the total motion time cost of the path after the ant completes a complete path, updating the pheromone according to the total motion time cost until convergence, obtaining the minimum total motion time cost, determining the optimal path and performing smoothing processing to generate the motion trajectory. That is, the scheme of the present application can realize the shortest motion time and high stability path search, and significantly improve the assembly efficiency and safety.

Description

Technical Field

[0001] This invention relates to the field of robot path planning and motion control technology. More specifically, this invention relates to a method for moving a six-axis robotic arm for XR equipment assembly. Background Technology

[0002] With the continuous advancement of modern industrial manufacturing technology, especially in the assembly of high-precision, complex electronic products such as XR equipment, active alignment (AA) is a crucial process. AA requires a six-axis robot to frequently and precisely perform module pick-and-place, assembly, and fine-tuning movements within narrow and structurally complex assembly cavities. To maximize production efficiency, a highly efficient AA process places extremely stringent operational requirements on the robot: it must not only maintain extremely high positioning accuracy during movement but also possess the ability to complete movements in the shortest possible time to meet the pace of industrial mass production.

[0003] To address the problem of low mobility in robotic arms, the industry currently tends to use heuristic path planning algorithms. For example, the Ant Colony Algorithm (ACO) is applied to digital twin environments, leveraging its ability to search for optimal solutions. This method aims to simulate the process of ants finding the shortest path in nature. By utilizing the accumulation effect of pheromones and iterative calculations of local heuristic functions, it searches for feasible movement paths in a virtual environment, attempting to plan the robotic arm's operational trajectory.

[0004] However, directly applying traditional ant colony optimization (ACO) algorithms to path planning in six-axis robotic arms presents significant technical limitations. Traditional ACO algorithms primarily rely on spatial Euclidean distance to calculate path costs. However, for a six-axis robotic arm with complex kinematic models, multi-joint coupling characteristics, and torque constraints, the shortest spatial distance at the geometric level often does not equate to the shortest physical motion time. Specifically, the shortest spatial path may lead to drastic changes in joint angles or highly uneven torque consumption. Understandably, when the robotic arm's path approaches a singularity in its workspace, even with a short spatial distance, some joints still require extremely high angular velocities and torques to pass through. This can easily cause vibration and impact to the robotic arm during actual execution, and may even necessitate forced deceleration to ensure assembly accuracy and robotic arm safety. Therefore, the shortest path planned by traditional ACO algorithms is not necessarily the fastest actual path. This is severely out of sync with the stringent requirements of high-speed movement in the active alignment process of XR equipment, thus limiting the improvement of overall production efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide a six-axis robotic arm movement method for XR equipment assembly, in order to solve the problem in the prior art that the inability to plan the actual fastest path leads to a disconnect from the active alignment process requirements, resulting in low overall production efficiency; to this end, the present invention provides a solution in one aspect.

[0006] This invention provides a method for moving a six-axis robotic arm for XR equipment assembly, comprising:

[0007] The workspace of the six-axis robot is discretized to obtain the nominal maximum angular velocity and maximum rated output torque of each joint. An improved ant colony algorithm is used for path search. When ants select path segments between adjacent nodes, the local motion time heuristic function value of the path segment is calculated. This value is negatively correlated with the theoretical lower bound of the path segment and the estimated dynamic coupling torque. After an ant completes a path, the total motion time cost of the path is calculated. This cost is the sum of the products of the theoretical lower bound of all path segments and the motion impedance penalty factor. The pheromone on the path is updated based on the total motion time cost until it converges, yielding the minimum total motion time cost and determining the optimal path. The optimal path is then smoothed to generate the motion trajectory of the six-axis robot.

[0008] Preferably, the local motion time heuristic function value of the calculated path segment includes: In the formula, For path segment The local motion time heuristic function value, , They are nodes ,node First The angle of each joint For the first The nominal maximum angular velocity of each joint, For the first Each joint is in the path segment The estimated dynamic coupling torque that the upper part can withstand, For the first The maximum rated output torque of each joint To customize the time penalty coefficient, It is a function with maximum value. It is an exponential function with the natural constant e as its base. It is the absolute value symbol.

[0009] Preferably, in the formula for calculating the local motion time heuristic function value of the path segment, For path segment The theoretical lower limit of the time was used to calculate the time required for the longest joint among the six joints; The torque safety margin penalty is applied to the estimated dynamic coupling torque. Punishment will be imposed.

[0010] Preferably, calculating the total motion time cost of the path includes: In the formula, For ants Path taken The total cost of movement time, , They are nodes ,node First The angle of each joint For the first The nominal maximum angular velocity of each joint, For the first The maximum rated output torque of each joint For path segment Upper The change in angle of each joint To customize the motion impedance penalty factor, It is a function with maximum value. It is the absolute value symbol.

[0011] Preferably, in the formula for calculating the total travel time cost of the path, For path segment The theoretical lower bound of time; For six joints in the path segment The square root of the sum of squares of normalized torque consumption is used to evaluate motion resistance.

[0012] Preferably, updating the pheromone on the path based on the total movement time cost includes: using the ratio of the pheromone constant to the total movement time cost of the ant's path as the pheromone increment to update the pheromone on all path segments in the ant's path.

[0013] Preferably, the method for smoothing the optimal path is as follows: using Spline curve interpolation method.

[0014] Preferably, after discretizing the workspace of the six-axis manipulator, the method further includes: obtaining the initial pose and target pose of the six-axis manipulator; and using the inverse kinematics of the manipulator, converting the initial pose and target pose into a set of angles for six joints, which are defined as the joint space initial angle and target angle, respectively.

[0015] Preferably, after obtaining the nominal maximum angular velocity and maximum rated output torque of each joint, the method further includes: initializing the parameters of the ant colony algorithm, including the total number of ants. pheromone factors Heuristic function factor pheromone volatile factors and pheromone constant .

[0016] Preferably, the custom motion impedance penalty coefficient Take 50.

[0017] The beneficial effects of this invention are as follows:

[0018] 1. This invention integrates the kinematic parameters of a six-axis robot, such as joint speed, torque consumption, and theoretical time depth, into the path evaluation mechanism of the ant colony algorithm, achieving a fundamental shift from the traditional shortest spatial distance to the shortest motion time. By introducing a local motion time heuristic function value for the path segment in the local decision-making process and a motion impedance penalty factor in the global cost evaluation, this invention ensures that the optimal path searched by the ant colony algorithm not only meets the requirement of the shortest time but also possesses high stability and low impact industrial-grade motion quality. This significantly improves the movement efficiency and safety of the active alignment process of XR equipment, thereby increasing overall production efficiency.

[0019] 2. By directly optimizing the total motion time cost of the path, the present invention compresses the movement time of the robot to the maximum extent. At the same time, the motion impedance penalty mechanism effectively avoids the robot from operating in areas with high torque consumption and uneven joint movement, ensuring the stability and safety margin of the precision assembly process, and providing a reliable motion control basis for assembly tasks with micron-level precision. Attached Figure Description

[0020] Figure 1 This schematically illustrates a flowchart of a six-axis robotic arm movement method for XR equipment assembly in this embodiment;

[0021] Figure 2 The diagram illustrates the convergence curve of the total motion time cost of the optimal path in this embodiment as a function of the number of iterations.

[0022] Figure 3 This diagram illustrates a comparison of the total motion time cost between the final optimal path and the existing optimal path in this embodiment. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0024] like Figure 1As shown, a six-axis robotic arm movement method for XR equipment assembly in this embodiment includes steps S1 to S4:

[0025] Step S1: Discretize the workspace of the six-axis robot to obtain the nominal maximum angular velocity and maximum rated output torque of each joint.

[0026] In one embodiment, a three-dimensional grid map is established in the digital simulation system based on the actual three-dimensional geometry of the XR device assembly cavity, the size and reach of the six-axis manipulator, and its range. This three-dimensional grid map discretizes the workspace of the six-axis manipulator into a series of feasible nodes, thereby completing the discretization process of the six-axis manipulator's workspace. The nodes represent the safe poses of the manipulator in joint space and are the search objects for the ant colony algorithm.

[0027] The nominal maximum angular velocity and maximum rated output torque of each joint are obtained from the robot control system or specifications, and these are kinematic parameters required by the ant colony algorithm.

[0028] In one embodiment, after discretizing the workspace of the six-axis robot, the method further includes:

[0029] Obtain the initial pose and target pose of the six-axis robot; using the inverse kinematics of the robot, convert the initial pose and target pose into a set of angles for the six joints, which are defined as the joint space initial angle and target angle, respectively.

[0030] Specifically, after completing the discretization of the workspace of the six-axis robot, the initial pose of the six-axis robot was also obtained. and target pose And using the inverse kinematics algorithm of the robot arm, the acquired initial pose is... and target pose This is converted into a set of angles for six joints, which is also the starting angle in joint space. and target angle The inverse kinematics algorithm for the robotic arm is a well-known technique in the field and will not be described in detail here.

[0031] In one embodiment, after obtaining the nominal maximum angular velocity and maximum rated output torque of each joint, the method further includes: initializing the parameters of the ant colony algorithm, including the total number of ants. pheromone factors Heuristic function factor pheromone volatile factors and pheromone constant .

[0032] Specifically, the parameters for initializing the ant colony algorithm are the parameter settings for the mathematical model of the ant colony algorithm itself; among them, the total number of ants... It refers to the number of ants sent out in each iteration; pheromone factor. It refers to the degree to which ants pay attention to historical paths; heuristic function factor. It represents the degree of importance that ants attach to the current local movement time heuristic function value; pheromone volatile factor. This sets the decay rate of pheromones after each iteration to prevent premature convergence of the algorithm; pheromone constant. This sets the baseline for the total intensity of pheromone release. These parameters are typically input directly into the algorithm controller as preset constants, and are based on empirical values ​​or obtained through pre-tuning.

[0033] Through the above process, an accurate digital twin model can be established and the physical constraint parameters of the robot can be obtained, which in turn provides an accurate computational basis for subsequent kinematic time cost optimization and path search.

[0034] Step S2: Path search is performed using an improved ant colony algorithm; wherein, when ants select path segments between adjacent nodes, the local motion time heuristic function value of the path segment is calculated, and the local motion time heuristic function value of the path segment is negatively correlated with the theoretical time lower limit and the estimated dynamic coupling torque that the path segment can withstand.

[0035] In one embodiment, the calculation of the local motion time heuristic function value of the path segment includes:

[0036] ;

[0037] In the formula, For path segment The local motion time heuristic function value, , They are nodes ,node First The angle of each joint For the first The nominal maximum angular velocity of each joint, For the first Each joint is in the path segment The estimated dynamic coupling torque that the upper part can withstand, For the first The maximum rated output torque of each joint To customize the time penalty coefficient, It is a function with maximum value. It is an exponential function with the natural constant e as its base. It is the absolute value symbol.

[0038] In the formula for calculating the local motion time heuristic function value of the path segment, For path segment The theoretical lower limit of the time was used to calculate the time required for the longest joint among the six joints; The torque safety margin penalty is applied to the estimated dynamic coupling torque. Punishment will be imposed.

[0039] Specifically, the ant at the current node The decision is made to go to the adjacent node. When processing, via path segment The theoretical time lower limit and torque safety margin penalty term calculation path segment The local motion time heuristic function value. Furthermore, For the node Heading to the node First The distance each joint needs to rotate, i.e., the change in angle; this is calculated from the node. Heading to the node The time required for the longest joint out of the six joints Obtain path segment Theoretical time lower limit; path segment The shorter the theoretical lower bound, the shorter the corresponding path segment. Local motion time heuristic function value Only then can there be a chance to maximize. For example, from node... Heading to the node The first joint of the six-axis robotic arm. The angle change is radians, nominal maximum angular velocity is Radians per second, calculated sequentially, until the 6th joint is calculated. Angle change radians, nominal maximum angular velocity is radians per second; path segment The theoretical lower bound of time is: Taking 0.5 as the maximum value among the six values ​​as an example, then the path segment The theoretical lower limit of the time is 0.5 seconds.

[0040] For the first Each joint is in the path segment The estimated dynamic coupling torque is estimated by using a simplified dynamic model, such as the Newton-Euler method or the Lagrange method, to reflect the actual load. For the first The normalized coupling torque consumption of each joint is calculated, and the torque safety margin penalty term is obtained by summing the normalized coupling torque consumption of all joints. The torque safety margin penalty term penalizes the estimated dynamic coupling torque; in the torque safety margin penalty term... A custom time penalty coefficient is used to adjust the weight of the torque safety margin penalty, and its specific value can be obtained through empirical tuning. When the sum of the normalized coupling torque consumption of all joints is larger, it indicates that the torque consumption of that path segment is higher, the safety margin is lower, and the value of the exponential torque safety margin penalty term is larger. Local motion time heuristic function value The smaller the value, the better; conversely, the smaller the sum of normalized coupling torque consumption of all joints, the smaller the value of the exponential torque safety margin penalty term, and the smaller the path segment. Local motion time heuristic function value The larger the value, the better. For example, when the calculated torque safety margin penalty is 1.2, then the path segment... Local motion time heuristic function value This demonstrates that the algorithm prioritizes the path segment with the shortest travel time.

[0041] The larger the local motion time heuristic function value of a path segment, the higher the probability that ants will choose that path segment. Furthermore, by constructing a local motion time heuristic function that integrates kinematics and torque constraints, and calculating the local motion time heuristic function value of a path segment, the local decision-making mechanism of the ant colony algorithm is improved. This allows the algorithm to no longer rely solely on spatial distance when selecting a path segment, but rather to be guided by the shortest motion time and high torque safety margin. This enables the ant colony algorithm to favor paths with short motion times and high torque safety margins at the micro-decision level, effectively preventing the ant colony algorithm from exploring ineffective regions with high kinematic costs and preventing the path cost from becoming disconnected from the actual motion time in traditional ant colony algorithms.

[0042] Step S3: After the ant completes a complete path, calculate the total movement time cost of the path. The total movement time cost is the sum of the products of the theoretical lower limit of the path segment and the motion impedance penalty factor.

[0043] In one embodiment, calculating the total motion time cost of the path includes:

[0044] ;

[0045] In the formula, For ants Path taken The total cost of movement time, , They are nodes ,node First The angle of each joint For the first The nominal maximum angular velocity of each joint, For the first The maximum rated output torque of each joint For path segment Upper The change in angle of each joint To customize the motion impedance penalty factor, It is a function with maximum value. It is the absolute value symbol.

[0046] In the formula for calculating the total motion time cost of the path, For path segment The theoretical lower bound of time; For six joints in the path segment The square root of the sum of squares of normalized torque consumption is used to evaluate motion resistance.

[0047] In one embodiment, the custom motion impedance penalty factor Take 50.

[0048] Specifically, in ants Complete a full path from the starting point to the end point. Then, according to the ants The total motion time cost of the path is calculated by using the theoretical lower bound and motion impedance penalty factor of all path segments in the path. .

[0049] For each path segment Calculate path segments The theoretical time lower limit and motion impedance penalty factor The product of; where path segments The theoretical lower bound is The motion resistance penalty factor includes the motion resistance penalty term. And 1. Custom motion impedance penalty coefficient In the motion impedance penalty factor, 1 indicates that the theoretical time is used as a reference to prevent zero cost; a custom motion impedance penalty coefficient is defined. Based on experience, a value of 50 is chosen to amplify the effect of the motion impedance penalty term; the motion impedance penalty term... It is the square root of the sum of the squares of the normalized torque consumption of the six joints on the path segment. This indicates that from the node Heading to the node Joint The motion requirement is the normalized ratio between the distance to be rotated and the driving capability. The distance to be rotated is represented by the change in angle, and the driving capability is the maximum rated output torque. and Only numerical values ​​are used for calculation; by using the change in angle... Representing the torque consumption trend, using the maximum rated output torque. For the change in angle Penalized scaling calculates the motion impedance penalty term, obtaining the total motion time cost of the path. This ensures the selection of path regions with uniform torque consumption and smooth motion. When the path passes near singularities or requires rapid changes in joint angles, the motion demands of some joints will far exceed those of others, leading to an increase in the motion impedance penalty term and thus an increase in the motion impedance penalty factor.

[0050] Understandably, the complete path taken consists of only a single path segment, a single path segment. The theoretical lower limit of the time is 0.5 seconds, and the six joints are in the path segment. The sum of squares of normalized torque consumption =0.04, another path segment The theoretical lower limit of the time is 0.6 seconds, and the six joints are in the path segment. The sum of squares of normalized torque consumption Taking 0.0004 as an example, the path segment is calculated. Motion resistance penalty term Six joints in the path segment The motion is more uniform, and the path segment is calculated. Motion resistance penalty term Thus, the path segment is obtained. The total motion time cost is seconds, path segment The total motion time cost is The algorithm will penalize paths with high motion impedance, i.e., unstable motion, within seconds. Prioritize choosing a path with smoother motion. When the complete path consists of multiple path segments, the product of the theoretical lower limit of the path segment and the motion impedance penalty factor needs to be added together to obtain the total motion time cost of the path.

[0051] By integrating motion impedance penalty into the total motion time cost of the path, an accurate total motion time cost is assigned to the complete path completed by the ants. Paths with high motion impedance are evaluated and penalized. This guides the ant colony algorithm to actively avoid path regions with uneven torque consumption, high motion impedance (i.e., uneven distribution of joint torque) and easy vibration and impact. This ensures that the finally searched path is not only the shortest in time, but also has high motion stability, low impact, and strong executability.

[0052] Step S4: Update the pheromone on the path according to the total motion time cost until the total motion time cost converges, obtain the minimum total motion time cost and determine the optimal path; smooth the optimal path to generate the motion trajectory of the six-axis robot.

[0053] In one embodiment, the iterative and convergence process of the ant colony algorithm includes state transition, pheromone update, optimal path output, and trajectory smoothing and reconstruction. State transition is based on the ant's position at the current node. Pheromones and local motion time heuristic function value Combined with preset weighting coefficients, i.e., combined with pheromone factors Heuristic function factor Calculate the state transition probability and select the next node based on the state transition probability. The formula for calculating the state transition probability is a well-known technique in this field and will not be elaborated here.

[0054] In one embodiment, updating the pheromones on the path based on the total motion time cost includes:

[0055] The ratio of the pheromone constant to the total movement time cost of the ant's path is used as the pheromone increment to update the pheromone on all path segments in the ant's path.

[0056] Specifically, in ants Complete a full path from the starting point to the end point. Then, the total motion time cost calculated in step S3 is used. This updates the pheromone levels for each path segment within the path. The pheromone increment uses an ant-cycle model, where the ratio of the pheromone constant to the total movement time cost of the ant's path is used as the pheromone increment, thus updating the pheromone levels for each path segment. Regarding the total movement time cost... The shorter the path, the higher the pheromone reward, and the more likely it will be chosen by subsequent ants.

[0057] The expression for the pheromone increment is: In the formula, For the increase of pheromones, For ants Path taken The total cost of movement time, This is a pheromone constant.

[0058] In this iteration After all ants have traversed the complete path, the pheromone levels are updated. The updated pheromone levels satisfy the expression:

[0059] ;

[0060] In the formula, For path segment Updated pheromones For path segment The original pheromones, As a pheromone volatile factor, For ants Path taken The total cost of movement time, For pheromone constants, The total number of ants. This indicates the residual rate.

[0061] Specifically, the ratio of the pheromone constant to the total movement time cost of the paths taken by each ant. As the increment of pheromones on all path segments in the path taken by each ant; the path segments traversed The increments of pheromones left by each ant are summed to form a path segment. Total increase in upper pheromones; for those not traversed by path segment Ants on the path segment The added pheromone is 0; path segment Total new pheromone amount and path segment The remaining pheromones are added together to obtain the path segment. The pheromone is updated, thus enabling the pheromone to be updated along the entire path.

[0062] Repeat the iterative search until the total motion time cost is reached. The path tends to stabilize, or reaches the preset maximum number of iterations, yielding the minimum total motion time cost. The path corresponding to the minimum total motion time cost is then output as the optimal path. For example... Figure 2As shown in the figure, the line graph represents the number of ant colony iterations on the horizontal axis and the cost of the optimal path (in seconds) on the vertical axis, which is the total movement time cost of the optimal path. The graph shows the convergence curve of the total movement time cost of the optimal path in this invention as a function of the number of iterations. As can be seen from the figure, the convergence curve fluctuates in the early stage of the ant colony algorithm. As the number of iterations increases, the convergence curve drops rapidly and tends to stabilize. The final convergence value is significantly lower than the actual time cost baseline of the prior art, thus proving the superiority of the total movement time cost calculation proposed in this invention in terms of search efficiency and path quality.

[0063] In one embodiment, the method for smoothing the optimal path is as follows: using Spline curve interpolation method.

[0064] Specifically, in order to transform the discrete point sequence in the optimal path into a smooth, continuous motion that the robot can perform, trajectory smoothing and reconstruction are required. This embodiment employs... Spline curve interpolation is used to smooth discrete optimal path points, generating the final motion trajectory of the six-axis robot. The smoothed trajectory is then sent to the actual six-axis robot controller to complete the efficient and high-precision assembly and movement of the XR equipment. Figure 3 As shown in the bar chart, the horizontal axis represents the two optimal paths, specifically the actual travel time of the existing optimal path and the actual travel time of the optimal path of the present invention. The actual travel time is also the total motion time cost. The vertical axis represents the path motion time cost (seconds), showing a comparison of the total motion time cost between the final optimal path of the present invention and the existing optimal path. The chart provides a clear comparison of the actual motion time cost of the existing technology and the final motion time cost of the present invention, i.e., comparing the shortest Euclidean distance path with the shortest motion time path. It clearly demonstrates that the path searched by the present invention significantly reduces the actual travel time, thus directly proving the significant improvement of the active alignment process movement efficiency of the present invention.

[0065] By using the total motion time cost as the basis for pheromone reward, it ensures that the path with the smaller the total motion time cost receives the higher the pheromone reward, fundamentally realizing the essential transformation from shortest path to shortest time, and providing the optimal movement scheme for the active alignment process of XR.

[0066] In the description of this specification, "multiple" means at least two, such as two, three or more, etc., unless otherwise expressly and specifically defined.

[0067] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention.

Claims

1. A method for moving a six-axis robotic arm for XR equipment assembly, characterized in that, include: The workspace of the six-axis robot is discretized to obtain the nominal maximum angular velocity and maximum rated output torque of each joint; An improved ant colony algorithm is used for pathfinding; when ants select path segments between adjacent nodes, the local motion time heuristic function value of the path segment is calculated, including: , Path segment The local motion time heuristic function value, , They are nodes ,node First The angle of each joint For the first The nominal maximum angular velocity of each joint, For the first Each joint is in the path segment The estimated dynamic coupling torque that the upper part can withstand, For the first The maximum rated output torque of each joint To customize the time penalty coefficient, It is a function with maximum value. It is an exponential function with the natural constant e as its base. It is the absolute value symbol; Path segment The theoretical lower limit of the time was used to calculate the time required for the longest joint among the six joints; The torque safety margin penalty is applied to the estimated dynamic coupling torque. Punishment; After an ant completes a full path, the total movement time cost of the path is calculated. The total movement time cost is the sum of the products of the theoretical lower limit of the path segment and the motion impedance penalty factor. The pheromone on the path is updated according to the total motion time cost until the total motion time cost converges, the minimum total motion time cost is obtained and the optimal path is determined; the optimal path is smoothed to generate the motion trajectory of the six-axis robot.

2. The six-axis robotic arm movement method for XR equipment assembly according to claim 1, characterized in that, The calculation of the total motion time cost of the path includes: ； In the formula, For ants Path taken Total movement time cost , They are nodes ,node First The angle of each joint For the first The nominal maximum angular velocity of each joint, For the first The maximum rated output torque of each joint Path segment Upper The change in angle of each joint To customize the motion impedance penalty factor, It is a function with maximum value. It is the absolute value symbol.

3. A six-axis robotic arm movement method for XR equipment assembly according to claim 2, characterized in that, In the formula for calculating the total motion time cost of the path, Path segment The theoretical lower bound of time; For six joints in the path segment The square root of the sum of squares of normalized torque consumption is used to evaluate motion resistance.

4. A six-axis robotic arm movement method for XR equipment assembly according to claim 2, characterized in that, The step of updating the pheromone on the path based on the total motion time cost includes: The ratio of the pheromone constant to the total movement time cost of the ant's path is used as the pheromone increment to update the pheromone on all path segments in the ant's path.

5. A six-axis robotic arm movement method for XR equipment assembly according to claim 1, characterized in that, The method for smoothing the optimal path is as follows: using... Spline curve interpolation method.

6. A six-axis robotic arm movement method for XR equipment assembly according to claim 1, characterized in that, After discretizing the workspace of the six-axis robot, the method further includes: Obtain the initial pose and target pose of the six-axis robot; using the inverse kinematics of the robot, convert the initial pose and target pose into a set of angles for the six joints, which are defined as the joint space initial angle and target angle, respectively.

7. A six-axis robotic arm movement method for XR equipment assembly according to claim 1, characterized in that, After obtaining the nominal maximum angular velocity and maximum rated output torque of each joint, the method further includes: Initialize the parameters for the ant colony algorithm, including the total number of ants. pheromone factors Heuristic function factor pheromone volatile factors and pheromone constant .

8. A six-axis robotic arm movement method for XR equipment assembly according to claim 2, characterized in that, The custom motion impedance penalty coefficient Take 50.

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