WO2021114653A1 - 三维空间爬壁打磨抛光机器人及打磨能效控制方法 - Google Patents

三维空间爬壁打磨抛光机器人及打磨能效控制方法 Download PDF

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Publication number
WO2021114653A1
WO2021114653A1 PCT/CN2020/101053 CN2020101053W WO2021114653A1 WO 2021114653 A1 WO2021114653 A1 WO 2021114653A1 CN 2020101053 W CN2020101053 W CN 2020101053W WO 2021114653 A1 WO2021114653 A1 WO 2021114653A1
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Prior art keywords
polishing
wall
robot
climbing
grinding
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PCT/CN2020/101053
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English (en)
French (fr)
Inventor
杨青龙
方磊
戚宝运
吕路
王运泽
章琦
许自力
李臻
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中国电子科技集团公司第二十八研究所
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Priority to GB2018316.6A priority Critical patent/GB2586426B/en
Publication of WO2021114653A1 publication Critical patent/WO2021114653A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B24—GRINDING; POLISHING
    • B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
    • B24B27/00—Other grinding machines or devices
    • B24B27/0007—Movable machines
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B24—GRINDING; POLISHING
    • B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
    • B24B27/00—Other grinding machines or devices
    • B24B27/0076—Other grinding machines or devices grinding machines comprising two or more grinding tools
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B24—GRINDING; POLISHING
    • B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
    • B24B7/00—Machines or devices designed for grinding plane surfaces on work, including polishing plane glass surfaces; Accessories therefor
    • B24B7/10—Single-purpose machines or devices
    • B24B7/18—Single-purpose machines or devices for grinding floorings, walls, ceilings or the like
    • B24B7/182—Single-purpose machines or devices for grinding floorings, walls, ceilings or the like for walls and ceilings
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J11/00—Manipulators not otherwise provided for
    • B25J11/005—Manipulators for mechanical processing tasks
    • B25J11/0065—Polishing or grinding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects

Definitions

  • the invention relates to a wall-climbing robot and a control method, in particular to a three-dimensional space wall-climbing polishing robot and a polishing energy efficiency control method.
  • polishing after painting plays an important role in the surface quality.
  • the polishing of existing cabins is mainly done by workers climbing manually, which has high labor costs, low production efficiency and operating environment.
  • a wall-climbing robot that can be polished is needed.
  • the existing wall-climbing robots work on a plane and lack the ability to work in a three-dimensional space.
  • a wall-climbing polishing and polishing robot that can work in three-dimensional space in order to realize automatic polishing of cabins and vehicles, while ensuring safe adsorption, polishing quality and full traversal during the polishing process, which is the premise of ensuring safe polishing quality Enhance the grinding efficiency and improve the overall operation efficiency of coating grinding and polishing.
  • the technical problem to be solved by the present invention is to provide a three-dimensional space wall-climbing polishing robot and a polishing energy efficiency control method, which solves the lack of wall-climbing polishing robots that can work in three-dimensional space, and realizes the wall-climbing robot in three-dimensional Free movement and polishing in the space, and complete the optimization of the entire polishing path, and finally achieve the shortest polishing movement time.
  • the three-dimensional wall-climbing and polishing robot of the present invention includes a first wall-climbing robot, a second wall-climbing robot and a turning mechanism connecting them.
  • the lower ends of the first wall-climbing robot and the second wall-climbing robot are provided with polishing Mechanism
  • the turning mechanism includes a stepping motor, a sector gear reducer, a coupling, a pitch shaft, a sliding bearing, a connecting hoop and a first fixed angle.
  • the stepping motor is connected with the sector gear reducer, and the pitch shaft passes through the coupling.
  • the device is connected to the sector gear reducer. Both ends of the pitch shaft are equipped with sliding bearings.
  • the first wall-climbing robot is connected to the pitch shaft through the connecting hoop, and the second wall-climbing robot is fixed to the sector gear reducer by the first fixed angle. on.
  • first wall-climbing robot and the second wall-climbing robot have the same structure, each including a shell and a chassis.
  • the chassis is provided with a mecanum wheel, a drive motor, a motion shaft, a battery pack, a polishing mechanism, a vacuum negative pressure mechanism, and Centralized control module.
  • mecanum wheels There are 4 mecanum wheels, which are set on the bottom of the outer side of the chassis. Each mecanum wheel is connected to a driving motor through a motion shaft.
  • the grinding mechanism and the vacuum negative pressure mechanism run through the bottom of the chassis, and the vacuum negative pressure
  • the mechanism is used for the robot to be adsorbed on the wall when climbing the wall, and the centralized control module is used for the control and data interaction feedback of the drive motor, the polishing mechanism, the vacuum negative pressure mechanism and the turning mechanism.
  • the chassis also includes a second fixed corner and a third fixed corner.
  • the second fixed corner is used to fix the drive motor on the chassis
  • the third fixed corner is used to fix the polishing mechanism on the chassis.
  • polishing mechanisms which are symmetrically arranged on both sides of the transverse central axis of the chassis
  • vacuum negative pressure mechanisms which are symmetrically arranged on both sides of the longitudinal central axis of the chassis.
  • the grinding mechanism includes a grinding motor, an elastic coupling, a grinding force sensitive sensor, a connecting flange and an elastic grinding disc.
  • the grinding motor drives the elastic coupling to rotate at a high speed
  • the elastic coupling is connected to the grinding force.
  • the grinding force-sensitive sensor transmits feedback grinding force to the centralized control module in real time.
  • the connecting flange is connected with an elastic grinding disc.
  • the elastic grinding disc is driven by the connecting flange to rotate at a high speed for grinding operation.
  • the polishing energy efficiency control method of the three-dimensional space wall-climbing polishing robot of the present invention is based on the above-mentioned three-dimensional space wall-climbing polishing robot, and includes a polishing area modeling method, a polishing quality modeling method, a motion process modeling method, and a polishing path Smart planning method,
  • the polishing area modeling method establishes a three-dimensional space model according to the polishing environment, extracts the intersection points between the inflection points of each movement and the surface in the three-dimensional space model, generates the polishing motion link points in the entire three-dimensional space, and divides the polishing path into straight-line segments for polishing. Convert the robot's polishing path planning problem to the connection problem of the end point of the straight line of motion;
  • the polishing quality modeling method establishes a polishing quality model, and obtains the value range of the polishing movement speed of the robot according to the polishing surface roughness, the rotation speed of the polishing motor, and the load pressure of the polishing motor;
  • the motion process modeling method divides the motion of the wall-climbing robot into 4 stages to perform mechanical analysis of the motion process, obtain the mechanical model of each stage of the polishing, combine the polishing motion speed and the rated no-load speed to establish the polishing motion process model, and obtain the robot's Movement acceleration value range;
  • the intelligent polishing path planning method establishes a polishing energy efficiency control model, substituting the motion speed and motion acceleration into the efficiency control model, and solves the problem through the ant colony algorithm to obtain the optimal polishing path.
  • polishing area modeling method is specifically:
  • the grinding area is roughly divided, and the grinding area is discretized into ring-shaped areas to be polished surface by surface, and then the ring-shaped grinding area is finely divided into linear polishing areas, and each two-dimensional area in the area to be polished is obtained in turn Plan the contour lines of all walls and windows, one by one, with the set positive and negative polishing step h, the inner contour is offset outwards, and the outer contour is offset inwards, to initially generate a polished area in the plane. For those with intersections In the interference loop, traverse the inflection points in the opposite direction from the intersection point in turn, until a closed loop is formed;
  • the XY point coordinates are established as a structure for storage, and the circular path is split into straight path segments with the inflection point as the starting point.
  • polishing quality modeling method is specifically:
  • ⁇ is the grinding surface roughness
  • v p is the grinding movement speed
  • n is the rotation speed of the grinding motor
  • F N is the load pressure of the grinding motor
  • x, y, and z are the coefficients of the grinding experience model.
  • motion process modeling method is specifically:
  • the grinding motor applies pressure force to move linearly at the grinding movement speed v p and acceleration a p .
  • the movement time t 0 in the grinding stage is:
  • v s0 is the initial motion speed determined in the previous motion stage
  • L 0 is the path length of the polishing stage
  • the wall-climbing grinding and polishing robot first lifts the grinding disc to a safe plane, and performs linear motion with acceleration a k and movement speed v k , a k >a p , v k >v p , and no-load operation
  • the phase movement time t 1 is,
  • v s1 is the initial motion speed determined in the previous motion stage
  • L 1 is the path length during the no-load operation stage
  • v s2 is the initial motion speed determined in the previous motion stage
  • l 2 is the moving distance of the robot in the X direction or Y direction during the turning process
  • the polishing and polishing robot is absorbed by a single machine, and the servo motor of the flipping mechanism is controlled to drive another robot to flip motion at an angular velocity w r to achieve dihedral transition.
  • the motion time t 3 of the cross-wall phase is,
  • ⁇ is the angle between the walls
  • R is the radius of the motor shaft.
  • polishing energy efficiency control model is
  • the optimization goal is the shortest polishing time T ij;
  • loading robot can traverse all of the links point information, initialization of the point taboo table, and probability weighting factor affecting factors, provided the maximum path k m and the maximum number of iterations for each iteration the choice of the number N cm, the initial iteration
  • the number k and the initial path selection number N c of each iteration are 0, and the number of link points to be connected in the three-dimensional space to be polished is n 1 ;
  • step (8) Determine whether the traversal number i of this iteration is equal to N cm , if it is equal to N cm , then this iteration is completed, and go to step (8), if it is not equal to N cm , then the current iteration is not completed, and go to step (3 ) Continue to execute;
  • Step (9) determines whether the number of iterations is equal to the maximum number of iterations k m k T ij time whether or grinding reaches the set optimization target, if any of the above conditions are met, then the best path and the output ends, otherwise skip to Step (2) Continue to execute.
  • the present invention can realize a three-dimensional space wall-climbing polishing robot and a polishing efficiency control method.
  • the robot is composed of two independent wall-climbing polishing robots and a pitching mechanism, which can realize the adsorption of one of the two robots during the movement process.
  • the flipping mechanism drives another robot to achieve flipping motion within a certain angle range, until it is close to the target plane to complete the adsorption, to achieve the transition of the spatial dihedral angle, and then to achieve the free movement of the wall-climbing polishing robot in the three-dimensional space.
  • the efficiency control method takes the shortest polishing time as the goal, and establishes an efficiency control model with the constraints of safe adsorption, polishing quality, and full traversal.
  • the robot can realize efficient, safe and green intelligent operation in the field of polishing and polishing after painting in three-dimensional spaces such as home walls, military and civilian square cabins, wood furniture and the like.
  • FIG. 1 is a perspective view of the three-dimensional wall climbing and polishing robot in this embodiment
  • Figure 2 is a structural diagram of the wall-climbing robot in this embodiment
  • Figure 3 is a top view of the wall-climbing robot chassis in this embodiment
  • Figure 4 is a bottom schematic view of the wall-climbing robot in this embodiment
  • Figure 5 is a structural diagram of the polishing mechanism in this embodiment.
  • Fig. 6 is a flow chart of the polishing efficiency control method in this embodiment.
  • Figure 7 is the force analysis diagram of the robot climbing the wall
  • FIG. 8 is a schematic diagram of a ring-shaped area to be polished, which is roughly divided by a certain plane of the space to be polished;
  • Figure 9 is a flowchart of the algorithm for coarse division of the polishing area
  • Figure 10 is an algorithm flow chart of path optimization solution
  • Figure 11 is a schematic diagram of the optimized spatial motion trajectory of the wall-climbing polishing robot.
  • FIG. 1 to 4 The structure of the three-dimensional space wall-climbing polishing robot in this embodiment is shown in Figures 1 to 4, including two independent and identical wall-climbing polishing robots and a flip mechanism connecting them.
  • the two robots are the first wall-climbing robots.
  • the turning mechanism is composed of a stepping motor 2, a sector gear reducer 3, a coupling 5, a pitching shaft 6, a sliding bearing 7, a connecting hoop 8, and a fixed bending angle 9.
  • the stepping motor 2 is connected with the sector gear reduction box 3, the reduction box is connected with the second wall climbing robot 4 through a fixed angle 9, the pitch rotating shaft 6 is connected with the sector gear reduction box 3 through a coupling 5, and the two ends of the rotating shaft Equipped with a sliding bearing 7, the pitch rotating shaft 6 can rotate under the drive of the stepping motor 2 and stop rotating under the action of the sector gear reducer 3.
  • the first wall-climbing robot 1 is connected to the pitch axis 6 through the connecting hoop 8.
  • the second wall-climbing robot 4 is connected to the sliding bearing through the bearing seat in addition to the fixed bending angle 9, and the pitch axis 6 is relative to the second climbing axis.
  • the wall robot 4 is driven by the stepping motor 2 to make a rotating motion, and then takes the first wall-climbing robot 1 to make a turning motion.
  • the transition of the dihedral angle of the robot is realized by the coordinated control of the turning mechanism and the vacuum negative pressure mechanism of the robot.
  • the first wall-climbing robot 1 and the second wall-climbing robot 4 are independently movable wall-climbing polishing robots, each including a housing 10 and a chassis 11, and a mecanum wheel 12 and a driving motor 13 are provided in the chassis 11 ,
  • Four mecanum wheel drive motors 13 are fixed on the chassis 11 through the second fixed corner 14, and the drive motor 13 and the mecanum wheel 12 are connected through a motion shaft 20 to realize the independent driving of each mecanum wheel, and then Realize the flexible steering of the robot in space.
  • grinding mechanisms 17 There are two grinding mechanisms 17, which are symmetrically arranged on both sides of the transverse central axis of the chassis 11, and there are two vacuum negative pressure mechanisms 18, which are symmetrically arranged on both sides of the longitudinal central axis of the chassis 12, and they all penetrate from the inside of the chassis 11 to the outside of the bottom plate.
  • Two grinding mechanisms 17 and two vacuum negative pressure mechanisms 18 are set and fixed in a cross shape, and a centralized control module 19 is set in the center of the interior.
  • the centralized control module 19 is used to realize the assembly control of the various modules of the system and the interaction with data Feedback.
  • the vacuum negative pressure mechanism 18 is used to realize the adsorption of the robot when the robot performs wall climbing operations.
  • the battery pack 15 includes 4 independent batteries, which are respectively arranged on the side of the driving motor 13 and used for power supply of the entire system.
  • the grinding mechanism 17 is connected with the chassis 11 and fixed by the third fixed angle 16, which can realize the grinding operation of the robot.
  • the grinding mechanism 17 includes a grinding motor 25, an elastic coupling 23, a grinding force sensitive sensor 24, a connecting flange 22 and an elastic grinding disc 21.
  • the grinding motor 25 drives the elastic coupling 23 during the grinding operation.
  • the elastic coupling 23 is connected to the grinding force sensitive sensor 24 and the connecting flange 22, the grinding force sensitive sensor 24 transmits the feedback grinding force to the centralized control module 19 in real time, and the connecting flange 22 is connected to the elastic grinding disc 21.
  • the grinding disc 21 is driven by the connecting flange to rotate at a high speed for grinding operation.
  • the control system of the first wall-climbing robot 1 receives the signal and issues a dihedral transition command
  • the stepping motor 2 works to drive the first wall-climbing robot 1 to make a flip motion
  • the motion wheel drive motor of the second wall-climbing robot 4 drives the mecanum wheel of the robot to adjust the rotational angular speed, so that the first wall-climbing robot 1 can approach Target wall
  • the stepping motor 2 works to drive the second wall-climbing robot 4 to make a flip motion.
  • the first wall-climbing robot 1's motion wheel drive motor drives the mecanum wheel of the robot to adjust the rotational angular speed, so that the second wall-climbing robot 4 can approach the target wall;
  • the sanding efficiency control method in this embodiment specifically includes sanding area modeling, sanding quality modeling, sanding motion process modeling, and intelligent planning and solving of sanding paths.
  • First establish a three-dimensional space model according to the polishing environment; extract the intersection points between the inflection points of each motion and the surface in the three-dimensional space model to generate the polishing motion connection points in the entire three-dimensional space, divide the polishing path into polishing straight segments, and use the wall-climbing polishing robot
  • the problem of polishing path planning is converted to the connection problem of the end point of the straight line of motion;
  • the polishing motion process model is established to obtain the range of the robot's motion acceleration;
  • the polishing quality model is established again to obtain the range of the robot's motion speed; finally, the motion speed and motion acceleration Substituting the efficiency control model into the ant colony algorithm to obtain the optimal polishing path.
  • the polishing area modeling mainly includes the data import of the geometric model of the area to be polished, the acquisition of the contour curve of the polishing area, the coarse division of the polishing area based on the two-dimensional plane, and the fine division of the polishing area facing the three-dimensional space.
  • the specific steps are:
  • the data import of the geometric model refers to the path planning related interface developed by VISUL STUDIO to import the 3D model of the wall and cabin to be polished into the CAD or STP format into the UG software.
  • the UG software is the secondary developed UG Software, select the surface to be polished by clicking with the mouse, and manually select non-polished areas such as windows and power ports;
  • the acquisition of the contour curve of the polishing area refers to obtaining the geometric position information of the boundary contour of the plane to be polished in the space three-dimensional coordinate system according to the three-dimensional model, and at the same time obtaining the three-dimensional coordinates of the island area such as windows and orifices in the polishing area.
  • the geometric position information of the contour under the system and store it according to the plane;
  • the rough division of the polishing area based on the two-dimensional plane refers to reading the CAD geometric information of the drawing of the area to be polished, and sequentially obtaining the contours of all walls and windows of each two-dimensional plane in the area to be polished Line, take the wall as the polished boundary, and the non-polished areas such as windows and orifices as the wall islands.
  • the plane will be polished with the set positive and negative step h, the inner contour is biased outward, and the outer contour is biased inward, and a preliminary one is generated
  • the inflection point is traversed in the opposite direction from the intersection point in turn, until a closed loop is formed.
  • the rough-divided annular area to be polished is shown in Figure 8.
  • the fine division of the polishing area facing the three-dimensional space refers to the setting of link points on the movement path at the dihedral angle transition and turning point.
  • the mark of the link point is The serial number of the plane and its XY point coordinates in the two-dimensional coordinate system of the plane to establish a structure for storage, and split the circular path into straight path segments with the inflection point as the starting point.
  • the polishing trajectory of the space is set as a rectangular block with the width of the polishing line spacing and the length from the starting point to the next inflection point.
  • the generation of rectangular blocks starts from the entry point of the wall-climbing polishing robot, and traverses counterclockwise point by point from the outside to the inside to cover all the space area, and the polishing operation is completed. Therefore, in the subsequent path planning, connecting lines are generated between the end points of the polished rectangular blocks with the optimization scheme, and the end points of each rectangular block are regarded as the selectable nodes of the polished path optimization algorithm.
  • the grinding quality modeling is mainly to set a suitable grinding motor speed, grinding motor load pressure, and grinding movement speed to ensure the grinding quality during the grinding process.
  • set the robot's wall-climbing polishing parameters through the polishing quality control method, set the polishing roughness in the program interface, select the polishing environment (metal wall or rough wall), and confirm to select the default polishing Force, grinding motor speed and grinding roughness empirical model correlation coefficients, through the grinding roughness empirical model in the program, substituting various parameters, inversely calculating the grinding feed rate corresponding to the grinding environment and grinding quality requirements.
  • the selection of parameters is based on the empirical model formula of grinding roughness obtained by sample least squares fitting:
  • ⁇ is the grinding surface roughness
  • v p is the grinding movement speed
  • n is the rotation speed of the grinding motor
  • F N is the load pressure of the grinding motor
  • x, y, and z are the coefficients of the grinding experience model.
  • the polishing test is carried out by setting multiple sets of different polishing parameters, measuring and recording the wall roughness after polishing as the test result, fitting a large amount of test data, eliminating singularities, and obtaining the corresponding model coefficients of different work objects. It is stored in the polishing path planning control program. Before the robot works, the roughness value set by the appropriate work object can be used to reverse the polishing movement speed value, which is an important parameter for the calculation of the movement time between nodes in the subsequent polishing optimal path planning algorithm.
  • Model the polishing movement process analyze the force of the wall-climbing polishing robot in no-load, polishing, wall-climbing, and turning, and calculate the kinematic model of each stage and the motion speed formula respectively, and obtain the four stages of Exercise time calculation formula.
  • the motion is divided into four stages to analyze the motion process mechanics and establish a model, and obtain the safety constraints of the wall-climbing, polishing and polishing robot in each stage.
  • the force analysis of the wall-climbing polishing robot is shown in Figure 7.
  • N 1 and N 2 are the positive pressures of the two driving wheels; N 3 and N 4 are the pressures of the driving wheels; G is the gravity of the robot; H is the distance between the center of gravity of the robot and the wall; L is the center of the robot The distance from the front and rear wheels; B is the distance between the left and right wheels of the robot; F p is the vacuum suction force provided by a single vacuum suction mechanism; F Nr is the positive pressure provided by the elastic grinding head; F Nv is the grinding force in the speed direction Component force; F fyw is the friction force of the moving wheel; F f is the friction force of the follower wheel; ⁇ is the angle between the moving direction of the wall-climbing polishing robot and the horizontal direction, and ⁇ 1 is the friction coefficient.
  • F P can be obtained by the product of the rated working negative pressure of the suction motor of the selected vacuum suction mechanism and the suction area.
  • F Nr is the polishing load, which is usually set as the rated value according to the wall conditions. It is set at 2 ⁇ 3N in the surface grinding; the grinding force is set at 5 ⁇ 8N in the grinding and polishing of metal materials. According to the characteristics of different stages of movement, the polishing process can be divided into the following four stages.
  • v p is the best grinding feed rate of the current wall surface, which is obtained from the grinding roughness empirical model formula (1), and calculates the movement time at this stage:
  • v s0 is the initial motion speed determined by the previous motion stage
  • L 0 is the path length of the polishing stage
  • this stage is the movement of the wall-climbing polishing robot between the connection points of the two polishing blocks, that is, the wall-climbing polishing robot first lifts to a safe plane, and then performs uniform acceleration linear motion with acceleration a k , After reaching the rated speed, do a uniform motion at the motion speed v k,
  • the motion time t 1 in the no-load operation phase is,
  • v s1 is the initial motion speed determined in the previous motion stage
  • L 1 is the path length during the no-load operation stage
  • the movement of the wall-climbing polishing robot needs to rotate the mecanum wheel of the wall-climbing polishing robot, combined with the calculation formula of the angular acceleration of the mecanum wheel and the specific force of the wall-climbing polishing robot of the present invention
  • the analysis can get its angular acceleration calculation formula to change the angular velocity direction of the mecanum wheel movement and realize the steering of the robot.
  • the direction of motion is adjusted by the motion of the mecanum wheel, turning with angular acceleration ⁇ pr,
  • the movement time t 2 in the turning phase is,
  • v s2 is the initial motion speed determined in the previous motion stage
  • l 2 is the moving distance of the robot in the X direction or Y direction during the turning process
  • one robot in the wall-crossing stage drives one of the modules to flip through the pitch motor.
  • the robot as a whole needs to make a steering movement.
  • the overall adsorption force of the robot is changed from the original two machines to one machine, until the two machines are both Finish the adsorption on the flat surface, and restart the grinding movement.
  • the robot is adsorbed by a single machine, and the servo motor that controls the robot's pitching motion drives another robot to flip at an angular velocity w r to achieve the wall-crossing.
  • the movement time t 3 in the transmural stage is,
  • ⁇ is the angle between the walls
  • R is the radius of the motor shaft.
  • the polishing path intelligent planning and calculation of the polishing path adopts a method of dynamic real-time evaluation for path optimization, that is, according to the actual situation of each path selection, real-time recording and adjustment of the selection result are used as the basis for the next path selection.
  • We do the following calibration set the maximum number of iterations k m , set the maximum number of path selections for each iteration N cm , and the number of link points to be connected in the three-dimensional space to be polished is n 1 . It is recorded that the connection of all n 1 points is completed once, which is recorded as a path selection; when the path selection is completed N cm times, it is recorded as an iteration.
  • each time a path is selected the path needs to traverse all nodes and each node can only be selected once on the path. Before selecting the next node, it is necessary to judge whether the node is on the same plane as the current node , And then determine whether the path between nodes includes the path to be polished, the empty path, the cross-wall path, and the turning path.
  • the second is path selection
  • allowed k can represent the next node available for selection
  • ⁇ ij (t) is the influence factor of the global connection probability from node i to node j on the path at time t, which is determined by the total time of the previous path selection (previous The sum of the movement time T ij between all nodes on the secondary path selection) is determined, and the calculation method is to set an initial value and the maximum and minimum value interval [ ⁇ min - ⁇ max ] before the algorithm starts, and select after completing one iteration Draw out the most efficient and least efficient paths in N cm times of path selection, increase the global probability of all nodes on the optimal path, reduce the global connection probability influence factor of all nodes on the worst path, and use [ ⁇ min- ⁇ max ] correspondingly limit the increase range;
  • ⁇ and ⁇ are the weight coefficients of the global connection probability and local connection probability, respectively, where ⁇ represents the relative importance of the global connection probability; ⁇ is the relative importance of the local connection probability ⁇ ; ⁇ ij (t) is the calculation formula
  • the third is the calculation method of the global connection probability influence factor
  • the global connection probability impact factor update algorithm of the present invention is a method for changing the global connection probability impact factor on the path between nodes (i, j) in each iteration, as follows:
  • ⁇ ij (t+n) (1- ⁇ ) ⁇ ij (t)+ ⁇ ij (t)
  • ⁇ is the attenuation rate constant of each generation of the global connection probability influence factor.
  • the main function is to ensure that the global selection probability factor on each node path does not exceed [ ⁇ min - ⁇ max ].
  • the solution formula of ⁇ ij k (t) is
  • Q represents the initial increase probability constant
  • T k represents the total time to polish the path in the kth iteration. It can be seen that the shorter the time, the greater the probability of being selected.
  • the coordinate information of each divided polishing area block is stored in the spatial polishing area connection table, and the polishing path is intelligently planned and calculated according to the polishing path, and the initial optimization parameters are set.
  • the value of k m is usually greater than 50.
  • the value of N cm is usually greater than 200.
  • loading robot can traverse all of the links point information, initialization of the point taboo table, and probability weighting factor affecting factors, provided the maximum path k m and the maximum number of iterations for each iteration the choice of the number N cm, the initial iteration
  • the number k and the initial path selection number N c of each iteration are 0, and the number of link points to be connected in the three-dimensional space to be polished is n 1 ;
  • step (8) Determine whether the traversal number i of this iteration is equal to N cm , if it is equal to N cm , then this iteration is completed, and go to step (8), if it is not equal to N cm , then the current iteration is not completed, and go to step (3 ) Continue to execute;
  • Step (9) determines whether the number of iterations is equal to the maximum number of iterations k m k T ij time whether or grinding reaches the set optimization target, if any of the above conditions are met, then the best path and the output ends, otherwise skip to Step (2) Continue to execute.
  • the best path is output, and the polishing path of the wall-climbing polishing robot generated by an optimization example is shown in Figure 11.
  • the generated optimized path is converted into various spatial motion information, that is, the path information is stored in the form of a linked list structure.
  • the stored information includes: the previous node and the next node information, the movement speed, acceleration, and rotation speed at the node. Convert the stored path information point by point into the motion instructions of the mecanum wheel drive motor of the robot, complete the optimization of the entire grinding path, and finally achieve the goal of the shortest grinding movement time, and the constraint conditions of safe adsorption, grinding quality, and full traversal Polishing efficiency control methods.

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Abstract

一种三维空间爬壁打磨抛光机器人及其打磨能效控制方法，其中三维空间爬壁打磨抛光机器人包括第一爬壁机器人(1)、第二爬壁机器人(4)和连接它们的翻转机构，第一爬壁机器人(1)和第二爬壁机器人(4)的下端设置打磨机构(17)。在运动过程中可实现两机器人其中之一吸附，由翻转机构带动另一机器人在一定角度范围内实现翻转运动，直至接近目标平面完成吸附，实现爬壁打磨抛光机器人在三维空间内的自由运动。打磨能效控制方法包括打磨运动过程建模、打磨质量建模、打磨区域生成以及打磨路径智能规划，以打磨运动时间最短为目标，建立效能控制模型，在保证打磨安全性、打磨质量的前提下最大程度的提升效率。可实现机器人在家居墙面、军民用方舱车、木材家具等三维空间进行高效、安全、绿色的打磨抛光。

Description

三维空间爬壁打磨抛光机器人及打磨能效控制方法 技术领域
本发明涉及一种爬壁机器人及控制方法，特别是涉及一种三维空间爬壁打磨抛光机器人及打磨能效控制方法。
背景技术
在军、民用客车、方舱的生产过程中，涂装后打磨对表面质量起着重要作用，现有舱车打磨主要由工人登高手工作业完成，具有人力成本高昂、生产效率低、作业环境恶劣等诸多缺陷，需要一款能够打磨的爬壁机器人。另外，现有的爬壁机器人是在平面上工作的，缺乏三维空间的工作能力。所以，我们需要一款能够在三维空间工作的爬壁打磨抛光机器人，以期实现舱、车的自动打磨，同时在打磨过程中能够保证安全吸附、打磨质量和全遍历，在保障安全打磨质量的前提下提升打磨效率，提升涂装打磨抛光的整体作业效能。
发明内容
发明目的：本发明要解决的技术问题是提供一种三维空间爬壁打磨抛光机器人及打磨能效控制方法，解决了缺乏能在三维空间工作的爬壁打磨抛光机器人的不足，实现爬壁机器人在三维空间内的自由运动和打磨，并完成整个打磨路径的优化，最终实现了打磨运动时间最短。
技术方案：本发明所述的三维空间爬壁打磨抛光机器人，包括第一爬壁机器人、第二爬壁机器人和连接它们的翻转机构，第一爬壁机器人和第二爬壁机器人的下端设置打磨机构，翻转机构包括步进电机、扇形齿轮减速箱、联轴器、俯仰转轴、滑动轴承、连接抱箍和第一固定弯角，步进电机与扇形齿轮减速箱相连接，俯仰转轴通过联轴器与扇形齿轮减速箱相连接，俯仰转轴两端装有滑动轴承，第一爬壁机器人通过连接抱箍连接在俯仰转轴上，第二爬壁机器人通过第一固定弯角固定在扇形齿轮减速箱上。
进一步的，第一爬壁机器人和第二爬壁机器人为相同结构，各包括壳体和底盘，底盘中设置麦克纳姆轮、驱动电机、运动轴、蓄电池组、打磨机构、真空负压机构和集中控制模块，麦克纳姆轮为4个，分别设置在底盘的外侧底部，每个麦克纳姆轮通过运动轴连接1个驱动电机，打磨机构和真空负压机构贯穿底盘的底部，真空负压机构用于爬壁时机器人吸附在墙壁上，集中控制模块用于驱动电机、打磨机构、真空负压机构和翻转机构的控制和数据交互反馈。
进一步的，底盘中还包括第二固定弯角和第三固定弯角，第二固定弯角用于将驱动电机固定在底盘上，第三固定弯角用于将打磨机构固定在底盘上。
进一步的，打磨机构为2个，对称设置在底盘的横向中心轴两侧，真空负压机构为2个，对称设置在底盘的纵向中心轴两侧。
进一步的，所述打磨机构包括打磨电机、弹性连轴器、打磨力敏传感器、连接法兰和弹性打磨盘，打磨作业时打磨电机带动弹性联轴器高速转动，弹性联轴器上连接打磨力敏传感器和连接法兰，打磨力敏传感器实时将反馈打磨力传递至集中控制模块，连接法兰上连接弹性打磨盘，弹性打磨盘受连接法兰带动高速旋转进行打磨作业。
本发明所述的三维空间爬壁打磨抛光机器人的打磨能效控制方法，基于上述的三维空间爬壁打磨抛光机器人，包括打磨区域建模方法、打磨质量建模方法、运动过程建模方法以及打磨路径智能规划方法，
所述打磨区域建模方法根据打磨环境建立三维空间模型，在三维空间模型中提取各运动拐点和面面之间交点，生成整个三维空间的打磨运动链接点，将打磨路径分割为打磨直线段，将机器人的打磨路径规划问题转换为运动直线段终点的连接问题；
所述打磨质量建模方法建立打磨质量模型，根据打磨表面粗糙度、打磨电机转速和打磨电机负载压力获得机器人打磨运动速度取值范围；
所述运动过程建模方法将爬壁机器人的运动划分为4个阶段进行运动过程力学分析，得到打磨各个阶段力学模型，结合打磨运动速度、额定空载速度得到建立打磨运动过程模型，获得机器人的运动加速度取值范围；
所述打磨路径智能规划方法建立打磨能效控制模型，将运动速度、运动加速度代入效能控制模型，通过蚁群算法进行解算，获取最优的打磨路径。
进一步的，所述打磨区域建模方法具体为：
(1)将待打磨墙壁、舱体的三维模型导入UG软件中，选取待打磨平面和非打磨区域；
(2)依据三维模型，获取待打磨平面和非打磨区域在空间三维坐标系下的轮廓几何位置信息；
(3)基于二维平面进行打磨区域粗划分，将打磨区域逐面离散为环状待打磨区域，再将环状打磨区域细划分为线条状的打磨区域，依次获取待打磨区域内各个二维平面所有墙壁和窗户的轮廓线，逐一平面依次以设定的正负打磨步距h，内轮廓向外偏置，外轮廓向内偏置，初步生成一个平面内的打磨区域，对于有交点的干涉环，则由交点依次沿反方向依次遍历各拐点，直至形成闭环；
(4)面向三维空间进行打磨区域细划分，在跨壁和环状打磨区域转向处设置爬壁打磨抛光机器人运动线上的链接点，将环状打机器人运动区域的链接点的标记以面和空间坐标系下XY点坐标建立结构体进行存储，并将环形路径拆分为以拐点为起始点的直线段路径段。
进一步的，所述打磨质量建模方法具体为：
λ＝kv p xn yF N z，
其中，λ为打磨表面粗糙度，v p为打磨运动速度，n为打磨电机的转速，F N为打磨电机负载压力，x、y、z为打磨经验模型系数，在机器人作业开始之前，依据作业对象类别，设定粗糙度值计算出所需的打磨运动速度v p。
进一步的，所述运动过程建模方法具体为：
(1)打磨阶段，打磨电机施加压力力，以打磨运动速度v p、加速度a p进行直线运动，打磨阶段运动时间t 0为，
Figure PCTCN2020101053-appb-000001
其中，v s0为上一运动阶段决定的运动初始速度，L 0为打磨阶段路径长度；
(2)空载运行阶段，爬壁打磨抛光机器人先将打磨盘提升至安全平面，以加速度a k和运动速度v k进行直线运动，a k＞a p，v k＞v p，空载运行阶段运动时间t 1为，
Figure PCTCN2020101053-appb-000002
其中，v s1为上一运动阶段决定的运动初始速度，L 1为空载运行阶段路径长度；
(3)转向阶段，通过麦克纳姆轮的运动调整运动方向，以角加速度ψ pr转向，转向阶段运动时间t 2为，
Figure PCTCN2020101053-appb-000003
其中，v s2为上一运动阶段决定的运动初始速度，l 2为转向过程中机器人在X方向或Y方向的移动距离；
(4)跨壁阶段，打磨抛光机器人单机吸附，通过控制翻转机构的伺服电机以角速度w r带动另外一个机器人进行翻转运动，实现二面角过渡，跨壁阶段运动时间t 3为，
t 3＝θ/(w r*R)
其中，θ为墙壁间角度，R为电机转轴半径。
进一步的，所述打磨能效控制模型为，
Figure PCTCN2020101053-appb-000004
优化目标为打磨时间T ij最短；
所述的解算过程具体为：
(1)初始化参数，载入机器人所有可以遍历的链接点信息，初始化各点禁忌表、权重系数及概率影响因子，设置最大迭代次数k m和每次迭代的最大路径选择次数N cm，初始迭代次数k和每次迭代的初始路径选择次数N c为0，待打磨三维空间所有待连接的链接点数量为n 1；
(2)N c＝0，进行下一条路径选择，N c＝N c+1，其中N c为每次迭代的初始路径选择次数；
(3)进行下一次遍历，i＝i+1，其中i为本次迭代的遍历次数；
(4)依据当前链接点的被选择概率，根据赌轮盘算法随机选择下一链接点，遍历下一个链接点，j＝j+1，其中j为已遍历节点数量；
(5)修改禁忌表，把已经遍历过的链接点标记为已遍历链接点，确保一个链接点只被遍历过一次；
(6)判断已遍历节点数量j是否等于当前空间的节点总数n 1，若等于n 1，则当前路径选择完成，进行链接点被选择概率的即时更新，进入步骤(7)，若不等于n 1，则路径选择没有完成，进入步骤(4)继续执行；
(7)判断本次迭代的遍历次数i是否等于N cm，若等于N cm，则本次迭代完成，进入步骤(8)，若不等于N cm，则本次迭代未完成，进入步骤(3)继续执行；
(8)完成一次迭代，k＝k+1，其中k为迭代次数；
(9)判断迭代次数k是否等于最大迭代次数k m或打磨时间T ij是否达到设定的优化目标，若满足上述任一条件，则输出最佳路径并结束，否则跳转到步骤(2)继续执行。
有益效果：本发明能够实现三维空间爬壁打磨抛光机器人及打磨效能控制方法，机器人由两个独立的爬壁打磨抛光机器人及俯仰机构组成，在运动过程中可实现两机器人其中之一吸附，由翻转机构带动另一机器人在一定角度范围内实现翻转运动，直至接近 目标平面完成吸附，实现空间二面角的过渡，进而实现爬壁打磨抛光机器人在三维空间内的自由运动。效能控制方法以打磨运动时间最短为目标，以安全吸附、打磨质量、全遍历为约束条件建立效能控制模型。首先将待打磨区域几何模型的导入和几何信息的提取，建立打磨区域模型；其次建立打磨运动过程模型获得机器人安全吸附约束条件；再次建立打磨质量模型获得机器人运动参数、打磨工艺参数的约束条件；最后将约束条件代入效能控制模型，通过蚁群算法进行解算获取最优的打磨路径。最终得到在保证打磨安全性、打磨质量的前提下最大程度的提升效率。通过本发明，可实现机器人在家居墙面、军民用方舱车、木材家具等三维空间的涂装后打磨抛光领域，高效、安全、绿色地智能作业。
附图说明
图1是本实施方式中三维空间爬壁打磨抛光机器人的立体图；
图2是本实施方式中爬壁机器人的结构图；
图3是本实施方式中爬壁机器人底盘的俯视图；
图4是本实施方式中爬壁机器人的底部示意图；
图5是本实施方式中打磨机构的结构图；
图6是本实施方式中打磨效能控制方法的流程图；
图7是机器人爬壁过程受力分析图；
图8是待打磨空间某一平面经过打磨区域粗划分的待打磨环状区域示意图；
图9是打磨区域粗划分的算法流程图；
图10是路径优化解算的算法流程图；
图11是优化后的爬壁打磨抛光机器人空间运动轨迹示意图。
具体实施方式
本实施方式中的三维空间爬壁打磨抛光机器人的结构如图1至图4所示，包括两个独立的相同的爬壁打磨抛光机器人及连接它们的翻转机构，两个机器人为第一爬壁机器人1和第二爬壁机器人4，翻转机构由步进电机2、扇形齿轮减速箱3、联轴器5、俯仰转轴6、滑动轴承7、连接抱箍8、固定弯角9组成。步进电机2与扇形齿轮减速箱3相连接，减速箱通过固定弯角9与第二爬壁机器人4相连接，俯仰转轴6通过联轴器5与扇形齿轮减速箱3相连接，转轴两端装有滑动轴承7，俯仰转轴6能够在步进电机2的带动下转动，在扇形齿轮减速箱3的作用下停止转动。第一爬壁机器人1通过连接抱箍8与俯仰转轴6相连接，第二爬壁机器人4除了通过固定弯角9连接外，还通过轴承座连接在滑动轴承上，俯仰转轴6相对第二爬壁机器人4在步进电机2的驱动下做旋转运动，进而带着第一爬壁机器人1做翻转运动。机器人的二面角过渡依靠翻转机构与机器人的真空负压机构协同控制实现。
所述第一爬壁机器人1和第二爬壁机器人4为可独立运行的爬壁打磨抛光机器人单体，各包括壳体10和底盘11，底盘11中设置麦克纳姆轮12、驱动电机13、运动轴20、蓄电池组15、打磨机构17、真空负压机构18和集中控制模块19。底盘11上通过第二固定弯角14固定有4个麦克纳姆轮驱动电机13，驱动电机13与麦克纳姆轮12通过运动轴20相连接，实现每个麦克纳姆轮的独立驱动，进而实现机器人在空间的灵活转向。打磨机构17为2个，对称设置在底盘11的横向中心轴两侧，真空负压机构18为2个，对称设置在底盘12的纵向中心轴两侧，均从底盘11内部贯穿到底板的外部。2个打磨机构17和2个真空负压机构18呈十字型设置固定，内部的中心位置上设置有集中控制模块19，集中控制模块19用于实现系统各模块的总成控制，与数据的交互反馈。真空负压机构18用于机器人进行爬壁作业时，实现机器人的吸附，其具体的设计和实现记载在中国发明专利公开说明书CN106181646B中，与其中的真空负压机构相同，这里不再赘述。蓄电池组15包含4个独立的蓄电池，分别设置在驱动电机13一侧，用于整套系统的供电。打磨机构17与底盘11相连接，通过第三固定弯角16固定，可实现机器人的打磨作业。
如图5所示，所述打磨机构17包括打磨电机25、弹性连轴器23、打磨力敏传感器24、连接法兰22和弹性打磨盘21，打磨作业时打磨电机25带动弹性联轴器23高速转动，弹性联轴器23上连接打磨力敏传感器24和连接法兰22，打磨力敏传感器24实时将反馈打磨力传递至集中控制模块19，连接法兰22上连接弹性打磨盘21，弹性打磨盘21受连接法兰带动高速旋转进行打磨作业。
机器人在进行二面角过渡时具体工作过程如下：
1)第一爬壁机器人1控制系统收到信号，发出二面角过渡指令；
2)第一爬壁机器人1的真空负压机构18停止工作，第一爬壁机器人1与墙壁吸附力降低至0，第二爬壁机器人4真空吸附压增大，真空负压机构18保持工作状态；
3)步进电机2工作，带动第一爬壁机器人1作翻转运动，第二爬壁机器人4的运动轮驱动电机带动机器人的麦克纳姆轮调整旋转角速度，使得第一爬壁机器人1可以接近目标墙壁；
4)当依据步进电机2反馈力矩可以判断出第一爬壁机器人1已与墙壁充分接触，发出指令使第一爬壁机器人1的真空负压机构开始工作第二爬壁机器人4的真空负压机构停止工作；
5)步进电机2工作，带动第二爬壁机器人4作翻转运动，第一爬壁机器人1运动轮驱动电机带动机器人的麦克纳姆轮调整旋转角速度，使得第二爬壁机器人4可以接近目标墙壁；
6)当依据步进电机2反馈力矩可以判断出第二爬壁机器人4已与墙壁充分接触，发 出指令使第二爬壁机器人4的真空负压机构开始工作；
7)机器人完成二面角过渡，重新开始打磨作业。
如图6所示，本实施方式中打磨效能控制方法具体包括打磨区域建模、打磨质量建模、打磨运动过程建模以及打磨路径智能规划和解算。首先根据打磨环境建立三维空间模型；在三维空间模型中提取各运动拐点和面面之间交点，生成整个三维空间的打磨运动连接点，将打磨路径分割为打磨直线段，将爬壁打磨抛光机器人的打磨路径规划问题转换为运动直线段终点的连接问题；其次建立打磨运动过程模型获得机器人的运动加速度取值范围；再次建立打磨质量模型获得机器人运动速度取值范围；最后将运动速度、运动加速度代入效能控制模型，通过蚁群算法进行解算获取最优的打磨路径。
所述打磨区域建模主要包括待打磨区域几何模型的数据导入，打磨区域轮廓曲线的获取，基于二维平面的打磨区域粗划分、面向三维空间的打磨区域细划分，具体步骤为：
(1)几何模型的数据导入是指，通过VISUL STUDIO开发的路径规划相关接口，将待打磨墙壁、舱体的CAD或STP格式的三维模型导入UG软件中，UG软件为经过二次开发的UG软件，通过鼠标点击选取待打磨平面，并可手动选择窗口、电源孔口等非打磨区域；
(2)所述打磨区域轮廓曲线的获取是指依据三维模型，获取待打磨平面在空间三维坐标系下边界轮廓的几何位置信息，同时获取打磨区域内窗口、孔口等岛屿区域在空间三维坐标系下的轮廓几何位置信息，并按所在平面进行存储；
(3)如图9所示，所述基于二维平面的打磨区域粗划分，是指读取待打磨区域图纸的CAD几何信息，依次获取待打磨区域内各个二维平面所有墙壁和窗户的轮廓线，取墙壁为打磨边界，窗户、孔口等非打磨区域为墙壁岛屿，逐一平面依次以设定的正负打磨步距h，内轮廓向外偏执，外轮廓向内偏置，初步生成一个平面内的环状待打磨区域，对于有交点的干涉环，则由交点依次沿反方向依次遍历各拐点，直至形成闭环。粗划分后的待打磨环状区域如图8所示。
(4)所述面向三维空间的打磨区域细划分，是指在二面角过渡和转向处设置运动路径上的链接点，同时为适应爬壁打磨抛光机器人的空间路径规划，链接点的标记以所在平面的序列号和及其在该平面二维坐标系下XY点坐标建立结构体进行存储，并将环形路径拆分为以拐点为起始点的直线段路径段，即将爬壁打磨抛光机器人在空间的打磨轨迹设置为以打磨行距为宽，起始点至下一个拐点为长的矩形块。矩形块的生成由爬壁打磨抛光机器人切入点开始生成，由外向内逆时针逐点遍历，将一个空间区域全部覆盖 完毕，即完成打磨作业。因此后续的路径规划，即以最优化方案在打磨矩形块各终点之间生成连接线，将各个矩形块的终点视为打磨路径优化算法的可选择节点。
所述打磨质量建模主要是设置合适的打磨电机转速、打磨电机负载压力、打磨运动速度来保证打磨过程中的打磨质量。在完成打磨区域的自动生成后，设置机器人的各爬壁打磨参数，通过打磨质量控制方法，在程序界面设置打磨粗糙度，选择打磨环境(金属墙面或毛坯墙面)，确认选取默认的打磨力、打磨电机转速及打磨粗糙度经验模型相关系数，通过程序中的打磨粗糙度经验模型，代入各参数，反算得到对应打磨环境和打磨质量要求下的打磨进给速度。参数的选择依据样本最小二乘法拟合得到的打磨粗糙度经验模型公式得到：
λ＝kv p xn yF N z   (1)
其中，λ为打磨表面粗糙度，v p为打磨运动速度，n为打磨电机的转速，F N为打磨电机负载压力，x、y、z为打磨经验模型系数，在机器人作业开始之前，依据作业对象类别，设定粗糙度值计算出所需的打磨运动速度v p。
通过设定多组不同的打磨参数进行打磨试验，测量并纪录打磨后墙壁粗糙度作为试验结果，并将大量试验数据进行拟合，剔除奇异点，得到对应的不同作业对象的模型系数，并在打磨路径规划控制程序中进行存储。在机器人作业之前，根据合适的作业对象设定的粗糙度值即可反推打磨运动速度值，作为后续打磨最优路径规划算法中的节点之间运动时间计算的重要参数。
打磨运动过程建模，分别对爬壁打磨抛光机器人在空载、打磨、爬壁、转向做受力分析，并分别计算得到各个阶段的运动学模型，和运动速度公式，得到这四个阶段的运动时间计算公式。即将运动划分为四个阶段进行运动过程力学分析并建立模型，得到各个阶段爬壁打磨抛光机器人的安全性约束。其中爬壁打磨抛光机器人受力分析如图7所示。
Figure PCTCN2020101053-appb-000005
其中，N 1、N 2为两个驱动轮所受的正压力；N 3、N 4为系驱动轮所受压；G为机 器人的重力；H为机器人重心离墙壁的距离；L为机器人中心离前后轮的距离；B为机器人左右轮之间的距离；F p为单个真空吸附机构所提供的真空吸附力；F Nr为弹性打磨头提供的正压力；F Nv为打磨力在速度方向的分力；F fyw为运动轮摩擦力；F f为随动轮摩擦力；θ为爬壁打磨抛光机器人运动方向和水平方向的夹角，μ 1为摩擦系数。
由爬壁打磨抛光机器人受力分析可推导得出，任意位姿下爬壁打磨抛光机器人允许的打磨状态下的加速度a p：
Figure PCTCN2020101053-appb-000006
对于爬壁打磨抛光机器人而言，F P可由所选择的真空吸附机构吸附电机的额定工作负压和吸附面积的乘积获得，F Nr为为打磨负载通常依据墙壁情况设定为额定值，在墙面打磨中设置为2～3N；在金属材料的打磨抛光中设置打磨力大小为5～8N。依据不同阶段运动的特点可将打磨过程划分为下列四个阶段。
1)打磨阶段
打磨阶段机器人由起点以公式(2)所示的速度匀加速至最佳打磨速度v p。v p为当前墙面的最佳打磨进给速度，由打磨粗糙度经验模型公式(1)所得，计算此阶段的运动时间：
Figure PCTCN2020101053-appb-000007
其中v s0为运动初始速度由上一运动阶段决定，L 0为打磨阶段路径长度；
(2)空载运行阶段，该阶段为爬壁打磨抛光机器人在两个打磨块连接点之间的运动，即爬壁打磨抛光机器人先抬至安全平面，再以加速度a k做匀加速直线运动，至达到额定速度后以运动速度v k做匀速运动，
Figure PCTCN2020101053-appb-000008
空载运行阶段运动时间t 1为，
Figure PCTCN2020101053-appb-000009
其中，v s1为上一运动阶段决定的运动初始速度，L 1为空载运行阶段路径长度；
(3)转向阶段，该阶段爬壁打磨抛光机器人的运动需转动爬壁打磨抛光机器人的麦克纳姆轮，结合麦克纳姆轮角加速度计算公式及本发明所述爬壁打磨抛光机器人具体受力分析可得出其角加速度计算公式以改变麦克纳姆轮运动的角速度方向，实现机器人的转向。通过麦克纳姆轮的运动调整运动方向，以角加速度ψ pr转向，
Figure PCTCN2020101053-appb-000010
转向阶段运动时间t 2为，
Figure PCTCN2020101053-appb-000011
其中，v s2为上一运动阶段决定的运动初始速度，l 2为转向过程中机器人在X方向或Y方向的移动距离；
(4)跨壁阶段，跨壁阶段机器人一台机器通过俯仰电机带动其中一个模块进行翻转，同时机器人整体需进行转向运动，机器人整体吸附力的提供由原来两机变为一机，直至双机都在平面上完成吸附，重新进行打磨运动。跨壁阶段，机器人单机吸附，通过控制机器人进行俯仰运动的伺服电机以角速度w r带动另外一个机器人进行翻转运动，实现跨壁。跨壁阶段运动时间t 3为，
t 3＝θ/(w r*R)
其中，θ为墙壁间角度，R为电机转轴半径。
所述打磨能效控制模型，即计算得到打磨三维空间内任意两个运动直线段终点的之间运动时间计算模型为，T ij＝t 0+t 1+t 2+t 3。
即：
Figure PCTCN2020101053-appb-000012
所述打磨路径智能规划打磨路径和解算采用动态实时评估进行路径优化的方法，即依据每次路径选择的实际情况实时记录、调整选择结果并作为下一次路径选择的依据。我们做下列标定，设置最大迭代次数k m，设置每次迭代的最大路径选择次数N cm，待打磨三维空间所有待连接的链接点数量为n 1。记完成一次所有n 1个点的连接，记为完成一 次路径选择；当完成N cm次路径选择记为完成一次迭代。
主要分为下列几个步骤：
一是设置合法路径
即设定路径选择规则，每选择一次路径，该条路径需遍历所有节点并且每个节点在路径上只能被选择一次，在选择下一个节点前，要判断该节是否与当前节点处于同一平面，进而判断节点之间路径是否包含待打磨路径，空载路径，跨壁路径、转向路径。
二是路径选择
即在求解过程中，根据全局和动态更新的该节点的被选择概率
Figure PCTCN2020101053-appb-000013
通过赌轮盘算法依据被选择概率在所有可选择节点中，在所有节点中选择概率最大的节点进行连接，节点选择概率计算公式为
Figure PCTCN2020101053-appb-000014
式中，allowed k可以表示可供选择的下一个节点，τ ij(t)为t时刻路径上节点i至节点j上的全局连接概率影响因子,该因子由前次路径选择的总时间(前次路径选择上所有节点之间运动时间T ij之和)决定，其计算方法为在算法开始之前设定一个初始值和最大最小取值区间[τ min-τ max]，在完成一次迭代后选择出N cm次路径选择中效率最高和最低路径，对最优路径上所有节点选择的全局概率进行增加，对最劣路径上所有节点的全局连接概率影响因子进行相应降低，并以[τ min-τ max]对增加范围做相应限制；α，β分别为全局连接概率影响因子和局布连接概率影响因子的权重系数，其中α表示全局连接概率的相对重要性；β为局部连接概率的相对重要性；η ij(t)是局部连接概率计算公式，为T ij倒数。
三是全局连接概率影响因子的计算方法
本发明所述全局连接概率影响因子更新算法即每次迭代在节点(i，j)之间路径上的全局连接概率影响因子的改变方法，如下式：
τ ij(t+n)＝(1-ρ)τ ij(t)+Δτ ij(t)
Figure PCTCN2020101053-appb-000015
公式中ρ为全局连接概率影响因子的每代衰减率常数，主要功能是为了确保每个节点路径上的全局选择概率因子不会超出[τ min-τ max]，ρ的取值范围为：
Figure PCTCN2020101053-appb-000016
Δτ ij(t)表示每次迭代完成后依据路径的优劣程度(爬壁打磨抛光机器人打磨效率的全局连接概率影响因子的提高，初始时刻Δτ ij(t)＝0，Δτ ij k(t)表示经过第k次迭代后，节点路径i，j上全局连接概率影响因子的变化量，Δτ ij k(t)的求解公式为
Figure PCTCN2020101053-appb-000017
其中Q表示初始增加概率常量，T k表示第k次迭代时打磨路径的总时间，由此可见，时间越短路径的被选择概率越大。
将已划分好的各个打磨区域块的坐标信息存入空间打磨区域连接表中，依据打磨路径智能规划打磨路径和解算，设定初始优化参数。设定路径最大循环迭代次数k m，k m取值通常大于50，设定每次循环迭代的最大路径遍历次数N cm，一般的，N cm取值通常大于200，初始化打磨路径、初始化打磨区域连接点存储表，初始化打磨系数，载入跨壁、转弯各运动速度计算方程。
如图10所示，打磨路径智能规划打磨路径和解算具体步骤如下：
(1)初始化参数，载入机器人所有可以遍历的链接点信息，初始化各点禁忌表、权重系数及概率影响因子，设置最大迭代次数k m和每次迭代的最大路径选择次数N cm，初始迭代次数k和每次迭代的初始路径选择次数N c为0，待打磨三维空间所有待连接的链接点数量为n 1；
(2)N c＝0，进行下一条路径选择，N c＝N c+1，其中N c为每次迭代的初始路径选择次数；
(3)进行下一次遍历，i＝i+1，其中i为本次迭代的遍历次数；
(4)依据当前链接点的被选择概率，根据赌轮盘算法随机选择下一链接点，遍历下一个链接点，j＝j+1，其中j为已遍历节点数量；
(5)修改禁忌表，把已经遍历过的链接点标记为已遍历链接点，确保一个链接点只被遍历过一次；
(6)判断已遍历节点数量j是否等于当前空间的节点总数n 1，若等于n 1，则当前路径选择完成，进行链接点被选择概率的即时更新，进入步骤(7)，若不等于n 1，则路径选择没有完成，进入步骤(4)继续执行；
(7)判断本次迭代的遍历次数i是否等于N cm，若等于N cm，则本次迭代完成，进入步骤(8)，若不等于N cm，则本次迭代未完成，进入步骤(3)继续执行；
(8)完成一次迭代，k＝k+1，其中k为迭代次数；
(9)判断迭代次数k是否等于最大迭代次数k m或打磨时间T ij是否达到设定的优化目标，若满足上述任一条件，则输出最佳路径并结束，否则跳转到步骤(2)继续执行。
方法结束后，输出最佳路径，一个优化实例所生成的爬壁打磨抛光机器人打磨路径如图11所示。将生成的优化路径，转化为各个空间运动信息，即以链表结构体的形式，存储路径信息，存储信息包括：上一节点和下一节点信息、该节点处的运动速度、加速度、转速。将存储的路径信息逐点转换为机器人麦克纳姆轮驱动电机的运动指令，完成整个打磨路径的优化，最终实现以打磨运动时间最短为目标，以安全吸附、打磨质量、全遍历为约束条件的打磨效能控制方法。

Claims (10)

  1. 一种三维空间爬壁打磨抛光机器人，其特征在于：包括第一爬壁机器人(1)、第二爬壁机器人(4)和连接它们的翻转机构，第一爬壁机器人(1)和第二爬壁机器人(4)的下端设置打磨机构(17)，翻转机构包括步进电机(2)、扇形齿轮减速箱(3)、联轴器(5)、俯仰转轴(6)、滑动轴承(7)、连接抱箍(8)和第一固定弯角(9)，步进电机(2)与扇形齿轮减速箱(3)相连接，俯仰转轴(6)通过联轴器(5)与扇形齿轮减速箱(3)相连接，俯仰转轴(6)两端装有滑动轴承(7)，第一爬壁机器人(1)通过连接抱箍(8)连接在俯仰转轴(6)上，第二爬壁机器人(4)通过第一固定弯角(9)固定在扇形齿轮减速箱(3)上。
  2. 根据权利要求1所述的三维空间爬壁打磨抛光机器人，其特征在于：第一爬壁机器人(1)和第二爬壁机器人(4)为相同结构，各包括壳体(10)和底盘(11)，底盘(11)中设置麦克纳姆轮(12)、驱动电机(13)、运动轴(20)、蓄电池组(15)、打磨机构(17)、真空负压机构(18)和集中控制模块(19)，麦克纳姆轮(12)为4个，分别设置在底盘(11)的外侧底部，每个麦克纳姆轮(12)通过运动轴(20)连接1个驱动电机(13)，打磨机构(17)和真空负压机构(18)贯穿底盘(11)的底部，真空负压机构(18)用于爬壁时机器人吸附在墙壁上，集中控制模块(19)用于驱动电机(13)、打磨机构(17)、真空负压机构(18)和翻转机构的控制和数据交互反馈。
  3. 根据权利要求2所述的三维空间爬壁打磨抛光机器人，其特征在于：底盘(11)中还包括第二固定弯角(14)和第三固定弯角(16)，第二固定弯角(14)用于将驱动电机(13)固定在底盘(11)上，第三固定弯角(16)用于将打磨机构(17)固定在底盘(12)上。
  4. 根据权利要求2所述的三维空间爬壁打磨抛光机器人，其特征在于：打磨机构(17)为2个，对称设置在底盘(11)的横向中心轴两侧，真空负压机构(18)为2个，对称设置在底盘(12)的纵向中心轴两侧。
  5. 根据权利要求2所述的三维空间爬壁打磨抛光机器人，其特征在于：所述打磨机构(17)包括打磨电机(25)、弹性连轴器(23)、打磨力敏传感器(24)、连接法兰(22)和弹性打磨盘(21)，打磨作业时打磨电机(25)带动弹性联轴器(23)高速转动，弹性联轴器(23)上连接打磨力敏传感器(24)和连接法兰(22)，打磨力敏传感器(24)实时将反馈打磨力传递至集中控制模块(19)，连接法兰(22)上连接弹性打磨盘(21)，弹性打磨盘(21)受连接法兰带动高速旋转进行打磨作业。
  6. 一种三维空间爬壁打磨抛光机器人的打磨能效控制方法，基于权利要求1至5任一项所述的三维空间爬壁打磨抛光机器人，其特征在于：包括打磨区域建模方法、打磨质量建模方法、运动过程建模方法以及打磨路径智能规划方法，
    所述打磨区域建模方法根据打磨环境建立三维空间模型，在三维空间模型中提取各 运动拐点和面面之间交点，生成整个三维空间的打磨运动链接点，将打磨路径分割为打磨直线段，将机器人的打磨路径规划问题转换为运动直线段终点的连接问题；
    所述打磨质量建模方法建立打磨质量模型，根据打磨表面粗糙度、打磨电机转速和打磨电机负载压力获得机器人打磨运动速度取值范围；
    所述运动过程建模方法将爬壁机器人的运动划分为4个阶段进行运动过程力学分析，得到打磨各个阶段力学模型，结合打磨运动速度、额定空载速度得到建立打磨运动过程模型，获得机器人的运动加速度取值范围；
    所述打磨路径智能规划方法建立打磨能效控制模型，将运动速度、运动加速度代入效能控制模型，通过蚁群算法进行解算，获取最优的打磨路径。
  7. 根据权利要求6所述的三维空间爬壁打磨抛光机器人的打磨能效控制方法，其特征在于，所述打磨区域建模方法具体为：
    (1)将待打磨墙壁、舱体的三维模型导入UG软件中，选取待打磨平面和非打磨区域；
    (2)依据三维模型，获取待打磨平面和非打磨区域在空间三维坐标系下的轮廓几何位置信息；
    (3)基于二维平面进行打磨区域粗划分，将打磨区域逐面离散为环状待打磨区域，再将环状打磨区域细划分为线条状的打磨区域，依次获取待打磨区域内各个二维平面所有墙壁和窗户的轮廓线，逐一平面依次以设定的正负打磨步距h，内轮廓向外偏置，外轮廓向内偏置，初步生成一个平面内的打磨区域，对于有交点的干涉环，则由交点依次沿反方向依次遍历各拐点，直至形成闭环；
    (4)面向三维空间进行打磨区域细划分，在跨壁和环状打磨区域转向处设置爬壁打磨抛光机器人运动线上的链接点，将环状打机器人运动区域的链接点的标记以面和空间坐标系下XY点坐标建立结构体进行存储，并将环形路径拆分为以拐点为起始点的直线段路径段。
  8. 根据权利要求6所述的三维空间爬壁打磨抛光机器人的打磨能效控制方法，其特征在于，所述打磨质量建模方法具体为：
    λ＝kv p xn yF N z，
    其中，λ为打磨表面粗糙度，v p为打磨运动速度，n为打磨电机的转速，F N为打磨电机负载压力，x、y、z为打磨经验模型系数，在机器人作业开始之前，依据作业对象类别，设定粗糙度值计算出所需的打磨运动速度v p。
  9. 根据权利要求6所述的三维空间爬壁打磨抛光机器人的打磨能效控制方法，其特征在于，所述运动过程建模方法具体为：
    (1)打磨阶段，打磨电机施加压力力，以打磨运动速度v p、加速度a p进行直线运动，打磨阶段运动时间t 0为，
    Figure PCTCN2020101053-appb-100001
    其中，v s0为上一运动阶段决定的运动初始速度，L 0为打磨阶段路径长度；
    (2)空载运行阶段，爬壁打磨抛光机器人先将打磨盘提升至安全平面，以加速度a k和运动速度v k进行直线运动，a k＞a p，v k＞v p，空载运行阶段运动时间t 1为，
    Figure PCTCN2020101053-appb-100002
    其中，v s1为上一运动阶段决定的运动初始速度，L 1为空载运行阶段路径长度；
    (3)转向阶段，通过麦克纳姆轮的运动调整运动方向，以角加速度ψ ψr转向，转向阶段运动时间t 2为，
    Figure PCTCN2020101053-appb-100003
    其中，v s2为上一运动阶段决定的运动初始速度，l 2为转向过程中机器人在X方向或Y方向的移动距离；
    (4)跨壁阶段，打磨抛光机器人单机吸附，通过控制翻转机构的伺服电机以角速度w r带动另外一个机器人进行翻转运动，实现二面角过渡，跨壁阶段运动时间t 3为，
    t 3＝θ/(w r*R)
    其中，θ为墙壁间角度，R为电机转轴半径。
  10. 根据权利要求9所述的三维空间爬壁打磨抛光机器人的打磨能效控制方法，其特征在于：所述打磨能效控制模型为，
    Figure PCTCN2020101053-appb-100004
    优化目标为打磨时间T ij最短；
    所述的解算过程具体为：
    (1)初始化参数，载入机器人所有可以遍历的链接点信息，初始化各点禁忌表、权重系数及概率影响因子，设置最大迭代次数k m和每次迭代的最大路径选择次数N cm， 初始迭代次数k和每次迭代的初始路径选择次数N c为0，待打磨三维空间所有待连接的链接点数量为n 1；
    (2)N c＝0，进行下一条路径选择，N c＝N c+1，其中N c为每次迭代的初始路径选择次数；
    (3)进行下一次遍历，i＝i+1，其中i为本次迭代的遍历次数；
    (4)依据当前链接点的被选择概率，根据赌轮盘算法随机选择下一链接点，遍历下一个链接点，j＝j+1，其中j为已遍历节点数量；
    (5)修改禁忌表，把已经遍历过的链接点标记为已遍历链接点，确保一个链接点只被遍历过一次；
    (6)判断已遍历节点数量j是否等于当前空间的节点总数n 1，若等于n 1，则当前路径选择完成，进行链接点被选择概率的即时更新，进入步骤(7)，若不等于n 1，则路径选择没有完成，进入步骤(4)继续执行；
    (7)判断本次迭代的遍历次数i是否等于N cm，若等于N cm，则本次迭代完成，进入步骤(8)，若不等于N cm，则本次迭代未完成，进入步骤(3)继续执行；
    (8)完成一次迭代，k＝k+1，其中k为迭代次数；
    (9)判断迭代次数k是否等于最大迭代次数k m或打磨时间T ij是否达到设定的优化目标，若满足上述任一条件，则输出最佳路径并结束，否则跳转到步骤(2)继续执行。
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* Cited by examiner, † Cited by third party
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DE102022122584A1 (de) 2022-09-06 2024-03-07 Jöst Gmbh Schleifroboter zum Schleifen einer Oberfläche mit einer Schleifeinrichtung

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CN111203766B (zh) * 2020-04-20 2020-08-14 广东博智林机器人有限公司 一种墙面打磨路径规划方法、装置、设备和介质
CN111976857B (zh) * 2020-08-25 2024-01-30 广东省智能制造研究所 一种自旋转双体爬壁机器人
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CN113352192B (zh) * 2021-06-11 2022-07-29 中国建筑土木建设有限公司 一种模板打磨器及其控制方法
CN113799152B (zh) * 2021-10-16 2022-12-02 艾普科模具材料（上海）有限公司 一种自行走打磨机器人、打磨系统及控制方法
CN117428581B (zh) * 2023-12-21 2024-02-27 苏州博宏源机械制造有限公司 基于机械抛光的处理效率优化方法、系统及存储介质

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH01111578A (ja) * 1987-10-26 1989-04-28 Ishikawajima Kensa Keisoku Kk 真空吸着式走行装置
CN201176192Y (zh) * 2008-02-22 2009-01-07 北京航空航天大学 关节连接小型双体负压爬壁机器人
CN201545089U (zh) * 2009-10-21 2010-08-11 昆山市工业技术研究院有限责任公司 一种可跨越交叉壁面的壁面移动机器人
CN102351017A (zh) * 2011-08-03 2012-02-15 南京理工大学 一种爬壁机器人的运动机构
CN104828170A (zh) * 2015-05-08 2015-08-12 浙江省特种设备检验研究院 一种壁面打磨爬壁机器人
CN106271941A (zh) * 2016-09-28 2017-01-04 朱德进 智能墙面打磨机器人
CN110860983A (zh) * 2019-12-09 2020-03-06 中国电子科技集团公司第二十八研究所 三维空间爬壁打磨抛光机器人及打磨能效控制方法

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH01111578A (ja) * 1987-10-26 1989-04-28 Ishikawajima Kensa Keisoku Kk 真空吸着式走行装置
CN201176192Y (zh) * 2008-02-22 2009-01-07 北京航空航天大学 关节连接小型双体负压爬壁机器人
CN201545089U (zh) * 2009-10-21 2010-08-11 昆山市工业技术研究院有限责任公司 一种可跨越交叉壁面的壁面移动机器人
CN102351017A (zh) * 2011-08-03 2012-02-15 南京理工大学 一种爬壁机器人的运动机构
CN104828170A (zh) * 2015-05-08 2015-08-12 浙江省特种设备检验研究院 一种壁面打磨爬壁机器人
CN106271941A (zh) * 2016-09-28 2017-01-04 朱德进 智能墙面打磨机器人
CN110860983A (zh) * 2019-12-09 2020-03-06 中国电子科技集团公司第二十八研究所 三维空间爬壁打磨抛光机器人及打磨能效控制方法

Cited By (2)

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DE102022122584A1 (de) 2022-09-06 2024-03-07 Jöst Gmbh Schleifroboter zum Schleifen einer Oberfläche mit einer Schleifeinrichtung
WO2024052352A1 (de) * 2022-09-06 2024-03-14 Jöst Gmbh Schleifroboter zum schleifen einer oberfläche mit einer schleifeinrichtung

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