CN117798918B - Space obstacle avoidance planning method and system for multi-machine suspension system based on collaborative optimization - Google Patents
Space obstacle avoidance planning method and system for multi-machine suspension system based on collaborative optimization Download PDFInfo
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- CN117798918B CN117798918B CN202410044480.5A CN202410044480A CN117798918B CN 117798918 B CN117798918 B CN 117798918B CN 202410044480 A CN202410044480 A CN 202410044480A CN 117798918 B CN117798918 B CN 117798918B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
- B25J9/1666—Avoiding collision or forbidden zones
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
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Abstract
The invention discloses a space obstacle avoidance planning method and system for a multi-machine suspension system based on collaborative optimization, and the method comprises the following steps: step S1, acquiring information of a lifting space in the process of carrying out a lifting task by a multi-machine suspension system; s2, obtaining a space collision-free feasible region of the multi-machine suspension system according to the information of the lifting space; s3, planning a collision-free feasible track of the lifted object, a track of the rope and a track of the tail end of the crane in a collision-free area; and S4, carrying out obstacle avoidance planning on the tail end of the crane and the rope, and finally obtaining the optimal obstacle avoidance track of the multi-crane suspension system. By adopting the technical scheme of the invention, the obstacle avoidance planning of the lifted object, the tail end of the crane and the rope in the multi-robot coordination suspension system is realized, so that the lifted object moves from the initial position to the target position according to the expected track.
Description
Technical Field
The invention belongs to the technical field of multi-robot lifting systems, and particularly relates to a space obstacle avoidance planning method and system for a multi-robot lifting system based on collaborative optimization.
Background
The research of the flexible cable type tightly coupled multi-robot system is mainly focused on the application of the multi-robot combined lifting of one weight, and the system has the advantages of expandable working space, high working efficiency and small movement impact. In reality, the task of lifting a large object can be completed by utilizing a tightly coupled multi-robot system. For example, in Wenchuan earthquake rescue and relief work, due to road collapse or blockage, under the condition that large rescue equipment cannot enter a disaster site, a plurality of helicopters can be utilized to coordinate and hoist the large rescue equipment to solve the problem of insufficient bearing capacity of a single machine. In addition, the maximum radio telescope in the world constructed in Guizhou is controlled by 6 flexible wires to control the 'big pot' with the caliber reaching 500m, so that the position and the posture of a plurality of 4400 reflecting surfaces are changed, and the maximum radio telescope technology in China leads the world for at least 20 years.
However, in the existing civil production field, the joint lifting of objects by a plurality of identical lifting robots is not commonly used, and the main reason is that how to ensure that the robots of a plurality of fixed bases do not collide with each other when the lifting robots are operated, how to avoid obstacles when encountering space obstacles, and how to solve the problems are one difficulty in the system research of the plurality of robots. Therefore, a multi-machine suspension system space obstacle avoidance planning method and system based on force-position collaborative optimization are provided.
Disclosure of Invention
The invention aims to solve the technical problem of providing a multi-machine suspension system space obstacle avoidance planning method and system based on collaborative optimization.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
A space obstacle avoidance planning method of a multi-machine suspension system based on collaborative optimization comprises the following steps:
step S1, acquiring information of a lifting space in the process of carrying out a lifting task by a multi-machine suspension system;
s2, obtaining a space collision-free feasible region of the multi-machine suspension system according to the information of the lifting space;
S3, planning a collision-free feasible track of the lifted object, a track of the rope and a track of the tail end of the crane in a collision-free area;
And S4, carrying out obstacle avoidance planning on the tail end of the crane and the rope, and finally obtaining the optimal obstacle avoidance track of the multi-crane suspension system.
Preferably, the overhead space information includes: the motion state of the lifted object, the rope state, the tail end state of the crane and the information of the obstacle in the lifting environment.
Preferably, in step S2, based on the state of motion of the lifted object, the state of rope and the state of the tail end of the crane, based on the static obstacle environment map, the spatial collision-free feasible region of the multi-machine suspension system is solved by using a convex optimization algorithm and a semi-definite programming method with the topology of the structure and the critical supporting lines between the obstacles as constraint conditions.
In step S3, preferably, an improved dung beetle optimization SDBO algorithm is used, the path length of the lifted object is used as an objective function, a collision-free feasible track of the lifted object is planned in a collision-free area, then the path length and track smoothness of the tail end of the crane are used as objective functions, obstacle avoidance planning is performed on the tail end of the crane and the rope, and the track of the tail end of the crane is obtained.
The invention also provides a multi-machine suspension system space obstacle avoidance planning system based on collaborative optimization, which comprises:
the acquisition device is used for acquiring information of a lifting space in the process of carrying out lifting tasks by the multi-machine suspension system;
the processing device is used for obtaining a space collision-free feasible region of the multi-machine suspension system according to the information of the lifting space;
Planning means for planning a collision-free feasible trajectory of the object to be lifted, a trajectory of the rope and a trajectory of the crane end in a collision-free area;
and the optimizing device is used for carrying out obstacle avoidance planning on the tail end of the crane and the rope, and finally obtaining the optimal obstacle avoidance track of the multi-crane suspension system.
Preferably, the overhead space information includes: the motion state of the lifted object, the rope state, the tail end state of the crane and the information of the obstacle in the lifting environment.
Preferably, the processing device solves the space collision-free feasible region of the multi-machine suspension system by using a convex optimization algorithm and a semi-definite planning method based on a static obstacle environment map and taking the topology of the structure and critical supporting lines among obstacles as constraint conditions according to the motion state of the lifted object, the rope state and the tail end state of the crane.
Preferably, the planning device utilizes an improved dung beetle optimization SDBO algorithm, uses the path length of a lifted object as an objective function, plans to obtain a collision-free feasible track of the lifted object in a collision-free area, and then utilizes force-position cooperative optimization to carry out obstacle avoidance planning on the tail end of the crane and the rope by using the path length and the track smoothness of the tail end of the crane as the objective function, so as to obtain the track of the rope and the tail end of the crane.
According to the invention, the object to be lifted is taken as a planning object, the obstacle avoidance at the tail end of the crane is taken as a planning intermediate link, and the rope obstacle avoidance is taken as a planning object, so that the obstacle avoidance planning of the object to be lifted, the tail end of the crane and the rope in the multi-robot coordination suspension system is realized, the object to be lifted moves from an initial position to a target position according to a desired track, the object to be lifted and the obstacles in the environment, the rope and the obstacles in the environment can not collide, and the movement of each crane also needs to be kept in a given position relation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a multi-machine suspension system according to an embodiment of the present invention;
fig. 2 is a flowchart of a multi-machine suspension system space obstacle avoidance planning method based on collaborative optimization according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1:
The embodiment of the invention provides a multi-machine suspension system space obstacle avoidance planning method based on collaborative optimization, as shown in fig. 1, the multi-machine suspension system comprises: the lifting robot comprises three lifting robots (called lifting machines for short) with the same structure, wherein a single lifting robot comprises a fixed base, 3 joint robots and ropes, and a lifted object is suspended below the three robots through being connected with the ropes. The robot has a rod length of (a i1,ai2,ai3), a joint angle of (θ i1,θi2,θi3),Pi is a connection point between the lifted object and the rope, b i is a position of a tail end point of the lifting crane, and L i is the rope.
As shown in fig. 2, the space obstacle avoidance planning method for the multi-machine suspension system according to the embodiment of the invention comprises the following steps:
step S1, acquiring information of a lifting space in the process of carrying out a lifting task by a multi-machine suspension system;
s2, obtaining a space collision-free feasible region of the multi-machine suspension system according to the information of the lifting space;
S3, planning a collision-free feasible track of the lifted object, a track of the rope and a track of the tail end of the crane in a collision-free area;
and S4, carrying out obstacle avoidance planning on the tail end of the crane and the rope, and finally obtaining the optimal obstacle avoidance track of the multi-crane suspension system.
As one implementation of the embodiment of the present invention, the overhead space information includes: the motion state of the lifted object, the rope state, the tail end state of the crane and the information of the obstacle in the lifting environment.
In step S2, according to the motion state of the lifted object, the rope state and the end state of the crane, based on the static obstacle environment map (in which there are three types of obstacles, i.e. a cylinder, a cube and a sphere), the spatial collision-free feasible region of the multi-machine suspension system is solved by using the convex optimization algorithm and the semi-definite planning method and taking the topology of the structure and the critical supporting line between the obstacles as constraint conditions.
Further, for the case where there is an obstacle in the overhead space, the individual cranes in the suspension system need to maintain a cooperative relationship with each other while avoiding the obstacle. Under the static obstacle, taking the characteristics of rope vectors into consideration, carrying out obstacle avoidance planning on the lifted object, the rope and the crane, and obtaining the optimal or feasible track of the lifted object, the rope and the crane in the lifting space.
In step S3, an improved dung beetle optimization SDBO algorithm is used to plan a collision-free feasible track of a lifted object in a collision-free area by taking the path length of the lifted object as an objective function, and then a crane tail end and a rope are subjected to obstacle avoidance planning by taking the path length and the track smoothness of the crane tail end as objective functions by using force-position cooperative optimization to obtain the track of the rope and the crane tail end.
In step S4, the rope is in a tension state, so that the rope is a space vector, and two ends of the vector are connected by a rigid body, so that the system is no longer a traditional rigid body obstacle avoidance problem, and an obstacle avoidance planning method of a space multi-vector multi-rigid body system is required to be studied. The track of the lifted object is optimized firstly, and then the track of the tail end of the crane is optimized on the basis. The rope obstacle avoidance is realized when the track of the tail end of the crane is optimized, and the tail end of the crane cannot collide.
The multi-machine suspension system has two requirements on the planned track, namely, the requirement on kinematics and dynamics is met, and the obstacle avoidance is achieved. According to the constraint (rope length, tension, rope obstacle avoidance and safe distance between cranes), the track at the tail end of the crane is used as an optimization variable, and the path length is used as an objective function, so that a feasible track conforming to the constraint is obtained.
Due to the limitation of the single node field of view, it is necessary to utilize a global collision free feasible region to constrain crane formation motion. And integrating the lifted environmental information to obtain global environmental information, calculating a collision-free feasible region of the formation by using an optimization algorithm, and calculating a convex envelope of the formation according to the current formation of the formation to obtain formation boundary points. The current collision free feasible region is calculated based on the boundary points of the formation. And when the robot of each boundary calculates the collision-free feasible region, the global collision-free feasible region can be obtained. The key point is set in the middle of the formation motion path, so that the robot formation can reach the target point. The motion trail of the robot formation can be restrained by utilizing the global collision-free feasible region, and the robot formation can avoid the obstacle in an integral mode, so that the robot is prevented from being unstable due to independent obstacle avoidance.
And calculating the next target position and the optimal target formation by using the collision-free feasible region. The current formation is calculated according to the position information of each robot before lifting, the position of a target point at the next moment is calculated according to a set calculation rule, then the optimal target formation of the robot formation is calculated according to the collision-free feasible region calculated before and the target point, and the target formation is calculated continuously and iteratively in the moving process so as to achieve the obstacle avoidance effect. And finally, distributing the robots to the respective coordinates by using the calculated target formation, and calculating the position of the lifted object according to the position of the robots.
Due to the diversity and complexity of the lifting tasks, the situation that the cranes encounter obstacles in the lifting process is considered, and the plurality of cranes can not only integrally avoid the obstacles in the obstacle avoidance process, but also independently avoid the obstacles through the coordination function of track consistency. On the one hand, the robots encounter obstacles and cannot avoid the whole, but part of the robots avoid the obstacles smoothly, and each crane independently performs track planning, so that the crane keeps the formation while avoiding the obstacles, distributes each robot according to the obtained optimal formation, and can recover the original formation after avoiding the obstacles, and further executes the lifting task. On the other hand, in the cooperative lifting process of a plurality of robots, the whole robot can avoid obstacles, and in the obstacle avoidance process, a formation is used as a whole to design an obstacle avoidance algorithm, so that the lifting effectiveness is improved.
In order to avoid collision between the two robots, the distance between the robots is larger than the safety distance, so that the robots keep a certain cooperative distance to move. The aim of the cooperative constraint of crane track consistency is to ensure that a plurality of cranes can reach a target position simultaneously, the cranes are in cooperation in space to finish tasks, the movement time of the cranes is within a certain time range, and the intersection is not empty. When the robot moves along a pre-planned track, obstacle information in the surrounding environment is continuously detected, and if an unknown static obstacle is found, a local track planning strategy based on a rolling window is immediately adopted to realize quick obstacle avoidance.
Assume that the positions of cranes n 1 and n 2 are respectivelyAnd/>The j-th sample point corresponding to trace p i, then the distance vector/>, of cranes n 1 through n 2 Can be expressed as
D safe,d denotes the minimum safe distance between cranes, defineTo select a hazard distance greater than d safe,d.
Aiming at the condition that no obstacle exists in the working space of the system, the main purpose of planning is to ensure the stable operation of the system, construct an objective function by using the system stability index, design a feasible planning algorithm and plan the system to obtain the optimal or feasible track in the lifting space. And constructing an objective function by indexes such as motion stability, motion smoothness, tension balance and the like of the multi-machine suspension system, and solving the optimal motion trail in the system motion trail and rope length solution set by adopting a multi-objective optimization algorithm.
Aiming at the problem that the lifted objects, ropes and the tail ends of the cranes in the multi-crane suspension system collide with environmental obstacles, the embodiment of the invention adopts a collaborative optimization optimal track solving method to carry out space multi-vector multi-rigid body obstacle avoidance planning on the lifted objects, ropes and the tail ends of the cranes. The rope is in a tensioning state in the lifting process, and the visible rope is a rigid vector; based on the OBB bounding box theory, an optimization model of an environmental obstacle and a system structure is established in a three-dimensional space, the topology of the structure and critical supporting lines among the obstacles are used as constraint conditions on the basis of the model, and a convex optimization algorithm and a semi-definite planning method are utilized to solve a collision-free feasible region of the space. And adopting an improved dung beetle optimization SDBO algorithm to carry out obstacle avoidance planning on the lifted objects in a space collision-free feasible area. And taking the consistency of the track planning of the multi-machine suspension system into consideration, and obtaining the obstacle avoidance planning of the multi-machine suspension system by utilizing force-position collaborative optimization.
Example 2:
The embodiment of the invention also provides a multi-machine suspension system space obstacle avoidance planning system based on collaborative optimization, which comprises the following steps:
the acquisition device is used for acquiring information of a lifting space in the process of carrying out lifting tasks by the multi-machine suspension system;
the processing device is used for obtaining a space collision-free feasible region of the multi-machine suspension system according to the information of the lifting space;
Planning means for planning a collision-free feasible trajectory of the object to be lifted, a trajectory of the rope and a trajectory of the crane end in a collision-free area;
and the optimizing device is used for carrying out obstacle avoidance planning on the tail end of the crane and the rope, and finally obtaining the optimal obstacle avoidance track of the multi-crane suspension system.
As one implementation of the embodiment of the present invention, the overhead space information includes: the motion state of the lifted object, the rope state, the tail end state of the crane and the information of the obstacle in the lifting environment.
As one implementation mode of the embodiment of the invention, the processing device solves the space collision-free feasible region of the multi-machine suspension system by using a convex optimization algorithm and a semi-definite programming method based on a static obstacle environment map and taking the topology of the structure and critical supporting lines among obstacles as constraint conditions according to the motion state of the lifted object, the rope state and the tail end state of the crane.
As one implementation mode of the embodiment of the invention, the planning device utilizes an improved dung beetle optimization SDBO algorithm to plan a collision-free feasible track of the lifted object in a collision-free area by taking the path length of the lifted object as an objective function, and then utilizes force-position cooperative optimization to carry out obstacle avoidance planning on the tail end of the crane and the rope by taking the path length of the tail end of the crane and the smoothness of the track as an objective function to obtain the track of the rope and the tail end of the crane.
The above embodiments are merely illustrative of the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present invention pertains are made without departing from the spirit of the present invention, and all modifications and improvements fall within the scope of the present invention as defined in the appended claims.
Claims (4)
1. A space obstacle avoidance planning method of a multi-machine suspension system based on collaborative optimization is characterized by comprising the following steps:
step S1, acquiring information of a lifting space in the process of carrying out a lifting task by a multi-machine suspension system;
s2, obtaining a space collision-free feasible region of the multi-machine suspension system according to the information of the lifting space;
s3, planning a collision-free feasible track of the lifted object, a track of the rope and a track of the tail end of the crane in a collision-free area;
s4, carrying out obstacle avoidance planning on the tail end of the crane and the rope to finally obtain an optimal obstacle avoidance track of the multi-crane suspension system;
In the step S2, according to the motion state of the lifted object, the rope state and the tail end state of the crane, based on a static obstacle environment map, taking the topology of the structure and a critical supporting line among obstacles as constraint conditions, and solving a space collision-free feasible region of the multi-machine suspension system by using a convex optimization algorithm and a semi-definite planning method;
In step S3, using an improved dung beetle optimization SDBO algorithm to plan a collision-free feasible track of the lifted object in a collision-free area by taking the path length of the lifted object as an objective function, and then using force-position collaborative optimization to carry out obstacle avoidance planning on the tail end of the crane and the rope by taking the path length and the track smoothness of the tail end of the crane as the objective function to obtain the track of the rope and the tail end of the crane.
2. The collaborative optimization-based multi-machine suspension system space obstacle avoidance planning method of claim 1, wherein the overhead space information comprises: the motion state of the lifted object, the rope state, the tail end state of the crane and the information of the obstacle in the lifting environment.
3. A multi-machine suspension system space obstacle avoidance planning system based on collaborative optimization is characterized by comprising:
the acquisition device is used for acquiring information of a lifting space in the process of carrying out lifting tasks by the multi-machine suspension system;
the processing device is used for obtaining a space collision-free feasible region of the multi-machine suspension system according to the information of the lifting space;
Planning means for planning a collision-free feasible trajectory of the lifted object, a trajectory of the rope and a trajectory of the crane end in a collision-free area;
the optimizing device is used for carrying out obstacle avoidance planning on the tail end of the crane and the rope to finally obtain an optimal obstacle avoidance track of the multi-crane suspension system;
The processing device solves the space collision-free feasible region of the multi-machine suspension system by using a convex optimization algorithm and a semi-definite programming method based on a static obstacle environment map and taking the topology of the structure and a critical supporting line among obstacles as constraint conditions according to the motion state of the lifted object, the rope state and the tail end state of the crane;
The planning device utilizes an improved dung beetle optimizing SDBO algorithm, takes the path length of a lifted object as an objective function, plans to obtain a collision-free feasible track of the lifted object in a collision-free area, and then utilizes force-position cooperative optimization to carry out obstacle avoidance planning on the tail end of the crane and the rope by taking the path length and the track smoothness of the tail end of the crane as the objective function, so as to obtain the track of the rope and the tail end of the crane.
4. A collaborative optimization-based multi-machine suspension space obstacle avoidance planning system according to claim 3 wherein said overhead space information comprises: the motion state of the lifted object, the rope state, the tail end state of the crane and the information of the obstacle in the lifting environment.
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