CN113589826A - Dynamic path planning auxiliary management system for mobile robot - Google Patents
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Abstract
The invention discloses a dynamic path planning auxiliary management system of a mobile robot, which relates to the technical field of mobile rescue robots, and comprises a rescue module: the processing module is used for searching data of the life body characteristics of one area and comprises: data obtained by the rescue module are processed, the position of the characteristics of the living body is determined, and the induction module: determining the positions of all obstacles in the area from the current position of the mobile robot to the characteristic position of the living body to form corresponding data information, and controlling the module to: the route planning management system can avoid local optimal traps, realize quick search of the global optimal route and enable post-disaster rescue efficiency to be higher.
Description
Technical Field
The invention belongs to the technical field of mobile rescue robots, and particularly relates to a dynamic path planning auxiliary management system for a mobile robot.
Background
The most profound impression of the intelligent robot is a unique 'living thing' which carries out self control, the main organs of the intelligent robot are not as delicate and complex as the real people, the intelligent robot is only provided with various internal information sensors and external information sensors, such as vision, hearing, touch and smell, and is also provided with effectors besides receptors as a means for acting on the surrounding environment, namely muscles, or self-leveling motors, which move hands, feet, long noses, tentacles and the like, so that the intelligent robot is also known to have at least three elements: sensory, reaction and thinking elements, we call this robot an autonomous robot, in order to distinguish it from the one previously mentioned, which is the result of the cybernetics advising the fact that: the purposeful behaviors of life and non-life are consistent in many ways, as is said by an intelligent robot maker, a robot is a functional description of a system that has been derived in the past only from the results of living cell growth, and that has become something we can make themselves.
An intelligent robot can understand human language, converse with an operator with human language, separately form an external environment in its own "consciousness" -an exhaustive model of the actual situation that enables it to "live", analyze the situations that occur, adjust its own actions to meet all the requirements set by the operator, draw up the desired actions, and perform them under conditions of insufficient information and rapid changes in the environment, which, of course, is not possible as much as our human thinking, but still attempts to create some kind of micro-world that a computer can understand.
The mobile robot is widely applied, for example, in the field of search and rescue, a rescue robot, a robot developed by adopting advanced science and technology for rescue, such as an earthquake rescue robot, which is a robot specially used for searching survivors in ruins of underground shopping malls after a major earthquake to execute a rescue task, the robot is provided with a color camera, a thermal imager and a communication system, in order to reach a specified place at the fastest speed, the motion track of the robot usually needs to be accurately calculated, after the disaster such as the earthquake and the like, a plurality of obstacles appear on the advancing road of the rescue robot, in order to ensure that the rescue robot crosses the obstacles and reaches the specified place at the fastest speed, a dynamic path needs to be planned, and therefore, an auxiliary management system for planning the dynamic path of the mobile robot is needed.
The conventional dynamic path planning auxiliary management system of a mobile robot accurately avoids each obstacle in the moving process, namely, when the front of the mobile robot meets the obstacle in the moving process, the mobile robot passes through the obstacle to reach the other side of the obstacle and continues to move forwards through self calculation, then repeatedly passes through each obstacle in the same mode and finally reaches a specified place, the conventional mobile robot only can ensure local optimization, namely, the existing mobile robot easily sinks into a local optimal trap when passing through a single obstacle with the shortest route, so that a new solution is necessary.
The conventional mobile robot dynamic path planning auxiliary management system is easy to get into a local optimal trap during route planning, and cannot ensure global optimization, so that the mobile robot dynamic path planning auxiliary management system is provided.
Disclosure of Invention
The invention aims to provide a dynamic path planning auxiliary management system for a mobile robot, which solves the problems that the existing management system is easy to get into a local optimal trap and cannot ensure global optimal when planning a route.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a dynamic path planning auxiliary management system of a mobile robot, which comprises a rescue module: the processing module is used for searching data of the life body characteristics of one area and comprises: data obtained by the rescue module are processed, the position of the characteristics of the living body is determined, and the induction module: determining the positions of all obstacles in the area from the current position of the mobile robot to the characteristic position of the living body to form corresponding data information, and controlling the module to: acquiring data information of the induction module, uploading the data signal to the calculation module, acquiring a global optimal path by the calculation module through a calculation method, returning the global optimal path to the control module, and controlling the mechanical module to move by the control module to reach the characteristic position of the life body through the global optimal path;
the rescue module comprises an infrared sensing device, a thermal imaging device and a color camera, and all data information in the area is accurately acquired through the infrared sensing device, the thermal imaging device and the color camera;
in the processing module, the specific position of the characteristics of the living body is finally obtained by analyzing the data information of the infrared sensing device, analyzing the data acquired by the thermal imaging device and analyzing the color image;
in the induction module, three-dimensional point cloud reaching the characteristic position of the living body from the observation point of the mobile robot is measured through a three-dimensional scanning instrument, the position coordinates of obstacles in the three-dimensional point cloud are analyzed, and finally generated data information is transmitted to the control module;
the calculation module starts to calculate the optimal path after acquiring the observation point position, the life body feature position and the barrier position between the observation point and the life body feature position of the mobile robot, and the calculation process adopts a grid method, namely the environment information that the mobile robot needs to work is used
Dividing the information into square grids with equal size, simulating by a computing module through a grid method matched with an improved ant colony algorithm, finding a shortest path in a static environment through the ant colony algorithm, detecting that a robot collides with a dynamic obstacle in the process of advancing, determining a grid safe from the dynamic obstacle as a local target point on the shortest path, determining a general motion range of the dynamic obstacle through information collected by a sensor, setting an environment array value of the grid in the range to be 1, advancing the robot along the grid with large pheromones, finding a path avoiding the dynamic obstacle, and determining an optimal combination of important parameters of the improved ant colony algorithm under a specific environment through a particle swarm algorithm, wherein initialization is carried outThe parameters include the maximum iteration number Ncmax for initializing the improved ant colony algorithm, the number num of colonies, a starting point S, a target point T, the row number M and the column number N of grids, an environment array Map (M, N) for representing obstacles, the current position of num ants is set as the starting point S, the grids which can be reached by the ants in the next step are found, the grids which can be reached by the ants in the next step are the grids which can be reached by the ants in 8 grids around the current ants and the environment array value of which is 0, then the ants select the next grid, and the corresponding selection probability of the reachable grids is calculated, wherein the calculation formula is as follows: the probability that ant a will transition from grid i to j at time t is defined as
，
Then:
in the formula, the heuristic function of the subsequent grid point is represented,
represents the concentration of pheromone remaining on the route < i, j > at time t; alpha and beta are each independently
(t) and
the weight of the influence on the whole transition probability;
representing the next allowed selected grid number of ant a, obtaining the next forward grid by roulette, setting the environment array value of the grid to 1, judging whether the ant reaches the target point, if the ant reaches the target point T within the specified maximum step number, recording the grid number and the length of the walking path of the ant, otherwise, removing the ant from the current group, updating pheromone, finding out the local optimal and worst ant reaching the target point, and then for each gridUpdating the pheromone, wherein the updating formula is as follows: (
) In the formula: after n moments, the colony completes a cycle of movements which leaves pheromones on the path travelled, the concentration of which diverges with time, the concentration of which on the path (i, j) at the moment t being defined as
Then, at time t +1, the concentration of pheromone is:
in the formula, rho is the volatilization coefficient of pheromone; delta
The method comprises the steps of representing the concentration of pheromones left on a path (i, j) by an a-th ant in the current cycle, judging whether the maximum cycle number is reached, if so, outputting the grid serial number and the length of the shortest path in the current group, finally, enabling the mobile robot to move along the shortest path searched, if the mobile robot is detected to collide with a dynamic obstacle, determining a local target point, searching a path avoiding the dynamic obstacle along a grid with high pheromone concentration, finally, simulating the method through a C + + program, searching the global optimal path of the mobile search and rescue robot, returning data information to a control module, and controlling the mechanical module to move.
Preferably, the processing module searches whether the area has the life characteristics or not through a thermal imaging technology, the distance from the processing module to the life characteristics is measured through infrared induction, an image is obtained through matching with a color camera, then a digital image processing technology is used for comparing a color image, a thermal imaging image and an infrared induction image with three layers of images, the positions of the life characteristics are found out, when the search and rescue robot determines the life characteristics, the rescue robot can also carry the life detection instrument, namely, the life detection instrument is provided with legs and feet, so that the life detection instrument can enter the interior of a collapsed building independently and is carried to the vicinity of the collapsed building for use by rescuers, based on personal safety, the action range is limited, the deeper part of the building can not be detected, and the detection area can be greatly improved through carrying the life detection instrument by the robot.
Preferably, the three-dimensional laser scanner in the sensing module is based on the technical field of three-dimensional scanning, and by a high-speed laser scanning measurement method, three-dimensional coordinate data of the surface of the measured object is rapidly acquired in a large area and a high resolution mode, and spatial point location information can be rapidly and massively acquired so as to acquire three-dimensional point cloud, and finally the position relation of obstacles is a disadvantage.
Preferably, in the grid method of the computing module, the grid with obstacles is marked as 1, the grid without obstacles is marked as 0, and the grid marked as 0 is a movable grid.
Preferably, the control module controls the mobile module to work according to the global optimal path calculated by the calculation module, wherein the mobile module can control the mobile search and rescue robot to move forwards, turn left and right, bypass the obstacle and cross the obstacle through a mechanical structure, and finally reach the position of the living body.
Preferably, the calculation method in the management system is simultaneously applicable to a global path optimization method for a static obstacle and also applicable to a global path optimization algorithm for a dynamic obstacle, in dynamic path planning, a shortest path in a static environment is found through an ant colony algorithm, if the robot detects that the robot collides with the dynamic obstacle in the process of advancing, a grid safe from the dynamic obstacle is determined as a local target point on the shortest path, a general movement range of the dynamic obstacle is determined through information collected by a sensor, an environment array value of the grid in the range is set to be 1, the robot advances along the grid with large pheromones, a path avoiding the dynamic obstacle is found, and finally, a global path optimal route is found under the dynamic obstacle.
Reference throughout this specification to the description of "one embodiment," "an example," "a specific example," or the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention, and the schematic representation of the terms in this specification does not necessarily refer to the same embodiment or example, and the particular feature, structure, material, or characteristic described may be combined in any suitable manner in any one or more embodiments or examples.
The invention has the following beneficial effects:
the invention provides a mobile robot path planning management system, firstly, life body characteristics in ruins under natural disasters such as earthquake and debris flow are quickly searched and rescued through a rescue module, the position of a life body is determined through processing returned information by a processing module, the position of an obstacle on a route from a starting point of a mobile robot to the position of the life body characteristic is obtained through a sensing module, a control module controls a calculation module to find a global optimal route through an improved ant colony algorithm, and finally the control module controls a mechanical module to advance along the calculated route, and a dynamic path planning method is carried out on the mobile robot through the improved ant colony algorithm, in the traditional ant colony algorithm, the route is gradually explored by ants, the searching efficiency of the algorithm is low, and the heuristic function is proposed to be adaptively adjusted according to a target point, so that the searching speed is greatly improved, aiming at the problem that the traditional ant colony algorithm is easy to fall into a local optimum value, the ant colony distribution principle is used for reference, ants of a local optimum path in each circulation are found, the amount of pheromones released by the ants is increased, the pheromones released by the ants on the local worst path are removed, the interference of the pheromones on the worst path is avoided, the convergence speed can be improved, the dynamic path planning of the mobile robot is realized by utilizing the improved ant colony algorithm, the local optimum traps can be avoided by the path planning management system, the global optimum path can be quickly found, the post-disaster rescue efficiency is higher, and the life and property safety of people is guaranteed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a system block diagram of a dynamic path planning auxiliary management system of a mobile robot according to the present invention;
fig. 2 is a block diagram of a flow of a dynamic path planning assistance management system of a mobile robot according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "upper", "middle", "outer", "inner", "lower", "around", and the like, indicate orientations or positional relationships, are used merely to facilitate the description of the present invention and to simplify the description, and do not indicate or imply that the referenced components or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be taken as limiting the present invention.
Referring to fig. 1-2, the present invention is a mobile robot dynamic path planning auxiliary management system, which includes a rescue module: the processing module is used for searching data of the life body characteristics of one area and comprises: data obtained by the rescue module are processed, the position of the characteristics of the living body is determined, and the induction module: determining the positions of all obstacles in the area from the current position of the mobile robot to the characteristic position of the living body to form corresponding data information, and controlling the module to: acquiring data information of the induction module, uploading the data signal to the calculation module, acquiring a global optimal path by the calculation module through a calculation method, returning the global optimal path to the control module, and controlling the mechanical module to move by the control module to reach the characteristic position of the life body through the global optimal path;
the rescue module comprises an infrared sensing device, a thermal imaging device and a color camera, and all data information in the area is accurately acquired through the infrared sensing device, the thermal imaging device and the color camera;
in the processing module, the specific position of the characteristics of the living body is finally obtained by analyzing the data information of the infrared sensing device, analyzing the data acquired by the thermal imaging device and analyzing the color image;
in the induction module, three-dimensional point cloud reaching the characteristic position of the living body from the observation point of the mobile robot is measured through a three-dimensional scanning instrument, the position coordinates of obstacles in the three-dimensional point cloud are analyzed, and finally generated data information is transmitted to the control module;
the calculation module starts to calculate the optimal path after acquiring the observation point position, the life body feature position and the barrier position between the observation point and the life body feature position of the mobile robot, and the calculation process adopts a grid method, namely the environment information that the mobile robot needs to work is used
The method comprises the steps of dividing information into square grids with equal size, simulating by a computing module through a grid method matched with an improved ant colony algorithm, finding a shortest path in a static environment through the ant colony algorithm, detecting that a robot collides with a dynamic obstacle in the process of advancing, determining a grid safe from the dynamic obstacle as a local target point on the shortest path, determining a general motion range of the dynamic obstacle through information collected by a sensor, setting an environment array value of the grid in the range to be 1, advancing the robot along the grid with large pheromones, searching a path avoiding the dynamic obstacle, determining an optimal combination of important parameters of the improved ant colony algorithm under a specific environment through a particle swarm algorithm, wherein initialization parameters comprise the maximum iteration number Ncmax for initializing the improved ant colony algorithm, the number num of the colony, a starting point S, a target point T, the number of rows M and the number of columns N of the grid, environment representing obstaclesThe method comprises the following steps of setting an array Map (M, N), setting the current position of num ants as a starting point S, finding out grids which can be reached by the ants in the next step, wherein grids with an environment array value of 0 in 8 grids around the current ants are grids which can be reached by the ants in the next step, selecting the next grid by the ants, and calculating the corresponding selection probability of the reachable grids, wherein the calculation formula is as follows: the probability that ant a will transition from grid i to j at time t is defined as
，
Then:
in the formula, the heuristic function of the subsequent grid point is represented,
represents the concentration of pheromone remaining on the route < i, j > at time t; alpha and beta are each independently
(t) and
the weight of the influence on the whole transition probability;
representing the next allowed selected grid number of the ant a, obtaining the next advancing grid by adopting a roulette method, setting the environment array value of the grid to be 1, judging whether the ant reaches the target point, if the ant reaches the target point T within the specified maximum step number, recording the grid number and the length of the walking path of the ant, otherwise, removing the ant from the current group, updating pheromone, finding out the locally optimal and worst ant reaching the target point, and then updating the pheromone of each grid, wherein the updating formula is as follows: (
) In the form ofThe method comprises the following steps: after n moments, the colony completes a cycle of movements which leaves pheromones on the path travelled, the concentration of which diverges with time, the concentration of which on the path (i, j) at the moment t being defined as
Then, at time t +1, the concentration of pheromone is:
in the formula, rho is the volatilization coefficient of pheromone; delta
The method comprises the steps of representing the concentration of pheromones left on a path (i, j) by an a-th ant in the current cycle, judging whether the maximum cycle number is reached, if so, outputting the grid serial number and the length of the shortest path in the current group, finally, enabling the mobile robot to move along the shortest path searched, if the mobile robot is detected to collide with a dynamic obstacle, determining a local target point, searching a path avoiding the dynamic obstacle along a grid with high pheromone concentration, finally, simulating the method through a C + + program, searching the global optimal path of the mobile search and rescue robot, returning data information to a control module, and controlling the mechanical module to move.
The processing module searches whether the area has the life body characteristics or not through a thermal imaging technology, the distance reaching the life body characteristics is measured through infrared induction, an image is obtained through matching with a color camera, then a digital image processing technology is used for comparing a color image, a thermal imaging image and an infrared induction image with three layers of images, the positions of the life body characteristics are found out, when the search and rescue robot determines the life body characteristics, the rescue robot can also carry the life detection instrument, namely, legs and feet are arranged on the life detection instrument, the life detection instrument can enter the interior of a collapsed building independently, the life detection instrument needs to be carried by rescuers to the vicinity of the collapsed building for use, the action range is limited based on personal safety, the deeper part of the building cannot be detected, and the detection area can be greatly improved through carrying the life detection instrument by the robot.
The three-dimensional laser scanner in the sensing module is based on the technical field of three-dimensional scanning, and by a high-speed laser scanning measurement method, three-dimensional coordinate data of the surface of a measured object can be rapidly acquired in a large area and a high resolution mode, space point location information can be rapidly and massively acquired, three-dimensional point cloud is acquired, and finally the position relation of obstacles is a defect.
In the grid method of the computing module, a grid with an obstacle is marked as 1, a grid without the obstacle is marked as 0, and the grid marked as 0 is a movable grid.
The control module controls the mobile module to work according to the global optimal path calculated by the calculation module, wherein the mobile module can control the mobile search and rescue robot to move forwards, turn left and right, bypass the obstacle and cross the obstacle through a mechanical structure, and finally reach the position of the life body.
In the dynamic path planning, the shortest path in a static environment is found through an ant colony algorithm, if the robot is detected to collide with a dynamic obstacle in the advancing process, a grid safe from the dynamic obstacle is determined as a local target point on the shortest path, the general movement range of the dynamic obstacle is determined through information collected by a sensor, the environment array value of the grid in the range is set to be 1, the robot advances along the grid with large pheromones, a path avoiding the dynamic obstacle is found, and finally the optimal path of the global path is found under the dynamic obstacle.
While the preferred embodiments of the present invention have been disclosed for illustrative purposes only, and not for purposes of limiting the same to all details, it is to be understood that numerous modifications and variations may be made in the details of the present disclosure, which were chosen and described in order to best explain the principles of the invention and the practical application, thereby enabling others skilled in the art to best understand and utilize the invention, the invention being limited only by the claims and their full scope and equivalents.
Claims (7)
1. The dynamic path planning auxiliary management system for the mobile robot is characterized by comprising a rescue module: the processing module is used for searching data of the life body characteristics of one area and comprises: data obtained by the rescue module are processed, the position of the characteristics of the living body is determined, and the induction module: determining the positions of all obstacles in the area from the current position of the mobile robot to the characteristic position of the living body to form corresponding data information, and controlling the module to: acquiring data information of the induction module, uploading the data signal to the calculation module, acquiring a global optimal path by the calculation module through a calculation method, returning the global optimal path to the control module, and controlling the mechanical module to move by the control module to reach the characteristic position of the life body through the global optimal path;
the rescue module comprises an infrared sensing device, a thermal imaging device and a color camera, and all data information in the area is accurately acquired through the infrared sensing device, the thermal imaging device and the color camera;
in the processing module, the specific position of the characteristics of the living body is finally obtained by analyzing the data information of the infrared sensing device, analyzing the data acquired by the thermal imaging device and analyzing the color image;
in the induction module, three-dimensional point cloud reaching the characteristic position of the living body from the observation point of the mobile robot is measured through a three-dimensional scanning instrument, the position coordinates of obstacles in the three-dimensional point cloud are analyzed, and finally generated data information is transmitted to the control module;
the calculation module starts to calculate the optimal path after acquiring the observation point position of the mobile robot, the position of the vital body feature position and the position of the obstacle between the observation point and the position of the vital body feature position.
2. The mobile robot dynamic path planning auxiliary management system according to claim 1, characterized in that the calculation process adopts a grid method, which is to divide the environment information that the mobile robot needs to work into square grids of equal size, the calculation module performs simulation by matching the grid method with an improved ant colony algorithm, finds the shortest path in the static environment through the ant colony algorithm, the robot detects that the robot collides with a dynamic obstacle in the process of advancing, determines a grid safe from the dynamic obstacle as a local target point on the shortest path, determines the general motion range of the dynamic obstacle through the information collected by the sensor, sets the environment array value of the grids in the range as 1, the robot advances along the grid with large pheromones, finds a path avoiding the dynamic obstacle, determines the optimal combination of important parameters of the improved ant colony algorithm under the specific environment through the particle swarm algorithm, wherein, the initialization parameters comprise a maximum iteration number Ncmax for initializing an improved ant colony algorithm, a number num of colonies, a starting point S, a target point T, a number M and a number N of columns of grids, an environment array Map (M, N) for representing obstacles, the current position of num ants is set as the starting point S, the grids which can be reached by the ants in the next step are found, the grids with the environment array value of 0 in 8 grids around the current ants are the grids which can be reached by the ants in the next step, then the ants select the next grid, the next advancing grid is obtained by calculating the corresponding selection probability of the reachable grids and adopting a roulette method, the environment array value of the grid is set as 1, then whether the ants reach the target point is judged, if the ants reach the target point T in the specified maximum step number, the grid serial number and the length of the walking path are recorded, otherwise, removing the ants from the current group, then updating pheromone, finding out locally optimal and worst ants reaching the target point, then updating the pheromone for each grid, then judging whether the maximum cycle number is reached, if so, outputting the grid serial number and the length of the shortest path in the current group, finally, the mobile robot advances along the shortest path searched, if the collision with the dynamic barrier is detected, determining the local target point, finding a path avoiding the dynamic barrier along the grid with high pheromone concentration, finally, simulating the method through a C + + program, finding the globally optimal path of the mobile search and rescue robot, returning data information to the control module, and controlling the movement of the mechanical module.
3. The system according to claim 1, wherein the processing module searches whether the area has the living body feature by using a thermal imaging technology, measures the distance to the living body feature by using infrared sensing, and finally acquires the image by using the color camera, and compares the color image, the thermal imaging image and the infrared sensing image with three layers of images by using a digital image processing technology to find the position of the living body feature.
4. The dynamic path planning auxiliary management system for the mobile robot according to claim 1 is characterized in that a three-dimensional laser scanner in the sensing module rapidly acquires three-dimensional coordinate data of the surface of a measured object in a large area and high resolution manner by a high-speed laser scanning measurement method based on the technical field of three-dimensional scanning, and can rapidly acquire a large amount of space point location information so as to acquire a three-dimensional point cloud and finally obtain the position relationship of a defect obstacle.
5. The system according to claim 1, wherein the grid method of the computing module is a grid method in which a grid with obstacles is marked as 1, a grid without obstacles is marked as 0, and the grid marked as 0 is a movable grid.
6. The dynamic path planning auxiliary management system for the mobile robot according to claim 1, wherein the control module controls the mobile module to work according to the global optimal path calculated by the calculation module, wherein the mobile module can control the mobile search and rescue robot to move forward, turn left and right, bypass the obstacle and cross the obstacle through a mechanical structure, and finally reach the position of the living body.
7. The mobile robot dynamic path planning auxiliary management system according to claim 1, wherein the calculation method in the management system is simultaneously applied to a global path optimization method for static obstacles and a global path optimization algorithm for dynamic obstacles.
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