CN111413962B - Search and rescue robot target search method based on path passing probability - Google Patents
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Abstract
The invention discloses a search and rescue robot target search method based on path passing probability, which comprises the following steps: s01: establishing a topological environment model of a working environment of the mobile robot, and recording paths among nodes; s02: establishing an evaluation model of the probability that the path between the nodes can pass through, and carrying out reliability evaluation; s03: performing search and rescue key node sequence planning according to the optimization index; s04: planning paths among nodes according to the path passability of the local environment; s05: searching is executed according to the obtained path, and the path passable probability in the topology data set is updated according to the actual condition; if the target to be rescued is found, sending the real-time image and the target position to rescuers; s06: and repeating the steps S03 to S05 until the search task is completed. The invention plans the search path according to the probability of the path, updates the topological model according to the current determined path condition, ensures that the robot passes through an uncertain area with the maximum probability, and efficiently finishes the task.
Description
Technical Field
The invention relates to the field of path planning of search and rescue robots, in particular to a search and rescue robot target searching method based on path passing probability.
Background
The mobile robot search and rescue in disaster and dangerous environment can obviously improve the efficiency of rescue workers and reduce the casualties of the rescue workers. The reasonable mobile robot motion planning scheme can provide efficient upper-layer decision for a search and rescue robot with a thoughtful-reaction mixed system structure, so that the robot can find a target to be rescued as soon as possible and provide site environment images and position information. The mobile robot target search and rescue planning under the environment is not determined to belong to a very complex task type, and the reasonable motion planning method can greatly improve the work efficiency of the search and rescue task.
The invention provides a dynamic path planning method for an urban rescue intelligent body, which improves an ant colony algorithm, belongs to the technical field of robot simulation, aims at the path planning problem of a dynamic change environment in robot rescue simulation, improves the classic ant colony algorithm, introduces a target dominance, modifies a calculation method of ant state transition probability, and updates pheromone rules to adapt to the situations of unknown road conditions, dynamic change, complex rescue intelligent body tasks and inconsistent path planning requirement in the rescue environment.
The evaluation index commonly used in the existing search and rescue robot motion planning method is the shortest path, even the search and rescue robot enters the scene in a remote control mode, and the lagged planning mode cannot be matched with the complex scene environment. In the above search methods, the former is difficult to cope with the real-time change of the path after the problems of temporary collapse, falling of the combustion object and the like are encountered, and the latter is completely dependent on the judgment of people, so that the efficiency is extremely low. The practical search and rescue task needs are extremely complex in working environment, the working environment is often semi-structured or even completely unstructured, the feasible road of the mobile robot is continuously updated along with disaster changes, if a wall collapses, a path can be permanently blocked, and a path can be blocked in a short time by a small amount of comburent, but the existing path planning technology lacks a search method aiming at such search and rescue conditions.
Disclosure of Invention
The invention provides a search and rescue robot target search method based on path passing probability, which is used for constructing a search and rescue path planning strategy in uncertain environment and mainly used for search tasks which are optimized according to the feasible probability of road conditions and have uncertain search and rescue reliability in search environment. And realizing real-time optimization and adjustment of the planning scheme according to the characteristics of the tasks.
The technical scheme of the invention is as follows.
A search and rescue robot target search method based on path passing probability comprises the following steps:
s01: establishing a topological environment model of a working environment of the mobile robot, and recording paths among nodes; s02: establishing an evaluation model of probability that a path between nodes can pass through, and carrying out reliability evaluation; s03: performing search and rescue key node sequence planning according to the optimization index; s04: planning paths among nodes according to the path passability of the local environment; s05: searching is carried out according to the obtained path, and the path passable probability in the topological data set is updated according to the actual condition; if the target to be rescued is found, sending the real-time image and the target position to rescuers; s06: and repeating the steps S03 to S05 until the searching task is completed or an instruction for stopping the searching is received.
Preferably, the process of step S01 includes: establishing an initial feature map by using the features of an environment to be rescued, manually setting key rescue node areas, automatically generating initial paths among nodes by using a bidirectional regression fast random tree algorithm, and simultaneously recording the path length and the features of the paths;
the mobile robot search and rescue area meets the following conditions:
wherein S i Is the ith delivery point, O, of the robot k Is the kth search and rescue node, R k Is a search and rescue area near the kth search and rescue node, L λ The method comprises the steps of searching and rescuing nodes, M is a regional topological environment model to be searched and rescued, and E is a total region to be searched and rescued.
Preferably, the process of step S02 includes: the topological environment model M (O, L) for the mobile robot to work comprises a rescue node set: o = { O k |O k E G, k =1,2,3,.., m } and set of rescue paths: l = { L = λ |L λ ∈M,k=1,2,3,...,n};
Probability of path reliabilityIs used to evaluate O i And O j The probability of smooth passing of the robot is shown, if tau factors possibly blocking the path exist, the passing probability caused by each factor isThenEvaluation by the following model
Probability of initial passageThe shooting analysis or the manual experience value setting can be carried out through the unmanned aerial vehicle.
M (O, L) has a reliability matrix of
Wherein
Preferably, the process of step S03 includes: for M (O, L), only the shortest path among the search and rescue nodes is reserved, redundant paths are deleted, and M ' (O, L ') is generated, wherein L ' is a route set after deletion; judging whether M '(O, L') is communicated and is an Euler diagram, and if the vertex degree is an odd number, supplementing the M '(O, L') to the Euler diagram through the original M (O, L); according toThe weighted average value of the weight and the path length in the process is used as the weight, the weight is 0.5, and a Fleury algorithm is used for generating a preliminary tour sequence of the key nodes.
Preferably, the process of step S04 includes: read-in global topology modelM (O, L), will reliability matrixThe elements and the path length are weighted, the weights are all 0.5 and are used as topological edge weights, a path between nodes is searched by using a minimum weight algorithm Dijkstra, and a path from a robot starting point to a search and rescue node and a path between the search and rescue nodes are generated; the actual paths among the nodes are automatically generated according to the constraint of the robot by using a bidirectional regression fast random tree algorithm.
Preferably, the process of step S05 includes: tracking the generated path by using a speedometer, an IMU (inertial measurement Unit) and a DGPS (differential global positioning system), and updating the feasibility of the path into a topological environment model by using the feasibility of continuously detecting the front path by using a radar, a panoramic camera, binocular vision and an infrared temperature measurement sensor;
for a safe and reliable path with good detected road condition and extremely low possibility of damage, the reliability is set to 1, and for a front path, unrecoverable damage occurs, such as: when the collapse happens, the passability is set to be 0, and the passability can be recovered only by manpower, so that the update can be realized;
for recoverable damage, such as: the combustibles can be set to pass through the road with 0, the feasibility is increased along with the time (the road is possibly unobstructed again because the combustibles are burnt out), and the feasibility is recoveredThe evaluation formula with respect to time can be calculated by the following formula,
the probability of feasibility at time t +1,the probability of feasibility at the moment t is, delta is a recovery speed coefficient, and is set according to the characteristics of recoverable sexual disorder, and delta t is the time interval between the updating time and the last updating, and the unit is second;
updating reliability matrix of topological environment model
Preferably, the process of performing the search in step S06 includes: according to the key rescue node sequence, IMU, DGP and odometer S are used for moving along a path generated by a bidirectional fast random tree algorithm, a radar and a panoramic camera are used for detecting obstacles, temporary obstacles or unrecoverable obstacles appearing in the path are detected, a target to be searched and rescued is identified by binocular vision, the position of the target and image information are obtained, an infrared temperature measurement sensor is used for detecting flame or high-temperature obstacles, obstacle avoidance and danger are taken as the highest priority, and if the target to be rescued is found, the image and the position of the target are sent to rescuers. And then moving with the next search and rescue node as a target until a search task is completed or an instruction for stopping searching is received.
By updating the reliability topological probability model in real time, the searching paths of the robot are guaranteed to be better schemes, and the schemes can be adjusted in time when the road conditions change, so that the searching efficiency is improved, and the target searching task under the uncertain environment is realized.
The substantial effects of the invention include: planning a search and rescue path of the robot according to the initial topological environment model and the passability information stored in the initial topological environment model, meanwhile, updating the passability information stored in the topological environment model according to road condition information in the execution process of the search and rescue task, and ensuring that the search and rescue path tracked by the robot is a better scheme through updating the passability topological probability model in real time.
Detailed Description
The following description will be given in conjunction with embodiments of the present application. In addition, numerous specific details are set forth below in order to provide a better understanding of the present invention. It will be understood by those skilled in the art that the present invention may be practiced without some of these specific details. In some instances, methods, procedures, components, and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present invention.
Example (b):
a search and rescue robot target search method based on path passable probability plans a search and rescue path of a robot according to an initial topological environment model and passability information stored in the initial topological environment model, meanwhile, in the execution process of a search and rescue task, the passability information stored in the topological environment model is updated according to road condition information, and the search and rescue path tracked by the robot is guaranteed to be a better scheme through real-time updating of the passability topological probability model.
The embodiment comprises the following steps:
s01: establishing a topological environment model of a working environment of the mobile robot, and recording paths among nodes;
the method comprises the steps of establishing an initial feature map by using the features of an environment to be rescued, manually setting key rescue node areas, automatically generating initial paths among nodes by using a bidirectional regression fast random tree algorithm, and simultaneously recording the path length and the features of the paths.
The mobile robot search and rescue area meets the following conditions:
wherein S i Is the ith delivery point, O, of the robot k Is the kth search and rescue node, R k Is a search and rescue area near the kth search and rescue node, L λ The method comprises the steps of searching and rescuing nodes, M is a regional topological environment model to be searched and rescued, and E is a total region to be searched and rescued.
Step S02: establishing an evaluation model of the probability that the path between the nodes can pass through, and carrying out reliability evaluation;
the process comprises the following steps: the topological environment model M (O, L) for the mobile robot to work comprises a rescue node set: o = { O k |O k E G, k =1,2,3,.., m } and set of rescue paths: l = { L = λ |L λ ∈M,k=1,2,3,...,n};
Probability of path reliabilityIs used to evaluate O i And O j The probability of smooth passing of the robot is shown, if tau factors possibly blocking the path exist, the passing probability caused by each factor isThenEvaluation by the following model
Probability of initial passageThe shooting analysis or the manual experience value setting can be carried out through the unmanned aerial vehicle.
M (O, L) has a reliability matrix of
Wherein
Step S03: performing search and rescue key node sequence planning according to the optimization index;
the process comprises the following steps: for M (O, L), only the shortest path among the search and rescue nodes is reserved, redundant paths are deleted, and M ' (O, L ') is generated, wherein L ' is a route set after deletion; judging whether M '(O, L') is communicated and is an Euler diagram, and if the vertex degree is an odd number, supplementing the M '(O, L') into the Euler diagram through the original M (O, L); according toThe weighted average value of the weight and the path length in the process is used as the weight, the weight is 0.5, and a Fleury algorithm is used for generating a preliminary tour sequence of the key nodes.
S04: planning paths among nodes according to the path passability of the local environment;
the process comprises the following steps: reading in the overall topological model M (O, L) and forming a reliability matrixThe element and the path length of the robot are weighted, the weight values are all 0.5 and are used as topological edge weights, the path between nodes is searched by utilizing a minimum weight algorithm Dijkstra, and the path from the starting point of the robot to the search and rescue node and the path between the search and rescue nodes are generated; the actual paths among the nodes are automatically generated by a bidirectional regression fast random tree algorithm according to the constraint of the robot.
S05: searching is executed according to the obtained path, and the path passable probability in the topological data set is updated according to the actual condition; if the target to be rescued is found, sending the real-time image and the target position to rescuers;
the process comprises the following steps: tracking the generated path by using a speedometer, an IMU (inertial measurement Unit) and a DGPS (differential global positioning system), and updating the feasibility of the path into a topological environment model by using the feasibility of continuously detecting the front path by using a radar, a panoramic camera, binocular vision and an infrared temperature measurement sensor;
for a safe and reliable path with good detected road condition and extremely low possibility of damage, the reliability is set to 1, and for a front path, unrecoverable damage occurs, such as: when the collapse happens, the passability is set to be 0, and the passability can be recovered only by manual work, so that the update can be realized;
for recoverable damage, such as: the trafficability of the comburent is set to 0, and the feasibility is increased along with the time (the road may be unobstructed again because the comburent is burnt out), and the feasibility is recoveredThe evaluation formula with respect to time can be calculated by the following formula,
the probability of feasibility at time t +1,the probability of feasibility at the moment t is, delta is a recovery speed coefficient, and is set according to the characteristics of recoverable sexual disorder, and delta t is the time interval between the updating time and the last updating, and the unit is second;
updating reliability matrix of topological environment model
S06: repeating the steps S03 to S05 until the search task is completed or an instruction for stopping the search is received;
the process comprises the following steps: according to the key rescue node sequence, IMU, DGP and odometer S are used for moving along a path generated by a bidirectional fast random tree algorithm, a radar and a panoramic camera are used for detecting obstacles, temporary obstacles or unrecoverable obstacles appearing in the path are detected, a target to be searched and rescued is identified by binocular vision, the position and image information of the target are obtained, an infrared temperature measuring sensor is used for detecting flame or high-temperature obstacles, obstacle avoidance and danger are taken as the highest priority, and if the target to be rescued is found, the image and the position of the target are sent to rescuers. And then moving with the next search and rescue node as a target until a search task is completed or an instruction for stopping searching is received.
The effect of the embodiment includes: planning a search and rescue path of the robot according to the initial topological environment model and the passability information stored in the initial topological environment model, simultaneously updating the passability information stored in the topological environment model according to road condition information in the execution process of the search and rescue task, and ensuring that the search and rescue path tracked by the robot is a better scheme through updating the passability topological probability model in real time.
Through the description of the above embodiments, those skilled in the art will understand that, for convenience and simplicity of description, only the division of the above functional modules is used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of a specific device may be divided into different functional modules to perform all or part of the above described functions.
In the embodiments provided in the present application, it should be understood that the disclosed method can be implemented in other ways. The technical solution of the embodiments of the present application may be essentially or partially contributed to the prior art, or all or part of the technical solution may be embodied in the form of a software product, where the software product is stored in a storage medium, and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and all the changes or substitutions should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (3)
1. A search and rescue robot target searching method based on path passing probability is characterized by comprising the following steps:
s01: establishing a topological environment model of a working environment of the mobile robot, and recording paths among nodes;
s02: establishing an evaluation model of the probability that the path between the nodes can pass through, and carrying out reliability evaluation;
the process of step S02 includes:
the topological environment model M (O, L) for the mobile robot to work comprises a rescue node set:
O={O k |O k ∈G,k=1,2,3,...,m}
and set of rescue paths
L={L λ |L λ ∈M,k=1,2,3,...,n}.
Probability of path reliabilityIs used to evaluate O i And O j The probability of smooth passing of the robot is shown in the specification, if tau factors which can block the path exist, the passing probability caused by each factor isThenEvaluation with the following model
Probability of initial passageShooting analysis or manual experience value setting can be carried out through an unmanned aerial vehicle;
m (O, L) has a reliability matrix of
Wherein
S03: performing search and rescue key node sequence planning according to the optimization index;
the process of step S03 includes:
for M (O, L), only the shortest path among the search and rescue nodes is reserved, redundant paths are deleted, and M ' (O, L ') is generated, wherein L ' is a route set after deletion; judging whether M '(O, L') is communicated and is an Euler diagram, and if the vertex degree is an odd number, supplementing the M '(O, L') to the Euler diagram through the original M (O, L); according toTaking the weighted average value of the weight and the path length as the weight, wherein the weights are all 0.5, and generating a preliminary tour sequence of the key nodes by using a Fleury algorithm;
s04: planning paths among nodes according to the path passability of the local environment;
the process of step S04 includes: reading in the overall topological model M (O, L) and forming a reliability matrixThe element and the path length of the robot are weighted, the weight is 0.5 and is used as a topological edge weight, the path between the nodes is searched by utilizing a minimum weight algorithm Dijkstra, and the path from the starting point of the robot to the search and rescue node and the path between the search and rescue nodes are generated; the actual paths among the nodes are automatically generated by a bidirectional regression fast random tree algorithm according to the constraint of the robot;
s05: searching is executed according to the obtained path, and the path passable probability in the topological data set is updated according to the actual condition; if the target to be rescued is found, sending the real-time image and the target position to rescuers;
s06: and repeating the steps S03 to S05 until the searching task is completed or an instruction for stopping the searching is received.
2. The search and rescue robot target search method based on the path passable probability as claimed in claim 1, wherein the process of step S01 comprises:
establishing an initial feature map by using the features of an environment to be rescued, manually setting key rescue node areas, automatically generating initial paths among nodes by using a bidirectional regression fast random tree algorithm, and simultaneously recording the path length and the features of the paths;
the mobile robot search and rescue area meets the following conditions:
wherein S i Is the ith delivery point, O, of the robot k Is the kth search and rescue node, R k Is a search and rescue area near the kth search and rescue node, L λ The method is characterized in that the method is a path among search and rescue nodes, M is a regional topological environment model to be searched and rescued, and E is a total region to be searched and rescued.
3. The search and rescue robot target search method based on the path passable probability as claimed in claim 1, wherein the process of step S05 comprises:
tracking the generated path by using a speedometer, an IMU (inertial measurement Unit) and a DGPS (differential global positioning system), and updating the feasibility of the path into a topological environment model by using the feasibility of continuously detecting the path ahead by using a radar, a panoramic camera, binocular vision and an infrared temperature measurement sensor;
for a safe and reliable path with good detected road conditions, the device can be set to be 1,
when the front path is irrecoverable and damaged, the passability is set to 0;
for recoverable impairments, then passability is set to 0, and passability is incremented over time, passability recoveryThe evaluation formula with respect to time can be calculated by the following formula,
the passability probability at time t +1,setting delta as a recovery speed coefficient according to the characteristics of the restorability obstacle, wherein delta t is the passability probability at the time t, delta is the time interval between the updating time and the last updating, and the unit is second;
updating reliability matrix of topological environment model
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