Subway vehicle inspection robot navigation method, device and readable storage medium in multi-track scenario

Abstract

The present invention proposes a method, device and readable storage medium for navigating a subway vehicle inspection robot in a multi-track scenario, aiming to solve many problems of inspection robot navigation in a multi-track scenario in the prior art. The method first creates a lifting platform template on a navigation map and configures parameters, then creates a TOPO map in combination with information such as the current position of the robot and the position of the target point, then uses an improved Dijkstra algorithm to generate a global navigation path with the shortest time consumption, then divides the global path to obtain sub-target points and related attributes, and finally monitors the state of the robot expected to pass through the lifting platform in real time while the robot is running and controls the movement of the robot. Through these steps, the present invention can quickly deploy the lifting platform, simplify the task configuration process, realize autonomous online planning of the optimal path, accurately control the state of the lifting platform, improve the work efficiency and operation flexibility of the robot, and effectively ensure the inspection quality and safe operation of subway vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile robots, and in particular to a navigation method, a device and a readable storage medium for a subway vehicle inspection robot in a multi-track scenario. Background Art

[0002] With the rapid development of urban rail transit, the safe operation of subway trains is of vital importance. To ensure the safety of subway trains, it is essential to detect abnormal parts. Traditional manual detection methods have problems such as low efficiency and missed detection, which makes it difficult to meet the growing needs of subway operations.

[0003] In recent years, the inspection robot technology has been continuously developing, providing new solutions for subway train inspection. In subway maintenance stations with multiple tracks and inconsistent track grooves and heights on both sides, which require lifting platforms to transfer robots, the solution of using mobile robots equipped with visual inspection equipment and using advanced artificial intelligence algorithms to accurately identify abnormalities in subway parts and report them to the intelligent management platform in a timely manner has gradually attracted attention. However, the existing solutions have some shortcomings.

[0004] In a multi-track scenario, a robot needs to participate in the inspection of trains parked on multiple tracks. When the robot relies on a lift to transfer, the existing navigation method mostly sends navigation points in a fixed order to the robot in order to control the robot's transfer path and the designated lift. This method is not real-time online planning and cannot generate the optimal path based on the actual status of the lift. For example, when the lift cannot be used, the robot will not be able to continue running, resulting in interruption of the inspection work.

[0005] In addition, in the existing solutions, the configuration process of the inspection robot's transfer task in the multi-track scenario is complicated, and it is necessary to manually configure the lift entry and exit points into the task and issue the task navigation points in sequence. This not only increases the difficulty of project implementation, but also makes it impossible for the inspection robot to perform autonomous path planning online according to the real-time status of the lift, which seriously affects work efficiency. At the same time, when the inspection robot encounters an abnormality in the lift that it is expected to pass through during operation, the existing method cannot re-plan the path in time, resulting in insufficient flexibility of the robot and inability to effectively respond to emergencies.

[0006] In summary, the existing navigation methods of subway vehicle inspection robots have many problems in multi-track scenarios. There is an urgent need for a navigation method that can simplify the task configuration process, realize online autonomous planning of the optimal path, autonomously determine the lifts expected to pass through and accurately control their status, so as to improve the inspection efficiency and the operation flexibility of the robot. Summary of the invention

[0007] The embodiments of the present invention provide a navigation method, device and readable storage medium for a subway vehicle inspection robot in a multi-track scenario. The navigation method of the existing subway vehicle inspection robot in a multi-track scenario has the problems of complex task configuration, inability to autonomously plan the path online in combination with the status of the lifting platform, and inability to re-plan the path when the lifting platform is abnormal, resulting in low inspection efficiency and insufficient flexibility.

[0008] The core technology of the present invention is mainly a navigation method for a subway vehicle inspection robot in a multi-track scenario, including creating a lift template, constructing a TOPO diagram, using an improved Dijkstra algorithm to generate a path, segmenting the path to obtain sub-target points and attributes, and monitoring the lift status to control the robot movement to achieve efficient inspection.

[0009] In a first aspect, the present invention provides a navigation method for a subway vehicle inspection robot in a multi-track scenario, the method comprising the following steps:

[0010] S00, creating a lifting platform template including key points and contour corner points on the navigation map, and generating corresponding key point and corner point coordinate parameters according to the coordinates of all lifting platforms on the navigation map;

[0011] Among them, the key point attributes include point ID, the ID of the lift platform to which it belongs, the current state of the lift platform to which it belongs, the required state when the robot passes through, the speed configuration to reach the key point, and the motion control mode;

[0012] S10, after receiving the target point, update the real-time status information of the lifting platform, obtain the static obstacle map, define the starting point and the target point as similar types to the key point, so as to create a topological map;

[0013] S20, using an improved Dijkstra search algorithm to generate a global navigation path with the shortest time consumption from the starting point to the target point and key points of the lifting platform;

[0014] Among them, the improved Dijkstra search algorithm includes the path running time cost and the time cost of waiting for the elevator to change its state when calculating the cost from the initial point to each node;

[0015] S30, segmenting the global navigation path according to the key points of the lift platform of the global path, setting the key points of the lift platform as sub-target points, and determining the lift platform to be passed and the required state and the motion parameters to reach the sub-target points according to the attributes of two consecutive sub-target points;

[0016] S40, monitoring the state of the lifting platform, predicting whether the state of the lifting platform is consistent with the required state, sending the lifting platform required state control instruction, and controlling the movement of the robot according to the state of the lifting platform.

[0017] Further, in step S00, a lifting platform template including five key points and four contour corner points is created on the navigation map, wherein the center point id of the five key points is 0, the key points for entering and exiting the two sides of the track are 1 and 3, and the key points for entering and exiting the track channel are 2 and 4, and the four contour corner points are A, B, C, and D in clockwise order;

[0018] Taking key point 0 as the reference point, calculate the positions of other key points and corner points of the lift according to the coordinate transformation matrix between the lift posture and the target key point 0, obtain the lift parameter list and save it locally, and automatically obtain it each time the robot navigation program is started.

[0019] Furthermore, in step S10, the connectivity rules of the topology graph include:

[0020] Connectivity rules between the starting point and the target point: Connectivity is achieved when the connection line passes the safety detection of the static obstacle map and does not intersect with the rectangular edge line of the corner point of the lift. If connectivity is achieved, no subsequent key point connection operation of the lift is required.

[0021] Internal connectivity rules for a single lift: When the lift is not offline, 0 and the odd-numbered ID key points, 0 and the even-numbered ID key points are bidirectionally connected; when it is offline, they are not connected;

[0022] Connectivity rules between multiple lifts: Under the restriction condition, all odd-numbered ID key points are bidirectionally connected, and all even-numbered ID key points are bidirectionally connected. A two-dimensional list of lift connectivity is established to facilitate connectivity search.

[0023] Connectivity rules between the starting point, target point and key points: find the key point closest to the starting point and target point to determine the ID of the nearest elevator, and find other elevators connected to the nearest elevator based on the connected two-dimensional list. The starting point is unidirectionally connected to the key point, and the key point is unidirectionally connected to the target point.

[0024] Furthermore, in step S20, the time cost of the change of the state of the lifting platform is dynamically calculated based on the key point combination for the state of the lifting platform, using the result of the combination of attributes of three consecutive connected points;

[0025] Among them, the time cost is 0 when the lift is in place or there is no request; when the demand status is inconsistent and the lift is rising or falling, the robot is stationary and the time cost is the time consumed by the lift rising or falling; when entering or exiting the lift and the demand status changes, the total cost is calculated based on the previous point, but it is not less than 0.

[0026] Furthermore, in step S30, the key point of the lifting platform is set as a sub-target point, and the specific steps of determining the lifting platform and the required state expected to pass and the motion parameters to reach the sub-target point according to the attributes of two consecutive sub-target points are as follows:

[0027] Configure the key point attributes according to the platform ID, point ID and point attribute configuration rules of the previous and next key points in the key point list. If three consecutive points belong to the same platform and the lifting platform demand status is consistent, eliminate the middle key point 0, retain the key points on both sides of the lifting platform, and generate a target point list;

[0028] According to the attributes of the target point, a list of required states expected to pass through the lift is obtained, and the first required state of all expected lifts is extracted to generate a pre-controlled lift list, which is used to control the lift state when the robot is running.

[0029] Furthermore, in step S40, after receiving the sub-target point, the robot extracts the lift platform ID and the required state into a list, and updates the pre-controlled lift platform list at the same time, and controls the robot's motion state according to different results by comparing the lift platform required states in the two lists.

[0030] Furthermore, in step S40, the real-time status of the lifting platform is obtained when the robot is running, and the steps of controlling the robot movement by comparing the lifting platform demand status in the two lists include:

[0031] If the lift is not in place in the list, send control commands at intervals;

[0032] If there is an abnormality on the lift, the robot will stop running and wait for the abnormality to be resolved. If the waiting time exceeds the threshold, the path will be re-planned. If the lift is not in place during re-planning, the lift status will be forcibly set to offline, and the lift status will be updated after the task is completed. If the number of planning times exceeds the maximum number, the task will be abandoned.

[0033] If the final target point is not reached, update the target point, motion mode and speed to continue execution.

[0034] In a second aspect, the present invention provides a navigation device for a subway vehicle inspection robot in a multi-track scenario, comprising:

[0035] The lifting platform template creation module creates a lifting platform template including key points and contour corner points on the navigation map, and generates corresponding key point and corner point coordinate parameters according to the coordinates of all lifting platforms on the navigation map; the key point attributes include point id, the lifting platform id to which it belongs, the current state of the lifting platform to which it belongs, the required state when the robot passes, the speed configuration to reach the key point, and the motion control mode;

[0036] The topology map creation module updates the real-time status information of the lift platform after receiving the target point, obtains the static obstacle map, and defines the starting point and target point as similar types to the key points to create a topology map;

[0037] The global navigation path module uses the improved Dijkstra search algorithm to generate the shortest global navigation path from the starting point to the target point and the key points of the lift. The improved Dijkstra search algorithm includes the path running time cost and the time cost of waiting for the lift to change its state when calculating the cost from the initial point to each node.

[0038] The processing module divides the global navigation path according to the key points of the lift platform of the global path, sets the key points of the lift platform as sub-target points, and determines the lift platform and the required state expected to be passed and the motion parameters to reach the sub-target points according to the attributes of two consecutive sub-target points;

[0039] The monitoring module monitors the status of the lift, estimates whether the status of the lift is consistent with the required status, sends the lift required status control instruction, and controls the movement of the robot according to the status of the lift.

[0040] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the above-mentioned subway vehicle inspection robot navigation method in a multi-track scenario.

[0041] In a fourth aspect, the present invention provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute a process. The process includes a navigation method for a subway vehicle inspection robot in the above-mentioned multi-track scenario.

[0042] The main contributions and innovations of the present invention are as follows:

[0043] 1. Create a lift template and configure parameters: The lift can be quickly deployed, task configuration can be simplified, and parameters such as entry and exit points, motion control mode, and speed can be modified according to specific circumstances.

[0044] 2. Build a TOPO (topology) diagram: Create a TOPO diagram based on the robot's position, target point and other information to clarify the connectivity rules between points. This eliminates the need for tasks to specify the lifts they are expected to pass through, and enables automatic determination.

[0045] 3. Improved Dijkstra algorithm to generate paths: When calculating the cost from the initial point to each node, not only the path running time cost is considered, but also the time cost of waiting for the lift to change its state is added. Through the above improvements, the algorithm can more accurately calculate the path cost in complex scenarios with multiple tracks and lifts, thereby searching for a list of key points for the optimal goal based on the shortest time consumption, and generating a global navigation path with the shortest time from the starting point to the target point and the key points of the lift. This enables the robot to better combine the status of the lift for path planning during the navigation process, improves the rationality and efficiency of path planning, avoids unnecessary waiting time caused by changes in the status of the lift, and enhances the adaptability and flexibility of the robot in inspection tasks in multi-track scenarios.

[0046] 4. Split the global path to obtain sub-target points and configure attributes: Generate a target point list and a pre-controlled lift list based on the key point list, which are used to control the lift status when the robot is running.

[0047] 5. Monitor the status of the lift and flexibly control the robot's movement: The robot can autonomously control the status of the lift during operation, and make timely strategic responses when the lift is abnormal, such as stopping operation and waiting for the abnormality to be resolved or replanning the path, etc., which improves the flexibility of the robot's operation and its ability to respond to emergencies.

[0048] The details of one or more embodiments of the invention are set forth in the following drawings and description so that other features, objects, and advantages of the invention are more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0050] Figure 1 It is a schematic diagram of the steps of the present invention;

[0051] Figure 2 It is a schematic diagram of the process of the present invention;

[0052] Figure 3 It is a schematic diagram of the position of the lifting platform template proposed in the present invention;

[0053] Figure 4 It is a schematic diagram of the TOPO created in the present invention;

[0054] Figure 5 is a first global navigation path result diagram of the search under different lifting platform states of the present invention;

[0055] Figure 6is a second global navigation path result diagram of the search under different lifting platform states of the present invention;

[0056] Figure 7 4 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of this specification. Instead, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0058] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0059] Most existing solutions manually configure the entry and exit points of the lift into the task, and then issue the task navigation points in sequence to meet the transition requirements of the inspection robot through the lift. This has the following shortcomings:

[0060] 1. The configuration of the inspection robot's transfer task in a multi-track scenario will become very complicated, which is not conducive to project implementation;

[0061] 2. The task requires specifying the lift platform to pass through, but the inspection robot cannot perform autonomous path planning online based on the real-time status of the lift platform, resulting in low work efficiency;

[0062] 3. When the inspection robot encounters an abnormality when passing through the lifting platform during operation, it is unable to replan the path, resulting in insufficient flexibility of the robot.

[0063] Based on this, the present invention solves the problems existing in the prior art by improving the Dijkstra search algorithm.

[0064] Embodiment 1

[0065] The present invention aims to propose a navigation method for a subway vehicle inspection robot in a multi-track scenario. Specifically, referring to Figure 1 and Figure 2 , the method comprises the following steps:

[0066] S00. Create a lifting platform template on the navigation map, including key points and contour corner points, and generate corresponding key point and corner point coordinate parameters according to the coordinates of all lifting platforms on the navigation map. Specifically, there are five key points and four contour corner points. The five key points are: the center point of the lifting platform has an id of 0, and the two key points corresponding to the center point of the lifting platform from the center of the track are numbered 1 and 3 respectively. The two centered key points corresponding to the center point of the lifting platform in and out of the track are 2 and 4. The five key points 0 is in the middle, and 1 to 4 are arranged in a clockwise order. Since the lifting platform needs to pass through two pillars in the track when entering and exiting the track from both sides, and the width of the two pillars is limited, the space width margin on both sides is very small when the robot passes through. In order to make the robot stably pass through the pillars and enter and exit the lifting platform, the motion control mode and speed size at this time can be configured according to the actual situation.

[0067] The specific implementation process is as follows: Figure 3 When generating a lift template, key points 0, 1, 2, 3, and 4 can be configured with parameters on a specific lift. It should be noted that key points 1 and 0, 3 and 0 should be kept on the center line of the import and export as much as possible to improve the stability of the robot operation. The key point attributes include: point id, lift id, current state of the lift currentState, request state of the lift robot when it passes through requestState, speed configuration vel when reaching the key point, and motion control mode mode when reaching the key point.

[0068] The point ID and the lift ID of the above key points are fixed values. Other attributes are configured after global path planning. They are configured according to the attributes of two consecutive key points and are configured under the same lift condition:

[0069] Rule 1: Key point 0, odd-numbered key points enter and exit the odd-numbered key points, and the request state of the lift is high, and the speed and control mode match the corresponding parameters. For example: key points 0, 1, and 3 enter and exit the lift mutually, and the requestState is high, vel is low speed, and mode is a special motion mode;

[0070] Rule 2: Key point 0, even-numbered key points enter and exit the even-numbered key points. The request state of the lift is low, and the speed and control mode match the corresponding parameters. For example: key points 0, 2, and 4 enter and exit the lift mutually. The requestState is low, vel is low speed, and mode is a special motion mode;

[0071] Rule 3: From the starting point to a non-zero key point or from a non-zero point to the target point, there is no demand state for the lift, and the speed and control mode match the command parameters. For example: the lift requestState is no request, vel defaults, and mode is dynamically planned;

[0072] The state of the corresponding lift platform, currentState, is updated according to the real-time state information transmitted from the lift platform control terminal.

[0073] The four contour corner points are the corner points numbered A between key points 4 and 1, and the corner points are ABCD in clockwise order. For example, the configuration of corner points A, B, C, and D is based on the contour of the lifting platform. Figure 3 The corner point position parameters are shown as follows. Here, only the x and y values ​​are needed.

[0074] Taking the key point 0 of the lifting platform template as the reference point, vector The direction is the reference direction 0, and the other lifting platform positions 0' to be configured are obtained on the navigation map. The platform lowering direction is perpendicular to the track direction. The coordinate transformation matrix between the position 0' and the target key point 0 can be obtained to obtain the positions of other key points and corner points of the newly built lifting platform, such as Figure 4 The scene shown finally obtains the lift parameter list liftList:[lift0,lfit1,lift2,lift3] on the entire map, which is generally The key points and motion control parameters of the specific lifting platform can be adjusted according to the on-site conditions. All lifting platform configuration parameters are saved locally and automatically obtained each time the robot navigation program is started.

[0075] S10. Create a TOPO (topology) diagram of the key points, starting points, and target points of the lift platform;

[0076] See also Figure 4 In the TOPO diagram shown, the lifting platform status is 0: BOT (low), 1: OUT (offline), 2: BOT (low), 3: BOT (low), the specific implementation process is as follows:

[0077] After receiving the target point, the real-time status information of the elevator is updated to each key point of the elevator. Combined with the static obstacle map, the starting point and target point are defined as the same type as the key point startKeyPt and endKeyPt. The attribute configuration is that the speed is the command speed, and all others are -1, which means there is no elevator attribute and special motion control mode.

[0078] Create a TOPO graph between the inspection robot's starting point startKeyPt, the target point endKeyPt, and the key points of the lift. When the TOPO graph is connected, the restriction condition is that the line between the two points passes the safety detection of the static obstacle map, and the line does not intersect with the rectangular edge lines formed by all the corner points of the lift. The specific rules are:

[0079] Connectivity rules between startKeyPt and endKeyPt: If the connection is possible under the restriction conditions, no more connection operation of the key points of the lift platform will be performed; otherwise, the internal connectivity rules of a single lift platform will be performed.

[0080] The internal connection rule of a single lift is: if the real-time status of the lift is not offline, key points 0 and 1, 3 (key points with odd IDs), and key points 0 and 2, 4 (key points with even IDs) are bidirectionally connected; if the real-time status of the lift is offline, no internal connection is performed;

[0081] The connection rules between different lifts are: under the restriction condition, all 1 and 3 key points (odd id key points) are bidirectionally connected, and all 2 and 4 key points (even id key points) are bidirectionally connected. According to the lift id, a two-dimensional list of connections, LiftsLinkFlagList, is established to facilitate the search by ID whether each pair of lifts is connected;

[0082] Connectivity rules between startKeyPt, endKeyPt and key points: Find the key points closest to startKeyPt and endKeyPt respectively to determine the ID of the nearest lift, find other lifts connected to the nearest lift according to LiftsLinkFlagList, connect startKeyPt and endKeyPt with the key points of the other lifts under restrictions, where startKeyPt is unidirectionally connected to the key point, and the key point is unidirectionally connected to endKeyPt.

[0083] Create a TOPO diagram according to the above rules.

[0084] In the present invention, the TOPO diagram is used to represent the connection relationship between the starting point, target point and key points of the inspection robot, and is constructed by defining specific connectivity rules. For example, when judging whether two points are connected, it is necessary to consider whether the connection line passes the safety detection of the static obstacle map, and whether the connection line intersects with the rectangular edge line formed by all the corner points of the lift. The construction of this topological diagram helps the robot navigation system to quickly find a feasible path based on these connection relationships when planning the path, thereby achieving efficient navigation tasks. At the same time, it can also adapt to layout changes in different scenarios because it emphasizes logical relationships rather than precise geometric positions.

[0085] S20, use the improved Dijkstra to search for the shortest global path, Figure 5 and Figure 6 is a schematic diagram of global path search under different lifting platform states. In this embodiment, cost liftUse=30s, the default running speed is 1m / s, the speed of entering and exiting the lifting platform is 0.2m / s, and the length of the simulated track is 20m. Note: The traditional Dijkstra algorithm mainly considers factors such as path length to calculate the cost.

[0086] Figure 5 The status of the middle lift table is 0: BOT (low position), 1: OUT (offline), 2: TOP (high position), 3: BOT (low position);

[0087] Figure 6 The status of the middle elevator is 0: BOT (low), 1: OUT (offline), 2: BOT (low), 3: BOT (low). The status of the elevator 2 is different, and the global path planned for the optimal goal based on the shortest time consumption is also different. If Figure 6 Select and Figure 5 For the same route, the robot needs to wait for the lift to rise at lift 2, while Figure 6 In the process of the robot reaching the lifting platform 3, the lifting platform is also rising at the same time. The waiting time of the robot at the lifting platform 3 is much shorter than the waiting time at the lifting platform 2. The improvement of the Dijkstra algorithm of the present invention is mainly reflected in that more practical factors are considered when calculating the cost from the initial point to each node. The specific improvements are as follows:

[0088] The improved Dijkstra algorithm includes the path running time cost when calculating the cost from the initial point to each node. pathT , and the time cost of waiting for the elevator to change state lfitT :

[0089] Cost=cost pathT + cost lfitT

[0090] The path running time cost is the time consumed by the current point and the search point at the specified speed:

[0091] cost pathT =Pathlength / vel

[0092] The time cost of changing the elevator status is:

[0093]

[0094] The time cost of changing the state of the lifting platform is based on the demand state of the lifting platform for different combinations of key points in the first aspect, using three consecutive connected points KeyPt pre ,KeyPt now ,KeyPt next Dynamic calculation method under attribute combination results, cost liftUseThe time it takes for the lift to rise or fall, cost pre KeyPt is the previous connection point from the initial point to the current point pre total consideration;

[0095] The conditions expressed by the formula are:

[0096] Condition 1: KeyPt now ,KeyPt next Not belonging to the same lift; KeyPt now ,KeyPt next Belongs to the same lift platform but with KeyPt pre Not belonging to the same lift, KeyPt now To KeyPt next The required state is consistent with the current state of the corresponding lift; KeyPt pre ,KeyPt now ,KeyPt next The key points corresponding to the same lifting platform, KeyPt pre To KeyPt now With KeyPt now To KeyPt next The demand status is consistent; in the above case, the time cost of the lifting platform is 0;

[0097] Condition 2: KeyPt pre ,KeyPt now ,KeyPt next The key points corresponding to the same lifting platform, and KeyPt pre To KeyPt now With KeyPt now To KeyPt next The required state is inconsistent. In this case, the robot needs to remain stationary on the lift and wait for the lift to change to the required state. The time cost of the lift is cost liftUse .

[0098] Condition 3: KeyPt now ,KeyPt next Belongs to the same lift platform but with KeyPt pre Not belonging to the same lift, KeyPt now To KeyPt next The required state is inconsistent with the current state of the corresponding lift. In this case, the robot synchronizes from KeyPt when the lift changes state. pre KeyPt now The time cost of moving the lifting platform should be considered as KeyPt pre Total time cost pre, the specific cost is: cost liftUse - cost pre It should be noted that if this value is less than 0, it is set to 0.

[0099] According to the shortest time consumption, search for the key point list keyPtsList as the optimal target. Figure 5 The corresponding key point list keyPtsList: [startKeypt, 23, 20, 21, 03, 00, 01, endKeypt].

[0100] S30, split the global path to obtain sub-target points and configure the target point attributes:

[0101] The global path is segmented and estimated to be obtained through the lifting platform. According to the platform ID and point ID of the front and rear key points in the searched key point list, the key point attributes are configured according to the point attribute configuration rules in the first aspect. To ensure the efficiency of robot movement and reduce unnecessary downtime, if three consecutive points belong to the same platform and the demand status for the lifting platform is the same, the middle key point 0 is eliminated and only the key points on both sides of the lifting platform are retained. Finally, a list of target points is generated:

[0102] .

[0103] like Figure 5 The target point list is targesList:[23,21,03,01,endKeypt]. According to the attributes of all target points, the expected passing lift and demand status list passLiftRequestList is obtained. Figure 5 The passLiftRequestList list {2:TOP,0:TOP}, according to the lift list, obtains all the first demand states expected to pass the lift to extract the pre-controlled lift list liftRequestList, Figure 5 The liftRequestList list {2:TOP,0:TOP} is used to synchronously control the lifting platform to change state when the robot is running.

[0104] S40, monitoring the expected state of the lifting platform, controlling the movement of the lifting platform and the robot, and in the embodiment, restoring the normal threshold value T after the timeout. MaxWait =50s, path planning maximum number threshold T MaxPlan =10, the interval between sending the control command of the lifting platform is T sendCmd =10s, the specific implementation process is as follows:

[0105] After receiving the sub-target point, the robot extracts the lift id and demand status to the list currentRequestList. If the current sub-target point does not have a lift demand, it will be empty. Figure 5 In the embodiment, the first sub-target point is from the starting point to the key point 23, and the currentRequestList is empty. The liftRequestList list is updated, and the liftRequestList list update formula is:

[0106] liftRequestList=liftRequestList -liftRequestList∩currentRequestList

[0107] When the robot is running, it obtains the real-time status of all lifts, compares the lift demand status in the two lists, and controls the robot's motion status according to different results:

[0108] If there is a lift that is not in place in the list, sendCmd Sending control instructions of the lifting platform that is not in place to the lifting platform control terminal at intervals;

[0109] If the lift in the list is offline or the lift in currentRequestList is not in place, it means that there is an abnormality in the lift. At this time, the robot stops running and waits for the lift to contact abnormally. If the waiting time exceeds the set threshold T MaxWait Then re-plan the path. If the elevator in the currentRequestList is not in place, the elevator status will be forcibly set to offline during re-planning. After the task is completed, the elevator status will be updated according to the real-time status. If the number of planning times exceeds the set maximum number of planning times T MaxPlan , give up this mission;

[0110] If the target point reached is not the last one, the target point, motion mode and speed are updated and the execution continues until the final mission point is reached.

[0111] In this embodiment, the TOPO map, namely the topology map, is a graphical representation method for representing network structure or spatial relationship, which mainly focuses on the connection relationship between objects rather than the actual physical location or shape.

[0112] Embodiment 2

[0113] Based on the same concept, the present invention also proposes a subway vehicle inspection robot navigation device in a multi-track scenario, comprising:

[0114] The lifting platform template creation module creates a lifting platform template including key points and contour corner points on the navigation map, and generates corresponding key point and corner point coordinate parameters according to the coordinates of all lifting platforms on the navigation map; the key point attributes include point id, the lifting platform id to which it belongs, the current state of the lifting platform to which it belongs, the required state when the robot passes, the speed configuration to reach the key point, and the motion control mode;

[0115] The topology map creation module updates the real-time status information of the lift platform after receiving the target point, obtains the static obstacle map, and defines the starting point and target point as similar types to the key points to create a topology map;

[0116] The global navigation path module uses the improved Dijkstra search algorithm to generate the shortest global navigation path from the starting point to the target point and the key points of the lift. The improved Dijkstra search algorithm includes the path running time cost and the time cost of waiting for the lift to change its state when calculating the cost from the initial point to each node.

[0117] The processing module divides the global navigation path according to the key points of the lift platform of the global path, sets the key points of the lift platform as sub-target points, and determines the lift platform and the required state expected to be passed and the motion parameters to reach the sub-target points according to the attributes of two consecutive sub-target points;

[0118] The monitoring module monitors the status of the lift, estimates whether the status of the lift is consistent with the required status, sends the lift required status control instruction, and controls the movement of the robot according to the status of the lift.

[0119] Embodiment 3

[0120] This embodiment also provides an electronic device, referring to Figure 7 , comprises a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to run the computer program to execute the steps in any of the above method embodiments.

[0121] Specifically, the processor 402 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0122] Among them, the memory 404 may include a large capacity memory 404 for data or instructions. For example, but not limitation, the memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In appropriate cases, the memory 404 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 404 may be inside or outside the data processing device. In a specific embodiment, the memory 404 is a non-volatile memory. In a specific embodiment, the memory 404 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable Programmable Read-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. In appropriate circumstances, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory 404 (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0123] The memory 404 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402 .

[0124] The processor 402 reads and executes the computer program instructions stored in the memory 404 to implement any one of the subway vehicle inspection robot navigation methods in the multi-track scenario in the above embodiments.

[0125] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408 , wherein the transmission device 406 is connected to the processor 402 , and the input / output device 408 is connected to the processor 402 .

[0126] The transmission device 406 can be used to receive or send data via a network. The specific examples of the above-mentioned network may include a wired or wireless network provided by a communication provider of the electronic device. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 406 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0127] Input / output devices 408 are used to input or output information.

[0128] Embodiment 4

[0129] This embodiment also provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute a process. The process includes a navigation method for a subway vehicle inspection robot in a multi-track scenario according to the first embodiment.

[0130] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0131] In general, various embodiments may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the boxes, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0132] Embodiments of the present invention may be implemented by computer software that is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and / or macros may be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer executable components configured to perform an embodiment when the program is run. One or more computer executable components may be at least one software code or a portion thereof. In addition, at this point, it should be noted that, for example, Figure 1 Any block of the logic flow in the program may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on physical media such as memory chips or storage blocks implemented within the processor, magnetic media such as hard disk or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. Physical media are non-transitory media.

[0133] Those skilled in the art should understand that the technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A navigation method for a subway vehicle inspection robot in a multi-track scenario, characterized in that: The following steps are involved: S00, creating a lifting platform template including key points and contour corner points on the navigation map, and generating corresponding key point and corner point coordinate parameters according to the coordinates of all lifting platforms on the navigation map; Among them, the key point attributes include point ID, the ID of the lift platform to which it belongs, the current state of the lift platform to which it belongs, the required state when the robot passes through, the speed configuration to reach the key point, and the motion control mode; S10, after receiving the target point, update the real-time status information of the lifting platform, obtain the static obstacle map, define the starting point and the target point as similar types to the key point, so as to create a topological map; S20, using an improved Dijkstra search algorithm to generate a global navigation path with the shortest time consumption from the starting point to the target point and key points of the lifting platform; The improved Dijkstra search algorithm includes the path running time cost and the time cost of waiting for the lift to change state when calculating the cost from the initial point to each node; S30, segmenting the global navigation path according to the key points of the lift platform of the global path, setting the key points of the lift platform as sub-target points, and determining the lift platform to be passed and the required state and the motion parameters to reach the sub-target points according to the attributes of two consecutive sub-target points; S40, monitoring the state of the lifting platform, predicting whether the state of the lifting platform is consistent with the required state, sending the lifting platform required state control instruction, and controlling the movement of the robot according to the state of the lifting platform.

2. The method for navigating a subway vehicle inspection robot in a multi-track scenario as claimed in claim 1, characterized in that: In step S00, a lifting platform template including five key points and four contour corner points is created on the navigation map, wherein the center point id of the five key points is 0, the key points for entering and exiting the track on both sides are 1 and 3, and the key points for entering and exiting the track channel are 2 and 4, and the four contour corner points are A, B, C, and D in clockwise order; Taking key point 0 as the reference point, calculate the positions of other key points and corner points of the lift according to the coordinate transformation matrix between the lift posture and the target key point 0, obtain the lift parameter list and save it locally, and automatically obtain it each time the robot navigation program is started.

3. The method for navigating a subway vehicle inspection robot in a multi-track scenario as claimed in claim 2, characterized in that: In step S10, the connectivity rules of the topology graph include: Connectivity rules between the starting point and the target point: Connectivity is achieved when the connection line passes the safety detection of the static obstacle map and does not intersect with the rectangular edge line of the corner point of the lift. If connectivity is achieved, no subsequent key point connection operation of the lift is required. Internal connectivity rules for a single lift: When the lift is not offline, 0 and the odd-numbered ID key points, 0 and the even-numbered ID key points are bidirectionally connected; when it is offline, they are not connected; Connectivity rules between multiple lifts: Under the restriction condition, all odd-numbered ID key points are bidirectionally connected, and all even-numbered ID key points are bidirectionally connected. A two-dimensional list of lift connectivity is established to facilitate connectivity search. Connectivity rules between the starting point, target point and key points: find the key point closest to the starting point and target point to determine the ID of the nearest elevator, and find other elevators connected to the nearest elevator based on the connected two-dimensional list. The starting point is unidirectionally connected to the key point, and the key point is unidirectionally connected to the target point.

4. The method for navigating a subway vehicle inspection robot in a multi-track scenario as claimed in claim 1, characterized in that: In step S20, the time cost of changing the state of the lift is dynamically calculated based on the key point combination and the demand state of the lift, using the combination result of the attributes of three consecutive connected points; Among them, the time cost is 0 when the lift is in place or there is no request; when the demand status is inconsistent and the lift is rising or falling, the robot is stationary and the time cost is the time consumed by the lift rising or falling; when entering or exiting the lift and the demand status changes, the total cost is calculated based on the previous point, but it is not less than 0.

5. The method for navigating a subway vehicle inspection robot in a multi-track scenario as claimed in claim 1, characterized in that: In step S30, the key point of the lifting platform is set as a sub-target point, and the specific steps of determining the lifting platform and the required state expected to pass and the motion parameters of reaching the sub-target point according to the attributes of two consecutive sub-target points are as follows: Configure the key point attributes according to the platform ID, point ID and point attribute configuration rules of the previous and next key points in the key point list. If three consecutive points belong to the same platform and the lifting platform demand status is consistent, eliminate the middle key point 0, retain the key points on both sides of the lifting platform, and generate a target point list; According to the target point attributes, a list of required states expected to pass through the lift is obtained, and the first required state of all expected lifts is extracted to generate a pre-controlled lift list, which is used to control the lift state when the robot is running.

6. A subway vehicle inspection robot navigation method in a multi-track scenario as described in any one of claims 1 to 5, characterized in that: In step S40, after receiving the sub-target point, the robot extracts the lift platform ID and the required state into the list, and updates the pre-controlled lift platform list at the same time. By comparing the lift platform required states in the two lists, the robot's motion state is controlled according to different results.

7. The method for navigating a subway vehicle inspection robot in a multi-track scenario as claimed in claim 6, characterized in that: In step S40, the real-time status of the lifting platform is obtained when the robot is running, and the steps of controlling the robot movement by comparing the lifting platform demand status in the two lists include: If the lift is not in place in the list, send control commands at intervals; If there is an abnormality on the lift platform, the robot will stop running and wait for the abnormality to be resolved. If the waiting time exceeds the threshold, the path will be re-planned. If the lift platform is not in place during re-planning, the lift platform status will be forcibly set to offline, and the lift platform status will be updated after the task is completed. If the number of planning times exceeds the maximum number, the task will be abandoned. If the final target point is not reached, update the target point, motion mode and speed to continue execution.

8. A subway vehicle inspection robot navigation device in a multi-track scenario, characterized in that: include: The lifting platform template creation module creates a lifting platform template including key points and contour corner points on the navigation map, and generates corresponding key point and corner point coordinate parameters according to the coordinates of all lifting platforms on the navigation map; the key point attributes include point id, the lifting platform id to which it belongs, the current state of the lifting platform to which it belongs, the required state when the robot passes, the speed configuration to reach the key point, and the motion control mode; The topology map creation module updates the real-time status information of the lift platform after receiving the target point, obtains the static obstacle map, and defines the starting point and target point as similar types to the key points to create a topology map; The global navigation path module uses the improved Dijkstra search algorithm to generate the shortest global navigation path from the starting point to the target point and the key points of the lift. The improved Dijkstra search algorithm includes the path running time cost and the time cost of waiting for the lift to change its state when calculating the cost from the initial point to each node. The processing module divides the global navigation path according to the key points of the lift platform of the global path, sets the key points of the lift platform as sub-target points, and determines the lift platform and the required state expected to be passed and the motion parameters to reach the sub-target points according to the attributes of two consecutive sub-target points; The monitoring module monitors the status of the lift, estimates whether the status of the lift is consistent with the required status, sends the lift required status control instruction, and controls the movement of the robot according to the status of the lift.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the navigation method of a subway vehicle inspection robot in a multi-track scenario as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: A computer program is stored in the readable storage medium, and the computer program includes a program code for controlling a process to execute a process, and the process includes the navigation method of a subway vehicle inspection robot in a multi-track scenario according to any one of claims 1 to 7.

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