CN114237240B - Intelligent dredging robot and walking control method thereof - Google Patents
Intelligent dredging robot and walking control method thereof Download PDFInfo
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
- G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G05—CONTROLLING; REGULATING
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- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
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- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
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Abstract
The disclosure relates to an intelligent dredging robot and a walking control method thereof, comprising the following steps: the angle sensor is arranged at the walking motor of the dredging robot and is used for acquiring the rotation angle of the rotating shaft of the walking motor; the photoelectric encoder is arranged at a walking motor of the dredging robot and is used for outputting a plurality of pulse signals corresponding to the rotating speed of the walking motor; the motion controller is arranged on the chassis of the dredging robot, is connected with the angle sensor and the photoelectric encoder, and is used for determining a first rotating speed of the walking motor based on the rotating angle, determining a second rotating speed of the walking motor based on the pulse signals, determining a third rotating speed based on the first rotating speed and the second rotating speed, generating a PWM driving signal based on the third rotating speed and the set rotating speed, and sending the PWM driving signal to a walking motor driver of the dredging robot so that the walking motor driver drives the walking motor.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of robots, in particular to an intelligent dredging robot and a walking control method thereof.
Background
In development and construction of coal mine factories, the underground water bin is an important facility for preventing mine floods, and is also a production system which each mine must be equipped with. The underground sump has severe working conditions, the water content in the sludge is high, and the sludge often becomes semi-fluid or fluid after stirring, so that the sludge is difficult to thoroughly clean.
At present, the dredging process adopted in the coal industry still mainly comprises manual sludge cleaning and sludge carrying by matching with a tank truck. The traditional cleaning mode has the defects of high labor intensity, low working efficiency, long cleaning period, influence on safe production and certain danger, cannot realize real-time monitoring and early warning and rapid dredging of sump sludge, and cannot meet the requirement of safe production.
In order to realize rapid cleaning and real-time monitoring of sump silt, the urgent need of improving sump utilization rate and guaranteeing mine water safety is met. In the related art, an intelligent dredging robot is proposed to realize automatic cleaning and real-time monitoring, but the walking control accuracy of the current intelligent dredging robot in a coal mine factory is required to be further improved.
Disclosure of Invention
In order to solve the technical problems or at least partially solve the technical problems, an embodiment of the disclosure provides an intelligent dredging robot and a walking control method thereof.
In a first aspect, an embodiment of the present disclosure provides an intelligent dredging robot, including:
the angle sensor is arranged at the walking motor of the dredging robot and is used for acquiring the rotation angle of the rotating shaft of the walking motor;
the photoelectric encoder is arranged at a walking motor of the dredging robot and is used for outputting a plurality of pulse signals corresponding to the rotating speed of the walking motor;
the motion controller is arranged on the chassis of the dredging robot, is connected with the angle sensor and the photoelectric encoder, and is used for determining a first rotating speed of the walking motor based on the rotating angle, determining a second rotating speed of the walking motor based on the pulse signals, determining a third rotating speed based on the first rotating speed and the second rotating speed, generating a PWM driving signal based on the third rotating speed and the set rotating speed, and sending the PWM driving signal to a walking motor driver of the dredging robot so that the walking motor driver drives the walking motor.
In one embodiment, further comprising:
the ultrasonic sensor is arranged on the chassis of the dredging robot and is used for detecting whether an obstacle exists in the travelling direction of the dredging robot, and if so, outputting the distance between the dredging robot and the obstacle;
the motion controller is connected with the ultrasonic sensor and used for generating an obstacle avoidance PWM driving signal when the distance is smaller than a preset distance, and sending the obstacle avoidance PWM driving signal to the walking motor driver so that the walking motor driver drives the walking motor to operate, and therefore the dredging robot avoids obstacles.
In one embodiment, further comprising:
the laser radar is connected with the motion controller;
the main controller is in communication connection with the motion controller;
the temperature sensor is connected with the main controller and used for detecting the ambient temperature;
the gas sensor is connected with the main controller and is used for detecting the content of harmful gas in the environment; wherein the harmful gas comprises one or more of methane, gas, hydrogen sulfide and carbon monoxide;
the main controller is used for sending out an alarm signal when the ambient temperature is greater than a preset temperature and/or the content of the harmful gas is greater than a preset value;
the video acquisition device is connected with the main controller and used for acquiring scene images of the site of the dredging robot and comprises a cradle head camera and a network camera.
In one embodiment, the system further comprises an ethernet switch, which is connected with the cradle head camera and the network camera and is used for transmitting video information collected by the cradle head camera and the network camera to an external upper computer; and/or, still include setting up the mechanism that gathers materials on the desilting robot, gather materials the mechanism include spiral material collector, drive spiral material collector rotatory hydraulic motor, to hydraulic motor carry the hydraulic pressure pipe of hydraulic oil, with the hydraulic pump that liquid pipe connects, and hold the oil tank of hydraulic pump.
In one embodiment, the motion controller is further configured to obtain an environment map, determine a global map including a walking route of the dredging robot based on the environment map, perform path planning based on the global map, obtain a planned path, and control the motor driver to drive the walking motor based on the planned path, so that the dredging robot walks based on the planned path.
In one embodiment, the global map includes a plurality of nodes, and the connection relationship between the nodes represents a feasible route of the robot; the motion controller performs path planning based on the global map to obtain a planned path, and the method comprises the following steps:
determining intermediate nodes in the global map to form an intermediate node set;
determining a current position point of the dredging robot, executing a preset path searching algorithm to determine the shortest path between the current position point and each intermediate node in the intermediate node set, and selecting an intermediate node with the smallest path distance in the shortest path of each intermediate node as a new current position point;
executing a preset path searching algorithm to determine the shortest path between the new current position point and each rest of intermediate nodes in the intermediate node set, selecting the intermediate node with the smallest path distance in the shortest path of each rest of intermediate nodes as the new current position point, and repeatedly executing the preset searching algorithm until each intermediate node in the intermediate node set is traversed;
determining a target shortest path between a last traversed intermediate node in the intermediate node set and a target position point;
and merging the target shortest paths, and obtaining a planned path by the shortest path with the minimum path distance in the shortest paths of all the intermediate nodes, wherein the shortest paths are determined when a preset path searching algorithm is executed each time.
In one embodiment, the preset path search algorithm includes at least a disco tesla algorithm.
In one embodiment, the motion controller includes a PID controller that generates the PWM drive signal based on a difference between the third rotational speed and a set rotational speed.
In one embodiment, the system further comprises a pressure sensor arranged on the oil tank, and the pressure sensor is connected with the main controller.
In a second aspect, an embodiment of the present disclosure provides a method for controlling walking of an intelligent dredging robot, where the dredging robot is provided with an angle sensor and a photoelectric encoder located at a walking motor of the dredging robot, and a motion controller located on a chassis of the dredging robot, the method includes:
the angle sensor obtains the rotation angle of the rotating shaft of the walking motor;
the photoelectric encoder outputs a plurality of pulse signals corresponding to the rotating speed of the walking motor;
the motion controller determines a first rotating speed of the walking motor based on the rotating angle, determines a second rotating speed of the walking motor based on the pulse signals, determines a third rotating speed based on the first rotating speed and the second rotating speed, generates a PWM driving signal based on the third rotating speed and the set rotating speed, and sends the PWM driving signal to a walking motor driver of the dredging robot so that the walking motor driver drives the walking motor.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
according to the intelligent dredging robot and the walking control method thereof, the angle sensor obtains the rotation angle of the rotating shaft of the walking motor, the photoelectric encoder outputs a plurality of pulse signals corresponding to the rotation speed of the walking motor, the motion controller determines the first rotation speed of the walking motor based on the rotation angle, determines the second rotation speed of the walking motor based on the plurality of pulse signals, determines the third rotation speed based on the first rotation speed and the second rotation speed, generates PWM driving signals based on the third rotation speed and the set rotation speed, and sends the PWM driving signals to the walking motor driver of the dredging robot so that the walking motor driver drives the walking motor. Therefore, the rotating speed of the walking motor, namely the third rotating speed, can be comprehensively determined based on the output signals of the angle sensor and the photoelectric encoder, then a corresponding PWM driving signal is generated based on the comprehensively determined rotating speed of the walking motor and the set rotating speed, and then the PWM driving signal is sent to the walking motor driver to enable the walking motor driver to drive the walking motor to operate, so that the accuracy of the operation control of the walking motor can be improved, and the walking control of the dredging robot is more accurate.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, the drawings that are required for the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic diagram of a control architecture of an intelligent dredging robot according to an embodiment of the disclosure;
FIG. 2 is a schematic diagram of a control architecture of an intelligent dredging robot according to another embodiment of the disclosure;
FIG. 3 is a schematic diagram of path planning in accordance with an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of an acceleration/deceleration curve according to an embodiment of the present disclosure;
fig. 5 is a flow chart of a walking control method of an intelligent dredging robot according to an embodiment of the disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
It should be understood that, hereinafter, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" is used to describe association relationships of associated objects, meaning that there may be three relationships, e.g., "a and/or B" may mean: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
Fig. 1 is a schematic diagram of a control architecture of an intelligent dredging robot according to an embodiment of the present disclosure, including: the angle sensor is arranged at a walking motor of the dredging robot, for example, the walking motors of the front wheel and the rear wheel are respectively provided with the angle sensor, and the angle sensor is used for acquiring the rotation angle of a rotating shaft of the walking motor; the photoelectric encoder is arranged at the walking motor of the dredging robot, for example, the walking motors of the front wheel and the rear wheel are respectively provided with the photoelectric encoder and are used for outputting a plurality of pulse signals corresponding to the rotating speed of the walking motor. The motion controller is arranged on the chassis of the dredging robot, is connected with the angle sensor and the photoelectric encoder, and is used for determining a first rotating speed of the walking motor based on the rotating angle, determining a second rotating speed of the walking motor based on the pulse signals, determining a third rotating speed based on the first rotating speed and the second rotating speed, generating a PWM driving signal based on the third rotating speed and the set rotating speed, and sending the PWM driving signal to a walking motor driver of the dredging robot so that the walking motor driver drives the walking motor.
The motion controller may for example determine the number of circles of rotation based on the rotation angle, determine a corresponding distance based on the number of circles, which distance is divided by time to determine a first rotation speed of the travelling motor, and may for example determine a second rotation speed of the travelling motor based on the number of pulse signals output per unit time, e.g. 1 second, and then determine a third rotation speed based on the first rotation speed and the second rotation speed, e.g. calculate an average of the first rotation speed and the second rotation speed to obtain the third rotation speed.
In this embodiment, the rotation speed of the walking motor, that is, the third rotation speed, can be comprehensively determined based on the output signals of the angle sensor and the photoelectric encoder, then a corresponding PWM driving signal is generated based on the comprehensively determined rotation speed of the walking motor and the set rotation speed, and then the PWM driving signal is sent to the walking motor driver to enable the walking motor driver to drive the walking motor to operate, so that the accuracy of the operation control of the walking motor can be improved, and the operation control of the dredging robot is more accurate.
In one embodiment, as shown in fig. 2, the device further comprises an ultrasonic sensor, which is arranged on the chassis of the dredging robot and is used for detecting whether an obstacle exists in the travelling direction of the dredging robot, and if so, outputting the distance between the dredging robot and the obstacle; the motion controller is connected with the ultrasonic sensor and used for generating an obstacle avoidance PWM driving signal when the distance is smaller than a preset distance, and sending the obstacle avoidance PWM driving signal to the walking motor driver so that the walking motor driver drives the walking motor to operate, and therefore the dredging robot avoids obstacles.
In one embodiment, as shown in fig. 2, further comprising: and the laser radar is connected with the motion controller, so that the navigation control of the robot can be realized based on the laser radar, and the accuracy of the navigation control is improved. The main controller is in communication connection with the motion controller, and can send control instructions such as start, stop and the like to the motion controller so as to trigger the motion controller to control the running motor to run or stop running and the like. The temperature sensor is connected with the main controller and used for detecting the ambient temperature. The gas sensor is connected with the main controller and is used for detecting the content of harmful gas in the environment. Wherein the harmful gas comprises one or more of methane, gas, hydrogen sulfide and carbon monoxide, and the corresponding gas sensor can be a corresponding type of gas sensor. And the main controller is used for sending out an alarm signal when the ambient temperature is greater than a preset temperature and/or the content of the harmful gas is greater than a preset value.
In some situations, there is unreliability in a full-automatic control manner of the dredging robot, in one embodiment, two lasers are symmetrically arranged, and the two lasers are used for respectively scanning to obtain the positions of preset target objects such as configured positioning markers in a coal mine plant area, the preset target objects move in a preset angle range relative to the two lasers, and the preset angle range is located in the scanning range of the two lasers, is in a sector shape and has an angle range of 65-110 degrees.
The main controller is used for generating position data of a target position, such as a two-dimensional space, in a coal mine plant area based on the positions of preset target objects, namely two position data, obtained by respective scanning of the two laser radars, generating PWM driving signals based on the target positions and sending the PWM driving signals to the walking motor driver so that the walking motor driver drives the walking motor to run, and the dredging robot walks. Therefore, the target position in the coal mine plant area can be synthesized by means of the position of the preset target object obtained through scanning based on the small-range movement of the preset target object, and the dredging robot is driven to walk in an auxiliary guiding mode according to the target position, so that unreliability of a full-automatic control mode is reduced, and the reliability of walking control of the dredging robot is improved.
The video acquisition device is connected with the main controller and used for acquiring scene images of the site of the dredging robot, and the video acquisition device comprises a tripod head camera and a network camera. In one embodiment, the system further comprises an ethernet switch, and the ethernet switch is connected with the cradle head camera and the network camera, and is used for transmitting video information collected by the cradle head camera and the network camera to an external upper computer so as to observe surrounding environment.
In one embodiment, the dredging device further comprises a collecting mechanism arranged on the dredging robot, the collecting mechanism comprises a spiral material collector, a hydraulic motor driving the spiral material collector to rotate, a hydraulic pipe for conveying hydraulic oil to the hydraulic motor, a hydraulic pump connected with the hydraulic pipe, and an oil tank (not shown) for containing the hydraulic pump, the collecting mechanism can be arranged on a machine body of the dredging robot, and the machine body is arranged on a chassis. The spiral collector can adsorb and clean sundries such as silt, dust and the like when rotating, and can collect the sundries. The hydraulic pump can be connected with the main controller, and the main controller controls the work of the hydraulic pump such as start and stop. In one embodiment, the system may further include a pressure sensor, where the pressure sensor is disposed on the oil tank and detects the pressure of the oil tank, and the pressure sensor is connected with the main controller, where the main controller may send an alarm signal, such as an audible and visual alarm signal, when the pressure sensor detects that the pressure of the oil tank is greater than a preset pressure.
In one embodiment, the motion controller is further configured to obtain an environment map, determine a global map including a walking route of the dredging robot based on the environment map, perform path planning based on the global map, obtain a planned path, and control the motor driver to drive the walking motor based on the planned path, so that the dredging robot walks based on the planned path.
In order to reduce the complexity of path planning, and improve the accuracy and efficiency of path planning. In one embodiment, the global map includes a plurality of nodes, and the connection relationship between the nodes represents a feasible route of the robot. The motion controller performs path planning based on the global map to obtain a planned path, and the method comprises the following steps: determining intermediate nodes in the global map to form an intermediate node set; determining a current position point of the dredging robot, executing a preset path searching algorithm to determine the shortest path between the current position point and each intermediate node in the intermediate node set, and selecting an intermediate node with the smallest path distance in the shortest path of each intermediate node as a new current position point; executing a preset path searching algorithm to determine the shortest path between the new current position point and each rest of intermediate nodes in the intermediate node set, selecting the intermediate node with the smallest path distance in the shortest path of each rest of intermediate nodes as the new current position point, and repeatedly executing the preset searching algorithm until each intermediate node in the intermediate node set is traversed; determining a target shortest path between a last traversed intermediate node in the intermediate node set and a target position point; and merging the target shortest paths, and obtaining a planned path by the shortest path with the minimum path distance in the shortest paths of all the intermediate nodes, wherein the shortest paths are determined when a preset path searching algorithm is executed each time. In one embodiment, the preset path search algorithm includes at least a diecktra algorithm, i.e., dijkstra algorithm.
Specifically, in the actual scenario that the dredging robot performs the dredging operation in the cell, it may need to pass through one or several intermediate nodes from a starting point, such as a current location point, to a target point, such as a target location point, so that it is necessary to find a shortest path through nodes in all the intermediate node sets when planning a path. The traditional Dijkstra algorithm can only solve the problem of the classical shortest path from a single source to other nodes, but cannot solve the situation of containing intermediate nodes. For this problem, the final result can be obtained by an exhaustive method, and the shortest path including all the intermediate nodes is finally found through each test, but the complexity of the algorithm increases exponentially with the complexity of the problem, that is, as the intermediate nodes increase, the intermediate node set becomes larger, and the path meeting the condition increases exponentially with the increase of the nodes, so that the space complexity and the time complexity of searching for the optimal shortest path are too high, which is obviously not feasible in practical application. In this embodiment, considering the actual scenario application situation of the dredging inspection robot, it is a feasible scheme to plan a feasible path including all the selected intermediate nodes in a limited time and search for an approximately optimal solution to replace the optimal solution. Thus, an improved path search algorithm comprising an intermediate set of nodes is proposed on the basis of the Dijkstra shortest path algorithm.
The basic process of the improved path searching algorithm comprising the intermediate node set is as follows:
the nodes are classified by whether they are intermediate nodes or not, and the intermediate node set C contains n+1 intermediate nodes Ki (where i=0, 1,2,..n). Intermediate nodes are nodes between a start point and an end point in a path.
And executing Dijkstra algorithm from the current starting point to find the shortest path from the current starting point to all intermediate nodes. Then based on the idea of greedy strategy, sorting the path distances from the current starting point to the shortest paths of all intermediate nodes according to the length, selecting the shortest path with the smallest path distance as a segmented path, and setting the intermediate node K corresponding to the shortest path with the smallest path distance i As a new current starting point, the Dijkstra algorithm steps are repeatedly executed until all intermediate nodes in the intermediate node set C are traversed and accessed.
When all the intermediate nodes finish access, searching the shortest path from the last accessed intermediate node to the target point as a segmented path, combining all the segmented paths, and finally obtaining a shortest path from the starting point to the target point, wherein the shortest path is approximately the optimal solution.
Further description is provided below in connection with a specific example. As shown in fig. 3, vertex 1 is selected as the current start point S (i.e., the current position point), and vertex 5 is the end point G (i.e., the target position point). The path planning is performed by using a conventional Dijkstra algorithm, and the result is that, for example, the distance from the start point S to the shortest path of the end point G is 20, and the shortest path is the connection line of the nodes 1- >3- >6- >5, as shown in fig. 4.
By adopting the Dijkstra improved path search algorithm including the intermediate node set of this embodiment, a one-dimensional array dist is defined to store the shortest path from the start point to each vertex, array S stores the vertex, i.e., the node set, array path stores the path from the start point to the target point, and array pathList stores all the segment paths. For example, vertex 2 and vertex 4 are placed in the set of intermediate nodes C. According to the algorithm, firstly, using the vertex 1 as a starting point, calculating a path distance dist [2] =7 to the vertex 2, a path distance dist [4] =20 to the vertex 4, traversing the intermediate nodes, and comparing the path distance from the starting point to each intermediate node, wherein dist [2] < dist [4], so that a segmented path 1- >2 is put into a path list; then, using the vertex 2 as a starting point, calculating a path distance dist [4] =15 to the vertex 4 according to an algorithm, and putting the path 2- >4 into a path list because only one intermediate node exists at the moment after the intermediate node traverses; the intermediate nodes have all visited, the last visited vertex 4 is taken as a starting point, the shortest path of the final point, namely vertex 5, namely dist [5] =6, the segmented path 4- >5 is put into a pathList, the segmented paths are combined, namely, the path from the starting point S to the final point G is found, and the final result is a path 1- >2- >4>5, as shown in fig. 3.
The optimal solution is found by segmentation, and after all nodes are found, the segmented paths are combined to obtain the approximate shortest path, so that a feasible path comprising all the selected intermediate nodes can be planned in a limited time, and the approximate optimal solution is searched to replace the optimal solution, thereby reducing the complexity of path planning and improving the accuracy and efficiency of path planning to a certain extent.
In one embodiment, the motion controller includes a PID controller that generates the PWM drive signal based on a difference between the third rotational speed and a set rotational speed. The principle of PID control can be understood with reference to the prior art, and the dredging robot walking control is realized based on the PID controller in the embodiment, so that the walking stability on a bumpy road surface can be improved.
In one embodiment, the motion controller is further configured to control the dredging robot to walk based on a preset acceleration and deceleration curve.
The preset acceleration and deceleration curve is shown in fig. 4, and is mainly composed of 7 speed stages, and the speed stages are respectively as follows in sequence: acceleration phase, uniform acceleration phase, deceleration phase, uniform velocity phase, acceleration-deceleration phase, uniform deceleration phase, and deceleration-deceleration phase (i.e., t) 1 ～t 7 Each corresponding curve segment). It can be seen that the speed connection is smooth between the curve segments of each stage, so that the robot operates more stably, the speed change is more stable, and the stability of the whole system is improved.
Specifically, the mathematical relation formula between the running speed, the acceleration and the jerk of the dredging robot is as follows:
wherein j (τ) is the jerk function of the dredging robot, and the acceleration function a (t) can be obtained by integrating the jerk function, and the speed function v (t) of the dredging robot can be obtained by integrating the acceleration function a (t) in time.
The jerk function for each speed stage is as follows:
wherein J represents jerk.
From the above equation, the acceleration equation for each speed stage can be derived as follows:
and then, the operation speed calculation formula of the dredging robot in each speed stage of the acceleration and deceleration curve can be deduced by the acceleration formula:
the embodiment of the disclosure provides a walking control method of an intelligent dredging robot, wherein an angle sensor and a photoelectric encoder which are positioned at a walking motor of the dredging robot and a motion controller positioned on a chassis of the dredging robot are arranged on the dredging robot, as shown in fig. 5, and the method comprises the following steps:
step S501: the angle sensor obtains the rotation angle of the rotating shaft of the walking motor;
step S502: the photoelectric encoder outputs a plurality of pulse signals corresponding to the rotating speed of the walking motor;
step S503: the motion controller determines a first rotating speed of the walking motor based on the rotating angle, determines a second rotating speed of the walking motor based on the pulse signals, determines a third rotating speed based on the first rotating speed and the second rotating speed, generates a PWM driving signal based on the third rotating speed and the set rotating speed, and sends the PWM driving signal to a walking motor driver of the dredging robot so that the walking motor driver drives the walking motor.
In one embodiment, the method comprises the steps of:
the motion controller acquires an environment map, and determines a global map containing a walking route of the dredging robot based on the environment map;
planning a path based on the global map to obtain a planned path;
and controlling the motor driver to drive the walking motor based on the planned path so as to enable the dredging robot to walk based on the planned path.
In one embodiment, the global map includes a plurality of nodes, and the connection relationship between the nodes represents a feasible route of the robot. The motion controller performs path planning based on the global map to obtain a planned path, and the method comprises the following steps: determining intermediate nodes in the global map to form an intermediate node set; determining a current position point of the dredging robot, executing a preset path searching algorithm to determine the shortest path between the current position point and each intermediate node in the intermediate node set, and selecting an intermediate node with the smallest path distance in the shortest path of each intermediate node as a new current position point; executing a preset path searching algorithm to determine the shortest path between the new current position point and each rest of intermediate nodes in the intermediate node set, selecting the intermediate node with the smallest path distance in the shortest path of each rest of intermediate nodes as the new current position point, and repeatedly executing the preset searching algorithm until each intermediate node in the intermediate node set is traversed; determining a target shortest path between a last traversed intermediate node in the intermediate node set and a target position point; and merging the target shortest paths, and obtaining a planned path by the shortest path with the minimum path distance in the shortest paths of all the intermediate nodes, wherein the shortest paths are determined when a preset path searching algorithm is executed each time.
In one embodiment, the preset path search algorithm includes at least a diecktra algorithm, i.e., dijkstra algorithm.
It should be noted that although the steps of the methods of the present disclosure are illustrated in the accompanying drawings in a particular order, this does not require or imply that the steps must be performed in that particular order or that all of the illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc. In addition, it is also readily understood that these steps may be performed synchronously or asynchronously, for example, in a plurality of modules/processes/threads.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An intelligent dredging robot, which is characterized by comprising:
the angle sensor is arranged at the walking motor of the dredging robot and is used for acquiring the rotation angle of the rotating shaft of the walking motor;
the photoelectric encoder is arranged at a walking motor of the dredging robot and is used for outputting a plurality of pulse signals corresponding to the rotating speed of the walking motor;
the motion controller is arranged on the chassis of the dredging robot, connected with the angle sensor and the photoelectric encoder, and used for determining a first rotating speed of the walking motor based on the rotating angle, determining a second rotating speed of the walking motor based on the pulse signals, determining a third rotating speed based on the first rotating speed and the second rotating speed, generating a PWM driving signal based on the third rotating speed and the set rotating speed, and sending the PWM driving signal to a walking motor driver of the dredging robot so that the walking motor driver drives the walking motor;
the motion controller is further used for acquiring an environment map, determining a global map containing a walking route of the dredging robot based on the environment map, planning a path based on the global map to obtain a planned path, and controlling the motor driver to drive the walking motor based on the planned path so that the dredging robot walks based on the planned path; the global map comprises a plurality of nodes, and the connection relation among the nodes represents a feasible route of the robot; the motion controller performs path planning based on the global map to obtain a planned path, and the method comprises the following steps:
determining intermediate nodes in the global map to form an intermediate node set;
determining a current position point of the dredging robot, executing a preset path searching algorithm to determine the shortest path between the current position point and each intermediate node in the intermediate node set, and selecting an intermediate node with the smallest path distance in the shortest path of each intermediate node as a new current position point;
executing a preset path searching algorithm to determine the shortest path between the new current position point and each rest of intermediate nodes in the intermediate node set, selecting the intermediate node with the smallest path distance in the shortest path of each rest of intermediate nodes as the new current position point, and repeatedly executing the preset searching algorithm until each intermediate node in the intermediate node set is traversed;
determining a target shortest path between a last traversed intermediate node in the intermediate node set and a target position point;
and merging the target shortest paths, and obtaining a planned path by the shortest path with the minimum path distance in the shortest paths of all the intermediate nodes, wherein the shortest paths are determined when a preset path searching algorithm is executed each time.
2. The dredging robot of claim 1, further comprising:
the ultrasonic sensor is arranged on the chassis of the dredging robot and is used for detecting whether an obstacle exists in the travelling direction of the dredging robot, and if so, outputting the distance between the dredging robot and the obstacle;
the motion controller is connected with the ultrasonic sensor and used for generating an obstacle avoidance PWM driving signal when the distance is smaller than a preset distance, and sending the obstacle avoidance PWM driving signal to the walking motor driver so that the walking motor driver drives the walking motor to operate, and therefore the dredging robot avoids obstacles.
3. The dredging robot of claim 2, further comprising:
the laser radar is connected with the motion controller;
the main controller is in communication connection with the motion controller;
the temperature sensor is connected with the main controller and used for detecting the ambient temperature;
the gas sensor is connected with the main controller and is used for detecting the content of harmful gas in the environment; wherein the harmful gas comprises one or more of methane, gas, hydrogen sulfide and carbon monoxide;
the main controller is used for sending out an alarm signal when the ambient temperature is greater than a preset temperature and/or the content of the harmful gas is greater than a preset value;
the video acquisition device is connected with the main controller and used for acquiring scene images of the site of the dredging robot and comprises a cradle head camera and a network camera.
4. The dredging robot as recited in claim 3, further comprising an ethernet switch connected to the pan-tilt camera and the webcam for transmitting video information collected by the pan-tilt camera and the webcam to an external host computer; and/or, still include setting up the mechanism that gathers materials on the desilting robot, gather materials the mechanism include spiral material collector, drive spiral material collector rotatory hydraulic motor, to hydraulic motor carry the hydraulic pressure pipe of hydraulic oil, with hydraulic pump that hydraulic pipe connects, and hold the oil tank of hydraulic pump.
5. The dredging robot of claim 1, wherein the preset path search algorithm comprises at least a disco tesla algorithm.
6. The dredging robot of any one of claims 1-5, wherein the motion controller comprises a PID controller that generates a PWM drive signal based on a difference between the third rotational speed and a set rotational speed.
7. The dredging robot of claim 4, further comprising a pressure sensor disposed on the oil tank, the pressure sensor being connected to the main controller.
8. The intelligent dredging robot walking control method is characterized in that the dredging robot is provided with an angle sensor and a photoelectric encoder which are positioned at a walking motor of the dredging robot, and a motion controller positioned on a chassis of the dredging robot, and the method comprises the following steps:
the angle sensor obtains the rotation angle of the rotating shaft of the walking motor;
the photoelectric encoder outputs a plurality of pulse signals corresponding to the rotating speed of the walking motor;
a motion controller determines a first rotating speed of the walking motor based on the rotating angle, determines a second rotating speed of the walking motor based on the plurality of pulse signals, determines a third rotating speed based on the first rotating speed and the second rotating speed, generates a PWM driving signal based on the third rotating speed and a set rotating speed, and sends the PWM driving signal to a walking motor driver of the dredging robot so that the walking motor driver drives the walking motor;
the motion controller is further used for acquiring an environment map, determining a global map containing a walking route of the dredging robot based on the environment map, planning a path based on the global map to obtain a planned path, and controlling the motor driver to drive the walking motor based on the planned path so that the dredging robot walks based on the planned path; the global map comprises a plurality of nodes, and the connection relation among the nodes represents a feasible route of the robot; the motion controller performs path planning based on the global map to obtain a planned path, and the method comprises the following steps:
determining intermediate nodes in the global map to form an intermediate node set;
determining a current position point of the dredging robot, executing a preset path searching algorithm to determine the shortest path between the current position point and each intermediate node in the intermediate node set, and selecting an intermediate node with the smallest path distance in the shortest path of each intermediate node as a new current position point;
executing a preset path searching algorithm to determine the shortest path between the new current position point and each rest of intermediate nodes in the intermediate node set, selecting the intermediate node with the smallest path distance in the shortest path of each rest of intermediate nodes as the new current position point, and repeatedly executing the preset searching algorithm until each intermediate node in the intermediate node set is traversed;
determining a target shortest path between a last traversed intermediate node in the intermediate node set and a target position point;
and merging the target shortest paths, and obtaining a planned path by the shortest path with the minimum path distance in the shortest paths of all the intermediate nodes, wherein the shortest paths are determined when a preset path searching algorithm is executed each time.
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