CN114397893B - Path planning method, robot cleaning method and related equipment - Google Patents
Path planning method, robot cleaning method and related equipment Download PDFInfo
- Publication number
- CN114397893B CN114397893B CN202111631979.9A CN202111631979A CN114397893B CN 114397893 B CN114397893 B CN 114397893B CN 202111631979 A CN202111631979 A CN 202111631979A CN 114397893 B CN114397893 B CN 114397893B
- Authority
- CN
- China
- Prior art keywords
- path
- area
- wall
- navigation
- narrow area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 85
- 238000004140 cleaning Methods 0.000 title claims abstract description 65
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 31
- 238000010276 construction Methods 0.000 claims description 29
- 230000009191 jumping Effects 0.000 claims description 14
- 230000008030 elimination Effects 0.000 claims description 11
- 238000003379 elimination reaction Methods 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 10
- 230000004888 barrier function Effects 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 5
- 238000010408 sweeping Methods 0.000 description 26
- 238000010586 diagram Methods 0.000 description 11
- 230000007547 defect Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 4
- 238000010845 search algorithm Methods 0.000 description 4
- 208000031481 Pathologic Constriction Diseases 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 238000012217 deletion Methods 0.000 description 2
- 230000037430 deletion Effects 0.000 description 2
- 230000036262 stenosis Effects 0.000 description 2
- 208000037804 stenosis Diseases 0.000 description 2
- 238000009966 trimming Methods 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000010339 dilation Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 210000003254 palate Anatomy 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 238000005201 scrubbing Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
- 238000005406 washing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- 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
- G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
- G05D1/0274—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means using mapping information stored in a memory device
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Aviation & Aerospace Engineering (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to the technical field of robots, provides a path planning method, a robot cleaning method and related equipment, and is used for solving the problems that a path area navigation mode planned along a wall mode cannot pass in the existing path planning scheme, so that a final planned path is overlong or the planning is failed. The method comprises the following steps: acquiring a grid map of an area where the intelligent equipment is located, the current position of the intelligent equipment and a target area; detecting whether a narrow area exists in a grid map between the current position and the target area or not when the intelligent device is in a navigation mode; if so, adjusting the obstacle in the narrow area based on the structural element corresponding to the intelligent equipment, and updating the grid map based on the adjustment result; and planning a path from the current position to the target area by using a jump point searching algorithm according to the updated grid map, and outputting a navigation path.
Description
Technical Field
The present invention relates to the field of robots, and in particular, to a path planning method, a robot cleaning method, and related devices.
Background
The sweeping robot can realize sweeping through two modes in the sweeping process, firstly, the global map is constructed through a wall-along mode, a sweeping area and a sweeping path are planned according to the pose of the robot after the map is constructed, the area of the map is swept through switching between the wall-along mode and the point-to-point navigation mode, and after all the sweeping is completed, a recharging point is detected and a recharging path is planned.
Currently, in the cleaning process, if the cleaning robot needs to be cleaned from a certain cleaning area to a next cleaning area or needs to return to a charging position after cleaning is completed, a point-to-point navigation mode needs to be used to realize path planning and navigation. However, in the navigation process, due to the laser precision, when the sensor unit along the wall senses that the path passing through in the wall-along mode is sometimes switched to the point-to-point navigation mode, the navigation mode cannot pass through the path area in the wall-along mode, so that the problems of navigation failure, overlong path to be navigated and the like are caused.
Disclosure of Invention
The invention provides a path planning method, a robot cleaning method and related equipment, which are used for solving the problems that in the existing path planning scheme, a path area navigation mode planned along a wall mode cannot pass, and a finally planned path is overlong or the planning fails.
The first aspect of the present invention provides a path planning method, the method comprising:
acquiring a grid map of an area where the intelligent equipment is located, the current position of the intelligent equipment and a target area;
detecting whether a narrow area exists in a grid map between the current position and the target area or not when the intelligent equipment is in a navigation mode, wherein the narrow area is a passable area in a wall-along mode and a passable area in a navigation mode;
if so, adjusting the obstacle in the narrow area based on the structural element corresponding to the intelligent equipment, and updating the grid map based on the adjustment result;
and planning a path from the current position to the target area by using a preset path construction algorithm according to the updated grid map, and outputting a navigation path.
In a possible embodiment, before said detecting whether there is a narrow area in the grid map between the current location and the target area, further comprising:
judging whether the intelligent equipment finishes cleaning along a wall;
if so, acquiring the cleaning path along the wall;
and extracting a path region with obstacles around the position of the grid point in the cleaning path to obtain a narrow region set.
In a possible implementation manner, before the adjusting the obstacle in the narrow area based on the structural element corresponding to the smart device and updating the grid map based on the result of the adjusting, the method includes:
acquiring the size parameters of the intelligent equipment, and constructing corresponding original structural elements based on the size parameters;
and performing angle removal treatment on the original structural element to obtain the structural element.
In a possible implementation manner, the adjusting the obstacle in the narrow area based on the structural element corresponding to the smart device, and updating the grid map based on the result of the adjustment, includes:
extracting a map of the narrow area;
traversing each grid point in the map of the narrow area by taking the structural element as an operator, and extracting the grid points with barriers;
and performing obstacle elimination processing on the extracted grid points, and updating the grid map based on the elimination result.
In a possible implementation manner, the traversing each grid point in the map of the narrow area by taking the structural element as an operator, extracting the grid point with the obstacle, includes:
filling each grid point in the map of the narrow area based on a wall-along mode by using the central position of the structural element, and marking to obtain a marked map;
Identifying whether an obstacle exists in the structural element range of the grid point corresponding to each mark based on each mark in the mark map;
if so, marking the corresponding grid points in a passable way until all the grid points with barriers are output after the narrow area is traversed.
In a possible implementation manner, after traversing each grid point in the map of the narrow area with the structural element as an operator and extracting the grid point with the obstacle, the method further comprises:
extracting an obstacle on the grid point, and performing expansion treatment on the obstacle according to the structural element to obtain an expanded obstacle;
and replacing the original obstacle in the grid map based on the expansion obstacle to obtain a new grid map.
In a possible implementation manner, the planning, according to the updated grid map, the path from the current location to the target area by using a preset path construction algorithm, and outputting a navigation path, includes:
determining the advancing direction of the intelligent device based on the current position and the coordinates of the target area;
searching all blank grid points meeting the jumping point conditions in the advancing direction, and in the horizontal component direction and the vertical component direction in the advancing direction by using the current position as a starting point and utilizing a jumping point searching algorithm;
And constructing a path based on the current position, the blank grid point and the target area, and outputting a navigation path.
A second aspect of the present invention provides a robotic cleaning method, the method comprising:
the robot finishes the wall-along cleaning in a wall-along mode, and an initial grid map is constructed to obtain the cleaning path along which the wall is cleaned;
the method comprises the steps that a robot is switched to a navigation mode, and whether a narrow area exists in a grid map between the current position of the robot and a target area to be cleaned or not is detected, wherein the narrow area is a passable area in a wall-following mode and a passable area in a navigation mode;
if the obstacle clearance exists in the narrow area, obstacle clearance processing is carried out on the narrow area, and a navigation path from the current position to the target area is constructed by utilizing a preset path construction algorithm based on the narrow area after obstacle clearance;
and navigating the robot to reach the target area based on the navigation path to clean.
A third aspect of the present invention provides a path planning apparatus comprising:
the data acquisition module is used for acquiring a grid map of the area where the intelligent equipment is located, the current position of the intelligent equipment and a target area;
the first detection module is used for detecting whether a narrow area exists in the grid map between the current position and the target area when the intelligent equipment is in a navigation mode, wherein the narrow area is a passable area in a wall-along mode and a passable area in a navigation mode;
The area adjusting module is used for adjusting obstacles in the narrow area based on structural elements corresponding to the intelligent equipment when the narrow area is detected, and updating the grid map based on an adjustment result;
and the path planning module is used for planning the path from the current position to the target area by using a preset path construction algorithm according to the updated grid map and outputting a navigation path.
A fourth aspect of the present invention provides a robot comprising:
the wall-along construction module is used for finishing wall-along cleaning in a wall-along mode, constructing an initial grid map and obtaining the cleaning path along which the wall is cleaned;
the second detection module is used for switching to a navigation mode and detecting whether a narrow area exists in a grid map between the current position of the robot and a target area to be cleaned, wherein the narrow area is a passable area in a wall-following mode and a passable area in a navigation mode;
the navigation construction module is used for carrying out obstacle clearance treatment on the narrow area when the narrow area is detected, and constructing a navigation path from the current position to the target area by utilizing a preset path construction algorithm based on the narrow area after obstacle clearance;
And the cleaning module is used for guiding the robot to reach the target area based on the navigation path to clean.
A fifth aspect of the present invention provides an intelligent device, comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the intelligent device to perform the steps of the path planning method described above or the steps of the robot cleaning method described above.
A sixth aspect of the present invention provides a computer readable storage medium having a computer program stored therein, which when run on a computer, causes the computer to perform the steps of the path planning method described above or the steps of the robot cleaning method described above.
In the technical scheme provided by the invention, by defining the narrow area in the wall-following mode and carrying out obstacle elimination on the narrow area in the map between the current position and the target area based on the structural element constructed by the intelligent equipment, the intelligent equipment can also pass through the path area in the wall-following mode in the navigation mode, so that the defect that the narrow area can not pass through the area in the wall-following mode is overcome, the compatible mode of navigation and wall-following is realized, meanwhile, the planning of the path is well optimized, and the optimal path is ensured.
Drawings
FIG. 1 is a schematic diagram of a first embodiment of a path planning method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a second embodiment of a path planning method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a third embodiment of a path planning method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a structural element according to an embodiment of the present invention;
FIG. 5 is a schematic view illustrating a treatment of a narrow region according to an embodiment of the present invention;
FIG. 6 is a schematic view of an obstacle expansion process according to an embodiment of the invention;
FIG. 7 is a schematic view of a walking path along a wall pattern in an embodiment of the present invention;
FIG. 8 is a schematic diagram of a grid map of an obstacle dilation process in accordance with an embodiment of the present invention;
FIG. 9 is a schematic diagram of a navigation path planning for a navigation mode according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of a JPS algorithm for planning a navigation path according to an embodiment of the present invention;
FIG. 11 is a schematic view of an embodiment of a robotic cleaning method in accordance with an embodiment of the present invention;
FIG. 12 is a schematic diagram of a path planning apparatus according to an embodiment of the present invention;
FIG. 13 is a schematic diagram of another configuration of a path planning apparatus according to an embodiment of the present invention;
FIG. 14 is a schematic view of a robot according to an embodiment of the present invention;
fig. 15 is a schematic view of an embodiment of the smart device according to the present invention.
Detailed Description
The embodiment of the invention provides a path planning method, a robot cleaning method and related equipment, which are used for solving the problem that an excessively long path is caused by the fact that a navigation mode in intelligent equipment and a planned path in a wall-following mode cannot be overlapped.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
It will be appreciated that the present invention may be applied to a smart device, which may be, by way of example and not limitation, a mobile robot, as described herein. The mobile robot may be any one of a sweeping robot, a sweeping and mopping robot, a floor scrubbing robot, a floor washing robot, and the like. The navigation type of the sweeping robot applied by the invention is not limited, and the sweeping robot can be a pure inertial navigation sweeping machine or other sweeping machines adopting navigation equipment. The invention can be applied to the control method and the equipment of the advancing direction with low requirement on the positioning precision. By way of example, and not limitation, the present invention is illustrated with a sweeping robot in a mobile robot.
For convenience of understanding, a specific flow of an embodiment of the present invention is described below, referring to fig. 1, and fig. 1 is a schematic diagram of a path planning method in an embodiment of the present invention, where an embodiment of the path planning method in the embodiment of the present invention includes:
s101, acquiring a grid map of an area where the intelligent device is located, the current position of the intelligent device and a target area.
In this step, the grid map is acquired by a sensing module of the robot, which in turn is composed of different sensor units, such as a lidar sensor unit, a collision sensor unit, a wall-following sensor unit.
When the robot starts to work, the robot is firstly switched to a wall-following mode, the robot is controlled to clean according to the wall-following mode of the wall-following sensor unit in the wall-following mode, and other environmental information in the area where the robot is located is collected through the laser radar sensor unit and the collision sensor unit, so that a palate map is constructed, the grid map comprises cleaning areas, obstacle information and the like, and meanwhile, the control module performs corresponding path planning control.
S102, detecting whether a narrow area exists in the grid map between the current position and the target area when the intelligent device is in the navigation mode.
In this embodiment, the narrow area is a passable area in the wall-along mode and a passable area in the navigation mode; the narrow area handling in the path planning method is proposed when the robot is in a navigation mode, in which the path along wall sensor unit perceives that the path along the wall mode is generated, sometimes switching to a point-to-point navigation mode, due to laser accuracy, the navigation mode cannot pass through the path areas along the wall mode, which are defined herein as narrow areas.
In this embodiment, the process of judging whether or not there is a narrow region includes:
judging whether the current position is positioned at the wall side or at the edge position in the grid map;
if yes, planning a first path based on the wall-following mode and planning a second path based on the navigation mode respectively;
intercepting a path area which is different from a second path in the first path, and judging whether the intelligent equipment can pass through the path area in a navigation mode;
if not, determining that a narrow area exists between the current position and the target area;
if yes, determining that a narrow area does not exist between the current position and the target area, and navigating the intelligent device to reach the target area in a first path.
In practical application, a direction vector can be calculated through the current position and the target area, and the current position is taken as a starting point to search whether a narrow area exists in the grid map in the direction of the direction vector; the search is specifically based on the marking of a pre-marked stenosis area in the grid map.
And S103, if the obstacle exists, adjusting the obstacle in the narrow area based on the structural element corresponding to the intelligent equipment, and updating the grid map based on the adjustment result.
In the step, the structural element is constructed based on the actual size of the intelligent device, wherein the construction mode mainly comprises edge trimming, namely deleting redundant grid points in the grid image corresponding to the intelligent device to obtain an approximately circular grid image.
Based on the structural element, the obstacle defined as a narrow area is adjusted, wherein the adjustment includes local deletion, movement, and the like, and the adjustment is preferably performed by adopting a local deletion mode in the embodiment.
In this embodiment, the adjustment may be performed on the narrow area in the map range before the current position reaches the target area, or may be performed on all the narrow areas in the entire grid map.
In this embodiment, the structural element is used as an operator to traverse the narrow area, and after judging that the structural element does not traverse to a grid point, it is judged whether an obstacle exists in the range of the structural element after the grid point, if so, the obstacle at the position in the narrow area is eliminated, so that the structural element can pass through. And replacing the corresponding narrow area in the grid map based on the adjusted narrow area to obtain a new grid map.
S104, planning a path from the current position to the target area by using a preset path construction algorithm according to the updated grid map, and outputting a navigation path.
In this step, the path construction algorithm may be an a-algorithm or the hop search algorithm (jump point search, JPS), preferably, the present application is implemented by adopting a JPS algorithm, where the JPS is a path planning algorithm for constructing a path by searching for a hop or a forced neighbor of a parent node, and by performing path planning through the algorithm, the number of calculation of navigation points may be reduced.
In this embodiment, the current position is taken as a starting point, the starting point is taken as a parent node, a blank grid point meeting the requirement of the jump point is searched in the direction of the direction vector from the current position to the target area and is taken as a jump point of the parent node, the path from the parent node to the jump point is connected, then the jump point is taken as the parent node, the jump point or the forced neighbor point is searched based on the same mode, and when the direct jump point or the forced neighbor point is taken as a point or an end point in the target area, the search is stopped, and a path from the current position to the target area is constructed based on all the searched points.
In the embodiment of the invention, the narrow area is set based on the wall-along mode, and when a path is planned in the navigation mode, the narrow area meets the path planning requirement in the navigation mode in a mode of adjusting the obstacles in the narrow area, so that compatibility between the wall-along mode and the navigation mode is realized, the path planning length on the basis can be optimal, and the problem of overlong path caused by excessive obstacle avoidance is avoided.
Referring to fig. 2, in another embodiment of a path planning method according to the present invention, the method includes path planning along a wall mode and path planning in a navigation mode, and is mainly applied to a sweeping robot, where the sweeping robot includes a control module, a sensing module, and a motion module, and the sensing module includes, for example, a lidar sensor unit, a collision sensor unit, and a along-wall sensor unit, and the path planning method includes:
s201, powering up the sweeping robot to start sweeping.
S202, constructing an initial map along a wall mode.
Specifically, the control module controls the floor sweeping robot to work in a wall-following mode, then the robot is enabled to move along a wall or a boundary through the motion module, map data in the moving process and map data on the periphery are collected through the sensing module, and a standard coordinate system is utilized to draw a map based on the collected map data, so that an initial map is obtained.
S203, constructing a cleaning path along the wall mode, and collecting raster data through the sensing module based on the cleaning path.
S204, constructing a global grid map based on the collected grid data.
Specifically, after the grid map is built, judging whether the sweeping robot completes the wall-following sweeping task, if so, jumping to the next step, if not, continuing to sweep along the wall, collecting grid data, and updating the grid map based on the grid data.
In this embodiment, a narrow area is defined based on the constructed grid map, specifically:
judging whether the intelligent equipment finishes cleaning along a wall;
if so, acquiring the cleaning path along the wall;
and extracting a path region with obstacles around the position of the grid point in the cleaning path to obtain a narrow region set. Further, marking in the grid map based on each narrow region in the set of narrow regions is included.
S205, switching to a navigation mode to clean or recharge.
S206, detecting whether a narrow area exists in the grid map between the current position and the target area.
Specifically, in the navigation mode, based on a pre-planned navigation line, whether a narrow area exists around the line is judged, if so, the next step is skipped, otherwise, the navigation line is output.
S207, if the map exists, extracting the map of the narrow area.
And S208, traversing each grid point in the map of the narrow area by taking the structural element as an operator, and extracting the grid points with barriers.
In this embodiment, the structural element is a raster image of the sweeping robot, and is specifically constructed by the following manner:
acquiring the size parameters of the intelligent equipment, and constructing corresponding original structural elements based on the size parameters;
and performing angle removal treatment on the original structural element to obtain the structural element.
As shown in fig. 4, a matrix of the sweeping robot is constructed based on the size parameters to obtain an original structural element, as shown in fig. 4 (a), and then the edge trimming treatment is performed on the original structural element in a corner removing manner to make the length of the connecting diagonal line be ∈50=7, so that the calculated amount is reduced, and the designed structural element is shown in fig. 4 (b).
S209, performing an obstacle removal process on the extracted grid points, and updating the grid map based on the result of the removal.
In this embodiment, specifically, the central position of the structural element is used to fill each grid point in the map of the narrow area based on the wall-along mode, and marking is performed to obtain a marked map;
Identifying whether an obstacle exists in the structural element range of the grid point corresponding to each mark based on each mark in the mark map;
if so, marking the corresponding grid points in a passable way until all the grid points with barriers are output after the narrow area is traversed.
And finally, performing obstacle elimination processing on all grid points to obtain a new grid map.
For example, the map is traversed in the following traversal manner based on the structural elements in fig. 4 (b). Taking a narrow area as an example, fig. 5 (a) is a map based on a narrow area defined along a wall mode, wherein the central position of a structural element, namely, the coordinate position of a filling mark along the wall mode is taken as a reference in the traversing process, if an obstacle exists in the structural element in the coordinate range, the traversing process is enabled to pass, the filling is enabled to be 0, the obstacle is eliminated, and the map shown in fig. 5 (b) is obtained, namely, the narrow area after the obstacle is eliminated.
S210, expanding the obstacle in the grid map and updating the grid map.
In this step, the expansion treatment is specifically performed with the obstacle in the narrowed region after the obstacle is removed, specifically:
extracting an obstacle on the grid point in a narrow area, and performing expansion treatment on the obstacle according to the structural element to obtain an expanded obstacle;
And replacing the original obstacle in the grid map based on the expansion obstacle to obtain a new grid map.
S211, planning a path from the current position to the target area by using a preset path construction algorithm according to the updated grid map, and outputting a navigation path.
Specifically, determining the advancing direction of the intelligent device based on the current position and the coordinates of the target area;
searching all blank grid points meeting the jumping point conditions in the advancing direction, and in the horizontal component direction and the vertical component direction in the advancing direction by using the current position as a starting point and utilizing a jumping point searching algorithm;
and constructing a path based on the current position, the blank grid point and the target area, and outputting a navigation path.
In practical application, the obstacle in the map processed in fig. 5 (b) is traversed according to similar structural elements, the central element of the structural elements is replaced by an obstacle mark of "1", and if "1" exists in the structural elements in the traversing process, the central element in the structural elements is set as the obstacle, so that expansion of the obstacle in the map is completed. Fig. 6 (a) is a path through a stenosis area along a wall mode robot, fig. 6 (b) is a map expanded by structural elements, and "J" is a navigation track generated by a JPS navigation algorithm.
Further, the road sweeping robot is regarded as a particle, the size of which is one map grid, and the road is planned in the map processed in fig. 6 (b). The path planning uses a JPS path navigation algorithm with smaller calculation amount, the implementation flow of the specific algorithm is shown in figure 3, and the specific method comprises the following steps:
s301, acquiring a grid map;
s302, judging whether a wall-following mode exists;
s303, if so, acquiring a narrow area;
s304, eliminating obstacles in a narrow area according to a wall-along mode;
s305, expanding the obstacle with the radius of the robot, and performing navigation planning on the expanded grid map;
s306, judging whether the navigation is successful;
s307, if successful, generating a navigation path by using a JPS algorithm;
s308, if the narrow area is unsuccessful, determining that the narrow area cannot pass;
s309, ending.
The above method will be described in detail in a practical scenario. In an actual scene test, the acquired map size was 78×89, where the lidar resolution was 0.05. The map is a map in which the loading of the surrounding environment is perceived by a laser radar carried by the sweeping robot, and meanwhile, the sweeping process in the wall-following mode is completed, wherein a black area is an obstacle, and X rays represent a wall-following path in the sweeping process of the robot, as shown in fig. 7.
In cleaning, the robot needs to navigate from one point along the wall to another cleaning point and pass through a narrow area shown in the map. First, the map is cleared of obstacles along the wall path by the robot as shown in fig. 8 (a), and then the obstacles are inflated according to the radius of the robot as shown in fig. 8 (b).
Finally, the robot navigates from one point to another during the course of the wall, while following an optimal path through the stricture. As shown in fig. 9, 9 (a) is a route in which the narrow area navigation is not performed, and 9 (b) is a route in which the narrow area navigation is performed. The black point is the starting point of the robot, the star point is the target point of the robot, the solid track is the navigation motion track of the robot, and the narrow area of the middle section can pass through.
The following illustrates the path-finding flow of the JPS algorithm planning navigation path. As shown in fig. 10, the grid of 5*5, the diagonally filled portions represent the barrier regions, S being the start and E being the end. The JPS first initiates the addition of the start point S to openset to find the shortest path from S to E. Taking the point S with the minimum F value from the openset, deleting the point S from the openset, adding the closing set, searching the jumping point along eight directions when the current direction of the S is empty, in the figure, starting from the S, only moving downwards, rightwards (relative to the absolute reference system of the screen: upwards, downwards, leftwards, rightwards relative to the reference system of the player: front, backwards, leftwards and rightwards, the absolute reference system and the relative reference system can be switched to be used for convenience of description), searching downwards to reach the boundary, searching to reach the F to reach the right, and therefore, no jumping point is found, then searching the jumping point along the right-downwards direction, and at the point G, the parent (G) is S, the parent (G) moves diagonally to the S, and the G moves vertically (moves downwards) to reach the jumping point I, so that the G is the jumping point, and the point G is added into the openset. Taking out the point G with the minimum F value from the openset, deleting the point G from the openset, adding the closed set, searching the jump point in the right (horizontal component of the current direction), the lower (vertical component of the current direction) and the lower right (current direction) directions because the current direction of the G is the diagonal direction (the direction from S to G), and starting from the G in the figure, only downwards, searching the jump point I according to the jump point definition, and adding the I into the openset. The point I with the minimum F value is taken out of the openset, deleted from the openset and added to the closed set, because the current direction of I is a straight line direction (the direction from G to I), the left rear of I can not walk and the left and front can walk at the point I, so the jump points are found along the left, the left and the front, but the left and the front meet the boundary, and only the jump point Q is found to the left (according to the jump point definition), so the Q is added to the openset. The point Q with the minimum F value is taken out of the openset, deleted from the openset and added to the closed set, and because the current direction of Q is a straight line direction, the left rear of Q can not move and the left front can move, so the jump point is found along the left front, the left front and the front, but the left front and the front meet the boundary, and only the jump point E is found to the left (according to the jump point definition), so the E is added to the openset. The point E with the smallest F value is fetched from openet, and the path is S, G, I, Q, E because E is the target point and the seek is completed. Note that the case of going from H to K is not considered here, because there is a block on the diagonal, if H to K is required to reach directly, the path is S, G, H, K, M, P, E, the calculation method of the jump point is modified, but if H to K can reach directly in the game, the block on the right of H will be traversed.
In the embodiment of the invention, through the method and the visualization of the actual running module, the defect that the navigation mode can not pass through a narrow area in the wall-following mode is overcome, so that the compatible mode of navigation and wall-following is realized, and meanwhile, the planning of a path is well optimized, and the optimal path is ensured.
Referring to fig. 11, a method for cleaning a robot according to an embodiment of the present application includes the following steps:
s401, the robot finishes the wall-along cleaning in a wall-along mode, an initial grid map is constructed, and the cleaning path along the wall cleaning is obtained.
In this step, when the robot is in the wall-following mode, a wall or a boundary is detected by a laser sensor or a wall-following sensor unit, and the robot is controlled to scan map data along the wall or the boundary, and a wall-following cleaning path is constructed based on a start point and a destination, and cleaning is performed, in which, in the cleaning process, environmental data outside the wall or the boundary is acquired by the laser sensor, an initial grid map is constructed based on the environmental data and the wall-following cleaning path, while a narrow area is defined based on the wall-following cleaning path, which refers to a path area in the wall-following cleaning path through which the wall-following mode is passable and through which the navigation mode is not passable, and the path area is defined as a narrow area.
S402, the robot is switched to a navigation mode, and whether a narrow area exists in a grid map between the current position of the robot and a target area to be cleaned is detected.
In this embodiment, the narrow area is a passable area in the wall-along mode and a passable area in the navigation mode; specifically, firstly constructing a structural element based on the occupied area of a robot, wherein the dimensional parameter of the intelligent equipment is obtained when the structural element is constructed, and the corresponding original structural element is constructed based on the dimensional parameter; and performing angle removal treatment on the original structural element to obtain the structural element.
And (3) traversing each grid point in a narrow area in a wall-following mode in the grid map based on the structural element, judging the position of the grid point, and determining whether an obstacle exists in the range of the structural element or not, if so, determining that the narrow area exists.
S403, if the obstacle clearance exists in the narrow area, obstacle clearance processing is carried out on the narrow area, and a navigation path from the current position to the target area is constructed by using a preset path construction algorithm based on the narrow area after obstacle clearance.
Specifically, extracting a map of the narrow area; traversing each grid point in the map of the narrow area by taking the structural element as an operator, and extracting the grid points with barriers; extracting an obstacle on the grid point, and performing expansion treatment on the obstacle according to the structural element to obtain an expanded obstacle; and replacing the original obstacle in the grid map based on the expansion obstacle to obtain a new grid map.
Further, determining a forward direction of the intelligent device based on the current position and coordinates of the target area;
searching all blank grid points meeting the jumping point conditions in the advancing direction, and in the horizontal component direction and the vertical component direction in the advancing direction by using the current position as a starting point and utilizing a jumping point searching algorithm;
and constructing a path based on the current position, the blank grid point and the target area, and outputting a navigation path.
S404, the robot is navigated to reach the target area based on the navigation path to clean.
In the step, when the robot is controlled to move to the target area based on the navigation path, the method further comprises the steps of detecting whether a new obstacle exists after the robot is controlled to move to the navigation point in the navigation path, if so, selecting the nearest navigation point to reconstruct the navigation path by using an A-algorithm, and then, the robot enters the target area based on the new navigation path to clean the target area.
In this embodiment, by defining a narrow area in the wall-following mode, the obstacle elimination is performed on the narrow area in the map between the current position and the target area based on the structural element constructed by the intelligent device, so that the intelligent device can also pass through the path area in the wall-following mode in the navigation mode, thereby overcoming the defect that the narrow area is passed in the wall-following mode and the navigation mode cannot pass through the area, and realizing the compatible mode of navigation and wall-following.
The method for path planning in the embodiment of the present invention is described above, and the path construction device in the embodiment of the present invention is described below, referring to fig. 12, where an embodiment of the path construction device in the embodiment of the present invention includes:
the data acquisition module 901 is used for acquiring a grid map of an area where the intelligent device is located, the current position of the intelligent device and a target area;
a first detecting module 902, configured to detect, when the smart device is in a navigation mode, whether a narrow area exists in a grid map between the current location and the target area;
the area adjustment module 903 is configured to adjust, when detecting that a narrow area exists, an obstacle in the narrow area based on a structural element corresponding to the smart device, and update the grid map based on an adjustment result;
and the path planning module 904 is configured to plan a path from the current position to the target area by using a jump point search algorithm according to the updated grid map, and output a navigation path.
The function implementation of each module in the path construction device corresponds to each step in the path planning method embodiment, and the function and implementation process thereof are not described in detail herein.
In the embodiment of the invention, by defining the narrow area in the wall-following mode and carrying out obstacle elimination on the narrow area in the map between the current position and the target area based on the structural element constructed by the intelligent equipment, the intelligent equipment can also pass through the path area in the wall-following mode in the navigation mode, thereby overcoming the defect that the narrow area can not pass through the area in the wall-following mode, realizing the compatible mode of navigation and wall-following, and simultaneously well optimizing the planning of the path and ensuring the optimal path.
Referring to fig. 13, another embodiment of the path construction apparatus according to the present invention includes:
the data acquisition module 901 is used for acquiring a grid map of an area where the intelligent device is located, the current position of the intelligent device and a target area;
a judging module 905, configured to judge whether the intelligent device has completed cleaning along a wall;
a path obtaining module 906, configured to obtain a cleaning path along the wall when it is determined that the cleaning along the wall is completed;
a definition module 907, configured to extract a path area in which an obstacle exists around a position where a grid point in the cleaning path is located, to obtain a narrow area set;
A first detecting module 902, configured to detect, when the smart device is in a navigation mode, whether a narrow area exists in a grid map between the current location and the target area;
the area adjustment module 903 is configured to adjust, when detecting that a narrow area exists, an obstacle in the narrow area based on a structural element corresponding to the smart device, and update the grid map based on an adjustment result;
and the path planning module 904 is configured to plan a path from the current position to the target area by using a jump point search algorithm according to the updated grid map, and output a navigation path.
Optionally, the robot further includes: a construction module 908 for
Acquiring the size parameters of the intelligent equipment, and constructing corresponding original structural elements based on the size parameters;
and performing angle removal treatment on the original structural element to obtain the structural element.
Optionally, the area adjustment module 903 includes:
an extraction unit 9031 for extracting a map of the narrow region;
a traversing unit 9032, configured to traverse each grid point in the map of the narrow area with the structural element as an operator, and extract a grid point where an obstacle exists;
the eliminating unit 9033 is configured to perform obstacle elimination processing on the extracted grid points, and update the grid map based on the result of the elimination.
Optionally, the traversing unit 9032 is specifically configured to:
filling each grid point in the map of the narrow area based on a wall-along mode by using the central position of the structural element, and marking to obtain a marked map;
identifying whether an obstacle exists in the structural element range of the grid point corresponding to each mark based on each mark in the mark map;
if so, marking the corresponding grid points in a passable way until all the grid points with barriers are output after the narrow area is traversed.
Optionally, the traversing unit 9032 may be further specifically configured to:
extracting an obstacle on the grid point, and performing expansion treatment on the obstacle according to the structural element to obtain an expanded obstacle;
and replacing the original obstacle in the grid map based on the expansion obstacle to obtain a new grid map.
Optionally, the path planning module 904 includes;
a direction determining unit 9041 for determining a forward direction of the smart device based on the current position and coordinates of the target area;
a searching unit 9042, configured to search, with the current position as a starting point, for all blank grid points that meet a skip point condition in the forward direction, and in a horizontal component direction and a vertical component direction in the forward direction, respectively, using a skip point search algorithm;
A planning unit 9043, configured to construct a path based on the current position, the blank grid point, and the target area, and output a navigation path.
In the embodiment of the invention, the mode of defining the narrow area is adopted to adjust the obstacles in the narrow area so as to realize that the path in the wall-following mode can be used in the navigation mode, so that the defect that the narrow area can be passed in the wall-following mode and the navigation mode can not be passed in the area is overcome, the compatible mode of navigation and wall-following is realized, and meanwhile, the planning of the path is well optimized so as to ensure the optimal path.
The method for cleaning a robot in the embodiment of the present invention is described above, and the following describes a road robot in the embodiment of the present invention, referring to fig. 14, an embodiment of the robot in the embodiment of the present invention includes:
a wall-along construction module 1401, configured to complete wall-along cleaning in a wall-along mode, construct an initial grid map, and obtain the cleaning path along the wall cleaning;
a second detection module 1402, configured to switch to a navigation mode, and detect whether a narrow area exists in a grid map between a current position of the robot and a target area to be cleaned, where the narrow area is a passable area in a wall-along mode and a passable area in a navigation mode;
The navigation construction module 1403 is configured to perform obstacle clearing processing on a narrow area when the narrow area is detected, and construct a navigation path from the current position to the target area by using a preset path construction algorithm based on the cleared narrow area;
a cleaning module 1404 for navigating the robot to the target area based on the navigation path to perform cleaning.
The function implementation of each module in the robot corresponds to each step in the embodiment of the cleaning method of the robot, and the robot is implemented by adopting the path planning method when implementing the construction of the navigation path, and the functions and implementation processes thereof are not repeated here.
In the embodiment of the invention, by defining the narrow area in the wall-following mode and carrying out obstacle elimination on the narrow area in the map between the current position and the target area based on the structural element constructed by the intelligent equipment, the intelligent equipment can also pass through the path area in the wall-following mode in the navigation mode, thereby overcoming the defect that the narrow area can not pass through the area in the wall-following mode, realizing the compatible mode of navigation and wall-following, and simultaneously well optimizing the planning of the path and ensuring the optimal path.
12-14 describe the path construction device and the robot in the embodiment of the present invention in detail from the point of view of modularized functional entities, and describe the intelligent device based on unit decomposition in the embodiment of the present invention in detail from the point of view of hardware processing.
Fig. 15 is a schematic structural diagram of a smart device according to an embodiment of the present invention, where the smart device 1100 may vary considerably in configuration or performance, and may include one or more processors (central processing units, CPU) 1110 (e.g., one or more processors) and memory 1120, one or more storage mediums 1130 (e.g., one or more mass storage devices) that store applications 1133 or data 1132. Wherein the memory 1120 and the storage medium 1130 may be transitory or persistent. The program stored on the storage medium 1130 may include one or more modules (not shown), each of which may include a series of instruction operations in the smart device 1100. Still further, the processor 1110 may be configured to communicate with a storage medium 1130, and to perform a series of instruction operations in the storage medium 1130 on the smart device 1100.
The smart device 1100 may also include one or more power supplies 1140, one or more wired or wireless network interfaces 1150, one or more input/output interfaces 1160, and/or one or more operating systems 1131, such as Windows Serve, mac OS X, unix, linux, freeBSD, and the like. It will be appreciated by those skilled in the art that the smart device architecture shown in fig. 13 is not limiting of the smart device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, or may be a volatile computer readable storage medium, in which a computer program is stored, which when run on a computer causes the computer to perform the steps of the path planning method or the robot cleaning method.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a storage medium, comprising a number of computer programs for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute all or part of the steps of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (11)
1. A path planning method, characterized in that the path planning method comprises:
acquiring a grid map of an area where the intelligent equipment is located, the current position of the intelligent equipment and a target area;
detecting whether a narrow area exists in a grid map between the current position and the target area or not when the intelligent equipment is in a navigation mode, wherein the narrow area is a passable area in a wall-along mode and a passable area in a navigation mode;
if so, adjusting the obstacle in the narrow area based on the structural element corresponding to the intelligent equipment, and updating the grid map based on the adjustment result, wherein the adjustment comprises deleting part or movement;
and planning a path from the current position to the target area by using a preset path construction algorithm according to the updated grid map, and outputting a navigation path.
2. The path planning method according to claim 1, characterized by further comprising, before the detecting whether there is a narrow area in a grid map between the current position and the target area:
judging whether the intelligent equipment finishes cleaning along a wall;
if so, acquiring the cleaning path along the wall;
And extracting a path region with obstacles around the position of the grid point in the cleaning path to obtain a narrow region set.
3. The path planning method according to claim 1, characterized by comprising, before the adjusting the obstacle in the narrow area based on the structural element corresponding to the smart device and updating the grid map based on the result of the adjusting:
acquiring the size parameters of the intelligent equipment, and constructing corresponding original structural elements based on the size parameters;
and performing angle removal treatment on the original structural element to obtain the structural element.
4. The path planning method according to claim 3, wherein the adjusting the obstacle in the narrow area based on the structural element corresponding to the smart device and updating the grid map based on the result of the adjustment comprises:
extracting a map of the narrow area;
traversing each grid point in the map of the narrow area by taking the structural element as an operator, and extracting the grid points with barriers;
and performing obstacle elimination processing on the extracted grid points, and updating the grid map based on the elimination result.
5. The path planning method according to claim 4, wherein traversing each grid point in the map of the narrow region with the structural element as an operator, extracting the grid point where the obstacle exists, comprises:
Filling each grid point in the map of the narrow area based on a wall-along mode by using the central position of the structural element, and marking to obtain a marked map;
identifying whether an obstacle exists in the structural element range of the grid point corresponding to each mark based on each mark in the mark map;
if so, marking the corresponding grid points in a passable way until all the grid points with barriers are output after the narrow area is traversed.
6. The route planning method according to any one of claims 1 to 5, wherein the planning the route from the current location to the target area using a preset route construction algorithm according to the updated grid map, and outputting a navigation route, includes:
determining the advancing direction of the intelligent device based on the current position and the coordinates of the target area;
searching all blank grid points meeting the jumping point conditions in the advancing direction, and in the horizontal component direction and the vertical component direction in the advancing direction by using the current position as a starting point and utilizing a jumping point searching algorithm;
and constructing a path based on the current position, the blank grid point and the target area, and outputting a navigation path.
7. A robotic cleaning method, the robotic cleaning method comprising:
the robot finishes the wall-along cleaning in a wall-along mode, and an initial grid map is constructed to obtain the cleaning path along which the wall is cleaned;
the method comprises the steps that a robot is switched to a navigation mode, and whether a narrow area exists in a grid map between the current position of the robot and a target area to be cleaned or not is detected, wherein the narrow area is a passable area in a wall-following mode and a passable area in a navigation mode;
if the obstacle clearance exists in the narrow area, obstacle clearance processing is carried out on the narrow area, and a navigation path from the current position to the target area is constructed by utilizing a preset path construction algorithm based on the narrow area after obstacle clearance;
and navigating the robot to reach the target area based on the navigation path to clean.
8. A path planning apparatus, characterized in that the path planning apparatus comprises:
the data acquisition module is used for acquiring a grid map of the area where the intelligent equipment is located, the current position of the intelligent equipment and a target area;
the first detection module is used for detecting whether a narrow area exists in the grid map between the current position and the target area when the intelligent equipment is in a navigation mode, wherein the narrow area is a passable area in a wall-along mode and a passable area in a navigation mode;
The area adjusting module is used for adjusting obstacles in a narrow area based on structural elements corresponding to the intelligent equipment when the narrow area is detected, and updating the grid map based on an adjustment result, wherein the adjustment comprises deleting part or movement;
and the path planning module is used for planning the path from the current position to the target area by using a preset path construction algorithm according to the updated grid map and outputting a navigation path.
9. A robot, the robot comprising:
the wall-along construction module is used for finishing wall-along cleaning in a wall-along mode, constructing an initial grid map and obtaining the cleaning path along which the wall is cleaned;
the second detection module is used for switching to a navigation mode and detecting whether a narrow area exists in a grid map between the current position of the robot and a target area to be cleaned, wherein the narrow area is a passable area in a wall-following mode and a passable area in a navigation mode;
the navigation construction module is used for carrying out obstacle clearance treatment on the narrow area when the narrow area is detected, and constructing a navigation path from the current position to the target area by utilizing a preset path construction algorithm based on the narrow area after obstacle clearance;
And the cleaning module is used for guiding the robot to reach the target area based on the navigation path to clean.
10. An intelligent device, the intelligent device comprising: a memory and at least one processor, the memory having a computer program stored therein;
the at least one processor invokes the computer program in the memory to cause the smart device to perform the steps of the path planning method of any one of claims 1-6 or the steps of the robotic cleaning method of claim 7.
11. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the path planning method according to any one of claims 1-6 or the steps of the robot cleaning method according to claim 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111631979.9A CN114397893B (en) | 2021-12-28 | 2021-12-28 | Path planning method, robot cleaning method and related equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111631979.9A CN114397893B (en) | 2021-12-28 | 2021-12-28 | Path planning method, robot cleaning method and related equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114397893A CN114397893A (en) | 2022-04-26 |
| CN114397893B true CN114397893B (en) | 2024-02-02 |
Family
ID=81228634
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111631979.9A Active CN114397893B (en) | 2021-12-28 | 2021-12-28 | Path planning method, robot cleaning method and related equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114397893B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117472067A (en) * | 2023-12-27 | 2024-01-30 | 江苏中科重德智能科技有限公司 | Robot narrow channel passing method and system based on multilayer grid map |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011150473A (en) * | 2010-01-20 | 2011-08-04 | Ihi Aerospace Co Ltd | Autonomous traveling object |
| CN107168344A (en) * | 2017-05-17 | 2017-09-15 | 哈尔滨工程大学 | A kind of UUV approaches air route generation method during seabed operation |
| CN107990903A (en) * | 2017-12-29 | 2018-05-04 | 东南大学 | A kind of indoor AGV paths planning methods based on improvement A* algorithms |
| CN108646765A (en) * | 2018-07-25 | 2018-10-12 | 齐鲁工业大学 | Based on the quadruped robot paths planning method and system for improving A* algorithms |
| CN108775902A (en) * | 2018-07-25 | 2018-11-09 | 齐鲁工业大学 | The adjoint robot path planning method and system virtually expanded based on barrier |
| JP2018185633A (en) * | 2017-04-25 | 2018-11-22 | トヨタ自動車株式会社 | Autonomous mobile body |
| CN109540155A (en) * | 2019-02-01 | 2019-03-29 | 西安全志科技有限公司 | A kind of path planning and navigation method, computer installation and the computer readable storage medium of sweeping robot |
| CN110702133A (en) * | 2019-09-29 | 2020-01-17 | 安克创新科技股份有限公司 | Path planning method, robot and device with storage function |
| CN110850871A (en) * | 2019-10-21 | 2020-02-28 | 深圳市银星智能科技股份有限公司 | Machine path planning method and mobile robot |
| CN111067440A (en) * | 2019-12-31 | 2020-04-28 | 深圳飞科机器人有限公司 | Cleaning robot control method and cleaning robot |
| CN112132929A (en) * | 2020-09-01 | 2020-12-25 | 北京布科思科技有限公司 | Grid map marking method based on depth vision and single line laser radar |
| CN112180931A (en) * | 2020-09-30 | 2021-01-05 | 小狗电器互联网科技（北京）股份有限公司 | Sweeping path planning method and device of sweeper and readable storage medium |
| CN112180924A (en) * | 2020-09-28 | 2021-01-05 | 珠海市一微半导体有限公司 | Movement control method for navigating to dense obstacles |
| WO2021008611A1 (en) * | 2019-07-18 | 2021-01-21 | 广东宝乐机器人股份有限公司 | Robot trapping detection and de-trapping method |
| CN113156970A (en) * | 2021-05-08 | 2021-07-23 | 珠海市一微半导体有限公司 | Path fusion planning method for passing area, robot and chip |
| CN113219975A (en) * | 2021-05-08 | 2021-08-06 | 珠海市一微半导体有限公司 | Route optimization method, route planning method, chip and robot |
| CN113693493A (en) * | 2021-02-10 | 2021-11-26 | 北京石头世纪科技股份有限公司 | Regional map drawing method and device, medium and electronic equipment |
| CN113791617A (en) * | 2021-08-31 | 2021-12-14 | 金华市浙工大创新联合研究院 | Global path planning method based on physical dimension of fire-fighting robot |
-
2021
- 2021-12-28 CN CN202111631979.9A patent/CN114397893B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011150473A (en) * | 2010-01-20 | 2011-08-04 | Ihi Aerospace Co Ltd | Autonomous traveling object |
| JP2018185633A (en) * | 2017-04-25 | 2018-11-22 | トヨタ自動車株式会社 | Autonomous mobile body |
| CN107168344A (en) * | 2017-05-17 | 2017-09-15 | 哈尔滨工程大学 | A kind of UUV approaches air route generation method during seabed operation |
| CN107990903A (en) * | 2017-12-29 | 2018-05-04 | 东南大学 | A kind of indoor AGV paths planning methods based on improvement A* algorithms |
| CN108646765A (en) * | 2018-07-25 | 2018-10-12 | 齐鲁工业大学 | Based on the quadruped robot paths planning method and system for improving A* algorithms |
| CN108775902A (en) * | 2018-07-25 | 2018-11-09 | 齐鲁工业大学 | The adjoint robot path planning method and system virtually expanded based on barrier |
| CN109540155A (en) * | 2019-02-01 | 2019-03-29 | 西安全志科技有限公司 | A kind of path planning and navigation method, computer installation and the computer readable storage medium of sweeping robot |
| WO2021008611A1 (en) * | 2019-07-18 | 2021-01-21 | 广东宝乐机器人股份有限公司 | Robot trapping detection and de-trapping method |
| CN110702133A (en) * | 2019-09-29 | 2020-01-17 | 安克创新科技股份有限公司 | Path planning method, robot and device with storage function |
| CN110850871A (en) * | 2019-10-21 | 2020-02-28 | 深圳市银星智能科技股份有限公司 | Machine path planning method and mobile robot |
| CN111067440A (en) * | 2019-12-31 | 2020-04-28 | 深圳飞科机器人有限公司 | Cleaning robot control method and cleaning robot |
| CN112132929A (en) * | 2020-09-01 | 2020-12-25 | 北京布科思科技有限公司 | Grid map marking method based on depth vision and single line laser radar |
| CN112180924A (en) * | 2020-09-28 | 2021-01-05 | 珠海市一微半导体有限公司 | Movement control method for navigating to dense obstacles |
| CN112180931A (en) * | 2020-09-30 | 2021-01-05 | 小狗电器互联网科技（北京）股份有限公司 | Sweeping path planning method and device of sweeper and readable storage medium |
| CN113693493A (en) * | 2021-02-10 | 2021-11-26 | 北京石头世纪科技股份有限公司 | Regional map drawing method and device, medium and electronic equipment |
| CN113156970A (en) * | 2021-05-08 | 2021-07-23 | 珠海市一微半导体有限公司 | Path fusion planning method for passing area, robot and chip |
| CN113219975A (en) * | 2021-05-08 | 2021-08-06 | 珠海市一微半导体有限公司 | Route optimization method, route planning method, chip and robot |
| CN113791617A (en) * | 2021-08-31 | 2021-12-14 | 金华市浙工大创新联合研究院 | Global path planning method based on physical dimension of fire-fighting robot |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114397893A (en) | 2022-04-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP5402057B2 (en) | Mobile robot control system, route search method, route search program | |
| CN109240312B (en) | Cleaning control method and chip of robot and cleaning robot | |
| WO2019190395A1 (en) | Method and system for returning a displaced autonomous mobile robot to its navigational path | |
| CN112137529B (en) | Cleaning control method based on dense obstacles | |
| EP2870513B1 (en) | Autonomous mobile robot and method for operating the same | |
| JP7220285B2 (en) | route planning | |
| US9599987B2 (en) | Autonomous mobile robot and method for operating the same | |
| CN113156956B (en) | Navigation method and chip of robot and robot | |
| JP2010061293A (en) | Route searching device, route searching method, and route searching program | |
| CN108189039B (en) | Moving method and device of mobile robot | |
| CN113190010B (en) | Edge obstacle detouring path planning method, chip and robot | |
| JP2009053849A (en) | Path search system, path search method, and autonomous traveling body | |
| CN114397893B (en) | Path planning method, robot cleaning method and related equipment | |
| CN114343490B (en) | Robot cleaning method, robot, and storage medium | |
| CN112180924B (en) | Mobile control method for navigating to dense obstacle | |
| CN115008465A (en) | Robot control method, robot, and computer-readable storage medium | |
| CN114995458A (en) | Full-coverage real-time path planning method and device for cleaning robot | |
| CN114690753A (en) | Hybrid strategy-based path planning method, autonomous traveling equipment and robot | |
| KR102230362B1 (en) | Cleaning Robot Apparatus Using Rectangular Map Decomposition and Method for Planning Coverage Path Using the Same | |
| CN112033423B (en) | Robot path planning method and device based on road consensus and robot | |
| JP2022034861A (en) | Forklift, location estimation method, and program | |
| CN108363391B (en) | Robot and control method thereof | |
| CN114397889B (en) | Full-coverage path planning method based on unit decomposition and related equipment | |
| CN116149314A (en) | Robot full-coverage operation method and device and robot | |
| CN113485378B (en) | Mobile robot path planning method, system and storage medium based on traffic rules |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 518110 1701, building 2, Yinxing Zhijie, No. 1301-72, sightseeing Road, Xinlan community, Guanlan street, Longhua District, Shenzhen, Guangdong Province Applicant after: Shenzhen Yinxing Intelligent Group Co.,Ltd. Address before: 518110 Building A1, Yinxing Hi-tech Industrial Park, Guanlan Street Sightseeing Road, Longhua District, Shenzhen City, Guangdong Province Applicant before: Shenzhen Silver Star Intelligent Technology Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |