WO2022246750A1 - 轨迹生成方法、装置、可移动平台及存储介质 - Google Patents

轨迹生成方法、装置、可移动平台及存储介质 Download PDF

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Publication number
WO2022246750A1
WO2022246750A1 PCT/CN2021/096468 CN2021096468W WO2022246750A1 WO 2022246750 A1 WO2022246750 A1 WO 2022246750A1 CN 2021096468 W CN2021096468 W CN 2021096468W WO 2022246750 A1 WO2022246750 A1 WO 2022246750A1
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trajectory
leaf node
safety
target
point
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French (fr)
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高飞
纪佳林
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SZ DJI Technology Co Ltd
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SZ DJI Technology Co Ltd
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    • 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

Definitions

  • the present application relates to the technical field of path planning, and in particular, to a trajectory generation method, device, movable platform, and storage medium.
  • the mobile platform When the mobile platform performs a task, it must first plan a safe passage.
  • the planned safe passage is enough to avoid all obstacles, and then generate a guide on the basis of this known safe passage to guide the movement of the mobile platform. traces of.
  • the safety channel is composed of a plurality of safety areas.
  • a trajectory expressed by a section of Bezier curve is correspondingly generated in each safety area in the safety channel, and then by solving the QCQP (Quadratically constrained quadratic program, quadratic constrained quadratic programming) to optimize the trajectory expressed by the Bezier curve.
  • the optimization dimension will increase accordingly, which requires more computing resources and has low optimization efficiency.
  • one of the objectives of the present application is to provide a trajectory generation method, device, movable platform and storage medium.
  • the embodiment of the present application provides a trajectory generation method, including:
  • the embodiment of the present application provides a trajectory generation device, including:
  • processors one or more processors
  • the one or more processors execute the executable instructions, they are individually or jointly configured to:
  • the embodiment of the present application provides a mobile platform, including:
  • a power system arranged in the fuselage, for driving the movement of the movable platform
  • an embodiment of the present application provides a computer-readable storage medium, the computer-readable storage medium stores executable instructions, and when the executable instructions are executed by a processor, the method as described in the first aspect is implemented .
  • the trajectory generation method device, movable platform and storage medium provided in the embodiments of the present application, after a safe passage without obstacles is determined, the currently generated trajectory is checked for the trajectory segment outside the safe passage, Then generate a target trajectory point in the safe passage according to the trajectory segment, and optimize the trajectory by using the target trajectory point until the optimized trajectory is in the safe passage.
  • trajectories the number of trajectory segments outside the safe passage gradually decreases until all optimized trajectories are within the safe passage.
  • the trajectory generation method provided by this embodiment has nothing to do with the number of safe areas in the safe passage. Optimizing the trajectory corresponding to the safe region in the safe area, but using the inserted target trajectory points to optimize the overall trajectory can effectively reduce the trajectory optimization dimension and improve the trajectory optimization efficiency.
  • Figure 1 is a schematic diagram of a safe channel provided by an embodiment of the present application.
  • Fig. 2 is a schematic diagram of generating a trajectory in a safe passage provided by an embodiment of the present application
  • Fig. 3 is a schematic flowchart of a trajectory generation method provided by an embodiment of the present application.
  • Fig. 4 is a schematic diagram of a safety channel generated within the historical perception range of a depth sensor provided by an embodiment of the present application
  • Fig. 5 is a schematic diagram of a reliable area provided by an embodiment of the present application.
  • Fig. 6 is a schematic diagram of the growth of a path search tree provided by an embodiment of the present application.
  • FIG. 7 is a schematic diagram of determining a leaf node safety factor provided by an embodiment of the present application.
  • Fig. 8 is a schematic diagram of scoring a path search tree provided by an embodiment of the present application.
  • Fig. 9 is a schematic diagram of a path search tree pruning provided by an embodiment of the present application.
  • FIG. 10 is a schematic diagram of determining a safe channel based on a path search tree provided by an embodiment of the present application.
  • Fig. 11 is a schematic diagram of a trajectory optimization provided by an embodiment of the present application.
  • FIG. 12 is a schematic diagram of the quantitative relationship between target trajectory points and safe areas in a safe passage provided by an embodiment of the present application.
  • Fig. 13 is a schematic structural diagram of a trajectory generation device provided by an embodiment of the present application.
  • Fig. 14 is a schematic structural diagram of a mobile platform provided by an embodiment of the present application.
  • depth sensors such as depth cameras or laser radars
  • Depth information is the information necessary to ensure maneuver safety when mobile platforms (such as UAVs, mobile robots, etc.) perform tasks.
  • the movable platform maintains depth information about obstacles in the current detection environment collected by the depth sensor, so that a safe passage that can avoid obstacles can be subsequently determined based on the depth information maintained by the movable platform.
  • the depth information includes, but is not limited to, a grayscale image corresponding to a depth value, a color image corresponding to a depth value, a depth image or a point cloud frame.
  • the mobile platform will establish a unified spatial map representing the obstacles in the detection environment based on the above-mentioned depth information about obstacles, such as a three-dimensional grid map, an octree map or a European map.
  • the depth information of each frame collected by the depth sensor on the movable platform and the existing obstacle information on the map are reasonably calculated and fused to obtain the map obstacle distribution in the sense of maximum likelihood probability.
  • the obstacle query interface of the map can be called to obtain whether the queried spatial point is occupied by obstacles, so as to determine the safe passage that can avoid obstacles.
  • the movable platform can project the depth information of each frame collected by the depth sensor into a three-dimensional space to obtain a three-dimensional point cloud about obstacles, and store the three-dimensional point cloud data in a tree-like data structure , such as building a k-d tree or R tree based on the 3D point cloud of each frame, and providing an interface for nearest neighbor query, and then querying the obstacles closest to the spatial point to be measured through the provided interface, and determining the distance between the spatial point to be measured and The distance to the nearest obstacle is used to determine the safe passage that can avoid the obstacle.
  • the mobile platform uses the above-mentioned spatial map or tree data structure to detect the nearest neighbor obstacle, so as to obtain the multiple circular security Area composed of safe passages.
  • a trajectory expressed by a Bezier curve is correspondingly generated in each circular safety area in the safety channel, and then by solving the QCQP (Quadratically constrained quadratic program, quadratically constrained quadratic program, quadratically constrained quadratic program ) to optimize the trajectory expressed by the Bezier curve.
  • the optimization dimension will increase accordingly, which requires more computing resources and has low optimization efficiency.
  • the embodiment of the present application provides a trajectory generation method, after determining an obstacle-free safe passage, check the trajectory segments in the currently generated trajectory that are outside the safe passage, and then according to The trajectory segment generates a target trajectory point in the safety passage, and the trajectory is optimized using the target trajectory point until the optimized trajectory is in the safety passage.
  • trajectories the number of trajectory segments outside the safe passage gradually decreases until all optimized trajectories are within the safe passage.
  • the trajectory generation method provided by this embodiment has nothing to do with the number of safe areas in the safe passage. Optimizing the trajectory corresponding to the safe region in the safe area, but using the inserted target trajectory points to optimize the overall trajectory can effectively reduce the trajectory optimization dimension and improve the trajectory optimization efficiency.
  • the trajectory generation method provided in the embodiment of the present application can be applied to a trajectory generation device.
  • the trajectory generation device may be a mobile platform with data processing capability.
  • the mobile platform include but are not limited to unmanned aerial vehicle, unmanned vehicle, cloud platform, unmanned boat, mobile robot, sweeping robot, industrial mechanical arm or logistics transportation robot, etc.
  • the trajectory generation device may be a computer software product integrated in the mobile platform, and the computer software product includes an application program capable of executing the trajectory generation method provided by the embodiment of the present application.
  • the trajectory generation device may be a movable platform including at least a memory and a processor, and the processor in the movable platform may execute the program stored in the memory indicating the trajectory generation method provided by the embodiment of the present application. Executable instructions.
  • the trajectory generation device can also be a chip or an integrated circuit with data processing capabilities, and the trajectory generation device includes but is not limited to, for example, a central processing unit (Central Processing Unit, CPU), a digital signal processor (Digital Signal Processor) Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA), etc.
  • the obstacle information acquisition device can be installed in electronic equipment.
  • the unmanned aerial vehicle at least includes a fuselage, a power system installed in the fuselage, and a flight controller; the power system is used to drive the The unmanned aerial vehicle moves, and the flight controller is used to control the flight of the unmanned aerial vehicle; wherein, the trajectory generating device can be a flight controller in the unmanned aerial vehicle, that is, the flight controller in the unmanned aerial vehicle can Execute the trajectory generation method provided by the embodiment of the present application.
  • the trajectory generation method provided in this application can be applied in the fields of electric power inspection, fire rescue or security protection.
  • the movable platform can be controlled to move according to the trajectory, so as to realize The intelligent search and inspection process significantly reduces labor costs and improves inspection efficiency.
  • an unmanned aerial vehicle is used as an example for illustration: the unmanned aerial vehicle is equipped with a depth sensor, and the depth sensor includes but is not limited to a lidar or an RGBD camera.
  • the terminal waypoint to be reached by this trajectory generation task is determined under the operation of the user, and the current position is used as the starting waypoint.
  • the UAV needs to generate A safe trajectory avoiding obstacles.
  • the unmanned aerial vehicle can use its on-board depth sensor to collect depth information about obstacles, and then maintain the depth through a unified spatial map or tree-like data structure (such as k-d tree) information, then sample space points within the sensing range of the depth sensor, use the above-mentioned space map or tree data structure to perform nearest neighbor obstacle detection on the space point, determine the safe space around the space point, and pass several
  • the sampling of space points and the query of the surrounding safe space determine the obstacle-free flight corridor between the starting waypoint and the ending waypoint (in the field of unmanned aerial vehicles, the safe passage is called the flight corridor).
  • the trajectory generation method can be used. After determining an obstacle-free flight corridor, check the trajectory segments in the currently generated trajectory that are outside the flight corridor. The currently generated trajectory is based on the The starting waypoint and the ending waypoint are determined, or the currently generated trajectory is determined based on the starting waypoint, the ending waypoint and the target waypoint generated in the flight corridor last time; then the none The manned aircraft generates a target waypoint in the flight corridor according to the trajectory segment, uses the target waypoint to optimize the trajectory until the optimized trajectory is in the flight corridor, and finally generates a flight path from the initial waypoint A safe trajectory that avoids obstacles until the waypoint is terminated. Further, the trajectory generated by the embodiment of the present application can be applied in the fields of power inspection, fire rescue or security. For example, it can control the unmanned aerial vehicle to fly according to the generated trajectory. In this way, an intelligent search and inspection process is realized, which significantly reduces labor costs and improves inspection efficiency.
  • FIG. 3 is a schematic flow chart of a trajectory generation method provided by the embodiment of the present application.
  • the method can be executed by a trajectory generation device. Methods include:
  • step S101 a safe passage without obstacles is determined.
  • step S102 check the currently generated trajectory segments that are outside the safe channel.
  • step S103 a target trajectory point is generated in the safe channel according to the trajectory segment.
  • step S104 the trajectory is optimized using the target trajectory point until the optimized trajectory is within the safe channel.
  • the safe passage may be a passage determined by the device between the starting track point and the ending track point, which can avoid obstacles.
  • the starting track point and the ending track point can be specifically set according to the actual application scenario, and this embodiment does not impose any restrictions on this; for example, in the power inspection scene, the starting track can be determined according to the distribution of electric wires point and end track point; or in a security scene, the start track point and end track point can be determined according to the distribution of the area to be inspected; or the start track point and end track point can also be user Specify according to the actual situation.
  • the safe channel is obtained based on path search between the starting track point and the ending track point.
  • the device uses the starting track point as a root node to determine a path search tree extending to the end track point, and then determines a target leaf node from all leaf nodes in the path search tree, and finally according to A secure path from the target leaf node to the root node determines the secure channel.
  • the path search tree can be a forward spanning tree, RRT (rapidly-exploring random tree , fast search random tree) tree, RRT* tree or FMT (fast marching tree, fast marching number) tree, etc.
  • the acquisition process of the non-root node in the path search tree is described: taking the device installed on a movable platform as an example, and the movable platform is also equipped with a depth sensor.
  • the non-root nodes in the path search tree are spatial points indicating non-obstacles sampled by the device within the current perception range of the depth sensor.
  • the device can obtain the sampling result within the historical perception range of the depth sensor; then estimate the current perception range of the depth sensor according to the sampling result The reliable area of , and then sample the spatial point in the reliable area.
  • prior information is used to guide the sampling process within the current perception range, which is conducive to improving the validity of the sampled spatial points and further improving the trajectory generation efficiency.
  • the device can The spatial points are uniformly sampled within the current sensing range of the sensor.
  • the reliable area within the current sensing range of the depth sensor is determined according to the safe channel generated by the sampling result.
  • the movable platform needs to effectively detect the environment between the starting track point and the ending track point, and obtain obstacle information through a depth sensor.
  • the obstacle information includes The depth information of the depth sensor is used to collect the depth information of the obstacle within the sensing range of the depth sensor in the environment, and the depth information represents the relative distance between the obstacle within the current sensing range and the movable platform.
  • the movable platform gradually moves from the starting track point to the ending track point. With the movement of the movable platform, the environmental content within the sensing range of the depth sensor also gradually changes, thereby achieving the acquisition of the starting track point. Obstacle information of the environment between the start track point and the end track point.
  • the device Within each sensing range of the depth sensor, the device performs the following steps: the device samples a plurality of spatial points indicating non-obstacles within the current sensing range of the depth sensor according to the obstacle information collected by the depth sensor ; Then generate a path search tree extending to the end trajectory point according to the starting trajectory point and the plurality of space points, determine the safe passage according to the path search tree, and then use the trajectory generation method provided by the embodiment of the present application Generate a trajectory within the safe passage.
  • the device can To determine a reliable area within the current sensing range, wherein the historical sensing range overlaps with the current sensing range, so as to ensure that part or all of the environmental space where the safe passage corresponding to the historical sensing range is located is also within the current sensing range of the depth sensor, so that the device can effectively determine the reliable area based on the safe passage corresponding to the historical sensing range.
  • the reliable area includes at least the safe passage corresponding to the historical sensing range environment space.
  • the device may effectively determine the reliable area according to the safe channel corresponding to one or more historical sensing ranges, and the one or more historical sensing ranges and the current sensing range all have overlapping areas.
  • the device can determine the reliable area as shown in Figure 5 according to the environmental space where the safe passage is located as shown in Figure 4, and the safety probability of the reliable area is compared with other areas The security probability of is higher, where the historical perception range FoV(t) and the current perception range FoV(t+1) have an overlapping area.
  • the device may sample spatial points at a high sampling rate in the reliable area, And, sampling spatial points at a low sampling rate in the unreliable area of the current perception range.
  • the spatial point sampling process can be guided according to prior information, effectively improving the validity of the sampled spatial points, improving sampling efficiency and trajectory generation efficiency, and because the reliable area is based on the historical perception range
  • the safety channel generated by the sampling results is determined, which indirectly improves the overlapping range of the trajectory generated in the current perception range and the trajectory generated in the historical perception range, thereby improving the consistency with the perception information/historical planning trajectory.
  • the device After sampling the space point within the current sensing range of the depth sensor, the device also needs to check the safety of the space point.
  • the depth sensor is used to collect the depth information of obstacles within the sensing range of the depth sensor in the environment, and the depth information represents the relative distance between the obstacle within the current sensing range and the movable platform, and then the The device will maintain the depth information of obstacles collected by the depth sensor in the current sensing range and/or the historical sensing range; as an example, the current sensing range and/or the
  • the depth information of obstacles collected in the historical perception range constructs a unified spatial map such as a grid map; as an example, a tree-like data structure can be used to store the depth information of obstacles collected in each perception range.
  • the depth information of obstacles collected within the perception range is projected into a three-dimensional space to obtain a three-dimensional point cloud, and a k-d tree or R tree is established using the three-dimensional point cloud.
  • the device can determine the distance between the spatial point and the nearest The distance between adjacent obstacles realizes the safety inspection of the space point; wherein, the obstacle information includes the depth information about obstacles collected by the depth sensor within its sensing range, exemplary , the obstacle information also includes k-d tree, R tree and/or grid map constructed according to the depth information; and then the device can reserve the distance between the spatial point and the nearest neighbor obstacle greater than The spatial point of the preset distance threshold, wherein the preset distance threshold can be specifically set according to the actual application scenario, and this embodiment does not make any restrictions on this, for example, the preset distance threshold can be set according to the movable platform Size OK.
  • the spatial point may be added to the path search tree as a node of the path search tree.
  • the spatial points can be added to the path search tree according to the distance from the root node from near to far, so as to quickly obtain the Path search tree.
  • the spatial points may also be randomly added to the path search tree, so as to obtain a path search tree extending toward the termination track point.
  • the generated path search tree may appear as shown in FIG. 6 , that is, the leaf nodes of the path search tree grow toward obstacles, resulting in short-sighted appearance of local planning and falling into a dead end. Therefore, in order to improve the safety of the subsequently determined safety channel, after determining the path search tree extending to the termination track point, the device needs to perform pruning processing on the path search tree, and the scene shown in Figure 6 will appear The leaf nodes or branches are cut off.
  • the device determines the safety factor of each leaf node according to the relationship between each leaf node and the nearest neighbor obstacle in the path search tree; and then traverses the path search tree starting from each leaf node, Determine the safety factor of each non-leaf node; finally, perform pruning processing on the path search tree according to the safety factor of each node in the path search tree.
  • pruning is performed according to the result of the security assessment (i.e., the safety factor), so that most useless nodes can be quickly cut off, leaving a real feasible area for expressing the environment branches.
  • the nearest neighbor obstacle of each leaf node is obtained from pre-stored obstacle information based on the location of the leaf node; the obstacle information is obtained by the device in the current sensing range of the depth sensor and/or Obtained within the historical sensing range; the obstacle information includes depth information about obstacles collected by the depth sensor within its sensing range.
  • the obstacle information also includes Constructed k-d trees, R trees and/or raster maps.
  • the device After determining the nearest neighbor obstacle of each leaf node, the device can determine the safety factor of each leaf node in the following manner:
  • the device determines the direction of the leaf node towards the nearest neighbor obstacle, and then according to the angle between the direction and the growth direction of the leaf node , to determine the safety factor of the leaf node; wherein, the direction of the leaf node towards the nearest neighbor obstacle may be the direction of the perpendicular (or the shortest line, etc.) between the leaf node and the obstacle;
  • the growth direction of the leaf node is the direction of the connecting line between the leaf node and its parent node.
  • the safety factor of the leaf node is positively correlated with the included angle, please refer to Fig. 7, the larger the included angle ⁇ , it means that if the leaf node continues to grow, the growth direction of the branch will be different from the distance of the nearest neighbor obstacle. If the collision probability is low, the security of the leaf node is higher, and the corresponding safety factor is larger; on the contrary, the smaller the angle ⁇ is, it means that if the leaf node continues to grow, the growth direction of the branch is the same as that of the nearest neighbor. The higher the collision probability of obstacles, the lower the safety of the leaf node, and the smaller the corresponding safety factor.
  • the included angle threshold determines that the safety factor of the leaf node is a first preset value; otherwise, at the included angle If it is not greater than the preset angle threshold, it is determined that the safety factor of the leaf node is a second preset value, and the first preset value is greater than the second preset value.
  • the preset angle threshold, the first preset value and the second preset value can be specifically set according to the actual application scenario, and this embodiment does not make any limitation thereto; as an example, the preset angle The threshold value is greater than 90°, the first preset value is 1, and the second preset value is 0.
  • the device may determine the safety factor of the leaf node according to the distance between the leaf node and the nearest neighbor obstacle.
  • the safety factor of the leaf node is positively correlated with the distance, that is, the farther the distance is, the higher the safety of the leaf node is, and the closer the distance is, the lower the safety factor of the leaf node is.
  • the device may traverse the path search tree starting from each leaf node, and determine the safety factor of each non-leaf node.
  • the safety factor of the non-leaf node is the sum of the safety factors of its child nodes.
  • the safety factor of each leaf node is 0 or 1
  • the safety factor of each non-leaf node is the sum of the safety factors of its child nodes.
  • the device may perform pruning processing on the path search tree according to the safety factor of each node in the path search tree; Nodes in the path search tree whose safety factor is lower than a preset threshold are pruned.
  • the preset threshold can be specifically set according to actual application scenarios. In this embodiment, considering that if the nodes whose safety factor is lower than the preset threshold continue to be generated, the growth direction of the branch will have a higher collision probability with the nearest neighbor obstacle, so the nodes whose safety factor is lower than the preset threshold are cut off, leaving The branches that really express the feasible area of the environment are lowered, which is conducive to ensuring the accuracy of the subsequent safe passage.
  • the safety factor of each node in the path search tree can be normalized, and the The safety factor of each node in the path search tree is mapped to a preset range; exemplary, the normalization process includes: for each node, comparing its own safety factor with the maximum safety factor among nodes belonging to the same parent node The ratio between was determined as the normalized value.
  • the device may perform pruning processing on nodes in the path search tree whose safety factor is lower than a preset threshold.
  • the preset threshold can be specifically set according to actual application scenarios.
  • Figure 9 is the result of normalizing the path search tree shown in Figure 8.
  • a node with a safety factor of 0.25 its normalized safety factor of 0.25 is itself The ratio of the original safety factor of 1 to a safety factor of 4, the node with a safety factor of 4 belongs to the same parent node with the node and has the largest safety factor; and after the normalization process, the device can search the tree for the path Nodes whose middle safety factor is lower than the preset threshold (for example, lower than 0.3) are pruned, and the path search tree shown in Figure 9 is obtained, where the dotted line part is the useless node or branch cut off this time, thus leaving Branches that really express the feasible region of the environment.
  • the preset threshold for example, lower than 0.3
  • the device After determining the path search tree, the device determines a target leaf node from all leaf nodes in the path search tree; and then determines the safe channel according to a safe path from the target leaf node to the root node.
  • this embodiment does not impose any restrictions on the selection of the target leaf node, and specific settings can be made according to the actual application scenario; for example, in order to extend to the end track point more quickly, all leaf nodes can be Select the leaf node with the shortest distance from the termination track point as the target leaf node, that is, the distance between the target leaf node and the termination track point is the shortest; of course, the target node can also be selected based on other strategies, For example, the distance from the target node to the nearest neighbor obstacle is the farthest.
  • the device may generate multiple security areas on the security path, and then acquire a security channel composed of the multiple security areas.
  • the center points of the multiple security areas are all in the security path
  • the center point of one security area is the target leaf node
  • the center point of other security areas is the previous security area
  • the intersection with the safety path, and the size of each safety area is determined according to the distance between a part of the safety path corresponding to the safety area and the nearest neighbor obstacle, so as to obtain the multi-safety path as shown in Figure 10.
  • the safety area may be a circle, or other shapes, such as a rectangle, which is not limited in this embodiment.
  • the device checks the currently generated trajectory for the trajectory segment outside the safe passage; wherein, in the first optimization process, the current The generated trajectory is determined based on the initial trajectory point and the termination trajectory point; in the non-first optimization process, the currently generated trajectory is based on the initial trajectory point, the termination trajectory point and the last time in the safe channel
  • the target trajectory point generated within is determined; then the device can generate a target trajectory point in the safe passage according to the trajectory segment, and use the target trajectory point to optimize the trajectory until the optimized trajectory is in the safe passage Inside.
  • the trajectory generation method provided by this embodiment has nothing to do with the number of safety areas in the safety channel. It is not necessary to optimize the trajectory corresponding to the safety area in each safety area, but to use the inserted target trajectory points to optimize the overall trajectory, which can effectively reduce Trajectory optimization dimension to improve trajectory optimization efficiency.
  • the device determines the spatial position of the safe passage and the spatial position of the currently generated trajectory, and determines the position of the currently generated trajectory according to the non-overlapping part of the two spatial positions. Trajectory segments that are outside the safe passage.
  • the device may determine the target area in the safety passage according to the trajectory segment, for example, two intersection points of the trajectory segment and the safety passage may be respectively determined according to boundaries, and then determine the area within the two boundaries in the safe channel as the target area, and then generate target track points in the target area;
  • the generation of target trajectory points in the relevant target area is beneficial to improve the speed of trajectory optimization; and it is not necessary to generate target trajectory points in the entire safety channel, which is also conducive to improving the efficiency of target trajectory point generation.
  • the target trajectory point may be generated at the farthest distance from the trajectory segment in the target area, more specifically, the edge of the target area close to the trajectory segment In the position, the target trajectory point is generated at the farthest distance from the trajectory segment, so that the optimized trajectory based on the target trajectory point can be guaranteed to be within the safe channel as much as possible;
  • the target track point is generated at a random position in the area.
  • the device may generate a plurality of candidate trajectory points in the safe channel, and then, according to the distance between the candidate trajectory points and the trajectory segment, select Select the target track point; Exemplarily, the distance between the target track point and the track segment is the farthest; Exemplarily, the candidate track point with the second farthest distance from the track segment may also be selected as the target track point.
  • the target trajectory point is selected based on the distance between the candidate trajectory point and the trajectory segment, which is beneficial to improve the efficiency of trajectory optimization.
  • the safety channel is composed of a plurality of safety areas
  • the device may generate one or more candidate waypoints in the intersection area of the plurality of safety areas.
  • the candidate trajectory point is located at the edge position of the intersection area close to the side of the trajectory segment, so that on the premise of ensuring that the optimized trajectory is within the safe channel, the optimized trajectory is the same as the pre-optimized trajectory. The difference in trajectory is smaller.
  • the safety channel is composed of multiple safety areas
  • the device can determine the target safety area from the multiple safety areas according to the intersection position of the trajectory segment and the safety channel, For example, according to the positions of the two intersection points of the trajectory segment and the safety channel, two intersecting target safety areas can be determined respectively, and then the safety area between the two target safety areas can also be determined as the target safety area ; Then, the device generates a plurality of candidate track points within the target safety area; Exemplarily, the candidate track points are located at an edge position close to the side of the track segment in the intersection area of the target safety area.
  • choosing to generate candidate trajectory points in the target safety area related to the trajectory segment is conducive to improving the trajectory optimization speed; and it is not necessary to generate candidate trajectory points in the entire safe passage, which is also conducive to improving the generation of target trajectory points. efficiency.
  • the device uses a dynamic interpolation method to perform trajectory optimization.
  • the trajectory generated by the starting trajectory point and the ending trajectory point is obtained. , that is, the dotted line part in the safety passage in the upper part of Figure 11, and then check the trajectory segment outside the safety passage in the trajectory, according to the intersection position of the trajectory segment and the safety passage, from multiple Determining a target safe area in a safe area, and then generating a plurality of candidate track points in the target safe area, the candidate track points are located at the edge positions near the side of the track segment in the intersecting area of the target safe area, According to the distance between the candidate track point and the track segment, select the candidate track point with the farthest distance from the plurality of candidate track points as the target track point, and then the device uses the target track point to optimize the According to the above trajectory, the optimized trajectory of the solid line part in the upper part of the safety passage as shown in Figure 11 is obtained.
  • the device determines the target safety area from a plurality of safety regions according to the intersection position of the trajectory segment and the safety passage, and then A plurality of candidate trajectory points are generated in the target safety area, and the candidate trajectory points are located at an edge position close to the side of the trajectory segment in the intersection area of the target safety area, according to the candidate trajectory points and the trajectory segment distance, select the candidate track point with the farthest distance as the target track point from the plurality of candidate track points, then the device uses the target track point to optimize the track, and obtain the following as shown in Figure 11 Half of the optimized trajectories of the solid-line part of the safety passage, check that all of the optimized trajectories are in the safety passage, and end.
  • FIG. 12 shows the change of the number of target trajectory points that need to be generated as the number of safe areas in the safe passage increases.
  • the safe passage The number of safe areas in the system is generally about 10.
  • the trajectory generation method provided by the embodiment of the present application is aimed at the same number of safe areas (such as 10 safe areas) only need to insert 1 or 2 target trajectory points to cover most of the scenarios, which effectively improves the efficiency of trajectory generation and optimization.
  • the embodiment of the present application also provides a trajectory generation device 200, including:
  • memory 202 for storing executable instructions
  • processors 201 one or more processors 201;
  • processors 201 when the one or more processors 201 execute the executable instructions, they are individually or jointly configured to:
  • the processor 201 is further configured to: determine an obstacle-free safe passage between the starting track point and the ending track point.
  • the currently generated track is determined based on the start track point and the end track point; or, the currently generated track is determined based on the start track point, the end track point and the above A target trajectory point determination generated within the safety channel.
  • the processor 201 is further configured to: generate a plurality of candidate trajectory points in the safe channel; Select the target track point in .
  • the distance between the target track point and the track segment is the farthest.
  • the safe channel is composed of multiple safe areas.
  • the processor 201 is further configured to: generate a plurality of candidate waypoints in the intersecting areas of the plurality of safety areas.
  • the candidate track point is located at an edge position on a side of the intersection area close to the track segment.
  • the safe channel is composed of multiple safe areas.
  • the processor 201 is further configured to: determine a target safe area from multiple safe areas according to the intersection position of the track segment and the safe passage; generate multiple candidate track points in the target safe area.
  • the candidate track point is located at an edge position close to the side of the track segment in the intersecting area of the target safety area.
  • the processor 201 is further configured to: determine a target area in the safe passage according to the track segment, and generate target track points in the target area; wherein, the boundary of the target area It is determined according to the intersection position of the trajectory segment and the safety channel.
  • the processor 201 is further configured to: generate a target track point at a place farthest from the track segment in the target area.
  • the safety channel is obtained based on path search between the starting track point and the ending track point.
  • the processor 201 is further configured to: use the starting track point as a root node to determine a path search tree extending to the end track point; from all leaf nodes of the path search tree Determine the target leaf node; determine the security channel according to the security path from the target leaf node to the root node.
  • the distance between the target leaf node and the termination track point is the shortest.
  • the non-root nodes in the path search tree are spatial points indicating non-obstacles sampled within the current sensing range of the depth sensor.
  • the processor 201 when determining the spatial point, is further configured to: obtain a sampling result within the historical sensing range of the depth sensor; estimate the current sensing range according to the sampling result A reliable region within ; the spatial point is sampled within the reliable region.
  • the processor 201 is further configured to: sample spatial points at a high sampling rate in the reliable region, and sample spatial points at a low sampling rate in an unreliable region of the current perception range.
  • the reliable region is determined according to a safe channel generated from the sampling result.
  • the historical sensing range and the current sensing range have an overlapping area.
  • the processor 201 is further configured to: according to the obstacle information obtained in the current sensing range and/or the historical sensing range, Determine the distance between the spatial point and the nearest neighbor obstacle; retain the spatial point whose distance is greater than a preset distance threshold.
  • the spatial points are added to the path search tree in descending order of distance from the root node.
  • the processor 201 is further configured to: search the path according to the distance between each leaf node and the nearest neighbor obstacle in the path search tree. Relationship, determine the safety factor of each leaf node; traverse the path search tree from each leaf node, determine the safety factor of each non-leaf node; according to the safety factor of each node in the path search tree, the path Search the tree for pruning.
  • the safety factor of the non-leaf node is the sum of the safety factors of its child nodes.
  • the processor 201 is further configured to: for each leaf node, determine the direction of the leaf node towards the nearest obstacle; according to the distance between the direction and the growth direction of the leaf node The included angle determines the safety factor of the leaf node.
  • the safety factor of the leaf node is positively correlated with the included angle.
  • the safety factor of the leaf node if the included angle is greater than a preset angle threshold, the safety factor of the leaf node is a first preset value; otherwise, the safety factor of the leaf node is a second preset value, and the first A preset value is greater than the second preset value.
  • the growth direction of the leaf node is the direction of the connection line between the leaf node and its parent node.
  • the processor 201 is further configured to: for each leaf node, determine the safety factor of the leaf node according to the distance between the leaf node and the nearest neighbor obstacle.
  • the safety factor of the leaf node is positively correlated with the distance.
  • the processor 201 before performing pruning on the path search tree according to the safety factor of each node in the path search tree, is further configured to: The safety factor of each node is normalized.
  • the normalization process includes: for each node, determining a ratio between its own safety factor and the maximum safety factor among nodes belonging to the same parent node as a normalized value.
  • the pruning process includes: performing pruning processing on nodes in the path search tree whose safety factor is lower than a preset threshold.
  • the nearest neighbor obstacle is obtained from pre-stored obstacle information based on the location of the leaf node; the obstacle information is within the current sensing range and/or the depth sensor acquired within the context of the historical perception described above.
  • the secure channel is composed of multiple secure areas generated in the secure path.
  • the center points of the multiple security areas are all in the security path; the center point of one security area is the target leaf node, and the center points of other security areas are the previous security area and the target leaf node. The intersection point of the security path.
  • the size of each safety area is determined according to the distance between a part of the safety path corresponding to the safety area and the nearest obstacle.
  • the device embodiment since it basically corresponds to the method embodiment, for related parts, please refer to the part description of the method embodiment.
  • the device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network elements. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. It can be understood and implemented by those skilled in the art without creative effort.
  • Various implementations described herein can be implemented using a computer readable medium such as computer software, hardware, or any combination thereof.
  • the embodiments described herein can be implemented by using Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays ( FPGA), processors, controllers, microcontrollers, microprocessors, electronic units designed to perform the functions described herein.
  • ASICs Application Specific Integrated Circuits
  • DSPs Digital Signal Processors
  • DSPDs Digital Signal Processing Devices
  • PLDs Programmable Logic Devices
  • FPGA Field Programmable Gate Arrays
  • processors controllers, microcontrollers, microprocessors, electronic units designed to perform the functions described herein.
  • an embodiment such as a procedure or a function may be implemented with a separate software module that allows at least one function or operation to be performed.
  • the software codes can be implemented by a software application (or program
  • a movable platform 10 which includes:
  • the power system 110 is arranged in the fuselage and is used to drive the movement of the movable platform
  • the movable platform includes a depth sensor, and the depth sensor is used to collect depth information of obstacles within its sensing range.
  • non-transitory computer-readable storage medium including instructions, such as a memory including instructions, which are executable by a processor of an apparatus to perform the above method.
  • the non-transitory computer readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
  • a non-transitory computer-readable storage medium enabling the terminal to execute the above method when instructions in the storage medium are executed by a processor of the terminal.

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Abstract

一种轨迹生成方法、装置、可移动平台及存储介质，所述方法包括：确定一条无障碍物的安全通道（S101）；检查当前生成的轨迹中处于所述安全通道之外的轨迹段（S102）；根据所述轨迹段在所述安全通道内生成目标轨迹点（S103）；使用所述目标轨迹点优化所述轨迹，直到优化后的轨迹处于所述安全通道内（S104）。该方法可以有效降低轨迹优化维度，提高轨迹优化效率。

Description

轨迹生成方法、装置、可移动平台及存储介质 技术领域
本申请涉及路径规划技术领域，具体而言，涉及一种轨迹生成方法、装置、可移动平台及存储介质。
背景技术
现今社会中可移动平台的应用越来越广泛，例如扫地机器人，工业机械臂，物流运输机器人，无人飞行器或者无人驾驶车辆等等，都为人们的生活带去了许多便利。无论是何种形态的可移动平台，只要涉及运动，就需要考虑到运动过程中的避障问题与运动平滑性问题。
可移动平台在执行任务的时候要先进行安全通道规划，规划出来的安全通道足以避开所有的障碍物，然后再在这条已知的安全通道基础上生成用于指导所述可移动平台运动的轨迹。其中，所述安全通道由多个安全区域组成，相关技术中在获取安全通道之后，在安全通道中的每个安全区域内对应生成一段贝塞尔曲线所表达的轨迹，然后通过求解QCQP(Quadratically constrained quadratic program，二次型约束二次规划)来优化贝塞尔曲线所表达的轨迹。该种方法在安全区域较多的情况下，优化维度也会相应增加，需要耗费较多的计算资源，且优化效率较低。
发明内容
有鉴于此，本申请的目的之一是提供一种轨迹生成方法、装置、可移动平台及存储介质。
第一方面，本申请实施例提供了一种轨迹生成方法，包括：
确定一条无障碍物的安全通道；
检查当前生成的轨迹中处于所述安全通道之外的轨迹段；
根据所述轨迹段在所述安全通道内生成目标轨迹点；
使用所述目标轨迹点优化所述轨迹，直到优化后的轨迹处于所述安全通道内。
第二方面，本申请实施例提供了一种轨迹生成装置，包括：
用于存储可执行指令的存储器；
一个或多个处理器；
其中，所述一个或多个处理器执行所述可执行指令时，被单独地或共同地配置成：
确定一条无障碍物的安全通道；
检查当前生成的轨迹中处于所述安全通道之外的轨迹段；
根据所述轨迹段在所述安全通道内生成目标轨迹点；
使用所述目标轨迹点优化所述轨迹，直到优化后的轨迹处于所述安全通道内。
第三方面，本申请实施例提供了一种可移动平台，包括：
机身；
动力系统，设于所述机身内，用于驱动所述可移动平台运动；
以及，如第二方面所述的轨迹生成装置。
第四方面，本申请实施例提供了一种计算机可读存储介质，所述计算机可读存储介质存储有可执行指令，所述可执行指令被处理器执行时实现如第一方面所述的方法。
本申请实施例所提供的一种轨迹生成方法、装置、可移动平台及存储介质，在确定一条无障碍物的安全通道之后，检查当前生成的轨迹中处于所述安全通道之外的轨迹段，然后根据所述轨迹段在所述安全通道内生成目标轨迹点，使用所述目标轨迹点优化所述轨迹，直到优化后的轨迹处于所述安全通道内。本实施例中，仅需根据在安全通道之外的轨迹段，使用动态插点方式在所述安全通道内插入目标轨迹点，并利用插入的目标轨迹点来优化轨迹，通过这种方式来优化轨迹，处于安全通道之外的轨迹段的数量逐渐减少直到优化后的全部轨迹处于所述安全通道内，本实施例提供的轨迹生成方法与安全通道内的安全区域的数量无关，无需在每个安全区域内优化该安全区域对应的轨迹，而是利用插入的目标轨迹点来优化整体轨迹，可以有效降低轨迹优化维度，提高轨迹优化效率。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本申请一个实施例提供的安全通道的示意图；
图2是本申请一个实施例提供的一种在安全通道中生成轨迹的示意图；
图3是本申请一个实施例提供的一种轨迹生成方法的流程示意图；
图4是本申请一个实施例提供的在深度传感器的历史感知范围内生成的安全通道的示意图；
图5是本申请一个实施例提供的一种可靠区域的示意图；
图6是本申请一个实施例提供的一种路径搜索树生长情况的示意图；
图7是本申请一个实施例提供的一种确定叶子节点安全系数的示意图；
图8是本申请一个实施例提供的一种路径搜索树打分的示意图；
图9是本申请一个实施例提供的一种路径搜索树剪枝的示意图；
图10是本申请一个实施例提供的一种基于路径搜索树确定安全通道的示意图；
图11是本申请一个实施例提供的一种轨迹优化的示意图；
图12是本申请一个实施例提供的一种安全通道中目标轨迹点与安全区域的数量关系的示意图；
图13是本申请一个实施例提供的一种轨迹生成装置的结构示意图；
图14是本申请一个实施例提供的一种可移动平台的结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
可移动平台(如无人机、移动机器人等)在复杂环境中执行移动任务时，一般需要借助深度传感器，如深度相机或者激光雷达，获得环境中障碍物的相对距离信息。这种信息一般给出实际障碍物在观测方向上的投影距离，因此也称为深度信息。深度信息是可移动平台(如无人机、移动机器人等)执行任务时保证机动安全所必须的信息。
所述可移动平台维护有深度传感器采集的当前探测环境中有关于障碍物的深度信息，以便后续可以基于所述可移动平台所维护的深度信息来确定可以避开障碍物的安全通道。作为例子，所述深度信息包括但不限于是对应有深度值的灰度图、对应有深 度值的彩色图、深度图像或者点云帧。
在一种可能的实现方式中，可移动平台会基于上述的有关于障碍物的深度信息建立一个统一的表征探测环境中的障碍物的空间地图，如三维栅格地图、八叉树地图或欧氏符号距离等，该方法将可移动平台上的深度传感器采集的每一帧的深度信息和地图已有的障碍物信息进行合理的计算融合，得到最大似然概率意义下的地图障碍物分布，后续可以调取该地图的障碍物查询接口来获取被查询的空间点是否被障碍物占据，从而来确定可以避开障碍物的安全通道。在另一种可能的实现方式中，可移动平台可以将深度传感器采集的每一帧的深度信息投影到三维空间以获得有关于障碍物的三维点云，以树状数据结构存储三维点云数据，比如基于每一帧的三维点云建立k-d树或者R树，并提供一个最近邻查询的接口，后续通过提供的接口来查询与待测空间点最邻近的障碍物，确定待测空间点与最邻近障碍物的距离，根据该距离来确定可以避开障碍物的安全通道。
比如请参与图1，可移动平台在如图1所示的环境中，利用上述的空间地图或者树状数据结构进行最近邻障碍物检测，从而获取如图1所示的由多个圆形安全区域组成的安全通道。相关技术中在获取安全通道之后，在安全通道中的每个圆形安全区域内对应生成一段贝塞尔曲线所表达的轨迹，然后通过求解QCQP(Quadratically constrained quadratic program，二次型约束二次规划)来优化贝塞尔曲线所表达的轨迹。该种方法在安全区域较多的情况下，优化维度也会相应增加，需要耗费较多的计算资源，且优化效率较低。
针对于相关技术中的问题，本申请实施例提供了一种轨迹生成方法，在确定一条无障碍物的安全通道之后，检查当前生成的轨迹中处于所述安全通道之外的轨迹段，然后根据所述轨迹段在所述安全通道内生成目标轨迹点，使用所述目标轨迹点优化所述轨迹，直到优化后的轨迹处于所述安全通道内。本实施例中，仅需根据在安全通道之外的轨迹段，使用动态插点方式在所述安全通道内插入目标轨迹点，并利用插入的目标轨迹点来优化轨迹，通过这种方式来优化轨迹，处于安全通道之外的轨迹段的数量逐渐减少直到优化后的全部轨迹处于所述安全通道内，本实施例提供的轨迹生成方法与安全通道内的安全区域的数量无关，无需在每个安全区域内优化该安全区域对应的轨迹，而是利用插入的目标轨迹点来优化整体轨迹，可以有效降低轨迹优化维度，提高轨迹优化效率。
在一些实施例中，本申请实施例提供的轨迹生成方法可以应用轨迹生成装置中。
一方面，所述轨迹生成装置可以是具有数据处理能力的可移动平台。其中，所述 可移动平台的示例包括但不限于无人飞行器、无人驾驶车辆、云台、无人驾驶船只、移动机器人、扫地机器人，工业机械臂或者物流运输机器人等。
示例性的，所述轨迹生成装置可以是集成于所述可移动平台中的计算机软件产品，该计算机软件产品包括可以执行本申请实施例提供的轨迹生成方法的应用程序。示例性的，所述轨迹生成装置可以是至少包括存储器和处理器的可移动平台，所述可移动平台中的处理器可以执行所述存储器中存储的指示本申请实施例提供的轨迹生成方法的可执行指令。
另一方面，所述轨迹生成装置也可以是具有数据处理能力的芯片或者集成电路，所述轨迹生成装置包括但不限于例如中央处理单元(Central Processing Unit，CPU)、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)或者现成可编程门阵列(Field-Programmable Gate Array，FPGA)等。所述障碍物信息获取装置可以安装于电子设备中。
示例性的，以所述可移动平台为无人飞行器为例，所述无人飞行器至少包括有机身、安装于机身内的动力系统以及飞行控制器；所述动力系统用于驱动所述无人飞行器运动，所述飞行控制器用于控制所述无人飞行器的飞行；其中，所述轨迹生成装置可以是无人飞行器中的飞行控制器，即所述无人飞行器中的飞行控制器可以执行本申请实施例提供的轨迹生成方法。
示例性的，本申请提供的轨迹生成方法可应用电力巡检、火灾救援或者安防领域中，在使用本申请实施例提供的方法生成轨迹之后，可以控制可移动平台按照所述轨迹移动，从而实现智能化的搜查巡检过程，显著降低了人力成本也提高了巡检效率。
在一示例性的应用场景中，请参阅图2，以无人飞行器进行示例说明：无人飞行器搭载有深度传感器，所述深度传感器包括但不限于激光雷达或者RGBD相机。在当前探测环境中，比如在用户的操作下确定本次轨迹生成任务要到达的终止航点，以当前位置为起始航点，无人飞行器需要在起始航点和终止航点之间生成一条避开障碍物的安全的轨迹。
在当前探测环境内，所述无人飞行器可以利用其搭载的深度传感器采集有关于障碍物的深度信息，然后可以通过统一性的空间地图或者树状数据结构(如k-d树)来维护所述深度信息，接着在所述深度传感器的感知范围内采样空间点，利用上述的空间地图或者树状数据结构对所述空间点进行最近邻障碍物检测，确定所述空间点周围的安全空间，通过若干空间点的采样及其周围的安全空间的查询确定起始航点和终止航点之间的无障碍物的飞行走廊(在无人飞行器领域，将安全通道称为飞行走廊)。
接着，可以采用本实施例提供的轨迹生成方法，在确定一条无障碍物的飞行走廊之后，检查当前生成的轨迹中处于所述飞行走廊之外的轨迹段，所述当前生成的轨迹基于所述起始航点和所述终止航点确定，或者所述当前生成的轨迹基于所述起始航点、所述终止航点以及上一次在飞行走廊内生成的目标航点确定；然后所述无人飞行器根据所述轨迹段在所述飞行走廊内生成目标航点，使用所述目标航点优化所述轨迹，直到优化后的轨迹处于所述飞行走廊内，最终生成一条从起始航点飞行至终止航点的避开障碍物的安全的轨迹，进一步地，本申请实施例所生成的轨迹可应用电力巡检、火灾救援或者安防领域中，比如可以控制无人飞行器按照生成的轨迹飞行，从而实现智能化的搜查巡检过程，显著降低了人力成本也提高了巡检效率。
接下来对本申请实施例提供的轨迹生成方法进行说明：请参阅图3，图3为本申请实施例提供的一种轨迹生成方法的流程示意图，所述方法可以由轨迹生成装置来执行，所述方法包括：
在步骤S101中，确定一条无障碍物的安全通道。
在步骤S102中，检查当前生成的轨迹中处于所述安全通道之外的轨迹段。
在步骤S103中，根据所述轨迹段在所述安全通道内生成目标轨迹点。
在步骤S104中，使用所述目标轨迹点优化所述轨迹，直到优化后的轨迹处于所述安全通道内。
对于步骤S101，所述安全通道可以是所述装置在起始轨迹点和终止轨迹点之间确定的一条能够避开障碍物的通道。所述起始轨迹点和终止轨迹点可以依据实际应用场景进行具体设置，本实施例对此不做任何限制；例如在电力巡检场景中，可以根据电线的分布情况来确定所述起始轨迹点和终止轨迹点；又或者在安防场景中，可以根据要巡检区域的分布情况来确定所述起始轨迹点和终止轨迹点；或者所述起始轨迹点和终止轨迹点也可以是用户根据实际情况进行指定。
在一些实施例中，所述安全通道基于在所述起始轨迹点和所述终止轨迹点之间进行路径搜索得到。示例性的，所述装置以所述起始轨迹点为根节点，确定向所述终止轨迹点延伸的路径搜索树，然后从所述路径搜索树的全部叶子节点中确定目标叶子节点，最后根据所述目标叶子节点到所述根节点的安全路径，确定所述安全通道。可以理解的是，本申请对于路径搜索过程所使用的搜索算法不做任何限制，可依据实际应用场景进行具体设置，例如所述路径搜索树可以是前向生成树、RRT(rapidly-exploring random tree，快速搜索随机树)树、RRT*树或者FMT(fast marching tree，快速行进数)树等。
这里对所述路径搜索树中的非根节点的获取过程进行说明：以所述装置安装于可移动平台为例，所述可移动平台也搭载有深度传感器。所述路径搜索树中的非根节点为所述装置在深度传感器的当前感知范围内采样到的指示非障碍物的空间点。
在采样所述空间点时，为了提高采样的空间点的有效性，所述装置可以获取在深度传感器的历史感知范围内的采样结果；然后根据所述采样结果估计在深度传感器的当前感知范围内的可靠区域，进而在所述可靠区域内采样所述空间点。本实施例中，采用先验信息(历史感知范围内的采样结果)来引导在当前感知范围内的采样过程，有利于提高采样的空间点的有效性，进一步提升轨迹生成效率。
当然，在无法获取到在深度传感器历史感知范围内的采样结果的情况下，比如所述深度传感器当前感知范围为所述深度传感器在该环境下的首次感知过程，所述装置可以在所述深度传感器的当前感知范围内均匀采样空间点。
在一种可能的实施方式中，在深度传感器的当前感知范围内的可靠区域根据由所述采样结果生成的安全通道所确定。具体来说，可移动平台需要对所述起始轨迹点和终止轨迹点之间的环境进行有效探测，通过深度传感器来获取障碍物信息，示例性的，所述障碍物信息包括有关于障碍物的深度信息，所述深度传感器用于采集环境中在深度传感器感知范围内的障碍物的深度信息，该深度信息表征在当前感知范围内的障碍物与所述可移动平台的相对距离。在这个探测过程中，可移动平台逐渐从起始轨迹点移动至所述终止轨迹点，随着可移动平台的运动，深度传感器的感知范围内的环境内容也逐渐改变，从而实现获取所述起始轨迹点和终止轨迹点之间的环境的障碍物信息。
在深度传感器的各个感知范围内，所述装置执行以下步骤：所述装置根据所述深度传感器采集的障碍物信息，在所述深度传感器的当前感知范围内采样指示非障碍物的多个空间点；然后根据起始轨迹点和所述多个空间点生成向所述终止轨迹点延伸的路径搜索树，根据所述路径搜索树确定所述安全通道，进而利用本申请实施例提供的轨迹生成方法生成一条处于所述安全通道内的轨迹。
进一步地，考虑到安全通道所在环境空间的安全概率较高，因此所述装置在深度传感器的当前感知范围内采样空间点时，所述装置可以根据由历史感知范围内的采样结果生成的安全通道来确定在所述当前感知范围内的可靠区域，其中，所述历史感知范围与所述当前感知范围具有重叠区域，从而确保所述历史感知范围对应的安全通道所在的部分或全部环境空间也在深度传感器的当前感知范围内，这样所述装置才能基于所述历史感知范围对应的安全通道来有效确定所述可靠区域，示例性的，所述可靠区域至少包括所述历史感知范围对应的安全通道所在环境空间。示例性的，所述装置 可以根据一个或多个历史感知范围对应的安全通道来有效确定所述可靠区域，所述一个或多个历史感知范围与所述当前感知范围均具有重叠区域。
在一个例子中，请参阅图4，假设在t时刻，由深度传感器的历史感知范围FoV(t)内的采样结果生成的安全通道如图4所示，在t+1时刻，在深度传感器的当前感知范围FoV(t+1)内，所述装置可以根据如图4所示的安全通道所在的环境空间确定如图5所示的可靠区域，所述可靠区域的安全概率相较于其他区域的安全概率更高，其中，历史感知范围FoV(t)和当前感知范围FoV(t+1)具有重叠区域。
进而在确定所述当前范围内的可靠区域之后，由于所述可靠区域的安全概率相较于其他区域的安全概率更高，所述装置可以在所述可靠区域内以高采样率采样空间点，以及，在所述当前感知范围的非可靠区域以低采样率采样空间点。本实施例中能够根据先验信息来指导空间点采样过程，有效提高了所采样的空间点的有效性，提高采样效率和轨迹生成效率，并且由于所述可靠区域是基于由历史感知范围内的采样结果生成的安全通道来确定，其间接提升了在当前感知范围内生成的轨迹与在历史感知范围内生成的轨迹的重叠范围，从而提升了与感知信息/历史规划轨迹的一致性。在深度传感器的当前感知范围内采样所述空间点之后，所述装置还需对所述空间点的安全性进行检验。上述提到，所述深度传感器用于采集环境中在深度传感器感知范围内的障碍物的深度信息，该深度信息表征在当前感知范围内的障碍物与所述可移动平台的相对距离，进而所述装置会对所述深度传感器在所述当前感知范围和/或所述历史感知范围内采集到的障碍物的深度信息进行维护；作为例子，可以使用在所述当前感知范围和/或所述历史感知范围内采集到的障碍物的深度信息构建统一的空间地图如栅格地图；作为例子，可以使用树状数据结构存储在各个感知范围内采集到的障碍物的深度信息，比如将在各个感知范围内采集到的障碍物的深度信息投影到三维空间获取三维点云，利用所述三维点云建立k-d树或者R树等。
因此，在深度传感器的当前感知范围内采样所述空间点之后，所述装置可以根据在所述当前感知范围和/或所述历史感知范围内获得的障碍物信息，确定所述空间点与最近邻障碍物之间的距离，实现对所述空间点的安全性检验；其中，所述障碍物信息包括所述深度传感器在其感知范围内采集到的有关于障碍物的深度信息，示例性的，所述障碍物信息还包括根据所述深度信息构建的k-d树、R树和/或栅格地图；进而所述装置可以基于实际需求保留所述空间点与最近邻障碍物之间的距离大于预设距离阈值的空间点，其中，所述预设距离阈值可依据实际应用场景进行具体设置，本实施例对此不做任何限制，比如所述预设距离阈值可以根据所述可移动平台的尺寸确定。
在深度传感器的当前感知范围内采样所述空间点并确定所述空间点的安全性之后，可以将所述空间点作为路径搜索树的节点加入所述路径搜索树中。示例性的，为了加快路径搜索树的生成速度，可以将所述空间点按照与所述根节点的距离由近到远加入所述路径搜索树中，从而快速获得向所述终止轨迹点延伸的路径搜索树。示例性的，也可以将所述空间点随机加入所述路径搜索树中，从而获得向所述终止轨迹点延伸的路径搜索树。
在一些实施例中，考虑到生成的路径搜索树中可能会出现如图6所示的情况，即路径搜索树的叶子节点向障碍物上生长，导致局部规划的短视性显现，陷入死胡同。因此，为了提高后续确定的安全通道的安全性，在确定向所述终止轨迹点延伸的路径搜索树之后，所述装置需要对所述路径搜索树进行剪枝处理，将如图6的场景出现的叶子节点或者分支剪掉。示例性的，所述装置根据所述路径搜索树中各个叶子节点与最近邻障碍物之间的关系，确定各个所述叶子节点的安全系数；然后从各个叶子节点开始遍历所述路径搜索树，确定各个非叶节点的安全系数；最后根据所述路径搜索树中各个节点的安全系数，对所述路径搜索树进行剪枝处理。本实施例中，通过对路径搜索树中的节点进行安全评估，根据安全评估的结果(即安全系数)来进行剪枝处理，可以迅速剪掉大部分无用的节点，留下真正表达环境可行区域的树枝。
其中，各个叶子节点的最近邻障碍物基于所述叶子节点所处位置从预存的障碍物信息中查询得到；所述障碍物信息由所述装置在深度传感器的所述当前感知范围和/或所述历史感知范围内获得的；所述障碍物信息包括所述深度传感器在其感知范围内采集到的有关于障碍物的深度信息，示例性的，所述障碍物信息还包括根据所述深度信息构建的k-d树、R树和/或栅格地图。
在确定各个叶子节点的最近邻障碍物之后，所述装置可以通过以下方式确定各个所述叶子节点的安全系数：
在一种可能的实现方式中，对于各个叶子节点，所述装置确定所述叶子节点朝向所述最近邻障碍物的方向，然后根据所述方向与所述叶子节点的生长方向之间的夹角，确定所述叶子节点的安全系数；其中，所述叶子节点朝向所述最近邻障碍物的方向可以是所述叶子节点与所述障碍物的垂线(或者最短连线等)的方向；所述叶子节点的生长方向为所述叶子节点与其父节点的连线的方向。
其中，所述叶子节点的安全系数与所述夹角成正相关关系，请参阅图7，所述夹角α越大，表示所述叶子节点继续生长的话，树枝的生长方向与最近邻障碍物的碰撞概率较低，则所述叶子节点的安全性较高，其对应的安全系数越大；反之，所述夹角 α越小，表示所述叶子节点继续生长的话，树枝的生长方向与最近邻障碍物的碰撞概率较高，则所述叶子节点的安全性较低，其对应的安全系数越小。
示例性的，在获取所述叶子节点的所述夹角之后，若所述夹角大于预设角度阈值，确定所述叶子节点的安全系数为第一预设值；否则，在所述夹角不大于预设角度阈值的情况下，确定所述叶子节点的安全系数为第二预设值，所述第一预设值大于所述第二预设值。可以理解的是，所述预设角度阈值、第一预设值和第二预设值可依据实际应用场景进行具体设置，本实施例对此不做任何限制；作为例子，所述预设角度阈值为大于90°，所述第一预设值为1，所述第二预设值为0。
在第二种可能的实现方式中，对于各个叶子节点，所述装置可以根据所述叶子节点与所述最近邻障碍物之间的距离，确定所述叶子节点的安全系数。其中，所述叶子节点的安全系数与所述距离成正相关关系，即距离越远，所述叶子节点的安全越高，距离越近，所述叶子节点的安全系数越低。
在一些实施例中，在确定各个叶子节点的安全系数之后，所述装置可以从各个叶子节点开始遍历所述路径搜索树，确定各个非叶节点的安全系数。示例性的，所述非叶节点的安全系数为其孩子节点的安全系数之和。比如请参阅图8，各个叶子节点的安全系数为0或1，各个非叶节点的安全系数为其孩子节点的安全系数之和。
在确定所述路径搜索树中各个节点的安全系数之后，所述装置可以根据所述路径搜索树中各个节点的安全系数，对所述路径搜索树进行剪枝处理；示例性的，可以对对所述路径搜索树中安全系数低于预设阈值的节点进行剪枝处理。所述预设阈值可依据实际应用场景进行具体设置。本实施例中，考虑到安全系数低于预设阈值的节点如果继续生成，树枝的生长方向与最近邻障碍物的碰撞概率较大，因此将安全系数低于预设阈值的节点剪掉，留下真正表达环境可行区域的树枝，从而有利于保证后续生成的安全通道的准确性。
在一些实施例中，为了避免误差因素的影响，在确定所述路径搜索树中各个节点的安全系数之后，可以对所述路径搜索树中各个节点的安全系数进行归一化处理，将所述路径搜索树中各个节点的安全系数映射到预设范围内；示例性的，所述归一化处理包括：对于每个节点，将自身的安全系数与属于同一父节点的节点中的最大安全系数之间的比值确定为归一化后的值。在归一化处理之后，所述装置可以将对所述路径搜索树中安全系数低于预设阈值的节点进行剪枝处理。所述预设阈值可依据实际应用场景进行具体设置。
作为例子，请参阅图9，图9为对图8所示的路径搜索树进行归一化处理之后的 结果，例如针对于安全系数为0.25的节点，其归一化后的安全系数0.25为自身原先的安全系数1与安全系数为4的比值，安全系数为4的节点与该节点属于同一父节点且安全系数最大；并且在归一化处理之后，所述装置可以将对所述路径搜索树中安全系数低于预设阈值(比如低于0.3)的节点进行剪枝处理，得到如图9所示的路径搜索树，其中虚线部分为本次剪掉的无用的节点或分支，从而留下真正表达环境可行区域的树枝。
在确定所述路径搜索树之后，所述装置从所述路径搜索树的全部叶子节点中确定目标叶子节点；然后根据所述目标叶子节点到所述根节点的安全路径，确定所述安全通道。可以理解的是，本实施例对于所述目标叶子节点的选择不做任何限制，可依据实际应用场景进行具体设置；示例性的，为了更快延伸到所述终止轨迹点，可以从全部叶子节点中选择距所述终止轨迹点距离最近的叶子节点作为所述目标叶子节点，即所述目标叶子节点距所述终止轨迹点的距离最近；当然，还可以基于其他策略来选择所述目标节点，比如所述目标节点到最近邻障碍物的距离最远等。
示例性的，在确定所述目标叶子节点到所述根节点的安全路径之后，所述装置可以在所述安全路径上生成多个安全区域，进而获取由多个安全区域组成的安全通道。作为例子，请参阅图10，所述多个安全区域的中心点均处于所述安全路径中，其中一个安全区域的中心点为所述目标叶子节点，其他安全区域的中心点为上一个安全区域与所述安全路径的交点，并且各个所述安全区域的尺寸根据所述安全区域所对应的一部分安全路径与最近邻障碍物之间的距离确定，从而获取如图10所示的由多个安全区域组成的安全通道。其中，所述安全区域可以是圆形，也可以是其他形状，如矩形等，本实施例对此不做任何限制。
在步骤S102～步骤S104中，在确定一条无障碍物的安全通道之后，所述装置检查当前生成的轨迹中处于所述安全通道之外的轨迹段；其中，在首次优化过程中，所述当前生成的轨迹基于所述起始轨迹点和所述终止轨迹点确定；在非首次优化过程中，所述当前生成的轨迹基于所述起始轨迹点、所述终止轨迹点以及上一次在安全通道内生成的目标轨迹点确定；然后所述装置可以根据所述轨迹段在所述安全通道内生成目标轨迹点，使用所述目标轨迹点优化所述轨迹，直到优化后的轨迹处于所述安全通道内。本实施例提供的轨迹生成方法与安全通道内的安全区域的数量无关，无需在每个安全区域内优化该安全区域对应的轨迹，而是利用插入的目标轨迹点来优化整体轨迹，可以有效降低轨迹优化维度，提高轨迹优化效率。
示例性的，在确定所述轨迹段时，所述装置确定所述安全通道所在空间位置以及 所述当前生成的轨迹所在空间位置，根据两者的空间位置不重叠的部分确定当前生成的轨迹中处于所述安全通道之外的轨迹段。
这里对所述目标轨迹点的生成进行示例性说明：
在第一种可能的实现方式中，所述装置可以根据所述轨迹段在所述安全通道中确定目标区域，比如根据所述轨迹段与所述安全通道的两个交点的位置可以分别确定两个边界，然后将所述安全通道中处于所述两个边界内的区域确定为所述目标区域，进而在所述目标区域内生成目标轨迹点；本实施例中，选择在与所述轨迹段相关的目标区域内生成目标轨迹点，有利于提高轨迹优化速度；而且无需在整个安全通道中生成目标轨迹点，也有利于提高目标轨迹点生成效率。
示例性的，为了提高轨迹优化速度，可以在所述目标区域中距所述轨迹段最远距离处生成目标轨迹点，更具体说，可以在所述目标区域靠近所述轨迹段一侧的边缘位置中，在距所述轨迹段最远距离处生成目标轨迹点，从而可以保证基于所述目标轨迹点优化后的轨迹尽可能在所述安全通道内；示例性的，也可以在所述目标区域内随机位置处生成所述目标轨迹点。
在第二种可能的实现方式中，所述装置可以在所述安全通道中生成多个候选轨迹点，然后根据所述候选轨迹点与所述轨迹段的距离，从所述多个候选轨迹点中选择所述目标轨迹点；示例性的，所述目标轨迹点与所述轨迹段之间的距离最远；示例性，也可以选择与所述轨迹段距离次远的候选轨迹点作为目标轨迹点。本实施例基于候选轨迹点与所述轨迹段的距离来选择目标轨迹点，有利于提高轨迹优化效率。
在一示例性的实施方式中，所述安全通道由多个安全区域组成，所述装置可以在所述多个安全区域的相交区域内生成一个或多个候选航点。示例性的，所述候选轨迹点处于所述相交区域中靠近所述轨迹段一侧的边缘位置，从而在保证优化的轨迹处于所述安全通道内的前提下，使得优化后的轨迹与优化前的轨迹的差别更小。
在另一示例性的实施例中，所述安全通道由多个安全区域组成，所述装置可以根据所述轨迹段与所述安全通道的交点位置，从多个安全区域中确定目标安全区域，例如可以根据所述轨迹段与所述安全通道的两个交点的位置，分别确定相交的两个目标安全区域，进而将处于所述两个目标安全区域之间的安全区域也确定为目标安全区域；然后所述装置在所述目标安全区域内生成多个候选轨迹点；示例性的，所述候选轨迹点处于所述目标安全区域的相交区域中靠近所述轨迹段一侧的边缘位置。本实施例中，选择在与所述轨迹段相关的目标安全区域内生成候选轨迹点，有利于提高轨迹优化速度；而且无需在整个安全通道中生成候选轨迹点，也有利于提高目标轨迹点生成效率。
作为例子，请参阅图11，在确定一条无障碍物的安全通道以后，所述装置采用动态插点法来进行轨迹优化，在首次优化时，获取由起始轨迹点和终止轨迹点生成的轨迹，即处于图11中上半部分的安全通道中的虚线部分，然后检查所述轨迹中处于所述安全通道之外的轨迹段，根据所述轨迹段与所述安全通道的交点位置，从多个安全区域中确定目标安全区域，然后在所述目标安全区域内生成多个候选轨迹点，所述候选轨迹点处于所述目标安全区域的相交区域中靠近所述轨迹段一侧的边缘位置，根据所述候选轨迹点与所述轨迹段的距离，从所述多个候选轨迹点中选择距离最远的候选轨迹点作为所述目标轨迹点，然后所述装置使用所述目标轨迹点优化所述轨迹，得到如图11所示上半部分的安全通道中的实线部分的优化轨迹。
在第二次优化时，检查优化后的轨迹(图11中下半部分的安全通道中的虚线部分)中处于所述安全通道之外的轨迹段，所述优化后的轨迹由起始轨迹点、终止轨迹点以及上一次在安全通道内生成的目标轨迹点生成的轨迹；然后所述装置根据所述轨迹段与所述安全通道的交点位置，从多个安全区域中确定目标安全区域，然后在所述目标安全区域内生成多个候选轨迹点，所述候选轨迹点处于所述目标安全区域的相交区域中靠近所述轨迹段一侧的边缘位置，根据所述候选轨迹点与所述轨迹段的距离，从所述多个候选轨迹点中选择距离最远的候选轨迹点作为所述目标轨迹点，然后所述装置使用所述目标轨迹点优化所述轨迹，得到如图11所示下半部分的安全通道中的实线部分的优化轨迹，检查优化后的轨迹中均处于所述安全通道，结束。
在一些实施例中，经实验数据验证，请参阅图12，图12示出了随着安全通道中安全区域数量的增加，需要生成的目标轨迹点数量的变化情况，在复杂环境下，安全通道中的安全区域的个数一般都在10个左右，相关技术中需要在10个安全区域内分别优化一段轨迹，而通过本申请实施例提供的轨迹生成方法，针对于同样数量的安全区域(如10个安全区域)仅需插入1～2个目标轨迹点即可覆盖大部分情景，有效提升了轨迹生成效率及优化效率。
相应的，请参阅图13，本申请实施例还提供了一种轨迹生成装置200，包括：
用于存储可执行指令的存储器202；
一个或多个处理器201；
其中，所述一个或多个处理器201执行所述可执行指令时，被单独地或共同地配置成：
确定一条无障碍物的安全通道；
检查当前生成的轨迹中处于所述安全通道之外的轨迹段；
根据所述轨迹段在所述安全通道内生成目标轨迹点；
使用所述目标轨迹点优化所述轨迹，直到优化后的轨迹处于所述安全通道内。
在一实施例中，所述处理器201还用于：在起始轨迹点和终止轨迹点之间确定一条无障碍物的安全通道。
在一实施例中，所述当前生成的轨迹基于所述起始轨迹点和所述终止轨迹点确定；或者，所述当前生成的轨迹基于所述起始轨迹点、所述终止轨迹点以及上一次在安全通道内生成的目标轨迹点确定。
在一实施例中，所述处理器201还用于：在所述安全通道中生成多个候选轨迹点；根据所述候选轨迹点与所述轨迹段的距离，从所述多个候选轨迹点中选择所述目标轨迹点。
在一实施例中，所述目标轨迹点与所述轨迹段之间的距离最远。
在一实施例中，所述安全通道由多个安全区域组成。
所述处理器201还用于：在所述多个安全区域的相交区域内生成多个候选航点。
在一实施例中，所述候选轨迹点处于所述相交区域中靠近所述轨迹段一侧的边缘位置。
在一实施例中，所述安全通道由多个安全区域组成。
所述处理器201还用于：根据所述轨迹段与所述安全通道的交点位置，从多个安全区域中确定目标安全区域；在所述目标安全区域内生成多个候选轨迹点。
在一实施例中，所述候选轨迹点处于所述目标安全区域的相交区域中靠近所述轨迹段一侧的边缘位置。
在一实施例中，所述处理器201还用于：根据所述轨迹段在所述安全通道中确定目标区域，并在所述目标区域内生成目标轨迹点；其中，所述目标区域的边界根据所述轨迹段与所述安全通道的交点位置确定。
在一实施例中，所述处理器201还用于：在所述目标区域中距所述轨迹段最远距离处生成目标轨迹点。
在一实施例中，所述安全通道基于在所述起始轨迹点和所述终止轨迹点之间进行路径搜索得到。
在一实施例中，所述处理器201还用于：以所述起始轨迹点为根节点，确定向所述终止轨迹点延伸的路径搜索树；从所述路径搜索树的全部叶子节点中确定目标叶子节点；根据所述目标叶子节点到所述根节点的安全路径，确定所述安全通道。
在一实施例中，所述目标叶子节点距所述终止轨迹点的距离最近。
在一实施例中，所述路径搜索树中的非根节点为在深度传感器的当前感知范围内采样到的指示非障碍物的空间点。
在一实施例中，在确定所述空间点时，所述处理器201还用于：获取在所述深度传感器的历史感知范围内的采样结果；根据所述采样结果估计在所述当前感知范围内的可靠区域；在所述可靠区域内采样所述空间点。
在一实施例中，所述处理器201还用于：在所述可靠区域内以高采样率采样空间点，以及，在所述当前感知范围的非可靠区域以低采样率采样空间点。
在一实施例中，所述可靠区域根据由所述采样结果生成的安全通道所确定。
在一实施例中，所述历史感知范围与所述当前感知范围具有重叠区域。
在一实施例中，在所述可靠区域内采样所述空间点之后，所述处理器201还用于：根据在所述当前感知范围和/或所述历史感知范围内获得的障碍物信息，确定所述空间点与最近邻障碍物之间的距离；保留所述距离大于预设距离阈值的空间点。
在一实施例中，所述空间点按照与所述根节点的距离由近到远加入所述路径搜索树中。
在一实施例中，在所述确定向所述终止轨迹点延伸的路径搜索树之后，所述处理器201还用于：根据所述路径搜索树中各个叶子节点与最近邻障碍物之间的关系，确定各个所述叶子节点的安全系数；从各个叶子节点开始遍历所述路径搜索树，确定各个非叶节点的安全系数；根据所述路径搜索树中各个节点的安全系数，对所述路径搜索树进行剪枝处理。
在一实施例中，所述非叶节点的安全系数为其孩子节点的安全系数之和。
在一实施例中，所述处理器201还用于：对于各个叶子节点，确定所述叶子节点朝向所述最近邻障碍物的方向；根据所述方向与所述叶子节点的生长方向之间的夹角，确定所述叶子节点的安全系数。
在一实施例中，所述叶子节点的安全系数与所述夹角成正相关关系。
在一实施例中，若所述夹角大于预设角度阈值，所述叶子节点的安全系数为第一预设值；否则，所述叶子节点的安全系数为第二预设值，所述第一预设值大于所述第二预设值。
在一实施例中，所述叶子节点的生长方向为所述叶子节点与其父节点的连线的方向。
在一实施例中，所述处理器201还用于：对于各个叶子节点，根据所述叶子节点与所述最近邻障碍物之间的距离，确定所述叶子节点的安全系数。
在一实施例中，所述叶子节点的安全系数与所述距离成正相关关系。
在一实施例中，在所述根据所述路径搜索树中各个节点的安全系数，对所述路径搜索树进行剪枝处理之前，所述处理器201还用于：对所述路径搜索树中各个节点的安全系数进行归一化处理。
在一实施例中，所述归一化处理包括：对于每个节点，将自身的安全系数与属于同一父节点的节点中的最大安全系数之间的比值确定为归一化后的值。
在一实施例中，所述剪枝处理包括：对所述路径搜索树中安全系数低于预设阈值的节点进行剪枝处理。
在一实施例中，所述最近邻障碍物基于所述叶子节点所处位置从预存的障碍物信息中查询得到；所述障碍物信息在所述深度传感器的所述当前感知范围和/或所述历史感知范围内获得的。
在一实施例中，所述安全通道由在所述安全路径中生成的多个安全区域组成。
在一实施例中，所述多个安全区域的中心点均处于所述安全路径中；其中一个安全区域的中心点为所述目标叶子节点，其他安全区域的中心点为上一个安全区域与所述安全路径的交点。
在一实施例中，各个所述安全区域的尺寸根据所述安全区域所对应的一部分安全路径与最近邻障碍物之间的距离确定。
对于装置实施例而言，由于其基本对应于方法实施例，所以相关之处参见方法实施例的部分说明即可。以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性劳动的情况下，即可以理解并实施。
这里描述的各种实施方式可以使用例如计算机软件、硬件或其任何组合的计算机可读介质来实施。对于硬件实施，这里描述的实施方式可以通过使用特定用途集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理装置(DSPD)、可编程逻辑装置(PLD)、现场可编程门阵列(FPGA)、处理器、控制器、微控制器、微处理器、被设计为执行这里描述的功能的电子单元中的至少一种来实施。对于软件实施，诸如过程或功能的实施方式可以与允许执行至少一种功能或操作的单独的软件模块来实施。软件代码可以由以任何适当的编程语言编写的软件应用程序(或程序)来实施，软件代码可以存储在存储器中并且由控制器执行。
在示例性实施例中，请参阅图14，还提供了一种可移动平台10，所述可移动平台包括：
机身100；
动力系统110，设于所述机身内，用于驱动所述可移动平台运动；
以及，上述的轨迹生成装置200。
在一实施例中，所述可移动平台包括有深度传感器，所述深度传感器用于采集在其感知范围内的障碍物的深度信息。
在示例性实施例中，还提供了一种包括指令的非临时性计算机可读存储介质，例如包括指令的存储器，上述指令可由装置的处理器执行以完成上述方法。例如，非临时性计算机可读存储介质可以是ROM、随机存取存储器(RAM)、CD-ROM、磁带、软盘和光数据存储设备等。
一种非临时性计算机可读存储介质，当存储介质中的指令由终端的处理器执行时，使得终端能够执行上述方法。
需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
以上对本申请实施例所提供的方法和装置进行了详细介绍，本文中应用了具体个例对本申请的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本申请的方法及其核心思想；同时，对于本领域的一般技术人员，依据本申请的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本申请的限制。

Claims (74)

  1. 一种轨迹生成方法，其特征在于，包括：
    确定一条无障碍物的安全通道；
    检查当前生成的轨迹中处于所述安全通道之外的轨迹段；
    根据所述轨迹段在所述安全通道内生成目标轨迹点；
    使用所述目标轨迹点优化所述轨迹，直到优化后的轨迹处于所述安全通道内。
  2. 根据权利要求1所述的方法，其特征在于，所述确定一条无障碍物的安全通道，包括：
    在起始轨迹点和终止轨迹点之间确定一条无障碍物的安全通道。
  3. 根据权利要求2所述的方法，其特征在于，所述当前生成的轨迹基于所述起始轨迹点和所述终止轨迹点确定；
    或者，所述当前生成的轨迹基于所述起始轨迹点、所述终止轨迹点以及上一次在安全通道内生成的目标轨迹点确定。
  4. 根据权利要求1所述的方法，其特征在于，所述根据所述轨迹段在所述安全通道内生成目标轨迹点，包括：
    在所述安全通道中生成多个候选轨迹点；
    根据所述候选轨迹点与所述轨迹段的距离，从所述多个候选轨迹点中选择所述目标轨迹点。
  5. 根据权利要求4所述的方法，其特征在于，所述目标轨迹点与所述轨迹段之间的距离最远。
  6. 根据权利要求4所述的方法，其特征在于，所述安全通道由多个安全区域组成；
    所述在所述安全通道中确定生成多个候选轨迹点，包括：
    在所述多个安全区域的相交区域内生成多个候选航点。
  7. 根据权利要求6所述的方法，其特征在于，所述候选轨迹点处于所述相交区域中靠近所述轨迹段一侧的边缘位置。
  8. 根据权利要求4所述的方法，其特征在于，所述安全通道由多个安全区域组成；
    所述在所述安全通道中确定生成多个候选轨迹点，包括：
    根据所述轨迹段与所述安全通道的交点位置，从多个安全区域中确定目标安全区域；
    在所述目标安全区域内生成多个候选轨迹点。
  9. 根据权利要求8所述的方法，其特征在于，所述候选轨迹点处于所述目标安全 区域的相交区域中靠近所述轨迹段一侧的边缘位置。
  10. 根据权利要求1所述的方法，其特征在于，所述根据所述轨迹段在所述安全通道内生成目标轨迹点，包括：
    根据所述轨迹段在所述安全通道中确定目标区域，并在所述目标区域内生成目标轨迹点；其中，所述目标区域的边界根据所述轨迹段与所述安全通道的交点位置确定。
  11. 根据权利要求10所述的方法，其特征在于，所述在所述目标区域内生成目标轨迹点，包括：
    在所述目标区域中距所述轨迹段最远距离处生成目标轨迹点。
  12. 根据权利要求2所述的方法，其特征在于，所述安全通道基于在所述起始轨迹点和所述终止轨迹点之间进行路径搜索得到。
  13. 根据权利要求2或12所述的方法，其特征在于，所述在起始轨迹点和终止轨迹点之间确定一条无障碍物的安全通道，包括：
    以所述起始轨迹点为根节点，确定向所述终止轨迹点延伸的路径搜索树；
    从所述路径搜索树的全部叶子节点中确定目标叶子节点；
    根据所述目标叶子节点到所述根节点的安全路径，确定所述安全通道。
  14. 根据权利要求13所述的方法，其特征在于，所述目标叶子节点距所述终止轨迹点的距离最近。
  15. 根据权利要求13所述的方法，其特征在于，所述路径搜索树中的非根节点为在深度传感器的当前感知范围内采样到的指示非障碍物的空间点。
  16. 根据权利要求15所述的方法，其特征在于，所述空间点通过以下方式确定：
    获取在所述深度传感器的历史感知范围内的采样结果；
    根据所述采样结果估计在所述当前感知范围内的可靠区域；
    在所述可靠区域内采样所述空间点。
  17. 根据权利要求16所述的方法，其特征在于，所述在所述可靠区域内采样所述空间点，包括：
    在所述可靠区域内以高采样率采样空间点，以及，在所述当前感知范围的非可靠区域以低采样率采样空间点。
  18. 根据权利要求16所述的方法，其特征在于，所述可靠区域根据由所述采样结果生成的安全通道所确定。
  19. 根据权利要求16所述的方法，其特征在于，所述历史感知范围与所述当前感知范围具有重叠区域。
  20. 根据权利要求16所述的方法，其特征在于，在所述可靠区域内采样所述空间点之后，还包括：
    根据在所述当前感知范围和/或所述历史感知范围内获得的障碍物信息，确定所述空间点与最近邻障碍物之间的距离；
    保留所述距离大于预设距离阈值的空间点。
  21. 根据权利要求15所述的方法，其特征在于，所述空间点按照与所述根节点的距离由近到远加入所述路径搜索树中。
  22. 根据权利要求13所述的方法，其特征在于，在所述确定向所述终止轨迹点延伸的路径搜索树之后，还包括：
    根据所述路径搜索树中各个叶子节点与最近邻障碍物之间的关系，确定各个所述叶子节点的安全系数；
    从各个叶子节点开始遍历所述路径搜索树，确定各个非叶节点的安全系数；
    根据所述路径搜索树中各个节点的安全系数，对所述路径搜索树进行剪枝处理。
  23. 根据权利要求22所述的方法，其特征在于，所述非叶节点的安全系数为其孩子节点的安全系数之和。
  24. 根据权利要求22所述的方法，其特征在于，所述根据所述路径搜索树中各个叶子节点与最近邻障碍物之间的关系，确定各个所述叶子节点的安全系数，包括：
    对于各个叶子节点，确定所述叶子节点朝向所述最近邻障碍物的方向；
    根据所述方向与所述叶子节点的生长方向之间的夹角，确定所述叶子节点的安全系数。
  25. 根据权利要求24所述的方法，其特征在于，所述叶子节点的安全系数与所述夹角成正相关关系。
  26. 根据权利要求24所述的方法，其特征在于，若所述夹角大于预设角度阈值，所述叶子节点的安全系数为第一预设值；否则，所述叶子节点的安全系数为第二预设值，所述第一预设值大于所述第二预设值。
  27. 根据权利要求24所述的方法，其特征在于，所述叶子节点的生长方向为所述叶子节点与其父节点的连线的方向。
  28. 根据权利要求22所述的方法，其特征在于，所述根据所述路径搜索树中各个叶子节点与最近邻障碍物之间的关系，确定各个所述叶子节点的安全系数，包括：
    对于各个叶子节点，根据所述叶子节点与所述最近邻障碍物之间的距离，确定所述叶子节点的安全系数。
  29. 根据权利要求28所述的方法，其特征在于，所述叶子节点的安全系数与所述距离成正相关关系。
  30. 根据权利要求22所述的方法，其特征在于，在所述根据所述路径搜索树中各个节点的安全系数，对所述路径搜索树进行剪枝处理之前，还包括：
    对所述路径搜索树中各个节点的安全系数进行归一化处理。
  31. 根据权利要求30所述的方法，其特征在于，所述归一化处理包括：对于每个节点，将自身的安全系数与属于同一父节点的节点中的最大安全系数之间的比值确定为归一化后的值。
  32. 根据权利要求22至31任意一项所述的方法，其特征在于，所述剪枝处理包括：对所述路径搜索树中安全系数低于预设阈值的节点进行剪枝处理。
  33. 根据权利要求22所述的方法，其特征在于，所述最近邻障碍物基于所述叶子节点所处位置从预存的障碍物信息中查询得到；所述障碍物信息由深度传感器在其当前感知范围和/或历史感知范围内获得。
  34. 根据权利要求13所述的方法，其特征在于，所述安全通道由在所述安全路径中生成的多个安全区域组成。
  35. 根据权利要求34所述的方法，其特征在于，所述多个安全区域的中心点均处于所述安全路径中；
    其中一个安全区域的中心点为所述目标叶子节点，其他安全区域的中心点为上一个安全区域与所述安全路径的交点。
  36. 根据权利要求34或35所述的方法，其特征在于，各个所述安全区域的尺寸根据所述安全区域所对应的一部分安全路径与最近邻障碍物之间的距离确定。
  37. 一种轨迹生成装置，其特征在于，包括：
    用于存储可执行指令的存储器；
    一个或多个处理器；
    其中，所述一个或多个处理器执行所述可执行指令时，被单独地或共同地配置成：
    确定一条无障碍物的安全通道；
    检查当前生成的轨迹中处于所述安全通道之外的轨迹段；
    根据所述轨迹段在所述安全通道内生成目标轨迹点；
    使用所述目标轨迹点优化所述轨迹，直到优化后的轨迹处于所述安全通道内。
  38. 根据权利要求37所述的装置，其特征在于，所述处理器还用于：在起始轨迹 点和终止轨迹点之间确定一条无障碍物的安全通道。
  39. 根据权利要求38所述的装置，其特征在于，所述当前生成的轨迹基于所述起始轨迹点和所述终止轨迹点确定；
    或者，所述当前生成的轨迹基于所述起始轨迹点、所述终止轨迹点以及上一次在安全通道内生成的目标轨迹点确定。
  40. 根据权利要求37所述的装置，其特征在于，所述处理器还用于：
    在所述安全通道中生成多个候选轨迹点；
    根据所述候选轨迹点与所述轨迹段的距离，从所述多个候选轨迹点中选择所述目标轨迹点。
  41. 根据权利要求40所述的装置，其特征在于，所述目标轨迹点与所述轨迹段之间的距离最远。
  42. 根据权利要求40所述的装置，其特征在于，所述安全通道由多个安全区域组成；
    所述处理器还用于：在所述多个安全区域的相交区域内生成多个候选航点。
  43. 根据权利要求42所述的装置，其特征在于，所述候选轨迹点处于所述相交区域中靠近所述轨迹段一侧的边缘位置。
  44. 根据权利要求40所述的装置，其特征在于，所述安全通道由多个安全区域组成；
    所述处理器还用于：
    根据所述轨迹段与所述安全通道的交点位置，从多个安全区域中确定目标安全区域；
    在所述目标安全区域内生成多个候选轨迹点。
  45. 根据权利要求44所述的装置，其特征在于，所述候选轨迹点处于所述目标安全区域的相交区域中靠近所述轨迹段一侧的边缘位置。
  46. 根据权利要求37所述的装置，其特征在于，所述处理器还用于：根据所述轨迹段在所述安全通道中确定目标区域，并在所述目标区域内生成目标轨迹点；其中，所述目标区域的边界根据所述轨迹段与所述安全通道的交点位置确定。
  47. 根据权利要求46所述的装置，其特征在于，所述处理器还用于：在所述目标区域中距所述轨迹段最远距离处生成目标轨迹点。
  48. 根据权利要求38所述的装置，其特征在于，所述安全通道基于在所述起始轨迹点和所述终止轨迹点之间进行路径搜索得到。
  49. 根据权利要求38或48所述的装置，其特征在于，所述处理器还用于：
    以所述起始轨迹点为根节点，确定向所述终止轨迹点延伸的路径搜索树；
    从所述路径搜索树的全部叶子节点中确定目标叶子节点；
    根据所述目标叶子节点到所述根节点的安全路径，确定所述安全通道。
  50. 根据权利要求49所述的装置，其特征在于，所述目标叶子节点距所述终止轨迹点的距离最近。
  51. 根据权利要求49所述的装置，其特征在于，所述路径搜索树中的非根节点为在深度传感器的当前感知范围内采样到的指示非障碍物的空间点。
  52. 根据权利要求51所述的装置，其特征在于，在确定所述空间点时，所述处理器还用于：
    获取在所述深度传感器的历史感知范围内的采样结果；
    根据所述采样结果估计在所述当前感知范围内的可靠区域；
    在所述可靠区域内采样所述空间点。
  53. 根据权利要求52所述的装置，其特征在于，所述处理器还用于：在所述可靠区域内以高采样率采样空间点，以及，在所述当前感知范围的非可靠区域以低采样率采样空间点。
  54. 根据权利要求52所述的装置，其特征在于，所述可靠区域根据由所述采样结果生成的安全通道所确定。
  55. 根据权利要求52所述的装置，其特征在于，所述历史感知范围与所述当前感知范围具有重叠区域。
  56. 根据权利要求52所述的装置，其特征在于，在所述可靠区域内采样所述空间点之后，所述处理器还用于：
    根据在所述当前感知范围和/或所述历史感知范围内获得的障碍物信息，确定所述空间点与最近邻障碍物之间的距离；
    保留所述距离大于预设距离阈值的空间点。
  57. 根据权利要求51所述的装置，其特征在于，所述空间点按照与所述根节点的距离由近到远加入所述路径搜索树中。
  58. 根据权利要求49所述的装置，其特征在于，在所述确定向所述终止轨迹点延伸的路径搜索树之后，所述处理器还用于：
    根据所述路径搜索树中各个叶子节点与最近邻障碍物之间的关系，确定各个所述叶子节点的安全系数；
    从各个叶子节点开始遍历所述路径搜索树，确定各个非叶节点的安全系数；
    根据所述路径搜索树中各个节点的安全系数，对所述路径搜索树进行剪枝处理。
  59. 根据权利要求58所述的装置，其特征在于，所述非叶节点的安全系数为其孩子节点的安全系数之和。
  60. 根据权利要求58所述的装置，其特征在于，所述处理器还用于：
    对于各个叶子节点，确定所述叶子节点朝向所述最近邻障碍物的方向；
    根据所述方向与所述叶子节点的生长方向之间的夹角，确定所述叶子节点的安全系数。
  61. 根据权利要求60所述的装置，其特征在于，所述叶子节点的安全系数与所述夹角成正相关关系。
  62. 根据权利要求60所述的装置，其特征在于，若所述夹角大于预设角度阈值，所述叶子节点的安全系数为第一预设值；否则，所述叶子节点的安全系数为第二预设值，所述第一预设值大于所述第二预设值。
  63. 根据权利要求60所述的装置，其特征在于，所述叶子节点的生长方向为所述叶子节点与其父节点的连线的方向。
  64. 根据权利要求58所述的装置，其特征在于，所述处理器还用于：对于各个叶子节点，根据所述叶子节点与所述最近邻障碍物之间的距离，确定所述叶子节点的安全系数。
  65. 根据权利要求64所述的装置，其特征在于，所述叶子节点的安全系数与所述距离成正相关关系。
  66. 根据权利要求58所述的装置，其特征在于，在所述根据所述路径搜索树中各个节点的安全系数，对所述路径搜索树进行剪枝处理之前，所述处理器还用于：
    对所述路径搜索树中各个节点的安全系数进行归一化处理。
  67. 根据权利要求66所述的装置，其特征在于，所述归一化处理包括：对于每个节点，将自身的安全系数与属于同一父节点的节点中的最大安全系数之间的比值确定为归一化后的值。
  68. 根据权利要求58至67任意一项所述的装置，其特征在于，所述剪枝处理包括：对所述路径搜索树中安全系数低于预设阈值的节点进行剪枝处理。
  69. 根据权利要求58所述的装置，其特征在于，所述最近邻障碍物基于所述叶子节点所处位置从预存的障碍物信息中查询得到；所述障碍物信息由深度传感器在其当前感知范围和/或历史感知范围内获得。
  70. 根据权利要求37所述的装置，其特征在于，所述安全通道由在所述安全路径中生成的多个安全区域组成。
  71. 根据权利要求70所述的装置，其特征在于，所述多个安全区域的中心点均处于所述安全路径中；
    其中一个安全区域的中心点为所述目标叶子节点，其他安全区域的中心点为上一个安全区域与所述安全路径的交点。
  72. 根据权利要求70或71所述的装置，其特征在于，各个所述安全区域的尺寸根据所述安全区域所对应的一部分安全路径与最近邻障碍物之间的距离确定。
  73. 一种可移动平台，其特征在于，包括：
    机身；
    动力系统，设于所述机身内，用于驱动所述可移动平台运动；
    以及，如权利要求37至72任意一项所述的轨迹生成装置。
  74. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有可执行指令，所述可执行指令被处理器执行时实现如权利要求1至36任意一项所述的方法。
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