Path planning method for unmanned forklift

Abstract

Disclosed in the present invention is a path planning method for an unmanned forklift. The method comprises: step 1, drawing a feasible area for an unmanned forklift on a constructed grid map, moving the unmanned forklift to record coordinates of stations, and automatically generating a topological map consisting of nodes and edges; step 2, setting a starting point and a destination point for planning, and on the basis of cost weights of all the edges in the topological map, searching for an optimal route; and step 3, driving the unmanned forklift to move along the planned optimal route. In the path planning method for an unmanned forklift of the present invention, a topological map required for a path search is automatically generated, without the need for manual drawing, thereby increasing the efficiency; different attributes of a route are automatically set on the basis of a task type, an operation area, etc., such that the method is more applicable to unmanned forklifts; and if it is not possible to make a U-turn due to insufficient space on a main road, another feasible area can be used to complete the U-turn, thereby increasing the flexibility.

Description

A path planning method for unmanned forklifts Technical Field

[0001] The present invention relates to the technical field of unmanned forklifts, and in particular to a path planning method for unmanned forklifts. Background Art

[0002] Patent: CN106382944A This solution connects different nodes on a map through edges. Setting node rotation properties increases connectivity between nodes. Adjusting line segment weights adjusts the robot's movement path. The robot can dock anywhere along the line without adding nodes. However, this solution requires providing a map, drawing routes, and configuring node properties, which can be inefficient in large environments. It also doesn't distinguish between forward and reverse routes, making it unsuitable for unmanned forklift routing.

[0003] Patent: CN110872080A In this solution, the path planning module performs image modeling of the path and scene before debugging, plans the optimal path at the end and starting point, and can also re-plan the route in real time according to the conditions on the road. However, if the working environment is large, the efficiency of this solution is low; if the working environment is complex, there are blind spots in the sensor sensing, and there are certain safety hazards in re-routing. Summary of the Invention

[0004] The purpose of the present invention is to provide a path planning method for an unmanned forklift to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a method for planning a path for an unmanned forklift, comprising the steps of: drawing a feasible area for the unmanned forklift on a constructed grid map, moving the unmanned forklift to record the coordinates of each station, and automatically generating a topological map consisting of nodes and edges;

[0006] Step 2: Set the starting point and target point of the plan, and search for the optimal route based on the cost weights of all edges in the topological map;

[0007] Step three: drive the unmanned forklift along the planned optimal route.

[0008] Preferably, the specific steps of generating the topological map are:

[0009] S11: Move the unmanned forklift within the work area to build a grid map of the physical environment;

[0010] S12: Move the robot to each site and record the site as a node in the topological map;

[0011] S13: Draw the feasible area of ​​the unmanned forklift on the grid map;

[0012] S14: After drawing the feasible area and generating the topological map nodes, the algorithm automatically generates an edge (route) connecting the two nodes. The unmanned forklift drives along the generated route. The vehicle outline and cargo do not exceed the feasible area. The corresponding cost weight is automatically set according to the type of route.

[0013] S15. A complete topological map is formed by all nodes and edges.

[0014] Preferably, the optimal route is a step of setting a starting point and an optimal point:

[0015] S21: Design a priority queue consisting of multiple routes. Each route records the cost from the starting point to the current route and from the current route to the end point. The more turns, U-turns, and reverse movements a route has, the greater the cost. The priority queue automatically sorts routes by cost, facilitating the selection of the lowest-cost route during each iterative search. The specific process is as follows: Each iteration begins with the lowest-cost route in the priority queue and sequentially traverses connected routes, updating their costs and storing them in the priority queue. The search continues until the destination is reached. Each route is assigned a forklift travel direction: forward or reverse. Combining routes with different directions allows for unmanned forklifts to pick up and drop off goods, charge, and return to a standby point. If the angle between two connected routes is greater than 90°, the forklift must make a U-turn. If the main road is insufficient to prevent a U-turn, the cost of the route is set to infinite. This means that although the two routes are physically connected, they are considered disconnected during the route search. However, a U-turn can be completed within the mission's feasible area.

[0016] S22: After searching to the end point, trace back from the end point until it reaches the starting point and stops. The obtained route is the optimal route.

[0017] Preferably, the optimal route movement is to send the route parameters to the control module for execution after obtaining the optimal route, and drive the unmanned forklift to travel along the optimal route to the target.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. The present invention automatically generates the topological map required for path search, eliminating the need for manual drawing and improving efficiency;

[0020] 2. This invention automatically sets different route attributes based on task type, operation area, etc., making it more applicable to unmanned forklifts.

[0021] 3. In this invention, if there is insufficient space on the main road to make a U-turn, the U-turn can be completed in other feasible areas, increasing flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] FIG1 is a schematic structural diagram of the finishing process of the present invention; DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] Referring to FIG1 , the present invention provides a technical solution: a method for planning a path for an unmanned forklift, wherein, in step 1, a feasible area for the unmanned forklift is drawn on a constructed grid map, and the unmanned forklift is moved to record the coordinates of each station, and a topological map consisting of nodes and edges is automatically generated;

[0025] Step 2: Set the starting point and target point of the plan, and search for the optimal route based on the cost weights of all edges in the topological map;

[0026] Step three: drive the unmanned forklift along the planned optimal route.

[0027] Further: the specific steps of generating the topological map:

[0028] S11: Move the unmanned forklift within the work area to build a grid map of the physical environment;

[0029] S12: Move the robot to each site and record the site as a node in the topological map;

[0030] S13: Plot the feasible areas for the unmanned forklift on the grid map, as shown in Figure 1. Different feasible areas have different forklift driving directions: 1. The mission feasible area is the work area, where the forklift generally enters from the back and leaves from the forward direction. 2. The feasible area for the main road allows for the forklift's driving direction to be set arbitrarily, and multiple lanes can be generated, corresponding to multiple routes to facilitate mixed traffic. The generated routes automatically set relevant attributes such as obstacle avoidance and speed.

[0031] S14: After drawing the feasible area and generating the topological map nodes, the algorithm automatically generates the edge (route) connecting the two nodes. The unmanned forklift travels along the generated route. The outline of the entire vehicle and the cargo do not exceed the feasible area, and the corresponding cost weight will be automatically set according to the type of route. As shown in Figure 1, in the feasible area of ​​the task, node 1 is the recorded pick-up / drop-off site. The forklift goes from node 4 on the main road to node 1 to perform the pick-up / drop-off task, and returns to the main road after completion. The specific route is: Node 4→2 forklift backs up, node 2→1 forklift backs up, node 1→2 forklift moves forward, node 2→3 forklift moves forward, completing the pick-up / drop-off task and returning to the main road.

[0032] S15. A complete topological map is formed by all nodes and edges.

[0033] Further: The optimal route is the step of setting the starting point and the optimal point:

[0034] S21: Design a priority queue consisting of multiple routes. Each route records the cost from the starting point to the current route and from the current route to the end point. The more turns, U-turns, and reverse driving there are in a route, the greater the cost. The priority queue will automatically sort the routes according to the cost, facilitating the selection of the route with the lowest cost during each iterative search. The specific process is as follows: From the set starting point, the search iterates outward. Each iteration starts with the route with the lowest cost in the priority queue, traverses the connected routes in sequence, updates their costs and stores them in the priority queue, and stops after continuous iterations to the target point. Each route is set with a forklift travel direction: forward and reverse. The combination of routes with different directions can realize functions such as unmanned forklift pickup and delivery, charging, and returning to the standby point. When the angle between two connected routes is greater than 90°, the forklift needs to make a U-turn. If the main road is not spacious enough to prohibit U-turns, the cost of the route will be set to infinity. That is, although the two lines are physically connected, they will be considered disconnected during route search. However, the U-turn can be completed in the feasible area of ​​the task. As shown in Figure 1, if the planning starting point is 5 and the target point is 1, the forklift is facing right. Because the forklift can only move forward and not backward in the feasible area of ​​the main road, and U-turns are prohibited, in order to reach the target point 1, the forklift needs to make a U-turn at node 5 so that the front of the vehicle faces left. The U-turn can be completed in the feasible area 2 of the task. The specific planned route is: the forklift moves forward from node 5 to 7, moves backward from node 7 to 8, moves forward from node 8 to 6, and moves forward from node 6 to 5 to complete the U-turn.

[0035] S22: After searching to the end point, trace back from the end point until it reaches the starting point and stops. The route obtained is the optimal route

[0036] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for unmanned forklift path planning, characterized in that: Step 1: Draw the feasible area of ​​the unmanned forklift on the constructed grid map, move the unmanned forklift to record the coordinates of each site, and automatically generate a topological map consisting of nodes and edges; Step 2: Set the starting point and target point of the plan, and search for the optimal route based on the cost weights of all edges in the topological map; Step three: drive the unmanned forklift along the planned optimal route.

2. The unmanned forklift path planning method according to claim 1, characterized in that: The specific steps of generating the topological map are as follows: S11: Move the unmanned forklift in the work area and build a grid map of the physical environment; S12: Move the robot to each site and record the site as a node in the topological map; S13: Draw the feasible area of ​​the unmanned forklift on the grid map; S14: After drawing the feasible area and generating the topological map nodes, the algorithm automatically generates the edge (route) connecting the two nodes. The unmanned forklift drives along the generated route. The outline of the vehicle and the cargo do not exceed the feasible area, and the corresponding cost weight is automatically set according to the type of route. S15. A complete topological map is formed by all nodes and edges.

3. The unmanned forklift path planning method according to claim 1, characterized in that: The optimal route is the steps of setting the starting point and the optimal point: S21: Design a priority queue consisting of multiple routes. Each route records the cost from the starting point to the current route and from the current route to the end point. The more turns, U-turns, and reverse driving in the route, the greater the cost. The priority queue will automatically sort the routes according to the cost, so that the route with the minimum cost can be selected in each iterative search. The specific process is: iterative search from the set starting point to the outside. Each iteration starts from the route with the minimum cost in the priority queue, traverses the routes connected to it in turn, updates its cost and stores it in the priority queue, and stops after continuous iteration to the target point. Each route sets the direction of forklift travel: forward and backward. The combination of routes in different directions can realize the functions of unmanned forklift picking up and releasing goods, charging, and returning to the standby point. When the angle between the two connected routes is greater than 90°, the forklift needs to turn around. If the main road is not large enough to prohibit U-turns, the cost of the route will be set to infinity, that is, although the two lines are physically connected, they will be regarded as disconnected during route search, but the U-turn can be completed by passing through the feasible area of ​​the task; S22: After searching to the end point, trace back from the end point until it reaches the starting point and stops. The obtained route is the optimal route.

4. The unmanned forklift path planning method according to claim 1, characterized in that: The optimal route movement is to send the route parameters to the control module for execution after obtaining the optimal route, and drive the unmanned forklift to travel along the optimal route to the target.

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