Mobile Robot Path Planning Method Based on Improved A-Star Algorithm

Abstract

The present invention discloses a path planning method for a mobile robot based on an improved A-Star algorithm. Step 1: Based on the actual working environment of the mobile robot, perform environmental modeling and establish a two-dimensional coordinate system. Step 2: Use the A-Star algorithm to plan and obtain the global initial path of the mobile robot. Step 3: Adopt a fuzzy logic algorithm to track and obtain the globally optimized path. Step 4: Use the 2-opt algorithm to select the optimal path. This enables the robot to effectively track an ideal path in a complex environment and at the same time can well ensure the stability of the robot dynamics.

Description

Technical Field

[0001] The present invention relates to a path planning method for a mobile robot based on an improved A-Star algorithm, belonging to the technical field of robot autonomous navigation. Background Art

[0002] In recent years, with the development and wide application of robot technology, robots have been more and more widely used in industrial, agricultural, military and civilian fields. Autonomous intelligent mobile robots are a hot topic in the international robot research field today, and path planning is an important part of intelligent mobile robots. The research purpose is to enable the robot to autonomously plan paths in different environments. Environment perception is a key technology for mobile robots to perform autonomous path planning. The sensors for obtaining this information need a sufficiently large field of view to cover the entire working area and a relatively high acquisition rate to ensure real-time information can be provided in a moving environment. Adding a vision system is the development direction of mobile robots, and its autonomy and adaptability can be greatly improved.

[0003] In the prior art, genetic algorithms and ant colony algorithms have also been proposed for the path planning method of mobile robots; the former is not applicable to the path planning of irregular obstacles. This algorithm fails to timely utilize the feedback information of the network, so the search speed of this algorithm is relatively slow, and more training time is required to obtain a more accurate solution; the latter seeks paths by ants releasing pheromones, but after the ants complete one cycle, the pheromones need to be updated in real time, and it needs to be iterated multiple times with low efficiency. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a path planning method for a mobile robot based on an improved A-Star algorithm, enabling the robot to effectively track an ideal path in a complex environment and well ensuring the stability of the robot dynamics at the same time.

[0005] To achieve the above object, the present invention provides a path planning method for a mobile robot based on an improved A-Star algorithm, including:

[0006] Step 1, based on the real working environment of the mobile robot, perform environment modeling to obtain a two-dimensional coordinate system;

[0007] Step 2, use the A-Star algorithm to plan and obtain the global initial path of the mobile robot;

[0008] Step 3, adopt a fuzzy logic algorithm to track and obtain a globally optimized path;

[0009] Step 4, use the 2-opt algorithm to select the optimal path.

[0010] Preferably, in step 1, based on the constructed two-dimensional coordinate system, the coordinates of the obstacle, the starting position X of the mobile robot start and the target position X of the mobile robot goal ;

[0011] Set the safety range of the mobile robot.

[0012] Preferably, step 2 includes:

[0013] 1), Set the step size s and the target point threshold ε;

[0014] 2), Calculate the Euclidean distance between the starting position and the target position, and compare the Euclidean distance with the step size s;

[0015] If the Euclidean distance ≤ step size s, then connect the starting position and the target position linearly and use it as the initial path of the mobile robot, otherwise execute 3);

[0016] 3), Based on the heuristic function of the A-Star algorithm, obtain the global initial path of the mobile robot.

[0017] Preferably, the heuristic function is:

[0018]

[0019] In the formula, x is the abscissa of the current position of the mobile robot, y is the ordinate of the current position of the mobile robot, x goal is the abscissa of the target position, y goal is the ordinate of the target position.

[0020] Preferably, step 3 includes:

[0021] Based on the global initial path, use the fuzzy logic algorithm to control path tracking to obtain a local obstacle avoidance path;

[0022] Perform fuzzy processing on the global initial path and the local obstacle avoidance path to obtain a globally optimized path;

[0023] The fuzzy controller sets the autonomous motion time speed constant τ of the mobile robot v and the angular velocity time constant τ θ , the motion speed v of the mobile robot c and the angle θ of the mobile robot moving forward c .

[0024] Preferably, step 4 includes:

[0025] Randomly select two coordinate positions from the globally optimized path;

[0026] Reverse the target order between the two coordinate positions to obtain a new path;

[0027] If the length of the new path is less than the length of the globally optimized path, then the new path is taken as the optimal path for the mobile robot to move; otherwise, the globally optimized path is taken as the optimal path for the mobile robot to move.

[0028] Preferably, the fuzzy logic algorithm is:

[0029]

[0030]

[0031] wherein, v c is the moving speed of the mobile robot, θ c is the moving angle of the mobile robot, τ v is the time speed constant of the robot's autonomous movement, v is the initial speed of the mobile robot, τ θ is the angular velocity time constant, is the initial angular velocity of the mobile robot;

[0032] Let the distance of the mobile robot from the starting position be ρ, and the relative distance of the mobile robot from the starting position at this time is:

[0033] x0 = ρcosθ,

[0034] y0 = ρsinθ,

[0035] wherein, x0 is the horizontal distance of the mobile robot from the starting position, and y0 is the vertical distance of the mobile robot from the starting position; the derivative of the displacement Therefore, it is obtained that:

[0036]

[0037]

[0038] wherein, is the speed of the mobile robot in the horizontal direction from the starting position, is the speed of the mobile robot in the vertical direction from the starting position;

[0039] Let It is obtained that:

[0040]

[0041]

[0042]

[0043] Let

[0044] Then

[0045] Wherein, x n is the set of points of the abscissa obtained by the mobile robot in the fuzzy logic algorithm, and y n is the set of points of the ordinate obtained by the mobile robot in the fuzzy logic algorithm;

[0046] The fuzzy logic control objective is to design the control input U. The set of points of the abscissa of the actual movement trajectory of the mobile robot and the set of points of the ordinate of the corresponding actual movement trajectory of the mobile robot are combined one by one to obtain multiple continuous coordinate positions, denoted as the local obstacle avoidance path X;

[0047] Perform fuzzy processing on the global initial path and the local obstacle avoidance path to obtain the globally optimized path X compare-ideal .

[0048] Preferably, set the safety range d safe :

[0049] d safe = r + l

[0050] Wherein, r is the working radius of the mobile robot, and l is a set constant.

[0051] Advantages achieved by the present invention:

[0052] 1. Compared with the prior art, the present invention introduces a safety distance, which ensures that when the robot moves along the planned path, it will not collide with obstacles, thus ensuring the safety of the robot.

[0053] 2. Compared with the prior art, the present invention introduces the A-Star algorithm to plan a global path that not only meets the obstacle avoidance conditions but also suits the dynamic constraints of the robot itself, improving the efficiency during path planning and ensuring the traceability of the path.

[0054] 3. Compared with the prior art, the present invention cleverly integrates the robot dynamics model with the fuzzy logic algorithm, enabling the robot to not only track an ideal obstacle avoidance path in a complex environment but also maintain the stability of the robot dynamics.

[0055] 4. Compared with the prior art, the present invention introduces the 2-opt algorithm, and the selected path not only meets the dynamic requirements of the robot but also is a more suitable ideal path for the robot to move in a complex environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is the flowchart of the present invention;

[0057] Figure 2It is a simulation result diagram of the optimal path of the present invention at different safety distances;

[0058] Figure 3 It is a simulation result diagram of the optimal path of the present invention at different safety distances. Detailed implementation manners

[0059] The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be used to limit the protection scope of the present invention.

[0060] Based on the real working environment of the mobile robot, an environmental map is obtained through an aerial camera carried by a drone, and the environmental map is processed and transmitted to the core processor of the robot. A two-dimensional coordinate system is established for the environment where the mobile robot is located, the edges of the polygonal obstacles are sampled, and the vertex coordinates of the obstacles in the environmental map are obtained. The positions of the starting point and the target point are determined in this two-dimensional coordinate system, and a suitable safe movement range is established as the boundary condition for the A-Star algorithm to plan its safe path. A robot dynamics model is established. Assume that the robot body is circular, and the boundary conditions are: 1) For concave polygonal obstacles, obtain its vertex coordinates and connect its vertices to fill the concave polygon, that is, convexify the concave polygon. 2) For convex polygonal obstacles, set the safety distance as d safe , that is:

[0061] d safe = r + l,

[0062] where r is the working radius of the robot and l is a constant.

[0063] As Figure 1 shown, a path planning method for a mobile robot based on an improved A-Star includes:

[0064] Step 1: Processing of irregular obstacles in the environmental map, including:

[0065] Obtain the surrounding environmental information through an aerial camera carried by a drone, perform environmental modeling, establish a two-dimensional coordinate system, and obtain the starting position of the mobile robot and the target position of the mobile robot; set a safe movement range as the boundary condition for the A-Star algorithm to plan its safe path;

[0066] Step 2: Plan the global path of the mobile robot through the A-Star algorithm, including:

[0067] Initialize the A-Star algorithm, set the starting position as X start , the target position as X goal , the step size s and the target point threshold ε;

[0068] Calculate the Euclidean distance between the starting position and the target position, and compare the Euclidean distance with the step size s. If the Euclidean distance is less than or equal to the step size s, connect the starting position and the target position linearly as the initial path of the mobile robot; otherwise, execute 3).

[0069] The heuristic function H based on the A-Star algorithm n is used to quickly plan a global initial path, plan the initial path of the mobile robot and backtrack, and finally obtain the global initial path of the mobile robot.

[0070] Step 3: Adopt the fuzzy logic algorithm to effectively and smoothly track a more ideal local obstacle avoidance path and obtain a globally optimized path, including:

[0071] After the global initial path is planned by the above A-Star algorithm, the fuzzy logic algorithm is used to control path tracking to obtain a more ideal local obstacle avoidance path; the fuzzy logic control algorithm is implemented by setting a fuzzy controller.

[0072] Fuzzify the global initial path and the local obstacle avoidance path to obtain a globally optimized path. At the same time, the fuzzy controller sets the autonomous motion time speed constant τ of the mobile robot v and the angular velocity time constant τ θ , and set the speed of the mobile robot's motion and the angle of the mobile robot's advancement to achieve the optimization of the mobile robot's local path.

[0073] Step 4: The 2-opt algorithm selects the optimal path, and the steps include:

[0074] Randomly select two positions from the globally optimized path;

[0075] Reverse the target order between the two positions to obtain a new path;

[0076] If the length of the new path is less than the length of the globally optimized path, select the new path as the path for the mobile robot to move; otherwise, select the globally optimized path as the path for the mobile robot to move.

[0077] The heuristic function is:

[0078]

[0079] In the formula, x is the abscissa of the current position of the mobile robot, y is the ordinate of the current position of the mobile robot, x goal is the abscissa of the target position, and y goal is the ordinate of the target position.

[0080] In the fuzzy logic algorithm:

[0081]

[0082]

[0083] In the formula, v c is the moving speed of the mobile robot, θ c is the moving angle of the mobile robot, τ v is the time speed constant of the robot's autonomous movement, v is the initial speed of the mobile robot, τ θ is the angular velocity time constant, is the initial angular velocity of the mobile robot;

[0084] Let ρ be the distance of the mobile robot from the starting position. At this time, the relative distance of the mobile robot from the starting position is:

[0085] x0 = ρcosθ

[0086] y0 = ρsinθ,

[0087] x0 is the horizontal distance of the mobile robot from the starting position, and y0 is the vertical distance of the mobile robot from the starting position;

[0088] The derivative of displacement Therefore, it is obtained that:

[0089]

[0090]

[0091] In the formula, is the speed of the mobile robot in the horizontal direction from the starting position, is the speed of the mobile robot in the vertical direction from the starting position;

[0092] Let

[0093] That is, it is obtained that

[0094]

[0095]

[0096] Let

[0097] Then

[0098] In the formula, x n is the set of points of the abscissa of the actual movement trajectory of the mobile robot, y n is the set of points of the ordinate of the actual movement trajectory of the mobile robot,

[0099] The fuzzy logic control objective is to design the control input U. The point set of the abscissa of the actual movement trajectory of the mobile robot and the point set of the ordinate of the corresponding actual movement trajectory of the mobile robot are combined one by one to obtain multiple continuous coordinate positions, denoted as the local obstacle avoidance path X. The global initial path and the local obstacle avoidance path are fuzzily processed to obtain the global optimized path X compare-ideal . To verify the feasibility and effectiveness of the mobile robot path planning method provided by the present invention, in this embodiment, an environment of 50m×50m is set respectively, and the starting point is X start =(2.5, 3), the end point is X goal =(43.5, 40), the speed is 1.25m / s, the angle is 5°, when r is 1.0m, l is 0.1m, and the safety distance is 1.1m, the optimal path is as Figure 2 shown; assuming that r is 1.5m, l is 0.1m, and the safety distance is 1.6m, the optimal path is as Figure 3 shown.

[0100] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and deformations can be made, and these improvements and deformations should also be regarded as the protection scope of the present invention.

Claims

1. A path planning method for a mobile robot based on an improved A-Star algorithm, characterized in that, Including: Step 1: Based on the real working environment of the mobile robot, conduct environment modeling and establish a two-dimensional coordinate system; Step 2: Use the A-Star algorithm to plan and obtain the global initial path of the mobile robot; Step 3: Adopt the fuzzy logic algorithm to track and obtain the globally optimized path; Among them, the fuzzy logic algorithm is: where v c is the moving speed of the mobile robot, θ c is the moving angle of the mobile robot, τ v is the time speed constant of the robot's autonomous movement, v is the initial speed of the mobile robot, τ θ is the angular velocity time constant, and is the initial angular velocity of the mobile robot; Let the distance ρ from the mobile robot to the starting position, and the relative distance of the mobile robot to the starting position at this time is: x0 = ρcosθ, y0 = ρsinθ, In the formula, x0 is the horizontal distance from the mobile robot to the starting position, and y0 is the vertical distance from the mobile robot to the starting position; Derivative of displacement Thus, it is obtained that: wherein, is the horizontal velocity of the mobile robot to the starting position, is the vertical velocity of the mobile robot to the starting position; Set Obtain: Set Then where x n is the set of points of the abscissa obtained by the mobile robot in the fuzzy logic algorithm, and y n is the set of points of the ordinate obtained by the mobile robot in the fuzzy logic algorithm; The fuzzy logic control objective is to design the control input U. The point set of the abscissa of the actual movement trajectory of the mobile robot and the point set of the ordinate of the corresponding actual movement trajectory of the mobile robot are combined one by one to obtain multiple continuous coordinate positions, denoted as the local obstacle avoidance path X; Fuzzify the global initial path and the local obstacle avoidance path to obtain the globally optimized path X compare-ideal ; Step 4: Use the 2-opt algorithm to select the optimal path.

2. The path planning method for a mobile robot based on the improved A-Star algorithm according to claim 1, wherein In Step 1, based on the constructed two-dimensional coordinate system, obtain the coordinates of the obstacle, the starting position X of the mobile robot start and the target position X of the mobile robot goal ; Set the safety range of the mobile robot.

3. The path planning method for a mobile robot based on the improved A-Star algorithm according to claim 2, characterized in that, Step 2 includes: 1), Set the step size s and the target point threshold ε; 2), Calculate the Euclidean distance between the starting position and the target position, and compare the Euclidean distance with the step size s; If the Euclidean distance ≤ step size s, then directly connect the starting position and the target position and use it as the initial path of the mobile robot, otherwise execute 3); 3), Based on the heuristic function of the A-Star algorithm, obtain the global initial path of the mobile robot.

4. The path planning method for a mobile robot based on the improved A-Star algorithm according to claim 3, wherein The heuristic function is: where x is the abscissa of the current position of the mobile robot, y is the ordinate of the current position of the mobile robot, x goal is the abscissa of the target position, and y goal is the ordinate of the target position.

5. The path planning method for a mobile robot based on the improved A-Star algorithm according to claim 3, wherein Step 3 includes: Based on the global initial path, adopt the fuzzy logic algorithm to control path tracking to obtain the local obstacle avoidance path; Perform fuzzy processing on the global initial path and the local obstacle avoidance path to obtain the globally optimized path; The fuzzy controller sets the autonomous motion time speed constant τ of the mobile robot v , the angular velocity time constant τ θ , the motion speed v of the mobile robot c and the angle θ of the mobile robot moving forward c .

6. The path planning method for a mobile robot based on the improved A-Star algorithm according to claim 5, characterized in that, Step 4 includes: Randomly select two coordinate positions from the globally optimized path; Reverse the target order between the two coordinate positions to find a new path; If the length of the new path is less than the length of the globally optimized path, then use the new path as the optimal path for the mobile robot to move, otherwise use the globally optimized path as the optimal path for the mobile robot to move.

7. The path planning method for a mobile robot based on the improved A-Star algorithm according to claim 2, wherein Set the safety range d of the mobile robot safe : d safe ＝r+l In the formula, r is the working radius of the mobile robot, and l is a set constant.

Images