Mobile robot global path planning method adapting to different grid map complexity

Abstract

The invention discloses a mobile robot global path planning method adapted to different grid map complexities, which improves a grid map complexity-based adaptive ant colony optimization algorithm, and comprises the following steps: firstly, providing a grid map complexity quantification method based on the number and distribution of obstacles and map information entropy; secondly, designing a self-adaptive initial pheromone distribution strategy based on the complexity of the grid map; thirdly, optimizing the state transition probability; and fourthly, providing a pheromone dynamic updating mechanism based on the complexity of the grid map. According to the method, good adaptive adjustment capability and efficiency are shown in grid charts with different complexities.

Description

Technical Field

[0001] The invention belongs to the technical field of mobile robots, and in particular relates to a global path planning method for mobile robots that is adaptable to different grid map complexities. Background Art

[0002] As one of the core modules of the decision-making and planning system of mobile robots, global path planning plays a vital role in improving its operational efficiency, practicality and reliability. The effectiveness of global path planning is affected by multiple key factors, mainly including environment representation, environment modeling and path planning algorithm. There are complex interactions between these factors, which significantly affect the performance and applicability of global path planning. The choice of environment representation method is directly related to modeling accuracy and computational efficiency, while in the field of global path planning, the selection of algorithm is crucial, which directly affects the autonomous navigation efficiency, path optimization, resource utilization and adaptability of mobile robots in complex environments. Efficient path planning algorithms enable robots to achieve optimal path planning under multi-objective and multi-constraint conditions while maintaining good robustness and real-time performance.

[0003] At present, the technical solution of using ant colony algorithm for global path planning of mobile robots is suitable for path planning of mobile robots in static environments. It mainly includes: 1. System architecture environment modeling: obtain environmental information through sensors and build a real-time dynamic grid map. Each grid in the map represents a state, in which obstacle grids and free grids will be marked. Ant colony initialization: initialize the ant population, each ant randomly selects a starting point, and initializes the pheromone matrix and heuristic information matrix to preset values. 2. Path search process: Path selection: Ants select the next moving position according to the state transition probability. It is determined by the state transition probability formula. Path construction: While selecting a path, ants record the nodes passed and calculate the path cost, including path length, energy consumption, and the number of dangerous areas passed. 3. Pheromone update: Every time an ant walks through a path, the pheromone is locally updated according to the pheromone volatilization and path cost to encourage the exploration of new paths. After completing a round of search, the pheromone is updated according to the quality of the best path to enhance the attractiveness of the high-quality path. 4: The algorithm ends after running to the preset number of iterations or finding a path that meets the performance indicators.

[0004] Although the ant colony algorithm has many advantages in path planning, it also has some disadvantages. First, its convergence speed is slow, especially in complex environments, and it usually takes more iterations to find the optimal solution. In addition, the ant colony algorithm is prone to fall into the local optimal solution, especially when the path search space is large, the search effect may not be ideal. The algorithm is highly sensitive to parameter settings (such as pheromone importance α and heuristic information importance β), and improper parameter selection will lead to low search efficiency or unstable results. At the same time, if the pheromone evaporates too fast, good path information will be quickly forgotten, while if it evaporates too slowly, the pheromone may be too concentrated, affecting the exploration ability. In large-scale maps or high-dimensional spaces, the algorithm has a high computational complexity and may require a lot of computing resources, thus affecting real-time performance. Although the ant colony algorithm has a certain dynamic adaptability, the speed and accuracy of path adjustment are still insufficient in frequently changing environments. In addition, the choice of path evaluation indicators will also affect the algorithm decision-making, and a too single evaluation criterion may not fully reflect the pros and cons of the path. Therefore, optimization and improvement of these shortcomings are the key to improving the performance of the ant colony algorithm. Summary of the invention

[0005] Purpose of the invention: In view of the above problems, the present invention proposes a global path planning method for mobile robots that adapts to different grid map complexities, which satisfies the flexibility and adaptability of mobile robots in path planning in grid maps. It aims to improve the autonomous navigation capability of mobile robots in complex and dynamic environments by optimizing the algorithm design of global path planning, and meet the growing demand for intelligent applications.

[0006] Technical solution: To achieve the purpose of the present invention, the technical solution adopted by the present invention is: a global path planning method for a mobile robot adapted to different grid map complexities, comprising the following steps:

[0007] The first step is to obtain environmental information, build a real-time dynamic grid map, and calculate the map complexity based on the number, distribution, and connectivity of obstacles;

[0008] Step 2: Initialize the ant colony algorithm: Initialize the ant population, each ant randomly selects a starting point, and uses an adaptive initial pheromone allocation strategy based on grid mapping complexity to initialize the pheromone matrix and heuristic information matrix to preset values;

[0009] Step 3: Path search process:

[0010] Path selection: An improved ant colony state transfer strategy is adopted, and a predefined threshold that varies with the map complexity is introduced to provide a balanced deterministic and random selection of the search direction. Ants select the next moving location node based on the state transfer probability.

[0011] Path construction: When ants select a path, they record the nodes they pass through and calculate the path cost;

[0012] Step 4: Pheromone Update:

[0013] Every time an ant walks through a path, the pheromone is locally updated according to the pheromone volatilization and pheromone increase, and the grid map complexity and the number of iterations are introduced into the expressions of the pheromone evaporation coefficient and the intensity coefficient respectively to update the path;

[0014] The algorithm runs to the preset number of iterations or ends when a path that meets the performance indicators is found.

[0015] Furthermore, in the first step, the map complexity calculation process is as follows:

[0016] First, the obstacle grid ratio is calculated as a basic measure of the degree of environmental obstruction. Second, the distribution of obstacles is used to capture map features. Finally, the Shannon entropy theory is applied to evaluate the information complexity of the map.

[0017] The map complexity calculation formula is as follows:

[0018]

[0019] Among them, G represents the complexity of the grid map, and its value range is from 0 to 1. The closer the value is to 0, the lower the complexity is, and the closer it is to 1, the higher the complexity is. all Represents the sum of the number of obstacle grids and free grids, N os Indicates the number of obstacle grids, N ef Indicates the total number of free grids adjacent to the barrier grid; is the Shannon entropy formula, where n = 2 means that the grid map contains only two types of grids, and when i = 1, x i represents the obstacle grid, when i=2, x i represents a free grid, p(x i ) is the probability of occurrence of obstacle grid and free grid, x i It is used to calculate the free grid and obstacle grid values, and then calculate the ratio; θ1, θ2, and θ3 represent the weight coefficients corresponding to the obstacle proportion, obstacle distribution, and distribution chaos, respectively.

[0020] Furthermore, in the second step, the pheromone initialization process is as follows:

[0021] When the pheromone is initialized, the distribution of pheromones is dynamically adjusted at the beginning of the optimization according to the complexity of the grid map and the grid distance from the start and end lines. The expression is as follows:

[0022] τ ij (initial)′=e^G*τ ij(initial)*ln(N all ) (2),

[0023]

[0024] Among them, τ ij (initial)′ is the improved pheromone value, τ ij (initial) is the initial pheromone value, i and j are the current node and the next node respectively, G is the target node, N all Represents the sum of the number of obstacle grids and free grids; u ij (initial) is the pheromone distribution value determined by distance, d is the distance of the current node i from the starting point and the end point, the coordinates of node i are (x, y), and the coordinates of the starting point S are (x S ,y S ), the target point T coordinate is (x T ,y T ).

[0025] Furthermore, in the third step, the path search process includes:

[0026] Design an improved ant colony state transfer strategy, introduce a predefined threshold q0 that varies with the map complexity, and provide a balanced deterministic and random selection of search directions, such as formula (5) and formula (6);

[0027]

[0028] in, represents the probability of improving ant m to move from node i to node j in the kth iteration. The argmax function is used to determine the index of the maximum value in the matrix, τ ij (k) is the pheromone concentration, η ij (k) is the heuristic function, α and β are the exponents of the pheromone concentration and the heuristic function, respectively. is the state transition probability, q is a random variable uniformly distributed in the interval [0,2], q0 is a predefined threshold, G is the grid map complexity, k is the current iteration number, and K is the total iteration number;

[0029] According to formula (5), when q≤q0, the algorithm selects the node that maximizes the product of pheromone concentration and heuristic function; when q>q0, the algorithm switches to random selection mode and adopts the roulette wheel selection method in the traditional ant colony algorithm.

[0030] Furthermore, in the third step, the path search process also includes:

[0031] Based on the complexity of the grid map, the Euclidean distance from the ant colony to the end point and the turning angle of the ant colony path, an improved heuristic function η′ is designed by quantifying the Euclidean distance and path turning angle of the grid. ij , as shown in formula (7) to formula (10);

[0032]

[0033] Among them, η′ ij is the improved heuristic function, λ is the global adjustment coefficient used to control the overall strength of the heuristic function, w1 and w2 are weight coefficients that satisfy the constraint w1+w2=1; d ij is the distance from the current node i to the next node j, d iT Represents the Euclidean distance from the current node i to the target node T, and the calculation formula is (x i ,y i ) and (x T ,y T ) are the coordinates of node i and target node T respectively; a(i) represents the path turning angle, which is defined as the angle between the current node i, the previous node l and the next node j. is the vector of the ant colony from node l to node i, is the vector of the ant colony from node i to node j; d ST is the Euclidean distance from the starting point S to the target point T, k is the current iteration number, K is the total iteration number, and G is the adjustment parameter, that is, the complexity of the grid map.

[0034] Furthermore, in the third step, the path search process also includes:

[0035] Design a dynamic coefficient definition mechanism based on the cosine annealing strategy, and adjust the pheromone concentration and the exponents α and β of the heuristic function according to the iteration, as shown in formula (11) and formula (12);

[0036]

[0037]

[0038] Among them, α max , α min are the upper and lower boundaries of α, β max and β min are the upper and lower boundary values ​​of β, k is the current iteration number, and K is the total iteration number;

[0039] α max =ln(N all ) / e,α min =ln(N all ) / e 2 , βmax =lg(N all )*2e,β min =lg(N all ), where N all Represents the sum of the number of obstacle grids and free grids, and e is a natural number.

[0040] Furthermore, in the fourth step, the pheromone dynamic update mechanism is as follows:

[0041] Mapping complexity and iteration are introduced into the expressions of pheromone evaporation coefficient and intensity respectively. On this basis, the pheromone concentration is kept within the preset range according to the definition and calculation of map complexity, such as formula (13) to formula (16);

[0042]

[0043] Among them, ρ′ is the volatility coefficient, ρ is the pheromone evaporation coefficient, G is the complexity of the grid map, M is the number of ants, k is the current iteration number, K is the total iteration number; Q′ is the improved pheromone strength, Q is the pheromone strength; τ ij (k) is the pheromone concentration on the path; τ max and τ min They are τ ij The maximum and minimum values ​​of (k), τ ij (initial)′ is the improved initial pheromone distribution, L min represents the shortest path length found by the current ant after the kth iteration, N all Represents the sum of the number of barrier grids and free grids.

[0044] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0045] The present invention discloses a global path planning method for a mobile robot that is adaptable to different grid map complexities. The method quantifies the grid map complexity based on the number, distribution and connectivity of obstacles, designs an adaptive initial pheromone distribution strategy based on the grid mapping complexity, and optimizes the search strategy for global path planning. The method comprehensively considers the adaptive heuristic function of target point information, corner constraints and grid map complexity, and a dynamic pheromone update mechanism based on the grid map complexity, thereby improving the path planning capability of the mobile robot in different environments.

[0046] The new pheromone initialization definition speeds up the accumulation of pheromones on early paths and improves the spatial distribution of pheromones on early paths, which is conducive to the concentration of pheromones on high-quality paths and the convergence speed of optimization. The improved initial pheromone value can be dynamically adjusted according to the complexity and scale of the grid map. By dynamically adjusting the unevenness of the initial pheromone distribution, the algorithm enhances the priority processing ability of high-potential areas, thereby improving the efficiency of the initial search. By introducing the complexity of the grid map into the ant colony algorithm, the adaptability and efficiency of the ant colony algorithm are significantly improved while maintaining the inherent advantages of the ant colony algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is the overall flow chart of the method of the present invention.

[0048] Figure 2 is the distribution of the obstacle grid.

[0049] Figure 3 is the change in the initial value of the pheromone.

[0050] Figure 4 It is a schematic diagram of calculating the distance d.

[0051] Figure 5 is the adaptive change of q0.

[0052] Figure 6 It is the calculation diagram of turning angle a(i).

[0053] Figure 7 is the changing trend of α.

[0054] Figure 8 It is the changing trend of β.

[0055] Fig. 9 is the number of convergence iterations for the five ACO variants.

[0056] Fig.10 is the optimal path for the 5 ACO variants.

[0057] Fig.11 It is the best path for a 20×20 grid map scale.

[0058] Fig.12 is the number of convergence iterations for a 20×20 grid map scale.

[0059] Fig.13 It is a real-world experimental map.

[0060] Fig.14 It is the optimal path of the four algorithms in the grid map. DETAILED DESCRIPTION

[0061] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0062] The present invention discloses a global path planning method for a mobile robot that is adaptable to different grid map complexities. First, a grid map complexity quantification method based on the number, distribution and connectivity of obstacles is proposed. Second, an adaptive initial pheromone allocation strategy based on grid mapping complexity is designed. Third, the search strategy for global path planning is optimized. An adaptive heuristic function that comprehensively considers target point information, corner constraints and grid map complexity is proposed. Fourth, a dynamic pheromone update mechanism based on grid map complexity is proposed.

[0063] In order to improve the path planning ability of mobile robots in different environments, an adaptive ant colony algorithm based on grid mapping is proposed. By introducing the complexity of grid mapping into the ant colony algorithm, the adaptability and efficiency of the ant colony algorithm are significantly improved while maintaining the inherent advantages of the ant colony algorithm. The present invention adopts four optimization strategy modules.

[0064] First, the map complexity calculation module: first, the obstacle grid ratio is calculated to provide a basic measure of the degree of environmental obstruction; second, the distribution of obstacles is used to accurately capture the map features; finally, the Shannon entropy theory is applied to evaluate the information complexity of the map; as shown in Formula 1.

[0065]

[0066] Among them, G represents the complexity of the grid map, and its value range is 0 to 1. The closer the value is to 0, the lower the complexity is, and the closer it is to 1, the higher the complexity is. all Represents the sum of the number of obstacle grids and free grids, N os Indicates the number of obstacle grids, N ef Indicates the total number of free grids adjacent to the barrier grid. is the Shannon entropy formula, where p(x i ) is the probability of occurrence of obstacle grids and free grids, which is used to determine the information entropy of the grid map. In the grid map, the grid is an obstacle grid or a free grid, so the value of n is 2, and when i=1, x i represents the obstacle grid, when i=2, x i represents a free grid. θ1, θ2, and θ3 represent the weight coefficients corresponding to the proportion of obstacles, obstacle distribution, and distribution disorder, respectively. The complexity of the grid map mainly depends on the number of obstacles, so the value of θ1 is set to 0.6. Considering that the importance of obstacle distribution is equal to that of map information entropy, the values ​​of θ2 and θ3 are both set to 0.2.

[0067] Figure 2Different distribution patterns of obstacles in a grid map and their impact on map complexity are shown. Each obstacle grid is defined by four boundaries and surrounded by four adjacent grids. The distribution of obstacle grids directly affects the number of free grids around it, and thus the complexity of the overall grid map. The analysis shows that there are 4 free grids around one obstacle; 6 free grids around two connected obstacles; 8 free grids around two independent obstacles; 8 free grids around four connected obstacles; 10 free grids around four consecutive obstacles; and 16 free grids around four completely independent obstacles. The analysis reveals a significant rule: when the number of obstacles is the same, the fewer connections between obstacles, the more free grids there are around them. In other words, the dispersed obstacle distribution provides more potential path options, thereby increasing the complexity of path planning. This multi-dimensional complexity evaluation method can not only objectively and clearly quantify the differences between different grid maps, but also provide the necessary theoretical support for the key operations of the ant colony algorithm, including pheromone initialization, node search, and adaptive optimization of the pheromone update mechanism.

[0068] Second, an improved pheromone initialization module: the complexity of the grid map and the grid distance away from the start and end lines are induced during pheromone initialization, and the distribution of pheromones is dynamically adjusted at the beginning of optimization, as shown in formulas (2), (3) and (4).

[0069] τ ij (initial)′=e^G*τ ij (initial)*ln(N all ) (2),

[0070]

[0071]

[0072] Among them, τ ij (initial)′ is the improved pheromone value, τ ij (initial) is the initial pheromone value, i and j are the current node and the next node respectively, G is the target node, N all Represents the sum of the number of obstacle grids and free grids; i ij (initial) is the pheromone distribution value determined by distance, d is the distance of the current node i from the starting point and the end point line, the coordinates of node i are (x, y), the starting point is S, and its coordinates are (x S ,y S ), the target point is T, and its coordinates are (x T ,y T ).

[0073] The new pheromone initialization definition speeds up the accumulation of pheromones on early paths and improves the spatial distribution of pheromones on early paths, which quickly facilitates the concentration of pheromones on high-quality paths and optimizes the convergence speed. The improved initial pheromone value can be dynamically adjusted according to the complexity and scale of the grid map.

[0074] Figure 3 The dynamic adjustment curve of the initial pheromone is shown. This adaptive mechanism enhances the flexibility of the algorithm, enabling it to adapt to map environments of different complexity. For example, as the complexity of the grid map increases, appropriately increasing the initial pheromone level can accelerate the convergence process of the algorithm and enhance its adaptive adjustment ability. The reason for this phenomenon is that when ants find a path and return to the starting point, they release pheromones along the way. When the initial pheromone value is high, the accumulation rate of pheromones on the early paths is accelerated. This positive feedback mechanism quickly strengthens the pheromone concentration on high-quality paths, prompting more ants to choose these paths, thereby accelerating the convergence of the algorithm. As d increases, the distance from point i to the straight line connecting S and T increases, and its influence on the optimal path decreases, resulting in a decrease in the initial pheromone level. By dynamically adjusting the unevenness of the initial pheromone distribution, the algorithm enhances the priority processing ability of high-potential areas, thereby improving the efficiency of the initial search. Figure 4 This is an example of how the distance d is calculated.

[0075] Third, node search efficiency improvement module: For the ant colony algorithm, the state transition probability is the most critical factor that determines the optimization (node ​​search) efficiency and solution quality. Different from the traditional ant colony algorithm, the method of the present invention designs a new ant colony state transition strategy based on the ant colony mapping complexity, the Euclidean distance from the ant colony to the end point, and the turning angle of the ant colony path. Among them, in order to improve the convergence efficiency while maintaining the population diversity and global search ability, a predefined threshold q0 that varies with the map complexity is introduced to provide and balance the deterministic selection and random selection of the search direction, such as formulas (5) to (6). By quantizing the Euclidean distance and path turning angle a(i) of the grid, an improved heuristic function is derived to improve the global search ability of the ant colony algorithm and ensure the smoothness of the path, such as formulas (7) to (10). In addition, a dynamic coefficient definition mechanism based on the cosine annealing strategy is proposed, which adjusts the pheromone concentration and the exponents α and β of the heuristic function according to iteration, such as formulas (11) to (12).

[0076]

[0077]

[0078] in, represents the probability of improving ant m to move from node i to node j in the kth iteration; the argmax function is used to determine the index of the maximum value in the matrix, τ ij (k) is the pheromone concentration, η ij (k) is the heuristic function, α and β are the exponents of the pheromone concentration and the heuristic function, respectively. is the state transition probability, q is a random variable uniformly distributed in the interval [0,2], q0 is a preset threshold used to balance exploration and development, G is the complexity of the grid map, k is the current number of iterations, and K is the total number of iterations; According to formula (5), when q≤q0, the algorithm selects the point that maximizes the product of the pheromone concentration and the heuristic function. This deterministic selection mechanism helps improve the convergence efficiency and local search ability of the algorithm. On the contrary, when q>q0, the algorithm switches to a random selection mode and adopts the roulette selection method in the traditional ant colony algorithm. This randomness helps maintain population diversity and enhances the global search ability of the algorithm, thereby reducing the risk of falling into a local optimum. Figure 5 The adaptive variation range of q0 is shown. In the early stage, q0 takes a larger value to select a high-probability deterministic transition and speed up the search for the local optimal path. In the middle stage of the algorithm, q0 takes a smaller value to increase the probability of selecting a random transition and prevent local optimality. As the algorithm enters the later stage, its evolutionary direction is basically determined, and gradually increasing q0 can speed up the convergence speed. In addition, q0 is positively correlated with the complexity of the grid map, which enhances the adaptability of the algorithm. In formula (7), λ is the global adjustment coefficient, which is used to control the overall strength of the heuristic function. w1 and w2 are weight coefficients that satisfy the constraint w1+w2=1. d iT Represents the Euclidean distance from the current point i to the target point T, and the calculation formula is a(i) represents the path turning angle, which is defined as the angle between the current node i, the previous node l, and the next node j, as shown in formula (8) and Figure 6 As shown; d ST is the Euclidean distance from the starting point S to the target point T. As the number of iterations increases, the value of the numerator λ gradually decreases and the denominator increases, thereby gradually weakening the influence of the heuristic information in the subsequent iterations of the algorithm and accelerating the convergence process. In addition, by introducing the parameter G, the parameter can dynamically adjust its value according to the complexity of the grid map, thereby enhancing the adaptability of the algorithm to grid maps of different complexities. max , α min are the upper and lower boundaries of α, β max and β min are the upper and lower boundary values ​​of β respectively. Specifically, α max =ln(N all ) / e,α min =ln(N all ) / e 2 ,β max=lg(N all )*2e,β min =lg(N all ). Figure 7 and Figure 8 The changing trend of α and β is shown. In the early stage of the algorithm, the value of α is relatively small, while the value of β is relatively large. This configuration gives the heuristic function a larger state transition probability weight, thereby enhancing the guiding role of the target point in the ant path selection. As the number of iterations k increases, the algorithm gradually finds a suitable path, the value of α gradually increases, and the value of β gradually decreases. This change makes the ant colony more inclined to choose the successful path that has been discovered, promotes convergence to a known high-quality solution, and speeds up the overall search efficiency. This embodiment verifies the convergence iteration number through 5 ACO variants, such as Fig. 9 shown.

[0079] Fourth, a new dynamic pheromone update mechanism module: Since the pheromone evaporation coefficient and intensity are static, the traditional ant colony algorithm is prone to fall into a local optimal solution. In order to solve this problem, the present invention proposes a new pheromone dynamic update mechanism, which introduces mapping complexity and iteration into the expressions of pheromone evaporation coefficient and intensity respectively. On this basis, the area that keeps the pheromone concentration within a reasonable range is defined and calculated according to the map complexity, which can prevent the pheromone value from being too high or insufficient, and avoid premature convergence, as shown in formulas (13) to (16).

[0080]

[0081] Among them, ρ′ is the volatility coefficient, ρ is the pheromone evaporation coefficient, G is the complexity of the grid map, M is the number of ants, k is the current iteration number, K is the total iteration number; Q′ is the improved pheromone strength, Q is the pheromone strength; τ ij (k) is the pheromone concentration on the path; τ max and τ min They are τ ij The maximum and minimum values ​​of (k), τ ij (initial)′ is the improved initial pheromone distribution, L min represents the shortest path length found by the current ant after the kth iteration, N all Represents the sum of the number of barrier grids and free grids.

[0082] In formulas (13) and (14), as the number of iterations increases, the pheromone intensity Q′ gradually decreases, while the volatility coefficient ρ′ gradually increases. This effectively prevents the pheromone from being over-concentrated and prevents the algorithm from falling into a local optimum. In addition, the impact of the complexity of the grid map on the pheromone update is also considered. When the complexity of the grid map increases, the algorithm needs more time to quickly find the optimal path. Therefore, in order to improve the efficiency of the algorithm, it is necessary to dynamically reduce the volatility coefficient and increase the pheromone intensity. On the contrary, when the map is simple and the complexity is reduced, the algorithm can easily find the optimal path. At this time, the volatility coefficient should be dynamically increased and the pheromone intensity should be reduced to avoid falling into a local optimum. In formulas (15) and (16), τ ij (k) Pheromone concentration on the path, τ max and τ min They are τ ij The present invention introduces the complexity of the grid map into τ max and τ min , and proposed a new calculation method, such as formula (16), variable L min represents the shortest path length found by the current ant after the kth iteration. According to the result of formula (16), in τ ij In the limited range of (k), not only the shortest path length exists, but also improved parameters such as volatility coefficient and pheromone intensity are considered. This will help to find the optimal solution more easily in subsequent iterations. In addition, the impact of dynamic complexity changes is also considered. By keeping pheromones within a reasonable range, the algorithm can effectively avoid falling into local optimization too early.

[0083] In the environment modeling and global path planning of mobile robots, maps constructed by grid method are widely used. However, the complexity of grid maps in different engineering applications varies greatly, requiring the path planning algorithm to have high flexibility, efficiency and adaptability, which makes the development of general and robust efficient algorithms face great challenges. To solve this problem, the present invention proposes an improved adaptive ant colony optimization algorithm (GMCACO) based on grid map complexity. The algorithm effectively overcomes the shortcomings of the prior art through the following innovations: a grid map complexity quantification method based on the number and distribution of obstacles and map information entropy is proposed, an adaptive initial pheromone allocation strategy based on map complexity is designed, the state transition probability is optimized to improve the node search efficiency, and a dynamic pheromone update mechanism based on complexity is proposed, thereby enhancing the adaptability and robustness of the algorithm. In order to verify the performance of GMCACO, a large number of simulation and experimental studies were carried out. The effectiveness of the improved method of GMCACO was verified in the simulation and compared with the latest improved ant colony algorithm; the experiment compared GMCACO with the commonly used A* and Dijkstra algorithms. The results show that GMCACO has good adaptability and efficiency improvement on grid maps of different complexity.

[0084] Overall, the adaptive ant colony optimization algorithm shows significant comprehensive performance improvement in real maps. Fig.10 is the optimal path for the 5 ACO variants, Fig.11 It is the best path at the 20×20 grid map scale, showing the effectiveness of various methods respectively; Fig.12 is the number of convergence iterations for a 20×20 grid map scale, showing the comparative effect of the adaptive ant colony optimization algorithm and other different algorithms; Fig.13 It is an experimental map of the real world; Fig.14 The results are the optimal paths of GMCACO and the four commonly used algorithms, ACO, A* and Dijkstra, in the grid map, showing the comparative effect of the adaptive ant colony optimization algorithm in the actual experiment. These results not only verify the theoretical advantages of the adaptive ant colony optimization algorithm, but also prove the potential of the adaptive ant colony optimization algorithm in practical applications. The adaptive ant colony optimization algorithm shows excellent performance both in simulation experiments and in actual environments. Specifically, the adaptive ant colony optimization algorithm successfully accelerates the convergence speed of the algorithm by adjusting the initial pheromone distribution strategy, optimizing the state transition probability, and improving the heuristic function and its index, while reducing the path length, algorithm execution time and the number of path turns. In addition, the adaptive ant colony optimization algorithm introduces a dynamic pheromone evaporation mechanism, which effectively avoids the problem of the algorithm falling into the local optimal solution too early, so that the global optimal path can be generated more reliably, significantly improving the quality of the solution. These innovative improvements enable the adaptive ant colony optimization algorithm to show strong adaptability and excellent performance in path planning tasks under different environments.

Claims

1. A global path planning method for a mobile robot adapted to different grid map complexities, characterized in that: The following steps are involved: The first step is to obtain environmental information, build a real-time dynamic grid map, and calculate the map complexity based on the number, distribution, and connectivity of obstacles; Step 2: Initialize the ant colony algorithm: Initialize the ant population, each ant randomly selects a starting point, and uses an adaptive initial pheromone allocation strategy based on grid mapping complexity to initialize the pheromone matrix and heuristic information matrix to preset values; Step 3: Path search process: Path selection: An improved ant colony state transfer strategy is adopted, and a predefined threshold that varies with the map complexity is introduced to provide a balanced deterministic and random selection of the search direction. Ants select the next moving location node based on the state transfer probability. Path construction: When ants select a path, they record the nodes they pass through and calculate the path cost; Step 4: Pheromone Update: Every time an ant walks through a path, the pheromone is locally updated according to the pheromone volatilization and pheromone increase, and the grid map complexity and the number of iterations are introduced into the expressions of the pheromone evaporation coefficient and the intensity coefficient respectively to update the path; The algorithm runs to the preset number of iterations or ends when a path that meets the performance indicators is found.

2. A mobile robot global path planning method adapted to different grid map complexities according to claim 1, characterized in that: In the first step, the map complexity calculation process is as follows: First, the obstacle grid ratio is calculated as a basic measure of the degree of environmental obstruction. Second, the distribution of obstacles is used to capture map features. Finally, the Shannon entropy theory is applied to evaluate the information complexity of the map. The map complexity calculation formula is as follows: Among them, G represents the complexity of the grid map, and its value range is from 0 to 1. The closer the value is to 0, the lower the complexity is, and the closer it is to 1, the higher the complexity is. all Represents the sum of the number of obstacle grids and free grids, N os Indicates the number of obstacle grids, N ef Indicates the total number of free grids adjacent to the barrier grid; is the Shannon entropy formula, where n = 2 means that the grid map contains only two types of grids, and when i = 1, x i represents the obstacle grid, when i=2, x i represents a free grid, p(x i ) is the probability of occurrence of obstacle grid and free grid, x i It is used to calculate the free grid and obstacle grid values, and then calculate the ratio; θ1, θ2, and θ3 represent the weight coefficients corresponding to the obstacle proportion, obstacle distribution, and distribution chaos, respectively.

3. A mobile robot global path planning method adapted to different grid map complexities according to claim 1, characterized in that: In the second step, the pheromone initialization process is as follows: When the pheromone is initialized, the distribution of pheromones is dynamically adjusted at the beginning of the optimization according to the complexity of the grid map and the grid distance from the start and end lines. The expression is as follows: t ij (initial).=e^G*τ ij (initial)*ln(N all ) (2), Among them, τ ij (inital)′ is the improved pheromone value, τ ij (initial) is the initial pheromone value, i and j are the current node and the next node respectively, G is the target node, N all Represents the sum of the number of obstacle grids and free grids; u ij (initial) is the pheromone distribution value determined by distance, d is the distance of the current node i from the starting point and the end point, the coordinates of node i are (x, y), and the coordinates of the starting point S are (x S ,y S ), the target point T coordinate is (x T ,y T ).

4. A mobile robot global path planning method adapted to different grid map complexities according to claim 1, characterized in that: In the third step, the path search process includes: Design an improved ant colony state transfer strategy, introduce a predefined threshold q0 that varies with the map complexity, and provide a balanced deterministic and random selection of search directions, such as formula (5) and formula (6); in, represents the probability of improving ant m to move from node i to node j in the kth iteration. The argmax function is used to determine the index of the maximum value in the matrix, τ ij (k) is the pheromone concentration, η ij (k) is the heuristic function, α and β are the exponents of the pheromone concentration and the heuristic function, respectively. is the state transition probability, q is a random variable uniformly distributed in the interval [0,2], q0 is a predefined threshold, G is the grid map complexity, k is the current iteration number, and K is the total iteration number; According to formula (5), when q≤q0, the algorithm selects the node that maximizes the product of pheromone concentration and heuristic function; when q>q0, the algorithm switches to random selection mode and adopts the roulette wheel selection method in the traditional ant colony algorithm.

5. A mobile robot global path planning method adapted to different grid map complexities according to claim 4, characterized in that: In the third step, the path search process also includes: Based on the complexity of the grid map, the Euclidean distance from the ant colony to the end point and the turning angle of the ant colony path, an improved heuristic function η′ is designed by quantifying the Euclidean distance and path turning angle of the grid. ij , as shown in formula (7) to formula (10); Among them, η′ ij is the improved heuristic function, λ is the global adjustment coefficient used to control the overall strength of the heuristic function, w1 and w2 are weight coefficients that satisfy the constraint w1+w2=1; d ij is the distance from the current node i to the next node j, d iT Represents the Euclidean distance from the current node i to the target node T, and the calculation formula is (x i ,y i ) and (x T ,y T ) are the coordinates of node i and target node T respectively; a(i) represents the path turning angle, which is defined as the angle between the current node i, the previous node l and the next node j. is the vector of the ant colony from node l to node i, is the vector of the ant colony from node i to node j; d ST is the Euclidean distance from the starting point S to the target point T, k is the current iteration number, K is the total iteration number, and G is the adjustment parameter, that is, the complexity of the grid map.

6. A mobile robot global path planning method adapted to different grid map complexities according to claim 4, characterized in that: In the third step, the path search process also includes: Design a dynamic coefficient definition mechanism based on the cosine annealing strategy, and adjust the pheromone concentration and the exponents α and β of the heuristic function according to the iteration, as shown in formula (11) and formula (12); Among them, α max , α min are the upper and lower boundaries of α, β max and β min are the upper and lower boundary values ​​of β, k is the current number of iterations, and K is the total number of iterations; a max =ln(N all ) / e,a min =ln(N all ) / e 2 ,b max =lg(N all )*2e,β min =lg(N all ), Among them, N all Represents the sum of the number of obstacle grids and free grids, and e is a natural number.

7. A mobile robot global path planning method adapted to different grid map complexities according to any one of claims 1 to 6, characterized in that: In the fourth step, the pheromone dynamic update mechanism is as follows: Mapping complexity and iteration are introduced into the expressions of pheromone evaporation coefficient and intensity respectively. On this basis, the pheromone concentration is kept within the preset range according to the definition and calculation of map complexity, such as formula (13) to formula (16); Among them, ρ′ is the volatility coefficient, ρ is the pheromone evaporation coefficient, G is the complexity of the grid map, M is the number of ants, k is the current iteration number, K is the total iteration number; Q′ is the improved pheromone strength, Q is the pheromone strength; τ ij (k) is the pheromone concentration on the path; τ max and τ min They are τ ij The maximum and minimum values ​​of (k), τ ij (initial)′ is the improved initial pheromone distribution, K min represents the shortest path length found by the current ant after the kth iteration, N all Represents the sum of the number of barrier grids and free grids.

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