Robot path planning method based on multi-strategy improved Harris eagle algorithm

Abstract

The invention discloses a robot path planning method based on a multi-strategy improved Harris eagle algorithm. The method comprises the following steps: setting initial parameters; a chaos initialization strategy is used, and a Tent chaos mapping initialization population is introduced; calculating prey escape energy by using a fuzzy energy strategy; selecting an exploration stage or a development stage according to the escape energy; when E > = 1, the individual enters an exploration stage, and when E < 1, the individual enters a development stage; trying to jump out of a local optimal solution by using a sine and cosine perturbation method; judging whether the maximum number of iterations is reached or not, if so, outputting an optimal path, otherwise, returning to the prey escape energy calculation step; and constructing a path trajectory graph and a fitness curve graph of the robot in the position space. The invention aims to solve the problems that the path is not smooth, the convergence speed is low, and the robot is easy to fall into a dead zone when performing path planning.

Description

Technical Field

[0001] The invention belongs to the technical field of robot path planning, and relates to a robot path planning method based on a multi-strategy improved Harris Hawk algorithm. Background Art

[0002] The artificial potential field method is a commonly used robot path planning algorithm. Its basic idea is to construct a virtual potential field and use the force of the potential field to guide the robot to avoid obstacles and reach the target. The algorithm regards the target point as a source of gravity and applies attraction to the robot; it regards obstacles as sources of repulsion and applies repulsion to the robot. The robot moves under the action of this composite force. The robot starts from the starting point, calculates the total force (the combination of gravity and repulsion) at the current position, and moves along the direction of the force until it reaches the target point or encounters an obstacle that cannot be avoided.

[0003] A major disadvantage of the artificial potential field method is that it is easy to fall into a local optimal solution. For example, the robot may be trapped in a "power balance point" or "dead zone", making it unable to continue moving towards the target. This situation usually occurs in an environment with many obstacles or complex paths near the target point. And because the robot only moves in the direction of the current total power each time, the path planning process is relatively local, which may cause the overall path to be not smooth enough, affecting the efficiency and stability of the robot's movement. Summary of the invention

[0004] The technical solution adopted by the present invention is: a robot path planning method based on a multi-strategy improved Harris Eagle algorithm, aiming to solve the problems faced by the robot when performing path planning, such as uneven path, slow convergence speed, and easy falling into dead zones.

[0005] The robot path planning method based on multi-strategy improved Harris Eagle algorithm includes the following specific steps:

[0006] Step 1: Set initial parameters;

[0007] According to the range of the robot's motion space, the distribution of obstacles, etc., the initial parameters of the method used are determined, including the size of the position space, the starting and ending points of the robot, the population size and the maximum number of iterations. Different individuals are used to represent different paths in the position space, and the prey is used to represent the current optimal path. The population size represents the number of individuals existing at the same time, and the maximum number of iterations is the maximum number of iterations of the individual path. The quality of the path is calculated and evaluated by the objective function.

[0008] Step 2: Use the chaos initialization strategy and introduce the Tent chaos map to initialize the population;

[0009] In order to overcome the problem of insufficient initial population diversity in the prior art, the present invention introduces the Tent chaotic mapping method to initialize the algorithm population; the Tent chaotic mapping can generate an initial population with high distribution uniformity, ensuring the uniform distribution of the population in the entire position space, thereby improving the global search capability and initial diversity of the algorithm. This strategy helps to avoid the algorithm from falling into a local optimal solution during the search process and improves the overall effect of path planning.

[0010] Step 3: Calculate the prey escape energy using fuzzy energy strategy;

[0011] In the HHO algorithm (Harris Hawks Optimization, a population-based, nature-inspired optimization algorithm), the size of the escape energy E determines whether the search behavior of the Harris Hawk enters the exploration stage or the development stage, which has a huge impact on the convergence speed of the algorithm. The escape energy in the original algorithm uses the random number E0 as its initial value and decreases in a linear manner. It cannot meet the requirements of the algorithm to perform more exploration behavior in the early stage and more development in the later stage. Therefore, a nonlinear strategy is introduced to improve the HHO algorithm.

[0012] Step 4: Select the exploration phase or the development phase based on the escape energy;

[0013] When E ≥ 1, the individual enters the exploration stage, and when E < 1, the individual enters the development stage;

[0014] Step 6: Determine whether the maximum number of iterations has been reached. If so, output the optimal path. Otherwise, return to step 3.

[0015] Step 7: Construct the robot's path trajectory diagram and fitness curve diagram in the position space

[0016] According to the actual robot movement starting point, end point and position space obstacle distribution, a simulation graph is drawn, and the optimal path planning is performed according to the method proposed in the present invention, and the search situation of the optimal path in 50 iterations is plotted as a fitness curve.

[0017] Furthermore, the Tent chaotic mapping formula in the above step 2 is:

[0018]

[0019] Where: i is the size of the initial population, λ is a constant, T i D is a random number in the interval [0,1], is an i×D-dimensional chaotic sequence; the population can be initialized as:

[0020]

[0021] Where: X is the position of the individual, t is the current iteration number, LB and UB are the lower and upper limits of the individual position space, respectively.

[0022] Furthermore, in the above step 3, the prey escape energy calculation introduces a nonlinear strategy to improve the HHO algorithm. The improved prey escape energy model is as follows:

[0023]

[0024] Where: E is the escape energy of the prey, E0 is a random number in the interval (-1,1), indicating the initial energy of the prey, and T is the maximum number of iterations;

[0025] When the escape energy of the prey gradually decreases, the HHO algorithm will all choose to enter the development stage, and it is more likely to fall into the local optimal solution. Inspired by the simulated annealing algorithm, the exploration probability p is introduced, so that after the algorithm finds that it has entered the local optimal solution, it has a probability to ignore the escape energy of the prey and enter the exploration stage to get rid of the local optimal solution. The probability formula is as follows:

[0026]

[0027] Where: C is the temperature coefficient, which gradually decreases with the number of iterations, C = αC0, C0 is the initial temperature, α is the attenuation factor, F(x) is the fitness function, and ΔX(t) is the distance between the prey position and the current individual position.

[0028] Furthermore, the exploration phase or development phase selection method in the above step 4 is:

[0029] When E≥1, the individual enters the exploration stage, and when E<1, the individual enters the development stage:

[0030] 1) Exploration phase

[0031] In this stage, the individual will update its position according to the formula. q ≥0.5, the individual chooses to fly randomly. q When the value is less than 0.5, the individual moves closer to other individuals and performs convergent flight:

[0032]

[0033] λ(t)=X rand (t)-2r3X(t) (6)

[0034] K=LB+r4(UB-LB) (7)

[0035] Where: X is the position of the individual, t is the current iteration number, X rand(t) is the position of an individual randomly selected from the population during iteration, λ(t) represents the movement trend of the current individual toward the random individual, r1~r4 and q are all random numbers in the interval (0, 1), K is the random position in the position space, LB and UB are the lower and upper limits of the individual position space, respectively, and X rabbit is the location of the prey, which is the optimal solution so far, X m (t) is the average position of all individuals at the tth generation;

[0036] 2) Development phase

[0037] After entering the exploitation phase, the HHO algorithm selects one of the four exploitation strategies in (1)-(4) according to the escape energy E of the prey and the probability of escape r, where r is a random number in the interval (0, 1);

[0038] (1) When r ≥ 0.5 and |E| ≥ 0.5, the HHO algorithm selects the soft encirclement strategy, and the position update method is as follows:

[0039] X(t+1)=ΔX(t)-E|JX rabbit (t)-X(t)| (8)

[0040] ΔX(t)=X rabbit (t)-X(t) (9)

[0041] J=2(1-r5) (10)

[0042] Where: ΔX(t) is the distance between the prey position and the current individual position, J is the random jump intensity of the prey during the escape process, and r5 is a random number in the interval (0, 1);

[0043] (2) When r ≥ 0.5 and |E| < 0.5, the HHO algorithm selects the hard encirclement strategy, and the position update method is as follows:

[0044] X(t+1)=X rabbit (t)-E|X rabbit (t)-X(t)| (11)

[0045] (3) When r < 0.5 and E ≥ 0.5, the HHO algorithm selects the diving soft encirclement strategy and uses formula (12) to update the individual position. If the fitness is not improved, the Lévy flight is performed according to formula (13). If it is still not improved, the original position is used, as shown in formula (16):

[0046] Y1=X rabbit (t)-E|JX rabbit (t)-X(t)| (12)

[0047] Z1=Y1+S×L(D) (13)

[0048]

[0049] Where: D is the dimension of the problem to be solved, S is a D-dimensional random vector, L(x) is the levy flight function, u and v are both random numbers in the interval (0, 1), β is a constant, which is 1.5, F(x) is the fitness function, and Γ(x) is the Gamma function;

[0050] (4) When r < 0.5 and |E| < 0.5, the HHO algorithm selects the dive hard encirclement strategy and uses formula (17) to update the individual position. If the fitness is not improved, the Lévy flight is performed according to formula (18). If it is still not improved, the original position is used, as shown in formula (19):

[0051] Y2=X rabbit (t)-EJX rabbit (t)-X m (t) (17)

[0052] Z2=Y2+S×L(D) (18)

[0053]

[0054] Furthermore, the sine-cosine perturbation method in step 5 above adopts an adaptive sine-cosine algorithm, and its specific position update formula is as follows:

[0055]

[0056] φ=|r8·X rabbit (t)-X(t) (21)

[0057] Where: w(t) and λ(t) are adaptive weight coefficients, which are updated as follows, r6, r7 and r8 are random numbers in the intervals [0,2π], [0,1] and [-2,2] respectively;

[0058]

[0059] Furthermore, the objective function is determined as follows:

[0060] 1) Establish evaluation indicators for robot path planning, including path length, path smoothness, convergence speed, obstacle avoidance ability and global optimality;

[0061] 2) According to the evaluation index of step 1), the objective function of robot path planning effect evaluation is determined as follows:

[0062]

[0063] Where: p is the path, n is the total number of nodes, C is the penalty factor. If the path passes through an obstacle and a collision occurs, C takes a larger value, and if there is no collision, it takes a smaller value. i is the i-th node in the entire path. i and i are the horizontal and vertical coordinates of the i-th node respectively.

[0064] Beneficial effects of the present invention: Compared with the prior art, the effects of the present invention are as follows:

[0065] 1) Improvement of initial population diversity: The present invention introduces the Tent chaotic mapping method to initialize the initial population of the algorithm so that the population is evenly distributed in the entire position space. This measure effectively improves the diversity of the initial population, enhances the global search capability of the algorithm, and reduces the possibility of the algorithm converging to a local optimal solution in the early stage, thereby ensuring the comprehensiveness and accuracy of path planning;

[0066] 2) Acceleration of algorithm convergence speed: The present invention adopts an adaptive sine-cosine algorithm to dynamically adjust the search behavior during the path planning process, especially in the later stages of path planning, and accelerates the convergence process of the algorithm through precise search strategies. Compared with traditional methods, the present invention can find the optimal path more quickly, greatly improving the efficiency of path planning, and is particularly suitable for robot application scenarios that require real-time response;

[0067] 3) Avoid falling into the local optimal solution: In order to solve the problem that traditional algorithms are prone to falling into the local optimal solution, the present invention introduces a nonlinear fuzzy energy strategy and improves the search behavior of the Harris Eagle algorithm; by adjusting the nonlinear decreasing method of the escape energy, it ensures that sufficient exploration can be carried out in the early stage; in addition, the introduction of the exploration probability mechanism makes the algorithm have a probability to jump out when it is close to the local optimal solution, further avoiding the trouble of the local optimal solution and improving the global optimization ability of path planning;

[0068] 4) Enhanced smoothness and safety of path planning: The present invention reduces the number of turning points in the path by optimizing the algorithm structure, significantly improving the smoothness of the planned path, thereby improving the stability and efficiency of the robot's movement; at the same time, since the algorithm can effectively avoid obstacles, the safety of path planning is significantly improved, reducing the risk of collision of the robot in complex environments.

[0069] 5) Enhanced algorithm robustness: The present invention uses a combination of multiple strategies to make the algorithm exhibit high robustness under different environmental conditions and parameter settings; the algorithm can stably generate high-quality path planning results, has strong adaptability, and is suitable for a variety of complex application scenarios;

[0070] In summary, the present invention effectively solves the problems of many inflection points, slow convergence speed, and easy falling into local optimal solutions in the prior art through the comprehensive application of multiple strategies, significantly improves the path planning effect of the robot in a multi-obstacle environment, and has significant technological advancement and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a flow chart of a robot path planning method based on a multi-strategy improved Harris Eagle algorithm;

[0072] Figure 2 This is the escape energy model diagram of the original HHO algorithm;

[0073] Figure 3 It is a diagram of the improved escape energy model based on the multi-strategy improved Harris Hawk algorithm;

[0074] Figure 4 It is a robot path planning method based on multi-strategy improved Harris Eagle algorithm to generate the path trajectory diagram of the robot in a 100×100 simulation map;

[0075] Figure 5 It is a fitness curve diagram of the robot after 50 iterations in the simulation map using the robot path planning method based on the multi-strategy improved Harris Eagle algorithm. DETAILED DESCRIPTION

[0076] The present invention is further described below in conjunction with specific embodiments.

[0077] Example 1: Figure 1-5 As shown in FIG. 1 , the robot path planning method based on the multi-strategy improved Harris Eagle algorithm includes the following specific steps:

[0078] Step 1: Set initial parameters;

[0079] According to the range of the robot's motion space, the distribution of obstacles, etc., the initial parameters of the method used are determined, including the size of the position space, the starting and ending points of the robot, the population size and the maximum number of iterations. Different individuals are used to represent different paths in the position space, and the prey is used to represent the current optimal path. The population size represents the number of individuals existing at the same time, and the maximum number of iterations is the maximum number of iterations of the individual path. The quality of the path is calculated and evaluated by the objective function.

[0080] Step 2: Use the chaos initialization strategy and introduce the Tent chaos map to initialize the population;

[0081] In order to overcome the problem of insufficient initial population diversity in the prior art, the present invention introduces the Tent chaotic mapping method to initialize the algorithm population; the Tent chaotic mapping can generate an initial population with high distribution uniformity, ensuring the uniform distribution of the population in the entire position space, thereby improving the global search capability and initial diversity of the algorithm. This strategy helps to avoid the algorithm from falling into a local optimal solution during the search process and improves the overall effect of path planning;

[0082] The Tent chaos mapping formula in step 2 is:

[0083]

[0084] Where: i is the size of the initial population, λ is a constant, T i D is a random number in the interval [0,1], is an i×D-dimensional chaotic sequence; the population can be initialized as:

[0085]

[0086] Where: X is the position of the individual, t is the current iteration number, LB and UB are the lower and upper limits of the individual position space, respectively.

[0087] Step 3: Calculate the prey escape energy using fuzzy energy strategy;

[0088] In the HHO algorithm (Harris Hawks Optimization, a population-based, nature-inspired optimization algorithm), the size of the escape energy E determines whether the search behavior of the Harris Hawk enters the exploration phase or the development phase, which has a huge impact on the convergence speed of the algorithm. The escape energy in the original algorithm uses the random number E0 as its initial value and decreases in a linear manner, which cannot meet the requirements of the algorithm to conduct more exploration behaviors in the early stage and more development in the later stage. Therefore, a nonlinear strategy is introduced to improve the HHO algorithm. The improved escape energy model is shown as follows:

[0089]

[0090] Where: E is the escape energy of the prey, E0 is a random number in the interval (-1,1), indicating the initial energy of the prey, and T is the maximum number of iterations;

[0091] When the escape energy of the prey gradually decreases, the HHO algorithm will all choose to enter the development stage, and it is more likely to fall into the local optimal solution. Inspired by the simulated annealing algorithm, the exploration probability p is introduced, so that after the algorithm finds that it has entered the local optimal solution, it has a probability to ignore the escape energy of the prey and enter the exploration stage to get rid of the local optimal solution. The probability formula is as follows:

[0092]

[0093] Where: C is the temperature coefficient, which gradually decreases with the number of iterations, C = αC0, C0 is the initial temperature, α is the attenuation factor, F(x) is the fitness function, ΔX(t) is the distance between the prey position and the current individual position;

[0094] Step 4: Select the exploration phase or the development phase based on the escape energy;

[0095] When E≥1, the individual enters the exploration stage, and when E<1, the individual enters the development stage:

[0096] The method for selecting the exploration phase or development phase in step 4 is:

[0097] 1) Exploration phase

[0098] In this stage, the individual will update its position according to the following formula. When q≥0.5, the individual will choose to fly randomly. When q<0.5, the individual will move closer to other individuals and perform convergent flight:

[0099]

[0100] λ(t)=X rand (t)-2r3X(t) (6)

[0101] K=LB+r4(UB-LB) (7)

[0102] Where: X is the position of the individual, t is the current iteration number, X rand (t) is the position of an individual randomly selected from the population during iteration, λ(t) represents the movement trend of the current individual toward the random individual, r1~r4 and q are all random numbers in the interval (0, 1), K is the random position in the position space, LB and UB are the lower and upper limits of the individual position space, respectively, and X rabbit is the location of the prey, which is the optimal solution so far, X m (t) is the average position of all individuals at the tth generation;

[0103] 2) Development phase

[0104] After entering the exploitation phase, the HHO algorithm selects one of the four exploitation strategies in (1)-(4) according to the escape energy E of the prey and the probability of escape r, where r is a random number in the interval (0, 1);

[0105] (1) When r ≥ 0.5 and |E| ≥ 0.5, the HHO algorithm selects the soft encirclement strategy, and the position update method is as follows:

[0106] X(t+1)=ΔX(t)-E|JXrabbit (t)-X(t)| (8)

[0107] ΔX(t)=X rabbit (t)-X(t) (9)

[0108] J=2(1-r5) (10)

[0109] Where: ΔX(t) is the distance between the prey position and the current individual position, J is the random jump intensity of the prey during the escape process, and r5 is a random number in the interval (0, 1);

[0110] (2) When r ≥ 0.5 and |E| < 0.5, the HHO algorithm selects the hard encirclement strategy, and the position update method is as follows:

[0111] X(t+1)=X rabbit (t)-E|X rabbit (t)-X(t)| (11)

[0112] (3) When r < 0.5 and |E| ≥ 0.5, the HHO algorithm selects the diving soft encirclement strategy and uses formula (12) to update the individual position. If the fitness is not improved, the Lévy flight is performed according to the formula. If it is still not improved, the original position is used, as shown in formula (16):

[0113] Y1=X rabbit (t)-E|JX rabbit (t)-X(t)| (12)

[0114] Z1=Y1+S×L(D) (13)

[0115]

[0116]

[0117] Where: D is the dimension of the problem to be solved, S is a D-dimensional random vector, L(x) is the levy flight function, u and v are both random numbers in the interval (0, 1), β is a constant, which is 1.5, F(x) is the fitness function, and Γ(x) is the Gamma function;

[0118] (4) When r < 0.5 and |E| < 0.5, the HHO algorithm selects the dive hard encirclement strategy and uses formula (17) to update the individual position. If the fitness is not improved, the Lévy flight is performed according to formula (18). If it is still not improved, the original position is used, as shown in formula (19):

[0119] Y2=X rabbit (t)-EJX rabbit (t)-X m(t) (17)

[0120] Z2=Y2+S×L(D) (18)

[0121]

[0122] Step 5: Use the sine and cosine perturbation method to try to escape from the local optimal solution;

[0123] Since the HHO algorithm is prone to fall into the local optimal solution during the iteration process, in order to solve this problem, the adaptive sine-cosine algorithm is introduced in the final stage of the algorithm. The specific position update formula is as follows:

[0124]

[0125] φ=|r8·X rabbit (t)-X(t) (21)

[0126] Where: w(t) and λ(t) are adaptive weight coefficients, which are updated as follows, r6, r7 and r8 are random numbers in the intervals [0,2π], [0,1] and [-2,2] respectively;

[0127]

[0128]

[0129] Step 6: Determine whether the maximum number of iterations has been reached. If so, output the optimal path; otherwise, return to step 3.

[0130] Step 7: Construct the robot's path trajectory diagram and fitness curve diagram in the position space

[0131] According to the actual robot movement starting point, end point and position space obstacle distribution, a simulation graph is drawn, and the optimal path planning is performed according to the method proposed in this invention. The planning results are shown in the figure. Figure 4 As shown, the search for the optimal path in 50 iterations is plotted as a fitness curve, as shown in Figure 5 shown.

[0132] In summary, the present invention introduces a chaos initialization strategy, a fuzzy energy strategy, and a sine-cosine perturbation strategy. The present invention aims to improve the global search capability and convergence speed of the algorithm, and effectively avoid falling into a local optimal solution during the path planning process, thereby optimizing the overall path planning effect and operating efficiency of the robot, and effectively solving the problems faced by the robot during path planning, such as an uneven path, slow convergence speed, and easy falling into a dead zone.

Claims

1. A robot path planning method based on a multi-strategy improved Harris Eagle algorithm, characterized by: The method comprises the following steps: Step 1: Set initial parameters; The initial parameters include the size of the position space, the starting and ending points of the robot, the population size and the maximum number of iterations. Different individuals represent different paths in the position space, and the prey represents the current optimal path. The population size represents the number of individuals existing at the same time, and the maximum number of iterations is the maximum number of iterations of the individual path. The quality of the path is calculated and evaluated by the objective function. Step 2: Use the chaos initialization strategy and introduce the Tent chaos map to initialize the population; Step 3: Calculate the prey escape energy using fuzzy energy strategy; Step 4: Select the exploration phase or the development phase based on the escape energy; When |E|≥1, the individual enters the exploration stage, and when |E|<1, the individual enters the development stage; Step 5: Use the sine and cosine perturbation method to try to escape from the local optimal solution; Step 6: Determine whether the maximum number of iterations has been reached. If so, output the optimal path; otherwise, return to step 3. Step 7: Construct the path trajectory diagram and fitness curve diagram of the robot in the position space; According to the actual robot movement starting point, end point and position space obstacle distribution, draw the simulation graph, and perform the optimal path planning according to steps 1-7, and draw the search situation of the optimal path in 50 iterations into a fitness curve.

2. The robot path planning method based on the multi-strategy improved Harris Hawk algorithm according to claim 1 is characterized in that: The Tent chaos mapping formula in step 2 is: Where: i is the size of the initial population, λ is a constant, T i D is a random number in the interval [0,1], is an i×D-dimensional chaotic sequence, and the population is initialized as: Where: X is the position of the individual, t is the current iteration number, LB and UB are the lower and upper limits of the individual position space, respectively.

3. The robot path planning method based on the multi-strategy improved Harris Hawk algorithm according to claim 1 is characterized in that: In step 3, the prey escape energy calculation introduces a nonlinear strategy to improve the HHO algorithm. The improved prey escape energy model is as follows: Where: E is the escape energy of the prey, E0 is a random number in the interval (-1,1), indicating the initial energy of the prey, and T is the maximum number of iterations; The exploration probability p is introduced so that after the algorithm finds that it has entered the local optimum, it has a probability to ignore the escape energy of the prey and enter the exploration phase to get rid of the local optimal solution. The probability formula is as follows: Where: C is the temperature coefficient, which gradually decreases with the number of iterations, C = αC0, C0 is the initial temperature, α is the attenuation factor, F(x) is the fitness function, and ΔX(t) is the distance between the prey position and the current individual position.

4. The robot path planning method based on the multi-strategy improved Harris Hawk algorithm according to claim 3 is characterized in that: The method for selecting the exploration phase or development phase in step 4 is: 1) Exploration phase In this stage, the individual will update its position according to the formula. q ≥0.5, the individual chooses to fly randomly. q When the value is less than 0.5, the individual moves closer to other individuals and performs convergent flight: λ(t)=|X rand (t)-2r3X(t)| (6) K=LB+r4(UB-LB) (7) Where: X is the position of the individual, t is the current iteration number, X rand (t) is the position of an individual randomly selected from the population during iteration, λ(t) represents the movement trend of the current individual toward the random individual, r1~r4 and q are all random numbers in the interval (0, 1), K is the random position in the position space, LB and UB are the lower and upper limits of the individual position space, respectively, and X rabbit is the location of the prey, which is the optimal solution so far, X m (t) is the average position of all individuals at the tth generation; 2) Development phase After entering the exploitation phase, the HHO algorithm selects one of the four exploitation strategies in (1)-(4) according to the escape energy E of the prey and the probability of escape r, where r is a random number in the interval (0, 1); (1) When r ≥ 0.5 and |E| ≥ 0.5, the HHO algorithm selects the soft encirclement strategy, and the position update method is as follows: X(t+1)=ΔX(t)-E∣JX rabbit (t)-X(t)| (8) ΔX(t)=X rabbit (t)-X(t) (9) J＝2(1-r5) (10) Where: ΔX(t) is the distance between the prey position and the current individual position, J is the random jump intensity of the prey during the escape process, and r5 is a random number in the interval (0, 1); (2) When r ≥ 0.5 and |E| < 0.5, the HHO algorithm selects the hard encirclement strategy, and the position update method is as follows: X(t+1)=X rabbit (t)-E|X rabbit (t)-X(t)| (11) (3) When r < 0.5 and |E| ≥ 0.5, the HHO algorithm selects the diving soft encirclement strategy and uses formula (12) to update the individual position. If the fitness is not improved, the Lévy flight is performed according to formula (13). If it is still not improved, the original position is used, as shown in formula (16): Y1=X rabbit (t)-E|JX rabbit (t)-X(t)| (12) Z1=Y1+S×L(D) (13) Where: D is the dimension of the problem to be solved, S is a D-dimensional random vector, L(x) is the levy flight function, u and v are both random numbers in the interval (0, 1), β is a constant, F(x) is the fitness function, and Γ(x) is the Gamma function; (4) When r < 0.5 and |E| < 0.5, the HHO algorithm selects the dive hard encirclement strategy and uses formula (17) to update the individual position. If the fitness is not improved, the Lévy flight is performed according to formula (18). If it is still not improved, the original position is used, as shown in formula (19): Y2=X rabbit (t)-E|JX rabbit (t)-X m (t)| (17) Z2=Y2+S×L(D) (18) 5. The robot path planning method based on the multi-strategy improved Harris Hawk algorithm according to claim 4 is characterized in that: The sine-cosine perturbation method in step 5 adopts an adaptive sine-cosine algorithm, and its specific position update formula is as follows: φ=|r8·X rabbit (t)-X(t)| (21) Where: w(t) and λ(t) are adaptive weight coefficients, which are updated as follows, r6, r7 and r8 are random numbers in the intervals [0,2π], [0,1] and [-2,2] respectively; 6. The robot path planning method based on the multi-strategy improved Harris Hawk algorithm according to claim 1 is characterized in that: The objective function is determined as follows: 1) Establish evaluation indicators for robot path planning, including path length, path smoothness, convergence speed, obstacle avoidance ability and global optimality; 2) According to the evaluation index of step 1), the objective function of robot path planning effect evaluation is determined as follows: Where: p is the path, n is the total number of nodes, C is the penalty factor. If the path passes through an obstacle and a collision occurs, C takes a larger value, and if there is no collision, it takes a smaller value. i is the i-th node in the entire path. i and i are the horizontal and vertical coordinates of the i-th node respectively.

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