Warehouse logistics robot path planning method based on improved Donglong optimization algorithm

Abstract

The invention discloses a warehouse logistics robot path planning method based on an improved Donglong optimization algorithm. The method comprises the following steps: S1, creating a path grid map by taking a searching demand of a warehouse logistics robot as a target; s2, carrying out the improvement of a Runwanton optimization algorithm; and S3, inputting the path grid map created in the step S1 into an improved Rhouyanglong optimization algorithm, and outputting an optimal path. According to the method, the safe and timely obstacle avoidance capability of the robot is improved by improving the Rhouyanlong optimization algorithm, the robot can be ensured to effectively explore an unknown area in a complex environment and improve the optimization precision, and the self-adaptability and the autonomous decision-making capability of the warehouse logistics robot are enhanced.

Description

Technical Field

[0001] The invention relates to a path planning method for a storage logistics robot based on an improved Tyrannosaurus Rex optimization algorithm. Background Art

[0002] In recent years, e-commerce business has grown explosively, and the increasing number of packages has put forward higher requirements for the operating efficiency of warehousing and logistics systems. For manufacturing companies, it is particularly important to continuously improve the production system and optimize the warehouse design. Intelligent warehousing has gradually become the mainstay of my country's warehousing methods due to its easy management and high efficiency. One of the important components of intelligent warehousing is the warehousing logistics robot. The path planning quality of the warehousing logistics robot determines whether the robots can cooperate with each other, and also determines the safety and work efficiency of the robot's driving.

[0003] The most important thing for a warehouse logistics robot is to receive instructions in a timely manner, to be able to safely and stably transport goods from the starting point to the loading point, and then from the loading point to the unloading point, and finally to the designated location. Path planning is an indispensable part of the warehouse robot system. When the robot is in an environment with many obstacles, it can plan a safe path and successfully reach the designated area and perform the designated tasks. These are the key factors to measure whether the mobile robot can plan a reasonable path and whether it has the ability to avoid obstacles. Therefore, how to plan a safe, stable and optimal route in a changing environment so that the robot can reach its destination smoothly is a very urgent demand for the market.

[0004] Mobile robot path planning is directly related to the robot's travel efficiency. Path optimization algorithm is the core of path planning. According to different theories, mobile robot path planning technology can be divided into sampling-based graph search algorithm, node-based search algorithm, model-based method, biological heuristic algorithm and other methods.

[0005] With the rapid development of human society, the scale and complexity of optimization problems are increasing, and there is an urgent need to develop more general and more effective algorithms to solve these difficulties. The widespread application of metaheuristic algorithms in complex scientific and engineering challenges is attributed to their wide applicability, high computational efficiency, and ability to expand problems. Swarm intelligence optimization algorithms influenced by the intelligent behavior of biological swarms have attracted the interest of many scholars. Particle swarm optimization, ant colony optimization, gray wolf algorithm, dung beetle algorithm, etc. are all typical biological heuristic algorithms. Compared with traditional planning algorithms, they reduce dependence on mathematical models and have strong global optimization capabilities. They have been widely used in mobile robot path planning research.

[0006] Tyrannosaurus optimization (TROA) was proposed by Venkata SatyaDurga Manohar Sahu et al. in 2023. The algorithm simulates the hunting behavior of Tyrannosaurus Rex and finds the optimal solution through rapid search, which can meet the real-time requirements in the warehousing and logistics environment. At the same time, it simulates the hunting strategy of Tyrannosaurus Rex, increases the diversity and adaptability of the algorithm, and improves the adaptability of the algorithm in the face of complex warehousing environments. However, in the search process, it may converge to the local optimal solution too early and fail to find the global optimal solution. At the same time, in the iterative process of the algorithm, the population diversity may gradually decrease, resulting in a decrease in the global search capability of the algorithm, and it is necessary to improve the performance through improvement. Summary of the invention

[0007] The present invention is to solve the problems existing in the above-mentioned prior art and provide a warehouse logistics robot path planning method based on an improved Tyrannosaurus Rex optimization algorithm. The present invention effectively solves the problem that the Tyrannosaurus Rex optimization algorithm is prone to premature convergence when used for robot path planning, and the convergence accuracy is not high and the convergence speed is not fast enough when facing complex problems. It provides the best moving route for the robot in the warehouse environment, helps the robot avoid collisions with people, cars, goods and shelves, and enables it to safely reach the designated area to work.

[0008] The technical solutions adopted in the present invention are:

[0009] A warehouse logistics robot path planning method based on an improved Tyrannosaurus Rex optimization algorithm comprises the following steps:

[0010] S1: Create a path grid map based on the search needs of the warehouse logistics robot;

[0011] S2: Improve the Tyrannosaurus Rex optimization algorithm. The improvements are as follows:

[0012] 1. Introduce SPM chaotic mapping to initialize the position distribution of the Tyrannosaurus Rex population in the Tyrannosaurus Rex optimization algorithm, generate the initial position sequence of the Tyrannosaurus Rex population, and adopt a competitive learning strategy to make the generated initial position sequence biased towards the global optimal solution;

[0013] 2. Introduce a nonlinear factor into the Tyrannosaurus Rex optimization algorithm to improve the exploration accuracy of the Tyrannosaurus Rex optimization algorithm;

[0014] 3. Design an elite screening mechanism for the Tyrannosaurus Rex optimization algorithm to generate the best elite individuals;

[0015] 4. When the T. rex optimization algorithm selects prey, it updates the current position of the target prey through the designed elite dominance strategy;

[0016] S3: Input the path grid map created in S1 into the improved T-Rex optimization algorithm and output the optimal path.

[0017] Furthermore, the SPM chaotic mapping randomly generates the initialization population sequence, and the introduced SPM chaotic mapping formula is defined as:

[0018]

[0019] In the formula, z(t) is the value of the chaotic sequence, z i+1 is the generated composite sequence, mod is the modulo operation, when η∈(0,1),μ∈(0,1), the population is in a chaotic state, and r∈(0,1) is a random number.

[0020] 3. The path planning method for a storage logistics robot based on an improved Tyrannosaurus Rex optimization algorithm as claimed in claim 2 is characterized in that: the initial population sequence generated by the SPM chaotic mapping is Z n ={z1,z2,...,z n}, where n is the population size, and the new population position sequence generated by random reverse competitive learning is The competitive learning strategy is defined as:

[0021]

[0022] Where ub and lb are the upper and lower bounds of the search area respectively; α is the learning factor, F is the fitness function, and Z best is the best individual in the population, and Z is all populations.

[0023] Furthermore, the optimization algorithm of the Tyrannosaurus Rex includes chasing and capturing prey in childhood. When the estimated value of dispersed prey is reached, the Tyrannosaurus Rex starts to hunt randomly and continuously updates the position to hunt and chase prey. The specific formula for updating the position is:

[0024]

[0025] Er=rand(1-(t / Max iteration ))

[0026] In the formula, z new is the updated position, rand is a random number, Er represents the estimated value of reaching the scattered prey, that is, when the Tyrannosaurus Rex starts to hunt, the prey starts to escape, and the Tyrannosaurus Rex hunts the prey by updating its own position, Randon is the random position, t is the current iteration number, Max iteration is the maximum number of iterations;

[0027] The position update equation of Tyrannosaurus Rex when chasing prey is:

[0028] z new=z+rand()*sr*(t pos *tr-target*pr)

[0029] In the formula, z is the position before the update, rand is a random number, and t pos is the position of Tyrannosaurus Rex, sr∈[0.1,1] is the hunting success rate. When its value is 0, it means hunting has failed and the position of the corresponding prey must be updated;

[0030] target is the minimum distance from the prey to the Tyrannosaurus Rex; tr is the running speed of the Tyrannosaurus Rex; pr∈[0,1] is the running speed of the prey;

[0031] A nonlinear factor is introduced to represent tr, and the specific expression of tr is:

[0032]

[0033] In the formula, tr min =0.6, tr max =0.9, t represents the current number of iterations, T represents the maximum number of iterations, and λ∈(0,1) is the nonlinear adjustment factor.

[0034] Furthermore, when the Tyrannosaurus Rex fails in hunting, the position is updated through the elite screening mechanism, and the specific steps are as follows:

[0035] First, the greedy mechanism is used for screening, and then the best individual is selected through the elite screening mechanism to complete the position update. The greedy mechanism update formula is:

[0036]

[0037] In the formula, is the individual position before updating, fit(V i ) is the fitness value of the original Tyrannosaurus Rex; is the fitness value of the new Tyrannosaurus Rex; V i+1 is the initial position of the newly generated Tyrannosaurus Rex;

[0038] The elite screening mechanism formula is:

[0039]

[0040] In the formula, represents the best individual, Represents the current individual, represents the current best individual, r1∈(0,1) is a random number, t represents the current number of iterations, T represents the maximum number of iterations, and R represents a random number of -1 or 1.

[0041] Furthermore, the process of Tyrannosaurus Rex selecting prey depends on the current position and the previous position of the target prey. If the fitness function of the initial random prey position is less than the fitness function of the updated prey position, the current position is updated through the elite dominant strategy.

[0042] Furthermore, when Tyrannosaurus Rex chooses prey, the comparative fitness function equation is:

[0043]

[0044] Where f(Z) is the initial random prey position fitness function, f(Z new ) is the updated fitness function;

[0045] When the fitness function of the initial random prey position is less than the fitness function of the updated prey position, the current position is updated through the elite-dominated strategy. The elite-dominated strategy formula is:

[0046] Z(t+1)=A(Z a (t)-Z(t))+B(Z b (t)-Z(t))+C(Z c (t)-Z(t))

[0047] In the formula, Z a (t), Z b (t) and Z c (t) represents the first, second and third positions of the fitness value in the Tyrannosaurus Rex population in the tth iteration, Z(t) is the position before the update, and the coefficients A, B, and C are:

[0048]

[0049] Where F is the fitness function.

[0050] The present invention has the following beneficial effects:

[0051] 1. The present invention uses an improved Tyrannosaurus Rex optimization algorithm to plan the path of the warehousing logistics robot, helping the robot to calculate a more accurate and reliable moving route, improving the smoothness of the robot's operation, and further accelerating the operating efficiency of warehousing logistics.

[0052] 2. The present invention first introduces SPM chaotic mapping to initialize the position distribution of the Tyrannosaurus population, generates the initial position sequence of the Tyrannosaurus population, and then generates the final population initial position sequence through a competitive learning strategy. This helps to generate a population with higher diversity in the initial stage of the algorithm, avoids the algorithm from falling into a local optimum, and achieves a more uniform initial population distribution. At the same time, it improves the search space coverage, helps the algorithm to search globally, reduces the limitations of local searches, improves the convergence performance of the algorithm, avoids premature convergence, and ensures that the algorithm can continuously and effectively search for the optimal solution, thereby improving the robot's search range during path planning, improving the robot's safe and timely obstacle avoidance capabilities, and the timeliness of the robot's path planning.

[0053] 3. The present invention designs an adaptive running speed formula, which replaces the original constant running speed tr in the position update equation of the Tyrannosaurus Rex when chasing its prey with a nonlinear factor, thereby solving the problem of the Tyrannosaurus Rex's narrow search range and thus solving the problem of poor search accuracy caused by insufficient exploration of the search space by the robot.

[0054] 4. In the present invention, after the Tyrannosaurus Rex fails in hunting, an elite screening mechanism is implemented. Through the greedy strategy and the best individual screening mechanism, it is ensured that the algorithm can not only effectively promote the population to evolve towards the optimal solution, but also maintain sufficient diversity and avoid premature convergence, thereby improving the robot's search efficiency and reducing the risk of falling into local optimality in the robot's path planning.

[0055] 5. In the present invention, an elite-led strategy is introduced in the position update link of the Tyrannosaurus Rex selection process, which utilizes the classes within the population and selects the three elite Tyrannosaurus Rex with the best fitness for guided global search, thereby increasing information exchange between populations and improving the performance of global search, thereby enhancing the robot's adaptability and autonomous decision-making ability in complex environments, enabling the robot to explore unknown areas more effectively and improve optimization accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Schematic diagram of the implementation flow for improving the T-Rex optimization algorithm.

[0057] Figure 2 This is a raster map of the warehouse environment.

[0058] Figure 3 Flowchart of the original Tyrannosaurus optimization algorithm.

[0059] Figure 4 This is the improved population initialization flow chart.

[0060] Figure 5 It is the frequency histogram of SPM chaotic mapping.

[0061] Figure 6This is the population distribution diagram of SPM chaos mapping.

[0062] Figure 7 This is the improved flowchart of hunting and chasing phase.

[0063] Figure 8 This is the flow chart of the improved selection process. DETAILED DESCRIPTION

[0064] The present invention will be further described below in conjunction with the accompanying drawings.

[0065] Figure 1 As shown in the algorithm flow chart, the path planning method of the warehouse logistics robot based on the improved Tyrannosaurus Rex optimization algorithm of the present invention specifically includes the following steps:

[0066] S1: Create a path grid map based on the storage environment where the storage logistics robot is located;

[0067] S2: Improve the Tyrannosaurus Rex optimization algorithm. The improvements are as follows:

[0068] 1. Introduce SPM chaotic mapping to initialize the position distribution of the Tyrannosaurus Rex population and generate the initial position sequence of the Tyrannosaurus Rex population; adopt a competitive learning strategy to make the generated initial population tend to the global optimal solution, thereby improving the quality of the initial population;

[0069] 2. Hunting by Tyrannosaurus Rex involves chasing and capturing prey at a young age. When the Tyrannosaurus Rex starts hunting, it will hunt randomly. If it reaches the estimated value of scattered prey, the Tyrannosaurus Rex will start hunting and constantly update its position to hunt and chase prey. The original algorithm's action rate tr is constant. The nonlinear factor is introduced to compensate for the Tyrannosaurus Rex's insufficient exploration of the search space and the decrease in exploration accuracy.

[0070] 3. When hunting fails, that is, when the estimated value of dispersed prey is not reached, an elite screening mechanism is designed to generate the best elite individuals to enhance the algorithm's ability to jump out of the local optimum;

[0071] 4. The process of Tyrannosaurus Rex selecting prey depends on the current position and the previous position of the target prey. If the fitness function of the initial random prey position is less than the fitness function of the updated prey position, the current position is updated through the elite dominant strategy to increase interspecies information exchange and improve global search capabilities. If the Tyrannosaurus Rex fails to prey, the prey escapes or protects itself from hunting, the prey position will become zero and the algorithm stops.

[0072] S3: Input the path grid map created in S1 into the improved T-Rex optimization algorithm and output the optimal path.

[0073] like Figure 2,In step S1, first a grid map is constructed for the warehouse environment to ,clearly mark the positions of obstacles, the shelves are black rectangles, the initial positions of ,all robots are red rectangles, the forklifts are white rectangles, the human is a blue circle, the other ,working robots are green triangles,

[0074] like Figure 3 ,The original Tyrannosaurus Rex optimization algorithm is divided into three steps: the ,initialization of the Tyrannosaurus Rex population stage, the Tyrannosaurus Rex hunting and ,chasing stage, and the Tyrannosaurus Rex selection stage.

[0075] The original algorithm will be improved and upgraded from these three aspects to help the Tyrannosaurus Rex optimization algorithm achieve better results in warehouse logistics robot path planning.

[0076] like Figure 4 The improved Tyrannosaurus population initialization consists of two parts: SPM chaotic mapping randomly generates the initialization population sequence Z n ; Then improve the quality of the population through competitive learning strategy, and further optimize the generation of the final initialization population sequence The specific steps are as follows:

[0077] The specific process of randomly generating the initialization population sequence by SPM chaotic mapping is as follows: randomly generate an initial chaotic sequence Z1 in [0, 1], then bring in the SPM chaotic mapping formula to iteratively generate n-1 chaotic sequences, and finally generate a total of initialization population sequences Z n .

[0078] The introduced SPM chaotic mapping formula is defined as:

[0079]

[0080] In the formula, z(t) is the value of the chaotic sequence, z i+1 is the generated composite sequence, mod is the modulo operation, when η∈(0,1),μ∈(0,1), the population is in a chaotic state, and r∈(0,1) is a random number.

[0081] like Figure 5 and Figure 6 The SPM chaotic mapping frequency histogram and SPM chaotic mapping population distribution diagram show that the chaotic mapping distribution is relatively uniform, the frequency of each frequency band is not much different, and the generated initial solution distribution is relatively uniform, indicating that the chaotic initialization is effective for the population initialization of this algorithm.

[0082] The specific steps of competitive learning strategy are:

[0083] The initial population sequence generated by the SPM chaotic map is Z n ={z1,z2,...,z n}, where n is the population size, and the new population position sequence generated by competitive learning is This is used as the final result of improved population initialization.

[0084] The competitive learning strategy is defined as:

[0085]

[0086] Where ub and lb are the upper and lower bounds of the search area respectively; α is the learning factor, F is the fitness function, and Z best is the best individual in the population, and Z is all populations.

[0087] like Figure 7 , the hunting and chasing of Tyrannosaurus Rex is divided into two parts:

[0088] In the first part, the position of the Tyrannosaurus Rex meets the chasing conditions and starts to chase the prey. The original constant running rate tr is improved by nonlinear factors, and an adaptive position update formula is constructed to continuously update the position.

[0089] In the second part, the position of the Tyrannosaurus Rex does not meet the conditions, and the Tyrannosaurus Rex enters the hunting failure phase. The elite screening mechanism is used to screen out the best individual to update the position, and the greedy mechanism is combined to screen out the Tyrannosaurus Rex with the best fitness. Finally, the elite screening formula is used to screen out the best individual again to obtain the best position update. The hunting process of the Tyrannosaurus Rex is achieved by updating the position. The specific formula is:

[0090]

[0091] Er=rand(1-(t / Max iteration ))

[0092] In the formula, z new is the updated position, rand is a random number, Er represents the estimated value of reaching the scattered prey, that is, when the Tyrannosaurus Rex starts to hunt, the prey starts to escape, and the Tyrannosaurus Rex hunts the prey by updating its own position, Randon is the random position, t is the current iteration number, Max iteration is the maximum number of iterations; the position update equation of Tyrannosaurus Rex when chasing prey is:

[0093] z new =z+rand()*sr*(t pos *tr-target*pr)

[0094] In the formula, z is the position before the update, rand is a random number, and t pos is the position of Tyrannosaurus Rex, sr∈[0.1,1] is the hunting success rate. When its value is 0, it means hunting fails and the position of the corresponding prey must be updated.

[0095] target is the minimum distance from the prey to the Tyrannosaurus Rex; tr is the running speed of the Tyrannosaurus Rex; pr∈[0,1] is the running speed of the prey.

[0096] Among them, a nonlinear factor is introduced to represent tr, and the specific expression of tr is:

[0097]

[0098] In the formula, tr min =0.6, tr max = 0.9, t represents the current iteration number, T represents the maximum iteration number, and λ∈(0,1) is a nonlinear adjustment factor. By improving the constant tr, the running speed changes from slow to fast, so that the Tyrannosaurus Rex can sneak up on the prey at a slow speed in the initial hunting stage to avoid scaring the prey away. When it slowly approaches the prey and reaches the appropriate chasing distance, it must quickly chase the prey at a fairly fast speed.

[0099] like Figure 7 When the Tyrannosaurus Rex fails in hunting, it is necessary to remove the poor individuals in the population and select individuals with higher fitness values ​​in the population to avoid the position of the solution in the later stage of the algorithm becoming fixed and causing search stagnation, promote the evolution of the population, and finally achieve algorithm optimization with the elite among the elites. Therefore, an elite screening mechanism is designed, and the specific steps are as follows:

[0100] First, A Tyrannosaurus Rex with poor fitness is eliminated from each iteration, where A is a random integer in the interval [n / 2k,n / k], and k is the population screening factor, which is a natural number. Then, since there is no guarantee that the fitness value of the newly generated Tyrannosaurus Rex is better than the original solution, a greedy strategy is used. The greedy strategy uses the principle of survival of the fittest, so that the fitness value of the newly generated individual must be better than the original fitness value.

[0101] During the update process, if New individuals are regenerated through improved population initialization. When , stop generating, complete the update of one Tyrannosaurus Rex, repeat the above steps until the update of A Tyrannosaurus Rex is completed. The greedy mechanism update formula is as follows:

[0102]

[0103] In the formula, is the individual position before updating, fit(V i ) is the fitness value of the original Tyrannosaurus Rex; is the fitness value of the new Tyrannosaurus Rex; V i+1 is the initial position of the newly generated Tyrannosaurus Rex.

[0104] Finally, the best individual is selected through the elite screening mechanism. The formula of the elite screening mechanism is:

[0105]

[0106] In the formula, represents the best individual, Represents the current individual, represents the current best individual, r1∈(0,1) is a random number, t represents the current number of iterations, T represents the maximum number of iterations, and R represents a random number of -1 or 1.

[0107] like Figure 8 As shown in the figure, there are two situations for the selection process of Tyrannosaurus Rex: in the first situation, the updated fitness function value is lower than the initial random prey position fitness function value, and the Tyrannosaurus Rex will promptly update the Tyrannosaurus Rex position, target and fitness function through the elite dominant strategy; otherwise, it is the second situation, the Tyrannosaurus Rex's target value is zero, and the final path is generated. The comparative fitness function equation when Tyrannosaurus Rex selects prey is:

[0108]

[0109] Where f(Z) is the initial random prey position fitness function, f(Z new ) is the updated fitness function.

[0110] When the fitness function of the initial random prey position is less than the fitness function of the updated prey position, the current position is updated through the elite-dominated strategy. Although Tyrannosaurus Rex acts alone most of the time, it also hunts in groups in some cases. Group cooperative hunting will prompt Tyrannosaurus Rex to evolve group cooperative hunting capabilities, and there must be individual differences within the population among the groups. Therefore, this algorithm selects the three Tyrannosaurus Rex with the best fitness to guide the search. The specific steps are: first select the Tyrannosaurus Rex with the top three population fitness values ​​in t iterations, and then construct the elite-dominated strategy formula with the fitness function of the three Tyrannosaurus Rex to obtain high-quality position updates. The elite-dominated strategy formula is:

[0111] Z(t+1)=A(Z a (t)-Z(t))+B(Z b (t)-Z(t))+C(Z c (t)-Z(t))

[0112] In the formula, Z a (t), Z b (t) and Z c (t) represents the first, second and third positions of the fitness value in the Tyrannosaurus Rex population in the tth iteration, Z(t) is the position before the update, and the coefficients A, B, and C are:

[0113]

[0114] In the formula, F is the fitness function. The proportion of the fitness function is used as a coefficient to increase the role of superior Tyrannosaurus Rex, reduce the influence of inferior individuals, and accelerate the overall convergence.

[0115] The above description is only a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements without departing from the principle of the present invention, and these improvements should also be regarded as within the protection scope of the present invention.

Claims

1. A warehouse logistics robot path planning method based on an improved Tyrannosaurus Rex optimization algorithm, characterized in that: The steps include: S1: Create a path grid map based on the search needs of the warehouse logistics robot; S2: Improve the Tyrannosaurus Rex optimization algorithm. The improvements are as follows:

1. Introduce SPM chaotic mapping to initialize the position distribution of the Tyrannosaurus Rex population in the Tyrannosaurus Rex optimization algorithm, generate the initial position sequence of the Tyrannosaurus Rex population, and adopt a competitive learning strategy to make the generated initial position sequence biased towards the global optimal solution; 2. Introduce a nonlinear factor into the Tyrannosaurus Rex optimization algorithm to improve the exploration accuracy of the Tyrannosaurus Rex optimization algorithm; 3. Design an elite screening mechanism for the Tyrannosaurus Rex optimization algorithm to generate the best elite individuals; 4. When the T. rex optimization algorithm selects prey, it updates the current position of the target prey through the designed elite dominance strategy; S3: Input the path grid map created in S1 into the improved T-Rex optimization algorithm and output the optimal path.

2. The warehouse logistics robot path planning method based on the improved Tyrannosaurus Rex optimization algorithm as claimed in claim 1 is characterized by: The initial population sequence is randomly generated by SPM chaotic mapping, and the introduced SPM chaotic mapping formula is defined as: In the formula, z(t) is the value of the chaotic sequence, z i+1 is the generated composite sequence, mod is the modulo operation, when η∈(0,1),μ∈(0,1), the population is in a chaotic state, and r∈(0,1) is a random number.

3. The warehouse logistics robot path planning method based on the improved Tyrannosaurus Rex optimization algorithm as claimed in claim 2 is characterized by: The initial population sequence generated by the SPM chaotic map is Z n ={z1,z2,...,z n }, where n is the population size, and the new population position sequence generated by random reverse competitive learning is The competitive learning strategy is defined as: Where ub and lb are the upper and lower bounds of the search area respectively; α is the learning factor, F is the fitness function, and Z best is the best individual in the population, and Z is all populations.

4. The warehouse logistics robot path planning method based on the improved Tyrannosaurus Rex optimization algorithm as claimed in claim 1 is characterized by: The optimization algorithm of Tyrannosaurus Rex includes chasing and capturing prey in childhood. When the estimated value of dispersed prey is reached, the Tyrannosaurus Rex starts to hunt randomly and continuously updates the position to hunt and chase prey. The specific formula for updating the position is: Is=rand(1-(t / Max iteration )) In the formula, z new is the updated position, rand is a random number, Er represents the estimated value of reaching the scattered prey, that is, when the Tyrannosaurus Rex starts to hunt, the prey starts to escape, and the Tyrannosaurus Rex hunts the prey by updating its own position, Randon is the random position, t is the current iteration number, Max iteration is the maximum number of iterations; The position update equation of Tyrannosaurus Rex when chasing prey is: z new =z+rand()*sr*(t pos *tr-target*pr) In the formula, z is the position before the update, rand is a random number, and t pos is the position of Tyrannosaurus Rex, sr∈[0.1,1] is the hunting success rate. When its value is 0, it means hunting has failed and the position of the corresponding prey must be updated; target is the minimum distance from the prey to the Tyrannosaurus Rex; tr is the running speed of the Tyrannosaurus Rex; pr∈[0,1] is the running speed of the prey; A nonlinear factor is introduced to represent tr, and the specific expression of tr is: In the formula, tr min =0.6, tr max =0.9, t represents the current number of iterations, T represents the maximum number of iterations, and λ∈(0,1) is the nonlinear adjustment factor.

5. The warehouse logistics robot path planning method based on the improved Tyrannosaurus Rex optimization algorithm as claimed in claim 1 is characterized by: When the Tyrannosaurus Rex fails in hunting, the position is updated through the elite screening mechanism. The specific steps are: First, the greedy mechanism is used for screening, and then the best individual is selected through the elite screening mechanism to complete the position update. The greedy mechanism update formula is: V i+1 =V i * ,fit(V i * )<fit(V i ) Where V i * is the individual position before updating, fit(V i ) is the fitness value of the original Tyrannosaurus Rex; fit(V i * ) is the fitness value of the new Tyrannosaurus Rex; V i+1 is the initial position of the newly generated Tyrannosaurus Rex; The elite screening mechanism formula is: In the formula, represents the best individual, Represents the current individual, represents the current best individual, r1∈(0,1) is a random number, t represents the current number of iterations, T represents the maximum number of iterations, and R represents a random number of -1 or 1.

6. The warehouse logistics robot path planning method based on the improved Tyrannosaurus Rex optimization algorithm as claimed in claim 5 is characterized by: The process of Tyrannosaurus Rex selecting prey depends on the current position and the previous position of the target prey. If the fitness function of the initial random prey position is less than the fitness function of the updated prey position, the current position is updated through the elite dominant strategy.

7. The path planning method for a storage logistics robot based on an improved Tyrannosaurus Rex optimization algorithm as claimed in claim 6, characterized in that: When Tyrannosaurus Rex chooses prey, the comparative fitness function equation is: Where f(Z) is the initial random prey position fitness function, f(Z new ) is the updated fitness function; When the fitness function of the initial random prey position is less than the fitness function of the updated prey position, the current position is updated through the elite-dominated strategy. The elite-dominated strategy formula is: Z(t+1)=A(Z a (t)-Z(t))+B(Z b (t)-Z(t))+C(Z c (t)-Z(t)) In the formula, Z a (t), Z b (t) and Z c (t) represents the first, second and third positions of the fitness value in the Tyrannosaurus Rex population in the tth iteration, Z(t) is the position before the update, and the coefficients A, B, and C are: Where F is the fitness function.

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