A spatiotemporal evaluation method and system for robot motion trajectory

Abstract

The present invention discloses a spatiotemporal evaluation method and system for a robot motion trajectory, the method comprising: dividing the evaluation grade of the robot motion trajectory into different levels using signal timing logic rules, and distinguishing and describing each level; evaluating the robot motion trajectory from two dimensions, namely, timing index and space index, to obtain the optimal timing-level STL parameter model and the optimal space-level STL parameter model for each level under the two indicators; combining and fusing the optimal timing-level STL parameter model and the optimal space-level STL parameter model for each level using logical connectors to obtain multiple-level fused STL parameter models; and selecting the model with the highest accuracy at each level as the optimal-level fused STL parameter model for this level; and simultaneously passing the robot motion trajectory through the optimal-level fused STL parameter models at different levels to evaluate the robot motion trajectory; the present invention can comprehensively reflect the quality of the robot motion trajectory.

Description

Technical Field

[0001] The present invention relates to the field of robot evaluation technology, and in particular to a spatiotemporal evaluation method and system for robot motion trajectory. Background Art

[0002] With the rapid development of autonomous robotics, the ability of a robot to navigate autonomously while performing tasks is becoming a key indicator for evaluating its performance. In the field of autonomous robotics, performance measurement involves multiple complex processes, including perception, decision-making, manipulation, and motion. Among these various evaluation metrics, robot trajectory analysis is widely considered a key method for reflecting autonomous navigation performance.

[0003] Traditional methods focus primarily on how a robot can reach its destination in the most efficient way possible, such as in the shortest time, across the shortest distance, and with the least energy consumption. However, they fail to continuously monitor the overall performance of the robot's trajectory over time. Continuous monitoring not only helps teams detect and correct deviations in real time, but also optimizes the robot's behavior and path planning, enhancing its environmental perception and ultimately improving its overall performance in various tasks and environments, significantly increasing its importance in the field of robot evaluation.

[0004] Signal Temporal Logic (STL) can be used to describe a robot's continuous position and timing information. Signal Temporal Logic (STL) is a formal language for describing and analyzing system behavior over time. It allows users to define the temporal constraints and conditions of system behavior in a mathematically rigorous manner. Existing evaluation systems often use general classification standards for the intelligence levels of unmanned systems as a reference, but these standards do not provide a clear and quantifiable method for evaluating the quality of motion trajectories.

[0005] In the prior art, patent publication number CN114489055A discloses a method, medium, and device for implementing multi-task robotic motion based on temporal logic. Based on temporal logic, this method encodes complex task specifications into interpretable wTLTL formulas, enabling rapid planning of motion trajectories for complex tasks tailored to user preferences with low computational complexity and high planning efficiency. This prior art is merely an application of temporal logic to motion planning for robotic systems, specifically for trajectory planning. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to solve the problem that the evaluation results of existing motion trajectories lack specific level quantitative indicators to describe them, and the existing evaluation methods only evaluate by total distance and total duration, and cannot distinguish the quality of the motion trajectory in a certain time period from the timing characteristics and continuous signals, and cannot comprehensively and objectively judge the quality of the robot's motion trajectory when performing tasks.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] A spatiotemporal evaluation method for a robot's motion trajectory comprises the following steps:

[0009] S100: The evaluation level of the robot's motion trajectory is divided into different levels using signal timing logic rules, and each level is distinguished and described;

[0010] S200, evaluating the robot's motion trajectory from two dimensions, namely, the timing index and the spatial index, to obtain the optimal timing-level STL parameter model for each level under the timing index, and the optimal spatial-level STL parameter model for each level under the spatial index;

[0011] S300, using a logical connector, combining and fusing the optimal temporal-level STL parameter model and the optimal spatial-level STL parameter model at each level to obtain a multi-level fused STL parameter model at each level;

[0012] S400, selecting the model with the highest accuracy from the multiple level fusion STL parameter models at each level as the optimal level fusion STL parameter model at this level;

[0013] S500 evaluates the robot's motion trajectory by placing the same robot's motion trajectory into the optimal level fusion STL parameter model at different levels. The evaluation results are then classified according to different levels to comprehensively reflect the pros and cons of the robot's motion trajectory.

[0014] Advantages: The present invention uses STL to describe the continuous position information and timing information of the robot, and then constructs a spatiotemporal fusion evaluation method for the robot's motion trajectory. The evaluation results are classified into different levels, which can comprehensively reflect the advantages and disadvantages of the robot's motion trajectory.

[0015] In one embodiment of the present invention, the evaluation level of the robot's motion trajectory is expressed by the following formula:

[0016] H i :STL[A i ,B i ](Y j ≤index≤Y k );

[0017] Where H i is the i-th level of the robot motion trajectory, where i = 1, 2, 3, ..., n, and n is the level of the level; STL is the signal timing logic rule symbol, including "□", "◇", "∧" and "∨"; where "□" represents the global property, which requires that the set STL formula constraint must always be satisfied within the set range; "◇" represents the final property, which requires that the set STL formula constraint must be satisfied at a certain moment within the set range; "∧" represents the logical AND, which means that two conditions must be satisfied at the same time; "∨" represents the logical OR, which requires that at least one of the two conditions be satisfied; [A i ,B i ] is the level area; index is the indicator value, Y j and Y k are the upper and lower limit values ​​of the indicator respectively.

[0018] In one embodiment of the present invention, a sensor, a visual system or a simulator is used to obtain the timing index and spatial index of the robot; wherein the timing index is represented by "projection distance-projection duration"; and the spatial index is represented by "xy" of the Cartesian coordinate system.

[0019] In one embodiment of the present invention, obtaining the optimal timing-level STL parameter model for each level under the timing indicator in step S200 includes the following steps:

[0020] S211, define the timing level STL initial model;

[0021] S212, initializing the parameters of the timing level STL initial model;

[0022] S213, calculating robustness: applying robustness to characterize the deviation distance between the trajectory signal point and the trajectory that meets the time series level STL initial model;

[0023] S214, defining the incentive function: for each level, the training objective function of the time series level STL initial model is used, and the function with the smallest robustness calculation result value is used as the reward function;

[0024] S215, establishing a reinforcement learning model: using a reinforcement learning algorithm, establishing an initial time series level STL parameter model, with the time series index as input and the STL model parameters of the initial time series level STL parameter model as output;

[0025] S216, training and acquiring models: Use the dataset to train the initial time-series level STL parameter model; in each training iteration, perform corresponding actions based on the current state and the model output, and observe the feedback from the environment; update and optimize the model parameters through the reward function to gradually learn and obtain the optimal time-series level STL parameter model for each level.

[0026] In one embodiment of the present invention, under the timing indicator, multiple robustness levels are obtained by the following formulas:

[0027]

[0028] Where x(t) is the monitoring signal at time t, l(x(t) is the evaluation result of the x signal at time t, l(x) is the timing evaluation result of the x signal, c is a constant, For different signal timing logic, It is expressed as the “handover” of tasks under the timing indicator. It is represented as the "and" of tasks under the timing indicator, and [a, b) is the time interval; among them, according to each level, there are multiple different timing level STL initial models, and each timing level STL initial model corresponds to a different robustness calculation; through the signal timing logic rule symbol, each timing level STL initial model is confirmed, and the corresponding robustness is calculated.

[0029] In one embodiment of the present invention, the steps of obtaining the optimal spatial-level STL parameter model for each level under the spatial index are the same as the steps of obtaining the optimal temporal-level STL parameter model for each level under the temporal index; the difference is that the multiple robustness under the spatial index is obtained by the following formula:

[0030]

[0031] In the formula, x(T) is the monitoring signal at position T, l(x(T) is the evaluation result of the x signal at position T, l(x) is the spatial evaluation result of the x signal, c is a constant, φ, φ1, and φ2 are different signal space logics, φ1∧φ2 represents the "intersection" of the task under the spatial indicator, φ1∨φ2 represents the "union" of the task under the spatial indicator, and [a, b) is the position point interval; according to each level, there are multiple different spatial level STL initial models, and each spatial level STL initial model corresponds to a different robustness calculation; through the signal temporal logic rule symbol, each spatial level STL initial model is confirmed, and the corresponding robustness is calculated.

[0032] In one embodiment of the present invention, a reinforcement learning model is established under the spatial index, with the spatial index as input and the STL model parameters of the initial spatial level STL parameter model as output.

[0033] In one embodiment of the present invention, under the spatial index, the training set for obtaining the optimal temporal level STL parameter model and the optimal spatial level STL parameter model at each level includes a temporal training set and a spatial training set; and the temporal training set includes temporal training subsets for training at different levels, and the spatial training set includes spatial training subsets for training at different levels.

[0034] In one embodiment of the present invention, the logical connectors are an “and” connector and an “or” connector.

[0035] The present invention also provides a system based on the above-mentioned spatiotemporal evaluation method of robot motion trajectory, comprising:

[0036] The level classification module is used to divide the evaluation level of the robot's motion trajectory into different levels using signal timing logic rules, and distinguish and describe each level;

[0037] The optimal parameter model module is used to evaluate the robot's motion trajectory from the two dimensions of time series indicators and space indicators, and obtain the optimal time-level STL parameter model for each level under the time series indicators, and obtain the optimal space-level STL parameter model for each level under the space indicators;

[0038] The level fusion module is used to fuse the optimal temporal level STL parameter model and the optimal spatial level STL parameter model at each level using logical connectors to obtain multiple level fusion STL parameter models at each level;

[0039] The optimal level fusion module is used to select the model with the highest accuracy from multiple level fusion STL parameter models at each level as the optimal level fusion STL parameter model at this level;

[0040] The evaluation level classification module is used to put the same robot motion trajectory into the optimal level fusion STL parameter model at different levels to evaluate the robot motion trajectory; and classify the evaluation results according to different levels to comprehensively reflect the advantages and disadvantages of the robot motion trajectory.

[0041] Compared with the existing technology, the present invention has the following advantages: a time-space (spatiotemporal) evaluation method based on the robot's continuous trajectory signal is proposed. By setting quantifiable grade intervals to identify the robot's motion trajectory, the robot's performance during continuous operation can be accurately quantified. An STL formula is then constructed to extract the robot's trajectory characteristics from the two dimensions of timing indicators and spatial position. A reinforcement learning algorithm is used to obtain the optimal spatial and temporal STL formula parameters. The two formulas are then connected to achieve a fusion evaluation of time and space. This invention not only improves evaluation accuracy but also provides a new theoretical basis and practical means for optimizing and improving the robot's autonomous navigation performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flow chart of a spatiotemporal evaluation method for a robot motion trajectory according to an embodiment of the present invention.

[0043] Figure 2 4 is a distribution diagram of motion trajectory curves at different levels according to an embodiment of the present invention.

[0044] Figure 3 Schematic diagram of projection distance definition according to an embodiment of the present invention.

[0045] Figure 4a Schematic diagram of timing information evaluation according to an embodiment of the present invention.

[0046] Figure 4b Schematic diagram of position data evaluation according to an embodiment of the present invention.

[0047] Figure 5 This is a block diagram of a spatiotemporal evaluation system for a robot motion trajectory according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To facilitate those skilled in the art to understand the technical solution of the present invention, the technical solution of the present invention is further described with reference to the accompanying drawings.

[0049] The terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0050] See also Figure 1 As shown, the present invention provides a spatiotemporal evaluation method for a robot motion trajectory, comprising the following steps:

[0051] S100, the evaluation level of the robot's motion trajectory is divided into different levels using signal timing logic rules, and each level is distinguished and described.

[0052] In one embodiment of the present invention, in the field of unmanned systems, the intelligence level of unmanned systems is generally divided into five levels according to the degree of their autonomy:

[0053] Level 1: Remotely operated, requiring a human operator.

[0054] Level 2: Assisted autonomy, capable of performing some basic automated functions.

[0055] Level 3: Partial autonomy, capable of autonomous navigation and decision-making in specific situations.

[0056] Level 4: Highly autonomous, capable of completing most tasks without human intervention.

[0057] Level 5: Full autonomy, capable of performing all tasks and operations without any human intervention.

[0058] Similarly, the evaluation level of the robot's motion trajectory is divided into 5 levels from H1 to H5. This embodiment is only explained with 5 levels, and is not limited to 5 levels. The specific level can be adjusted. The present invention uses STL formula to distinguish and describe different levels:

[0059] H i :STL[A i ,B i ](Y j ≤index≤Y k );

[0060] Where H i is the i-th level of the robot motion trajectory, where i=1,2,3,...,n, and n is the level. In this embodiment, n is 5; STL is the signal timing logic rule symbol, including "□", "◇", "∧" and "∨". Among them, "□" represents the global property, which requires that the set STL formula constraint must always be satisfied within the set range; "◇" represents the final property, which requires that the set STL formula constraint must be satisfied at a certain moment within the set range; "∧" represents the logical AND, which means that two conditions must be satisfied at the same time; "∨" represents the logical OR, which means that at least one of the two conditions must be satisfied; [A i ,B i ] is the level area; index is the indicator value, Y j and Y k are the upper and lower limit values ​​of the indicator value, j and k are different positive integers, and k>j.

[0061] See also Figure 2As shown, each level formula describes a rectangular area, and the curve of each level will pass through and only the curve of this level will pass through this rectangular area.

[0062] Because there are multiple symbols for signal timing logic rules, that is, at each level, there are multiple different STL formula constraints. Take the H1 level as an example: It means that within the AB time period, the index value index must be greater than or equal to Y1 and less than or equal to Y2 in order to determine that the evaluation result of this motion trajectory is at the H1 level.

[0063] S200 evaluates the robot's motion trajectory from two dimensions: timing indicators and spatial indicators, and obtains the optimal timing-level STL parameter model for each level under the timing indicators, and obtains the optimal spatial-level STL parameter model for each level under the spatial indicators.

[0064] See also Figure 1 and Figure 3 As shown in FIG, in one embodiment of the present invention, the total time consumed by the robot to reach the designated destination and complete the task is T, and the overall trajectory length is L. The quality of the robot's motion trajectory can be comprehensively evaluated from two dimensions: time and space.

[0065] like Figure 3 As shown, let the straight-line distance from the starting point O to the end point E be the optimal path L min , trajectory point P i (i=1,2,3,4) to the optimal path L min The foot of the perpendicular is M, and the length of OM is the projection distance L pro . It can be seen from the figure that when the projection distances of different trajectory points are the same, the shorter the time taken, the closer the motion trajectory is to the optimal path. Based on this, the position information and timing information of the robot under different motion trajectories can be used as features of the state space, and the acceleration, deceleration or turning behavior of the robot can be used as features of the action space. Use sensors, visual systems or simulators to obtain the timing information and position data of the robot, where the timing information is represented by "projection distance-projection duration", the motion space is simplified into a two-dimensional plane, and the position data is represented by "xy" in the Cartesian coordinate system. That is, in this embodiment, the timing information is used as a timing indicator, and the position data is used as a spatial indicator.

[0066] In this embodiment, obtaining the optimal timing level STL parameter model for each level under the timing indicator is illustrated as an example, including the following steps:

[0067] S211, define the timing level STL initial model. Each H i The motion trajectory of the level corresponds to an STL formula, as wait.

[0068] S212: Initialize the parameters of the temporal level STL initial model. According to the time limit and spatial threshold of the robot's motion process, initialize the parameters a, b, y1, and y2 of the STL initial model at different levels.

[0069] S213, calculating robustness: applying the robustness to characterize the deviation distance between the trajectory signal point and the trajectory that meets the timing level STL initial model.

[0070] In this embodiment, different timing level STL initial models have different structural expressions. For different timing level STL initial models, there are different corresponding robustness ρ calculation methods. In this embodiment, under the timing index, six forms are listed, covering all possible STL formulas:

[0071] Timing-Robustness Formula 1: ρ(x,(l(x) <c),t)=c-l(x(t));

[0072] Timing-robustness formula 2: ρ(x,(l(x)≥c),t)=l(x(t))-c;

[0073] Timing-Robustness Formula 3:

[0074] Timing-Robustness Formula 4:

[0075] Timing-Robustness Formula 5:

[0076] Timing-Robustness Formula 6:

[0077] Where x(t) is the monitoring signal at time t, l(x(t) is the evaluation result of the x signal at time t, l(x) is the timing evaluation result of the x signal, c is a constant, For different signal timing logic, It is expressed as the “handover” of tasks under the timing indicator. It is represented as the “and” of tasks under the timing indicator, and [a,b) is the time interval.

[0078] In this embodiment, there are multiple different timing level STL initial models at each level, and each timing level STL initial model is calculated corresponding to a different robustness. Through the signal timing logic rule symbol, each timing level STL initial model is confirmed and the corresponding robustness is calculated. For example: at the H1 level, there is and ◇[a1,b1](y1≤index≤y2) are used to express the robustness calculation under the timing index. The robustness of the expression of [a1, b1](y1≤index≤y2) is calculated using Timing-Robustness Formula 5. The robustness of the expression of [a1, b1](y1≤index≤y2) is calculated using Timing-Robustness Formula 6. If the signal timing logic rule symbol is omitted, the calculation is directly performed using Timing-Robustness Formula 1 or 2.

[0079] S214, define the incentive function: for each level, the training objective function of the time series level STL initial model is used, and the function with the smallest robustness calculation result value is used as the reward function.

[0080] S215, establishing a reinforcement learning model: using a reinforcement learning algorithm, establishing an initial time series level STL parameter model, with the time series index as input and the STL model parameters of the initial time series level STL parameter model as output.

[0081] In one embodiment of the present invention, a reinforcement learning algorithm, such as the Proximal Policy Optimization (PPO) algorithm, deep reinforcement learning, Q-Learning, or policy gradient method, is used to establish a model that can learn the optimal STL formula parameters. The model uses timing indicators as input and outputs the parameters of the STL parameter model.

[0082] S216, training and acquiring models: Use the dataset to train the initial time-series level STL parameter model; in each training iteration, perform corresponding actions based on the current state and the model output, and observe the feedback from the environment; update and optimize the model parameters through the reward function to gradually learn and obtain the optimal time-series level STL parameter model for each level.

[0083] In one embodiment of the present invention, the data set includes a temporal training set and a spatial training set, wherein the temporal training set is used herein. The temporal training set includes temporal training subsets for training at different levels, and the temporal training subset at each level corresponds to training a temporal level STL parameter model at the same level.

[0084] See also Figures 1 to 3 As shown, in one embodiment of the present invention, the steps of obtaining the optimal spatial-level STL parameter model for each level under the spatial index are the same as the steps of obtaining the optimal temporal-level STL parameter model for each level under the temporal index. The difference is that the multiple robustness ρ under the spatial index is obtained by the following formula:

[0085]

[0086] In the formula, x(T) is the monitoring signal at position T, l(x(T) is the evaluation result of the x signal at position T, l(x) is the spatial evaluation result of the x signal, c is a constant, φ, φ1, and φ2 are different signal space logics, φ1∧φ2 represents the "intersection" of the task under the spatial indicator, φ1∨φ2 represents the "union" of the task under the spatial indicator, and [a, b) is the position point interval; similarly, according to each level, there are multiple different spatial level STL initial models, and each spatial level STL initial model corresponds to a different robustness calculation. Through the signal temporal logic rule symbol, each spatial level STL initial model is confirmed, and the corresponding robustness is calculated.

[0087] In addition, the reinforcement learning model established under the spatial indicator takes the spatial indicator as input and the STL model parameters of the initial spatial level STL parameter model as output, and the spatial training set includes spatial training subsets for training at different levels. The spatial training subset of each level corresponds to the training of the spatial level STL parameter model at the same level.

[0088] S300: Using logical connectors, the optimal temporal level STL parameter model and the optimal spatial level STL parameter model at each level are combined and fused to obtain multiple level fused STL parameter models at each level.

[0089] In one embodiment of the present invention, taking five levels as an example, in this step, the optimal time-level STL parameter model and the optimal space-level STL parameter model for the five levels under the timing index are obtained. In this embodiment, the logical connectors are "and" connectors and "or" connectors, wherein the logical connectors can be used alone or in combination. Taking the H1 level as an example, the optimal time-level STL parameter model is: The optimal space-level STL parameter model is: Existence after combination and fusion form of expression.

[0090] S400, selecting the model with the highest accuracy from the multiple level fusion STL parameter models at each level as the optimal level fusion STL parameter model at this level.

[0091] In this embodiment, the data set also includes a test set, which also includes a timing test set and a spatial test set. The timing test set includes timing test subsets of different levels, and the spatial test set includes spatial test subsets of different levels. The timing test subsets and spatial test subsets of the same level are brought into the fused level fusion STL parameter model of the same level after fusion, and the level is evaluated and compared with the marked level. The model with the highest accuracy is selected as the optimal level fusion STL parameter model at this level.

[0092] S500 evaluates the robot's motion trajectory by placing the same robot's motion trajectory into the optimal level fusion STL parameter model at different levels. The evaluation results are then classified according to different levels to comprehensively reflect the pros and cons of the robot's motion trajectory.

[0093] In one embodiment of the present invention, when the tested robot motion trajectory passes through a square area of ​​a certain level of optimal level fusion STL parameter model, the evaluation result of the motion trajectory is considered to be H i class.

[0094] like Figure 4a and Figure 4b As shown, in one embodiment of the present invention, the evaluation results of the robot's timing information and position data are obtained after using the PPO algorithm to learn and train the STL formula. Figure 4a is the evaluation result of the timing information, Figure 4a is the evaluation result of the position data. When the curve composed of the dotted line representing the normal signal passes through the gray rectangle, it means that the evaluation result of this trajectory is level H5.

[0095] See also Figures 1 to 5 As shown, the present invention also provides a system according to the above-mentioned spatiotemporal evaluation method of the robot motion trajectory, comprising: a level division module, for dividing the evaluation level of the robot motion trajectory into different levels using signal timing logic rules, and distinguishing and describing each level. An optimal parameter model module, for evaluating the robot motion trajectory from two dimensions, namely, the timing index and the spatial index, to obtain the optimal timing-level STL parameter model of each level under the timing index, and to obtain the optimal spatial-level STL parameter model of each level under the spatial index. A level fusion module, for using logical connectors to combine and fuse the optimal timing-level STL parameter model and the optimal spatial-level STL parameter model under each level, and obtain multiple level fusion STL parameter models under each level. An optimal level fusion module, for selecting the model with the highest accuracy from the multiple level fusion STL parameter models under each level as the optimal level fusion STL parameter model under this level. The evaluation level classification module is used to simultaneously pass the robot's motion trajectory and fuse the optimal level at different levels with the STL parameter model to evaluate the robot's motion trajectory; and classify the evaluation results according to different levels to comprehensively reflect the advantages and disadvantages of the robot's motion trajectory.

[0096] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description. It is intended that all variations within the meaning and range of equivalents of the claims be embraced herein, and any reference signs in the claims should not be construed as limiting the claims to which they relate.

[0097] The above-mentioned embodiments merely represent the implementation methods of the invention. The protection scope of the present invention is not limited to the above-mentioned embodiments. For those skilled in the art, several variations and improvements can be made without departing from the concept of the present invention, which all fall within the protection scope of the present invention.

Claims

1. A spatiotemporal evaluation method for robot motion trajectory, characterized in that: The following steps are involved: S100: The evaluation level of the robot's motion trajectory is divided into different levels using signal timing logic rules, and each level is distinguished and described; S200, evaluating the robot's motion trajectory from two dimensions, namely, the timing index and the spatial index, to obtain the optimal timing-level STL parameter model for each level under the timing index, and the optimal spatial-level STL parameter model for each level under the spatial index; S300, using a logical connector, combining and fusing the optimal temporal-level STL parameter model and the optimal spatial-level STL parameter model at each level to obtain a multi-level fused STL parameter model at each level; S400, selecting the model with the highest accuracy from the multiple level fusion STL parameter models at each level as the optimal level fusion STL parameter model at this level; S500 evaluates the robot's motion trajectory by placing the same robot's motion trajectory into the optimal level fusion STL parameter model at different levels. The evaluation results are then classified according to different levels to comprehensively reflect the pros and cons of the robot's motion trajectory.

2. The spatiotemporal evaluation method of robot motion trajectory according to claim 1, characterized in that: The evaluation level of the robot's motion trajectory is expressed by the following formula: H i :STL[A i ,B i ](Y j ≤index≤Y k ); Where H i is the i-th level of the robot motion trajectory, where i = 1, 2, 3, ..., n, and n is the level of the level; STL is the signal timing logic rule symbol, including "□", "◇", "∧" and "∨"; where "□" represents the global property, which requires that the set STL formula constraint must always be satisfied within the set range; "◇" represents the final property, which requires that the set STL formula constraint must be satisfied at a certain moment within the set range; "∧" represents the logical AND, which means that two conditions must be satisfied at the same time; "∨" represents the logical OR, which means that at least one of the two conditions must be satisfied; [A i ,B i ] is the level area; index is the indicator value, Y j and Y k are the upper and lower limit values ​​of the indicator respectively.

3. The spatiotemporal evaluation method of robot motion trajectory according to claim 1, characterized in that: Use sensors or simulators to obtain the robot's timing and spatial metrics. The timing metric is expressed as "projected distance - projected duration." The projected distance is the length between the foot of the perpendicular from the trajectory point to the optimal path and the starting point. The optimal path is the straight-line distance from the starting point to the end point. The spatial metric is expressed as "xy" in the Cartesian coordinate system.

4. The spatiotemporal evaluation method of robot motion trajectory according to claim 1, characterized in that: Obtaining the optimal timing level STL parameter model for each level under the timing indicator in step S200 includes the following steps: S211, define the timing level STL initial model; S212, initializing the parameters of the timing level STL initial model; S213, calculating robustness: applying robustness to characterize the deviation distance between the trajectory signal point and the trajectory that meets the time series level STL initial model; S214, defining the incentive function: for each level, the training objective function of the time series level STL initial model is used, and the function with the smallest robustness calculation result value is used as the reward function; S215, establishing a reinforcement learning model: using a reinforcement learning algorithm, establishing an initial time series level STL parameter model, with the time series index as input and the STL model parameters of the initial time series level STL parameter model as output; S216, training and acquiring models: Use the dataset to train the initial time-series level STL parameter model; in each training iteration, perform corresponding actions based on the current state and the model output, and observe the feedback from the environment; update and optimize the model parameters through the reward function to gradually learn and obtain the optimal time-series level STL parameter model for each level.

5. The spatiotemporal evaluation method of robot motion trajectory according to claim 4, characterized in that: Under the timing indicators, multiple robustness is obtained by the following formula: Where x(t) is the monitoring signal at time t, l(x(t) is the evaluation result of the x signal at time t, l(x) is the timing evaluation result of the x signal, c is a constant, For different signal timing logic, It is expressed as the "delivery" of tasks under the time series indicator. It is represented as the "and" of tasks under the timing indicator, and [a,b) is the time interval. There are multiple different timing-level STL initial models at each level, and each timing-level STL initial model is calculated corresponding to a different robustness. The signal timing logic rule symbol is used to confirm each timing-level STL initial model and calculate the corresponding robustness.

6. The spatiotemporal evaluation method of robot motion trajectory according to claim 4, characterized in that: The steps for obtaining the optimal spatial-level STL parameter model for each level under spatial indicators are the same as those for obtaining the optimal temporal-level STL parameter model for each level under temporal indicators. The difference is that multiple robustness under spatial indicators are obtained by the following formula: Where x(T) is the monitoring signal at position T, l(x(T) is the evaluation result of the x signal at position T, l(x) is the spatial evaluation result of the x signal, c is a constant, φ, φ1, and φ2 are different signal space logics, φ1∧φ2 represents the "intersection" of tasks under spatial indicators, φ1∨φ2 represents the "union" of tasks under spatial indicators, and [a, b) is the position point interval; according to each level, there are multiple different spatial level STL initial models, and each spatial level STL initial model corresponds to a different robustness calculation; through the signal temporal logic rule symbol, each spatial level STL initial model is confirmed, and the corresponding robustness is calculated.

7. The spatiotemporal evaluation method of robot motion trajectory according to claim 6, characterized in that: Under the spatial indicator, the reinforcement learning model established takes the spatial indicator as input and the STL model parameters of the initial spatial level STL parameter model as output.

8. The spatiotemporal evaluation method of robot motion trajectory according to claim 1, characterized in that: Under the spatial indicators, the training set for obtaining the optimal time-level STL parameter model and the optimal space-level STL parameter model at each level includes a time-series training set and a space training set; and the time-series training set includes time-series training subsets for training at different levels, and the space training set includes space training subsets for training at different levels.

9. The spatiotemporal evaluation method of robot motion trajectory according to claim 1, characterized in that: Logical connectors are "and" and "or".

10. A system for spatiotemporal evaluation of robot motion trajectory according to any one of claims 1 to 9, characterized in that: include: The level classification module is used to divide the evaluation level of the robot's motion trajectory into different levels using signal timing logic rules, and distinguish and describe each level; The optimal parameter model module is used to evaluate the robot's motion trajectory from the two dimensions of time series indicators and space indicators, and obtain the optimal time-level STL parameter model for each level under the time series indicators, and obtain the optimal space-level STL parameter model for each level under the space indicators; The level fusion module is used to fuse the optimal temporal level STL parameter model and the optimal spatial level STL parameter model at each level using logical connectors to obtain multiple level fusion STL parameter models at each level; The optimal level fusion module is used to select the model with the highest accuracy from multiple level fusion STL parameter models at each level as the optimal level fusion STL parameter model at this level; The evaluation level classification module is used to put the same robot motion trajectory into the optimal level fusion STL parameter model at different levels to evaluate the robot motion trajectory; and classify the evaluation results according to different levels to comprehensively reflect the advantages and disadvantages of the robot motion trajectory.

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