Robot control method, device, equipment and robot

Abstract

The method comprises the following steps: in the process of variable-speed driving of the robot, first mass data of the robot at a current time is determined, and a predicted speed of the robot at a next time is predicted; according to the first mass data and the predicted speed, a driving speed of the robot at the next time is determined; and according to the driving speed, the running of the robot at the next time is controlled. The method of the present disclosure is used to achieve that the driving speed and other motion parameters of the robot in the process of variable-speed driving can adapt to the load of the robot, and the effect of improving the running stability of the robot is achieved.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent warehousing technology, and in particular to a robot control method, device, equipment, and robot. Background Technology

[0002] Robots have been gradually applied to various aspects of human life, such as controlling warehouse robots to climb shelves to pick up and put away goods.

[0003] In existing technologies, based on trapezoidal velocity planning algorithms, robots are controlled to accelerate at a constant acceleration during a preset acceleration phase, such as when climbing a shelf from the ground.

[0004] However, in the above method, since the robot's acceleration is constant during the acceleration phase, it is impossible to adjust the acceleration in time to adapt to the changes in robot load due to picking up and placing goods, which leads to the robot's operation becoming unbalanced or even running off the climbing track. Summary of the Invention

[0005] This disclosure provides a robot control method, apparatus, device, and robot to solve the technical problem that the robot's operation becomes unbalanced or even runs off the climbing track because the acceleration cannot be adjusted to adapt to changing robot loads.

[0006] In a first aspect, embodiments of this disclosure provide a robot control method, including:

[0007] During the robot's variable speed movement, the robot's first mass data at the current moment is determined, and the robot's predicted speed at the next moment is predicted; wherein, the first mass data represents the mass of the robot itself and the goods it is transporting;

[0008] Based on the first mass data and the predicted speed, the robot's travel speed at the next moment is determined;

[0009] The robot's operation at the next moment is controlled based on the travel speed.

[0010] In one possible implementation, determining the robot's travel speed at the next moment based on the first mass data and the predicted speed includes:

[0011] Based on the first mass data and the predicted velocity, the target acceleration of the robot at the next moment is determined;

[0012] Based on the target acceleration, the robot's travel speed at the next moment is determined.

[0013] In one possible implementation, determining the target acceleration of the robot at the next moment based on the first mass data and the predicted velocity includes:

[0014] Based on the first mass data and the predicted velocity, the robot's first acceleration at the next moment is determined;

[0015] Obtain the recommended acceleration of the robot at the next moment;

[0016] The target acceleration is determined based on the first acceleration and the recommended acceleration.

[0017] In one possible implementation, obtaining the robot's recommended acceleration at the next moment includes:

[0018] Based on the robot's current travel height, determine the robot's second mass data at the current moment; wherein, the travel height represents the height the robot has traveled during variable speed travel; and the second mass data represents the weight range of the stored goods that the robot is in at the current moment.

[0019] Based on the second mass data, the recommended acceleration of the robot at the next moment is determined.

[0020] In one possible implementation, determining the target acceleration based on the first acceleration and the recommended acceleration includes:

[0021] If it is determined that the first acceleration is less than or equal to the recommended acceleration, then the first acceleration is determined to be the target acceleration;

[0022] If it is determined that the first acceleration is greater than the recommended acceleration, then the recommended acceleration is determined to be the target acceleration.

[0023] In one possible implementation, determining the robot's first acceleration at the next moment based on the first mass data and the predicted velocity includes:

[0024] Based on the predicted velocity, determine the robot's second acceleration at the next moment;

[0025] Based on the first mass data, the second acceleration is corrected to obtain the first acceleration.

[0026] In one possible implementation, determining the robot's second acceleration at the next moment based on the predicted velocity includes:

[0027] Based on the predicted speed, the predicted height of the robot at the next moment is predicted; and a preset travel height is determined; wherein, the preset travel height represents the total height that the robot needs to travel during the variable speed travel process;

[0028] The second acceleration is determined based on the predicted speed, the predicted height, and the preset travel height.

[0029] In one possible implementation, predicting the robot's predicted speed at the next moment includes:

[0030] Based on the first mass data, predict the robot's acceleration at the next moment;

[0031] The predicted velocity is determined based on the predicted acceleration.

[0032] In one possible implementation, predicting the robot's predicted acceleration at the next moment based on the first mass data includes:

[0033] Determine the robot's current travel height and a preset travel height; wherein, the travel height represents the height already traveled by the robot during variable speed travel, and the preset travel height represents the total height that the robot needs to travel during variable speed travel;

[0034] Based on the travel height, the preset travel height, the first mass data, and the robot's travel speed at the current moment, predict the robot's acceleration at the next moment.

[0035] Secondly, embodiments of this disclosure provide a robot control device, comprising:

[0036] The first determining module is used to determine the first mass data of the robot at the current moment during the robot's variable speed travel, and to predict the predicted speed of the robot at the next moment; wherein, the first mass data represents the mass of the robot itself and the goods it is transporting;

[0037] The second determining module is used to determine the robot's travel speed at the next moment based on the first mass data and the predicted speed.

[0038] The control module is used to control the robot's operation in the next moment based on the travel speed.

[0039] In one possible implementation, the second determining module is specifically used to: determine the target acceleration of the robot at the next moment based on the first mass data and the predicted velocity; and determine the travel speed of the robot at the next moment based on the target acceleration.

[0040] In one possible implementation, the second determining module is specifically configured to: determine the first acceleration of the robot at the next moment based on the first mass data and the predicted velocity; obtain the recommended acceleration of the robot at the next moment; and determine the target acceleration based on the first acceleration and the recommended acceleration.

[0041] In one possible implementation, the second determining module is specifically used to: determine the robot's second mass data at the current moment based on the robot's current travel height; wherein the travel height represents the height the robot has traveled during variable speed driving; the second mass data represents the weight range of the stored goods that the robot is in at the current moment; and determine the recommended acceleration of the robot at the next moment based on the second mass data.

[0042] In one possible implementation, the second determining module is further configured to: if it is determined that the first acceleration is less than or equal to the recommended acceleration, then determine the first acceleration as the target acceleration; if it is determined that the first acceleration is greater than the recommended acceleration, then determine the recommended acceleration as the target acceleration.

[0043] In one possible implementation, the second determining module is further specifically used to: determine the robot's second acceleration at the next moment based on the predicted velocity; and perform correction processing on the second acceleration based on the first mass data to obtain the first acceleration.

[0044] In one possible implementation, the second determining module is further configured to: predict the robot's predicted height at the next moment based on the predicted speed; and determine a preset travel height; wherein the preset travel height represents the total height the robot needs to travel during variable speed driving; and determine the second acceleration based on the predicted speed, the predicted height, and the preset travel height.

[0045] In one possible implementation, the first determining module is specifically used to: predict the robot's predicted acceleration at the next moment based on the first mass data; and determine the predicted velocity based on the predicted acceleration.

[0046] In one possible implementation, the first determining module is specifically used to: determine the robot's current travel height and determine a preset travel height; wherein the travel height represents the height already traveled by the robot during variable speed travel, and the preset travel height represents the total height that the robot needs to travel during variable speed travel; and predict the robot's predicted acceleration at the next moment based on the travel height, the preset travel height, the first mass data, and the robot's current travel speed.

[0047] Thirdly, embodiments of this disclosure provide an electronic device, including: a memory and a processor;

[0048] The memory stores computer-executed instructions;

[0049] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0050] Fourthly, embodiments of this disclosure provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0051] Fifthly, embodiments of this disclosure provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0052] Sixthly, embodiments of this disclosure provide a robot in which electronic devices as described in the third aspect above are provided.

[0053] The robot control method, apparatus, device, and robot provided in this disclosure determine the robot's travel speed in the next moment based on the robot's own condition and the quality of the goods being transported at the current moment, as well as the robot's predicted speed in the next moment, during the robot's variable speed travel process, so as to control the robot's operation in the next moment; thereby, the robot's motion parameters such as travel speed during variable speed travel can adapt to the robot's load, the change in travel speed is smoother, and the robot's operational stability is improved. Attached Figure Description

[0054] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0055] Figure 1 This is a schematic diagram of an application scenario provided in this disclosure;

[0056] Figure 2 A schematic diagram illustrating the principle of a trapezoidal velocity planning method provided in this disclosure;

[0057] Figure 3 A flowchart illustrating a robot control method provided in one embodiment of this disclosure;

[0058] Figure 4 For this disclosure Figure 3A schematic diagram illustrating the principle of the robot's variable speed travel process in the illustrated embodiment;

[0059] Figure 5 A flowchart illustrating a robot control method provided in another embodiment of this disclosure;

[0060] Figure 6 A flowchart illustrating a robot control method provided in yet another embodiment of this disclosure;

[0061] Figure 7 This is a schematic diagram of the structure of a robot control device provided in one embodiment of the present disclosure;

[0062] Figure 8 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present disclosure;

[0063] Figure 9 This is a schematic diagram of the structure of a robot provided in one embodiment of the present disclosure.

[0064] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0065] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0066] Figure 1 This is a schematic diagram illustrating one application scenario provided in this disclosure. For example... Figure 1 As shown, the specific application scenario of this disclosure includes the electronic device 101 in the robot. When the robot starts to climb the shelf from the ground, it is controlled by the electronic device 101 to accelerate at a constant speed so that the robot can crawl on the vertical track on the shelf to the preset goods storage area to perform the work of picking up and putting away goods.

[0067] In one example, Figure 2This is a schematic diagram of the principle of a trapezoidal speed planning method provided in this disclosure. Based on electronic device 101, in the initial stage, the robot is controlled to accelerate uniformly at a constant acceleration. In the middle stage, the robot is controlled to run at a constant speed. In the final stage, the robot is controlled to decelerate uniformly at a constant acceleration, so that the robot can crawl on the vertical track on the shelf and reach the preset position to pick up and put away goods.

[0068] However, in the above method, since the robot's acceleration is constant during the acceleration phase, it is impossible to adjust the acceleration in time to adapt to the changes in robot load due to picking up and placing goods, which leads to the robot's operation becoming unbalanced or even running off the climbing track.

[0069] To address the aforementioned technical problems, this disclosure proposes the following technical concept: During the robot's variable-speed movement, the robot's speed at the next moment is determined based on the robot's current condition, the weight of the goods it is carrying, and the predicted speed of the robot at the next moment, thereby controlling the robot's operation at that moment. This solves the problem in existing technologies where the inability to adjust acceleration to adapt to changing robot loads leads to robot imbalance or even derailment from the climbing track.

[0070] The technical solutions of this disclosure and how they solve the aforementioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this disclosure will now be described with reference to the accompanying drawings.

[0071] Figure 3 This is a flowchart illustrating a robot control method provided in an embodiment of the present disclosure, as shown below. Figure 3 As shown, the method includes:

[0072] 201. During the robot's variable speed movement, determine the robot's first mass data at the current moment and predict the robot's predicted speed at the next moment; wherein, the first mass data represents the mass of the robot itself and the goods it is transporting.

[0073] For example, the execution subject of this embodiment may be an electronic device, hereinafter referred to as the device. The device may be a physical device or a virtual device used to execute the robot control method. During the robot's variable speed travel, such as the initial acceleration phase when the shelf robot is climbing the shelf or the final deceleration phase when it is about to reach the target position on the shelf, at each moment during this variable speed travel process, the device can obtain the mass of the robot itself and the goods it is carrying through the robot's internal weight sensor, thereby obtaining the mass status of the robot itself and the goods it is carrying, i.e., the first mass data; and, through a preset speed prediction method, predict the predicted speed of the robot at the next moment, for example, by calculating motion parameters such as the robot's current travel speed and speed changes in the previous time period, to obtain the predicted speed for processing.

[0074] 202. Based on the first mass data and the predicted speed, determine the robot's travel speed at the next moment.

[0075] For example, the device can calculate the robot's first mass data at the current moment and the predicted speed at the next moment based on a preset speed planning algorithm to obtain the robot's travel speed at the next moment.

[0076] 203. Control the robot's operation in the next moment based on its travel speed.

[0077] For example, the device controls the robot to change speed and travel to the next moment based on the operating parameters such as the current travel speed, so that the robot reaches the travel speed of the next moment obtained based on the first mass data of the robot at the current moment.

[0078] For example, Figure 4 For this disclosure Figure 3The schematic diagram of the robot's variable speed travel process in the illustrated embodiment is shown in the figure. When the robot is given a task to pick up and place goods, based on the known height the robot needs to climb on the shelf, after the robot engages with the track on the shelf, the target journey is divided into five segments during speed planning: the first segment is an acceleration segment with variable acceleration, the second segment is a constant speed segment, the third segment is a deceleration segment with uniform deceleration, the fourth segment is a crawling segment with uniform speed (i.e., a slowing-down phase), and the fifth segment is a stopping segment with uniform deceleration. The horizontal axis represents the time axis, and the vertical axis represents the robot's travel speed. The curve segment represents the change of the robot's travel speed over time in the above five stages, that is, the process from starting acceleration to deceleration and stopping. During the acceleration segment, the robot's speed can be controlled by processing the robot's first mass data at the current moment and the predicted speed at the next moment. During the deceleration process, the robot can accelerate as follows... Figure 4 The uniform deceleration process shown can also be controlled by calculating the travel speed at the next moment, as described above, to control the robot's deceleration.

[0079] This embodiment provides a robot control method. During the robot's variable speed driving process, the robot's travel speed at the next moment is determined based on the robot's own condition and the quality of the goods being transported at the current moment, as well as the robot's predicted speed at the next moment, so as to control the robot's operation at the next moment. In this way, the robot's motion parameters such as travel speed during variable speed driving can adapt to the robot's load, the change in travel speed is smoother, and the robot's operational stability is improved.

[0080] Figure 5 A flowchart illustrating a robot control method provided in another embodiment of this disclosure is shown below. Figure 5 As shown, the method includes:

[0081] 301. During the robot's variable speed movement, determine the robot's first mass data at the current moment and predict the robot's predicted speed at the next moment; wherein, the first mass data represents the mass of the robot itself and the goods it is transporting.

[0082] For example, this step can be referred to as step 201, which will not be repeated here.

[0083] 302. Based on the first mass data and the predicted velocity, determine the target acceleration of the robot at the next moment.

[0084] For example, the device can calculate the robot's first mass data at the current moment and the predicted speed at the next moment based on a preset acceleration planning algorithm to obtain the robot's target acceleration at the next moment, that is, the final variable speed travel acceleration of the robot from the current moment to the next moment.

[0085] In one example, step 302 includes the following steps:

[0086] The first step of step 302 is to determine the robot's first acceleration at the next moment based on the first mass data and the predicted velocity.

[0087] The second step of step 302 is to obtain the robot's recommended acceleration at the next moment.

[0088] The third step of step 302 is to determine the target acceleration based on the first acceleration and the recommended acceleration.

[0089] For example, the device can calculate the robot's first mass data at the current moment and the predicted velocity at the next moment based on a preset acceleration planning algorithm to obtain the robot's first acceleration at the next moment; obtain the robot's recommended acceleration at the next moment, which can be preset or calculated from the robot's motion parameters at the current moment; and perform comprehensive analysis and processing on the robot's first acceleration and recommended acceleration at the next moment based on the preset acceleration planning algorithm to determine the robot's target acceleration at the next moment.

[0090] In one example, the first step of step 302 includes:

[0091] Step 1: Based on the predicted velocity, determine the robot's second acceleration at the next moment.

[0092] Step 2: Based on the first mass data, correct the second acceleration to obtain the first acceleration.

[0093] Specifically, in order to obtain a more accurate acceleration and further improve the smoothness of speed changes, the device can process the predicted speed of the robot at the next moment and other motion parameters of the robot based on a preset prediction algorithm to obtain the second acceleration of the robot at the next moment. Based on a preset correction algorithm, the first mass data of the robot at the current moment and the second acceleration at the next moment are corrected and calculated to obtain the corrected second acceleration, i.e., the first acceleration.

[0094] For example, to simplify the calculation process, a speed increment step size can be set. This step size is determined based on the robot's final speed during the acceleration phase and the running time during that phase, representing the speed change. It can represent the robot's speed change throughout the entire variable-speed driving process, or it can represent the robot's speed change within a previous preset time period. Based on a preset algorithm, this speed increment step size motion parameter and the robot's predicted speed at the next moment are processed to obtain the robot's second acceleration at the next moment. Then, based on the robot's first mass data m at the current moment, the corresponding correction parameter k is determined. m According to the correction parameter k m The robot's second acceleration at the next moment is corrected to obtain the first acceleration.

[0095] In one example, step one of the first step of step 302 includes: predicting the robot's predicted height at the next moment based on the predicted speed; and determining a preset travel height; wherein the preset travel height represents the total height that the robot needs to travel during the variable speed driving process; and determining a second acceleration based on the predicted speed, the predicted height, and the preset travel height.

[0096] Specifically, the device can calculate the robot's predicted speed at the next moment based on a preset algorithm, and predict the robot's predicted height at the next moment; at the same time, it can determine the preset height, that is, the total height that the robot needs to travel on the shelf during acceleration or deceleration; it can call the preset algorithm and combine it with the preset speed increment step size to calculate the robot's predicted speed, predicted height, and preset travel height at the next moment, and obtain the second acceleration for correction processing.

[0097] For example, the device uses an encoder to determine the robot's current height on the shelf and can predict its speed V for the next moment based on a preset algorithm. 预测 The robot's current speed and height are calculated to obtain its predicted height H at the next moment. 预测 According to the preset formula The predicted velocity V of the robot at the next moment. 预测 Predicted height H 预测 Preset travel height H, preset speed increment step size V add The second acceleration 'a' is obtained through calculation.

[0098] In one example, the second step of step 302 includes:

[0099] Step 1: Determine the robot's second mass data at the current moment based on the robot's current travel height; where travel height represents the height the robot has traveled during variable speed travel; and second mass data represents the weight range of the stored goods that the robot is in at the current moment.

[0100] Step 2: Based on the second mass data, determine the recommended acceleration for the robot at the next moment.

[0101] Specifically, the device uses the robot's internal encoder to determine the weight range of the stored goods that the robot is currently in, i.e., its travel height. Based on the preset correspondence between each travel height and each second mass data, the device determines the second mass data corresponding to the robot's current travel height. Each second mass data represents the weight range of the stored goods that the robot is currently in. Based on the preset correspondence between each second mass data and each recommended acceleration, the recommended acceleration corresponding to the robot's second mass data at the current moment can be determined.

[0102] For example, three different weight ranges for the cargo boxes can be set along the height of the shelf. That is, three different current travel heights correspond to three different weight ranges of the stored goods, which can be determined based on the robot's maximum load capacity. Then, each weight range of goods has a corresponding recommended acceleration value, and the corresponding recommended acceleration value can be determined based on the current travel height.

[0103] In one example, the third step of step 302 includes: if it is determined that the first acceleration is less than or equal to the recommended acceleration, then the first acceleration is determined to be the target acceleration; if it is determined that the first acceleration is greater than the recommended acceleration, then the recommended acceleration is determined to be the target acceleration.

[0104] Specifically, the device compares the recommended acceleration of the robot at the next moment with the first acceleration. If it is determined that the first acceleration is less than or equal to the recommended acceleration, the first acceleration is determined as the target acceleration of the robot at the next moment. If it is determined that the first acceleration is greater than the recommended acceleration, the recommended acceleration is determined as the target acceleration of the robot at the next moment.

[0105] 303. Based on the target acceleration, determine the robot's speed at the next moment.

[0106] For example, based on the predetermined target acceleration of the robot at the next moment, the device can control the robot to travel at the target acceleration at the next moment, based on the motion parameters such as the current travel speed, so that the robot reaches a suitable operating speed at the next moment, which is the robot's travel speed at the next moment.

[0107] 304. Control the robot's operation in the next moment based on its travel speed.

[0108] For example, this step can be referred to as step 203, which will not be repeated here.

[0109] In this embodiment, based on the above embodiments, a recommended acceleration is determined based on the robot's current mass and travel height. Then, combined with the calculated first acceleration, a suitable target acceleration can be determined. This not only ensures the robot runs smoothly during operation based on the target acceleration, but also reduces impact deformation on the shelf and extends the service life of the shelf track.

[0110] Figure 6 A flowchart illustrating a robot control method provided in yet another embodiment of this disclosure is shown below. Figure 6 As shown, the method includes:

[0111] 401. During the robot's variable speed movement, determine the robot's first mass data at the current moment.

[0112] For example, this step can be referred to as step 201, which will not be repeated here.

[0113] 402. Based on the first mass data, predict the robot's acceleration at the next moment.

[0114] For example, the device can process the robot's first mass data at the current moment based on a preset prediction algorithm and the robot's motion parameters at the current moment, and predict the robot's predicted acceleration at the next moment.

[0115] In one example, step 402 includes:

[0116] Step 1: Determine the robot's current travel height and the preset travel height; whereby the travel height represents the height already traveled by the robot during the variable speed travel process, and the preset travel height represents the total height that the robot needs to travel during the variable speed travel process.

[0117] Step 2: Based on the travel height, preset travel height, first mass data, and the robot's travel speed at the current moment, predict the robot's predicted acceleration at the next moment.

[0118] Specifically, in the acceleration prediction process, the device first obtains the height already traveled by the robot during variable-speed travel from the encoder, such as the height it has climbed on the shelf, and determines the preset travel height, which is the total height the robot needs to travel during variable-speed travel; then, the device uses a preset algorithm, such as a calculation formula... The robot's current speed V t Travel height H h The preset travel height H, the first mass data m, and the preset speed increment step V add Calculations are performed to obtain the predicted acceleration 'a' of the robot at the next moment. 预测 The first mass data m can be the total weight of the robot itself and the goods it carries, or it can be a mass parameter obtained based on the total weight of the robot itself and the goods it carries.

[0119] 403. Determine the predicted velocity based on the predicted acceleration.

[0120] For example, the device can utilize the relationship between velocity and acceleration, such as predicting velocity V. 预测 =V t +a 预测 *Δt, combined with the robot's current first mass data, is used to calculate the robot's predicted acceleration, current speed, and the time interval between the current and next moments in the next moment, thereby predicting the robot's speed in the next moment, which is then used to calculate the robot's speed in the next moment.

[0121] 404. Based on the first mass data and the predicted speed, determine the robot's travel speed at the next moment.

[0122] For example, this step can be referred to as step 202, which will not be repeated here.

[0123] 405. Control the robot's operation in the next moment based on its travel speed.

[0124] For example, this step can be referred to as step 203, which will not be repeated here.

[0125] In this embodiment, based on the above embodiments, the robot's current mass condition is combined to predict the robot's acceleration at the next moment, thereby predicting a more accurate speed. This allows for a more accurate determination of the robot's operating speed at the next moment, resulting in smoother speed changes during variable-speed operation. This reduces wear between the robot's gears and racks, ensuring stable operation and extending the robot's lifespan.

[0126] Figure 7 This is a schematic diagram of the structure of a robot control device provided in one embodiment of the present disclosure, as shown below. Figure 7 As shown, the device 50 includes:

[0127] The first determining module 501 is used to determine the first mass data of the robot at the current moment during the robot's variable speed driving process, and predict the predicted speed of the robot at the next moment; wherein, the first mass data represents the mass of the robot itself and the goods it is transporting;

[0128] The second determining module 502 is used to determine the robot's travel speed at the next moment based on the first mass data and the predicted speed;

[0129] The control module 503 is used to control the robot's operation in the next moment based on the travel speed.

[0130] In one possible implementation, the second determination module 502 is specifically used to: determine the target acceleration of the robot at the next moment based on the first mass data and the predicted velocity; and determine the travel speed of the robot at the next moment based on the target acceleration.

[0131] In one possible implementation, the second determining module 502 is specifically used to: determine the first acceleration of the robot at the next moment based on the first mass data and the predicted velocity; obtain the recommended acceleration of the robot at the next moment; and determine the target acceleration based on the first acceleration and the recommended acceleration.

[0132] In one possible implementation, the second determining module 502 is specifically used to: determine the robot's second mass data at the current moment based on the robot's current travel height; wherein, the travel height represents the height the robot has traveled during variable speed driving; the second mass data represents the weight range of the stored goods in which the robot is located at the current moment; and determine the recommended acceleration of the robot at the next moment based on the second mass data.

[0133] In one possible implementation, the second determining module 502 is further specifically configured to: if it is determined that the first acceleration is less than or equal to the recommended acceleration, then determine the first acceleration as the target acceleration; if it is determined that the first acceleration is greater than the recommended acceleration, then determine the recommended acceleration as the target acceleration.

[0134] In one possible implementation, the second determining module 502 is further specifically used to: determine the robot's second acceleration at the next moment based on the predicted velocity; and perform correction processing on the second acceleration based on the first mass data to obtain the first acceleration.

[0135] In one possible implementation, the second determining module 502 is further specifically used to: predict the robot's predicted height at the next moment based on the predicted speed; and determine a preset travel height; wherein the preset travel height represents the total height that the robot needs to travel during variable speed driving; and determine a second acceleration based on the predicted speed, the predicted height, and the preset travel height.

[0136] In one possible implementation, the first determining module 501 is specifically used to: predict the robot's predicted acceleration at the next moment based on the first mass data; and determine the predicted velocity based on the predicted acceleration.

[0137] In one possible implementation, the first determining module 501 is specifically used to: determine the robot's current travel height and determine a preset travel height; wherein the travel height represents the height already traveled by the robot during variable speed travel, and the preset travel height represents the total height that the robot needs to travel during variable speed travel; and predict the robot's predicted acceleration at the next moment based on the travel height, the preset travel height, the first mass data, and the robot's current travel speed.

[0138] The apparatus in this embodiment can execute the technical solutions in the above method. Its specific implementation process and technical principles are the same, and will not be repeated here.

[0139] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. These modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented in software via processing element calls, while others are implemented in hardware. Each module can be a separate processing element, or it can be integrated into a chip within the device. Alternatively, it can be stored as program code in the device's memory, and its functions can be called and executed by a processing element. Furthermore, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the processor element or through software instructions.

[0140] Figure 8 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present disclosure, such as... Figure 8 As shown, the electronic device 60 includes: a memory 601 and a processor 602; the memory 601 is used to store instructions executable by the processor 602.

[0141] The processor 602 is configured to perform the method provided in the above embodiments.

[0142] The electronic device also includes a receiver 603 and a transmitter 604. The receiver 603 is used to receive instructions and data sent by other devices, and the transmitter 604 is used to send instructions and data to external devices.

[0143] The specific implementation process of the processor can be found in the above method embodiments, and its implementation principle and technical effect are similar, so it will not be repeated here.

[0144] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0145] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0146] For example, Figure 9 This is a schematic diagram of the structure of a robot provided in one embodiment of the present disclosure; as shown below. Figure 9 As shown, the handling robot 80 includes a chassis assembly 81, a climbing assembly 82, and a pick-and-place assembly 83. The robot 80 can move through the aisle via the chassis assembly 81. The climbing assembly 82 is disposed on the chassis assembly 81. The robot 80 can climb to different heights on the shelf through the cooperation of the climbing assembly 82 and the rack and pinion mechanism, and can pick up and place the material boxes at the storage locations on the shelf via the pick-and-place assembly 83.

[0147] This disclosure also provides a chip for executing instructions, which is used to execute the technical solutions of the processing methods described above.

[0148] This disclosure also provides a computer-readable storage medium storing computer-executable instructions that, when executed on a computer, cause the computer to perform the technical solution of the processing method described in the above embodiments.

[0149] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0150] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0151] This disclosure also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium, and when the at least one processor executes the computer program, it can implement the technical solution of the processing method described in the above embodiments.

[0152] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments.

[0153] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for controlling a robot, characterized in that, include: During the process of the robot moving at varying speeds along the vertical track of the shelf, the first mass data of the robot at the current moment is determined, the travel height of the robot at the current moment is determined, and the preset travel height is determined; wherein, the travel height represents the height already traveled by the robot during the variable speed travel, and the preset travel height represents the total height that the robot needs to travel during the variable speed travel. Based on the travel height, the preset travel height, the first mass data, and the robot's travel speed at the current moment, the predicted acceleration of the robot at the next moment is predicted; based on the predicted acceleration, the predicted speed of the robot at the next moment is predicted; wherein, the first mass data represents the mass of the robot itself and the cargo it is transporting; Based on the first mass data and the predicted speed, the robot's travel speed at the next moment is determined; The robot's operation at the next moment is controlled based on the travel speed.

2. The method according to claim 1, characterized in that, Determining the robot's travel speed at the next moment based on the first mass data and the predicted speed includes: Based on the first mass data and the predicted velocity, the target acceleration of the robot at the next moment is determined; Based on the target acceleration, the robot's travel speed at the next moment is determined.

3. The method according to claim 2, characterized in that, Based on the first mass data and the predicted velocity, the target acceleration of the robot at the next moment is determined, including: Based on the first mass data and the predicted velocity, the robot's first acceleration at the next moment is determined; Obtain the recommended acceleration of the robot at the next moment; The target acceleration is determined based on the first acceleration and the recommended acceleration.

4. The method according to claim 3, characterized in that, Obtaining the robot's recommended acceleration at the next moment includes: Based on the robot's current travel height, determine the robot's second mass data at the current moment; wherein, the travel height represents the height the robot has traveled during variable speed travel; and the second mass data represents the weight range of the stored goods that the robot is in at the current moment. Based on the second mass data, the recommended acceleration of the robot at the next moment is determined.

5. The method according to claim 4, characterized in that, Determining the target acceleration based on the first acceleration and the recommended acceleration includes: If it is determined that the first acceleration is less than or equal to the recommended acceleration, then the first acceleration is determined to be the target acceleration; If it is determined that the first acceleration is greater than the recommended acceleration, then the recommended acceleration is determined to be the target acceleration.

6. The method according to claim 3, characterized in that, Determining the robot's first acceleration at the next moment based on the first mass data and the predicted velocity includes: Based on the predicted velocity, determine the robot's second acceleration at the next moment; Based on the first mass data, the second acceleration is corrected to obtain the first acceleration.

7. The method according to claim 6, characterized in that, Based on the predicted velocity, determining the robot's second acceleration at the next moment includes: Based on the predicted speed, the predicted height of the robot at the next moment is predicted; and a preset travel height is determined; wherein, the preset travel height represents the total height that the robot needs to travel during the variable speed travel process; The second acceleration is determined based on the predicted speed, the predicted height, and the preset travel height.

8. A control device for a robot, characterized in that, include: The first determining module is used to determine the robot's first mass data at the current moment, the robot's current travel height, and a preset travel height during the robot's variable-speed travel along the vertical track of the shelf; wherein the travel height represents the height already traveled by the robot during the variable-speed travel, and the preset travel height represents the total height the robot needs to travel during the variable-speed travel; based on the travel height, the preset travel height, the first mass data, and the robot's current travel speed, predict the robot's predicted acceleration at the next moment; and based on the predicted acceleration, predict the robot's predicted speed at the next moment; wherein the first mass data represents the mass of the robot itself and the goods it is transporting; The second determining module is used to determine the robot's travel speed at the next moment based on the first mass data and the predicted speed. The control module is used to control the robot's operation in the next moment based on the travel speed.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

11. A computer program product comprising a computer program that, when executed by a processor, implements the method of any one of claims 1-7.

12. A robot, characterized in that, The robot is equipped with the electronic equipment described in claim 9.

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