A method and system for trajectory planning and tracking control of a flying robot under microgravity

Abstract

This invention discloses a method and system for trajectory planning and tracking control of a flying robot in microgravity. This method relates to the field of aircraft control technology and aims to address the problem that existing methods, in the confined space of a microgravity-normal air environment, are prone to collisions between the robot and astronauts or fixed or floating objects, thus failing to ensure safe and stable flight. The method includes: establishing an obstacle model to generate obstacle areas and navigable paths; establishing kinematic and dynamic models for a space intelligent flying robot; designing motion states and controller constraints based on the robot's maneuverability and mission requirements; establishing an optimization objective function to generate the robot's motion trajectory based on a tubular constraint model for tracking; establishing a robot posture dynamics model; designing a proportional-integral-differential control law to control the robot's posture; and establishing a flywheel unloading model for torque unloading.

Description

Technical Field

[0001] The present invention relates to the field of aircraft control technology, and in particular to a method and system for trajectory planning and tracking control of a flying robot under microgravity. Background Art

[0002] Currently, intelligent space flying robots, assisting astronauts in space operations, play an indispensable role in microgravity environment monitoring, space experiments, and astronaut life support. Research on intelligent space flying robots in microgravity environments and their control methods has essentially achieved autonomous flight capabilities, and their effectiveness and practicality have been demonstrated in on-orbit testing.

[0003] However, existing control schemes for intelligent flying robots in microgravity environments have yet to effectively address collisions between the robot and astronauts or fixed or floating objects. In particular, collisions with the robot during astronaut movements can be extremely dangerous. Therefore, addressing these safety risks requires developing safe trajectories for the robot and ensuring stable tracking and control of these planned trajectories. Summary of the Invention

[0004] The technical problems to be solved by the present invention are:

[0005] Existing intelligent flying robot control methods are prone to collisions between robots and astronauts or fixed / floating objects in the confined space of a microgravity-normal air environment, and cannot ensure the robot's safe and stable flight.

[0006] The present invention is to solve the above technical problems using the following technical solutions:

[0007] The present invention provides a method for trajectory planning and tracking control of a flying robot under microgravity, comprising the following steps:

[0008] Step S100, performing robot trajectory optimization based on the tubular constraint model, including:

[0009] Step S110: Establish static and dynamic obstacle models in a microgravity environment, predict the robot's motion risk area, and generate obstacle areas and navigable paths in three-dimensional space in real time;

[0010] Step S120: establishing kinematic and dynamic models of the space intelligent flying robot;

[0011] Step S130: Design motion states and controller constraints based on the robot's maneuverability and task requirements, including unknown obstacle avoidance constraints, posture angle and angular velocity constraints, and acceleration constraints.

[0012] Step S140: establishing an optimization objective function and generating a robot motion trajectory based on the tubular constraint model for robot tracking;

[0013] Step S200: Designing a bottom-level tracking controller to perform bottom-level control of the robot, including:

[0014] Step S210: define the control quantity in the control system as the robot's three-axis flywheel, and establish a robot posture dynamics model;

[0015] Step S220: Considering the constant interference torque on the flywheel, design a proportional-integral-differential control law, design robot control parameters, and control the robot posture;

[0016] Step S230: Establish a flywheel unloading model, and use a fan to perform torque unloading on the saturated flywheel.

[0017] Furthermore, step S110 includes the following process:

[0018] First, the movement patterns of the astronaut's torso and limbs are modeled. Secondly, the astronaut's future movement range is determined based on the astronaut's movement direction, speed, and acceleration level, and the risk area of ​​the robot's movement is generated. Finally, based on the human body positioning and human intention recognition results given by the navigation system, the position information of the human joints is predicted, and the obstacle area in the three-dimensional space is generated in real time according to the prediction results; combined with other static / dynamic obstacle information, the three-dimensional map is transformed into a The space is divided into three areas: barrier-free space, obstacle space, unknown space or risk space, and a traversable path is generated.

[0019] Furthermore, step S120 includes the following process:

[0020] The space intelligent flying robot is modeled as a six-degree-of-freedom rigid body, and its translational kinematic model is established as follows:

[0021]

[0022] in, Indicates the position of the center of gravity of the space intelligent flying robot, is the z-axis vector of the robot system, and are collective thrust and total mass respectively, is the gravity vector, It represents the exogenous aerodynamic drag during high-speed flight;

[0023] The rotational kinematics model of the space intelligent flying robot is established as:

[0024]

[0025] The dynamic equation of the space intelligent flying robot is established as:

[0026]

[0027] Among them, q represents the quaternion, represents the quaternion multiplication operator, represents the angular acceleration of the robot system, is the angular velocity of the robot system, represents the inertia matrix of the entire body, is the resulting torque produced by the rotor, is the interference torque.

[0028] Furthermore, the unknown obstacle avoidance constraint in step S130 is specifically for the situation where, when an obstacle that cannot be identified at a distance suddenly appears in the motion path, the obstacle is not updated in the map, and the trajectory planning algorithm cannot immediately update the trajectory to guide the robot to avoid it;

[0029] The posture angle and angular velocity constraints specifically limit the posture angle and angular velocity during trajectory tracking so that the robot can complete smooth movement:

[0030]

[0031]

[0032] in and are the angle and angular acceleration of the robot when it turns, and are the minimum and maximum angles when the robot turns, and are the minimum and maximum angular accelerations of the robot when turning;

[0033] The acceleration constraint specifically limits the acceleration output:

[0034]

[0035]

[0036]

[0037] in, 、 and are the accelerations along the x, y, and z directions, 、 and are the minimum accelerations along the x, y, and z directions, respectively, 、 and are the maximum accelerations along the x, y, and z directions, respectively.

[0038] Furthermore, the optimization objective function in step S140 is specifically:

[0039]

[0040] Among them, Q, R, S and are the appropriate dimensional weights for penalizing state tracking error, control action, control change rate, and violation of soft constraints, respectively. is the control input change rate, k is the cumulative time, To predict the time domain length, For reference of all states, is all the states from time t to time t+k, is all the control inputs from time t to time t+k, The rate of change of all control inputs from time t to time t+k is, Violation of soft constraints.

[0041] Furthermore, step S210 includes the following process:

[0042] The control quantity in the control system is defined as the robot's three-axis flywheel, and the robot's posture dynamics model is established as:

[0043]

[0044] in, is the robot's moment of inertia, is the robot's angular velocity, is the angular momentum of the flywheel relative to the robot, is the flywheel output torque, is the interference torque acting on the flywheel;

[0045] Since the robot's posture angles change at a relatively low speed during the execution of the task, the robot's posture dynamics model is simplified as follows:

[0046]

[0047]

[0048]

[0049] in 、 and are the moments of inertia of the robot in the x-axis, y-axis and z-axis directions, 、 and is the output torque of the flywheel in the x-axis, y-axis and z-axis directions, 、 and is the disturbance torque on the flywheel in the x-axis, y-axis and z-axis directions, is the x-axis angular acceleration, is the y-axis angular acceleration, is the z-axis angular acceleration.

[0050] Furthermore, step S220 includes the following process:

[0051] For the robot posture dynamics model, considering that the interference torque on the flywheel is a constant torque, the proportional-integral-differential control law is designed as follows:

[0052]

[0053] in, is the torque output by the controller, is the current posture of the robot, is the robot target posture, is the attitude control error, are proportional, differential, and integral coefficients respectively;

[0054] The closed-loop system model is:

[0055]

[0056] in, For the external disturbance torque, the control coefficient is designed through pole configuration to realize the control of the robot posture.

[0057] Furthermore, the flywheel unloading model in step S230 is specifically:

[0058]

[0059] in, is the aerodynamic torque generated by the fan, Indicates the deviation from the expectation, is the proportionality coefficient, is the current angular momentum of the robot, is the target angular momentum of the robot.

[0060] The present invention provides a trajectory planning and tracking control system for a flying robot under microgravity. The system has a program module corresponding to the steps of the method described in any one of the above technical solutions, and executes the steps in the above-mentioned trajectory planning and tracking control method for a flying robot under microgravity during operation.

[0061] The present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to implement the steps of the trajectory planning and tracking control method for a flying robot under microgravity described in any one of the above technical solutions when called by a processor.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] This invention provides a safe trajectory planning and tracking control method for a space intelligent flight robot operating in a confined space in a microgravity environment. By applying physical constraints such as unknown obstacle avoidance, attitude angle and angular velocity constraints, and acceleration constraints, the robot's state is constrained within a tubular invariant set. This allows the robot's flight control system to achieve asymptotic stability under bounded interference, ensuring that the generated control instructions guarantee the robot's safety and stability. This method combines trajectory optimization with model predictive control and a low-level proportional-integral-differential (PI / D) control scheme, achieving superior position tracking and attitude control accuracy compared to traditional cascaded PI / D control schemes.

[0064] The control scheme of the model predictive control trajectory optimizer + proportional-integral-differential underlying controller proposed in the present invention can effectively avoid collisions between the space intelligent flying robot and astronauts and floating objects in the cabin, ensure the robot's safe and stable flight, and has high engineering application value and good engineering application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a block diagram of robot trajectory planning and control in an embodiment of the present invention;

[0066] Figure 2 The following is a bottom-level control diagram of the space intelligent flying robot in an embodiment of the present invention, wherein Figure (a) is a control diagram of the robot's attitude loop, and Figure (b) is a control diagram of the robot's position loop;

[0067] Figure 3 This is a block diagram of the underlying control solution for trajectory optimization of a space intelligent flying robot in an embodiment of the present invention;

[0068] Figure 4 4 is a graph showing the quaternion control result of Tube-MPC over time in an embodiment of the present invention, wherein (a) shows the quaternion control result of the first two dimensions, and (b) shows the quaternion control result of the last two dimensions. DETAILED DESCRIPTION

[0069] In order to enable those skilled in the art to better understand the present invention, exemplary embodiments or examples of the present invention will be described below with reference to the accompanying drawings. Obviously, the described embodiments or examples are only some of the embodiments or examples of the present invention, and not all of them. Based on the embodiments or examples of the present invention, all other embodiments or examples obtained by those skilled in the art without creative work should fall within the scope of protection of the present invention.

[0070] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0071] Combine Figures 1 to 3 As shown, the present invention provides a method for trajectory planning and tracking control of a flying robot under microgravity, comprising the following steps:

[0072] Step S100, performing robot trajectory optimization based on the tubular constraint model, includes the following steps:

[0073] Step S110: Establish static and dynamic obstacle models in a microgravity environment, predict the robot's motion risk area, and generate obstacle areas and navigable paths in three-dimensional space in real time.

[0074] like Figure 1 As shown in the figure, first, an intelligent behavior model of astronauts and floating objects in a microgravity environment is established, and the movement patterns of the astronauts' torso and limbs are modeled by integrating deep learning and the motion recognition algorithm OpenPose; secondly, based on international human-machine safety standards, the astronauts' future movement range is judged according to the astronauts' movement direction, speed, and acceleration level, and the risk areas of the robot's movement are predicted; based on the human body positioning and human intention recognition results given by the navigation system, the Kalman filter algorithm is used to predict the position information of human joints, and the obstacle area in three-dimensional space is generated in real time according to the prediction results.

[0075] In addition to astronauts, static / dynamic obstacles will also constitute space obstacles during the robot's movement. Based on the obstacle information, the three-dimensional map established by the perception system will be Divided into:

[0076]

[0077] Where O, F, and U represent clear space, obstacle space, unknown space, or risk space, respectively. Considering that the robot's motion is affected by disturbances, there may be deviations between the actual trajectory and the desired trajectory. Therefore, the control algorithm of the present invention, which has good anti-interference performance, is required to achieve trajectory tracking control. Furthermore, the control variables output by the trajectory tracking algorithm—robot velocity, acceleration, and torque—must satisfy various constraints determined by the robot's physical properties and the available space.

[0078] Step S120: Establish kinematic and dynamic models of the space intelligent flying robot to describe the robot's translational and rotational motion processes; specifically:

[0079] The space intelligent flying robot is modeled as a six-degree-of-freedom rigid body, and its translational kinematic model is established as follows:

[0080]

[0081] in, Indicates the position of the center of gravity of the space intelligent flying robot, is the z-axis vector of the robot system, and are collective thrust and total mass respectively, is the gravity vector, It represents the exogenous aerodynamic drag during high-speed flight;

[0082] The rotational kinematics model of the space intelligent flying robot is established as:

[0083]

[0084] The dynamic equation of the space intelligent flying robot is established as:

[0085]

[0086] Among them, q represents the quaternion, represents the quaternion multiplication operator, represents the angular acceleration of the robot system; for The angular velocity, represents the inertia matrix of the entire body, is the resulting torque produced by the rotor, is the interference torque.

[0087] Step S130: Design motion states and controller constraints based on the robot's maneuverability and task requirements, including unknown obstacle avoidance constraints, attitude angle and angular velocity constraints, and acceleration constraints. Specifically:

[0088] In the robust control of robots, for a discrete system ,state , bounded interference satisfies If, under the conditions of satisfying input and state constraints, there exists an admissible control input sequence (in, ), so that , then it is called for Robust Step set, which means starting from the current state, in the case of disturbance, through N Step-wise input can bring the system into the desired state set ; Special, when is a robust one-step set.

[0089] The calculation method of the robust one-step set is:

[0090]

[0091] The calculation method of the robust N-step set is:

[0092]

[0093] in, is the union of the first set to the Mth set, Indicates the j A set of desired states.

[0094] By constraining the robot's state within a robust invariant set, model predictive control can achieve asymptotic stability in systems under bounded disturbances. After achieving adaptive disturbance rejection, to ensure that the control instructions generated by model predictive control can be executed by the robot, the following constraints need to be imposed based on the robot's maneuverability and task requirements during the objective function solution:

[0095] The unknown obstacle avoidance constraint is specifically: when an obstacle that cannot be identified at a distance suddenly appears in the motion path, such as when an astronaut's limbs move, the traditional MPC algorithm or the B-spline-based trajectory optimization algorithm cannot immediately update the trajectory to guide the robot to avoid the obstacle because the obstacle has not yet been updated in the map. At this time, emergency obstacle avoidance is achieved by considering the uncertainty of the obstacle. Taking the more common ellipsoid uncertainty obstacle area as an example, the following constraints need to be met in the model predictive control process:

[0096]

[0097] where x1, x2, and o x 、o y The center points of the robot and the obstacle are 、 The position component on the axis, represents the major axis of the ellipsoid, Represents the minor axis of the ellipsoid.

[0098] Attitude angle and angular velocity constraints: Since the robot's attitude angle and angular velocity have a significant impact on the sensor's sensing range, to ensure the normal operation of its navigation system, the attitude angle and angular velocity during trajectory tracking are restricted so that the robot can complete smooth movement:

[0099]

[0100]

[0101] in and are the angle and angular acceleration of the robot when it turns, and are the minimum and maximum angles when the robot turns, and are the minimum and maximum angular accelerations of the robot when turning;

[0102] Acceleration constraints: Due to the influence of thrust and resistance during robot motion, the normal and longitudinal accelerations that can be generated are limited. When the output of the model predictive control algorithm Tube-MPC exceeds the limit, the actuator will be unable to execute the control command. Therefore, the acceleration output needs to be limited:

[0103]

[0104] .

[0105]

[0106] in, 、 and are the accelerations along the x, y, and z directions, 、 and are the minimum accelerations along the x, y, and z directions, respectively, 、 and are the maximum accelerations along the x, y, and z directions, respectively.

[0107] The three constraints are combined into:

[0108]

[0109] in is the state of the system, is the control input, For interference.

[0110] Step S140: Establish an optimization objective function and generate a robot motion trajectory based on the tubular constraint model predictive control algorithm for robot tracking.

[0111] The robust model predictive controller uses the distance between the robot's current position and the corresponding point on the trajectory as the main optimization object, and solves a constrained optimal control problem at each sampling moment to calculate the optimal input sequence. At the same time, the calculated optimal control input sequence and the robot's state prediction trajectory under the control sequence are stored as nominal input and state trajectory. and The optimization objective function to be solved is:

[0112]

[0113] Among them, Q, R, S and are the appropriate dimensional weights for penalizing state tracking error, control action, control change rate, and violation of soft constraints, respectively. is the control input change rate, k is the cumulative time, To predict the time domain length, For reference of all states, is all the states from time t to time t+k, is all the control inputs from time t to time t+k, The rate of change of all control inputs from time t to time t+k is, Violation of soft constraints.

[0114] Step S200: Designing a bottom-level tracking controller to perform bottom-level control of the robot, including the following steps:

[0115] After calculating the robot's position and posture information according to the task requirements, the bottom-level control system converts the posture information into the flywheel and fan speed control instructions in the power module. According to the power mode of the flywheel and fan combined with the flywheel and fan adopted by the present invention, the proportional-integral-differential control law is designed for bottom-level control. The control framework is as follows: Figure 2 shown.

[0116] Step S210: define the control quantity in the control system as the robot's three-axis flywheel, and establish a robot posture dynamics model;

[0117] The control quantity in the control system is defined as the robot's three-axis flywheel. The flywheel mass is small relative to the robot mass. When the flywheel is used to control the robot's posture, the robot's posture dynamics model is established as follows:

[0118]

[0119] in, is the robot's moment of inertia, is the robot's angular velocity, is the angular momentum of the flywheel relative to the robot, is the flywheel output torque, is the interference torque acting on the flywheel;

[0120] Considering that the change speed of each posture angle of the robot is relatively low during the execution of the task, the robot posture dynamics model is simplified as follows:

[0121]

[0122]

[0123]

[0124] in 、 and are the moments of inertia of the robot in the x-axis, y-axis and z-axis directions, 、 and is the output torque of the flywheel in the x-axis, y-axis and z-axis directions, 、 and is the disturbance torque on the flywheel in the x-axis, y-axis and z-axis directions, is the x-axis angular acceleration, is the y-axis angular acceleration, is the z-axis angular acceleration.

[0125] Step S220 : Considering the constant interference torque on the flywheel, a proportional-integral-differential control law is designed, the robot control parameters are designed, and the robot posture control is realized.

[0126] The robot's three-axis attitude stabilization control is decoupled in all directions. For the robot attitude dynamics model, considering that the interference torque on the flywheel is a constant torque, the proportional-integral-differential control law is designed as follows:

[0127]

[0128] in, is the torque output by the controller, is the current posture of the robot, is the robot target posture, is the attitude control error, are proportional, differential, and integral coefficients respectively;

[0129] The closed-loop system model is:

[0130]

[0131] in, For the external disturbance torque, the control coefficient is designed through pole configuration to realize the control of the robot posture.

[0132] Step S230: Establish a flywheel unloading model, use a proportional control law to design a proportional coefficient, and use a fan to perform torque unloading on the saturated flywheel.

[0133] To solve the possible flywheel saturation problem, a fan is used to unload the torque. When the flywheel speed reaches the saturation speed, the corresponding fan is driven to generate the rotational torque of the robot body to unload the flywheel. The flywheel unloading model is as follows:

[0134]

[0135] in, is the aerodynamic torque generated by the fan, Indicates the deviation from the expectation, is the proportionality coefficient, is the current angular momentum of the robot, is the target angular momentum of the robot.

[0136] In summary, if Figure 3 As shown, the present invention maps the trajectory optimized by the model predictive controller in a differentially flat manner to obtain a series of position and attitude states. The obtained state quantities are used as the input of the underlying proportional-integral-differential controller to solve the control quantities of each actuator, which are executed by the flywheel and fan to achieve safe trajectory planning and tracking control of the space intelligent flying robot in the confined space of the microgravity environment.

[0137] The present invention proposes a method (algorithm) for trajectory planning and tracking control of a flying robot under microgravity, which is the underlying technical core of the present invention. Various products can be derived based on the algorithm.

[0138] Based on the method proposed in the present invention, a trajectory planning and tracking control system for a flying robot under microgravity is developed using a programming language. The system has program modules corresponding to the steps of the above-mentioned technical solution, and executes the steps in the above-mentioned trajectory planning and tracking control method for a flying robot under microgravity during operation.

[0139] The developed system (software) computer program is stored on a computer-readable storage medium. When called by a processor, the computer program is configured to implement the steps of the aforementioned method for trajectory planning and tracking control of a flying robot in microgravity. This materializes the present invention on a carrier, becoming a computer program product.

[0140] Various implementations of the systems and techniques described herein can be realized in digital electronic circuitry, integrated circuitry, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0141] The computer programs (also referred to as programs, software, software applications, or code) herein comprise machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., a magnetic disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0142] The beneficial effects of the present invention will be described below with reference to specific embodiments.

[0143] Example 1

[0144] By introducing the concept of tubular invariant sets, a certain amount of random wind disturbance and human walking intervention is added to the space intelligent flying robot on the microgravity experimental platform. The control sequence finally generated by the above-mentioned Tube-MPC algorithm can ensure that the robot's quaternion trajectory remains within the range under limited interference. Figure 4 Within the robust boundaries of medium blue and red, the unknown obstacle avoidance constraints, attitude angle and angular velocity constraints, and acceleration constraints can also be met.

[0145] Although the present invention is disclosed as above, the scope of protection disclosed by the present invention is not limited thereto. Those skilled in the art of the present invention may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for trajectory planning and tracking control of a flying robot under microgravity, characterized in that: The steps include: Step S100, performing robot trajectory optimization based on the tubular constraint model, including: Step S110: Establish static and dynamic obstacle models in a microgravity environment, predict the robot's motion risk area, and generate obstacle areas and navigable paths in three-dimensional space in real time; Step S120: establishing kinematic and dynamic models of the space intelligent flying robot; Step S130: Design motion states and controller constraints based on the robot's maneuverability and task requirements, including unknown obstacle avoidance constraints, posture angle and angular velocity constraints, and acceleration constraints. Step S140: establishing an optimization objective function and generating a robot motion trajectory based on the tubular constraint model for robot tracking; Step S200: Designing a bottom-level tracking controller to perform bottom-level control of the robot, including: Step S210: define the control quantity in the control system as the robot's three-axis flywheel, and establish a robot posture dynamics model; Step S220: Considering the constant interference torque on the flywheel, design a proportional-integral-differential control law, design robot control parameters, and control the robot posture; Step S230: Establish a flywheel unloading model, and use a fan to perform torque unloading on the saturated flywheel.

2. The method for trajectory planning and tracking control of a flying robot under microgravity according to claim 1, characterized in that: Step S110 includes the following process: First, the motion patterns of the astronaut's torso and limbs are modeled. Second, the astronaut's future range of motion is determined based on the astronaut's motion direction, speed, and acceleration level, generating risk areas for the robot's motion. Finally, based on the human body positioning and human intention recognition results provided by the navigation system, the position information of the human joints is predicted, and obstacle areas in three-dimensional space are generated in real time based on the predicted results. Combined with other static / dynamic obstacle information, the 3D map The space is divided into three areas: barrier-free space, obstacle space, unknown space or risk space, and a traversable path is generated.

3. The method for trajectory planning and tracking control of a flying robot under microgravity according to claim 2, wherein: Step S120 includes the following process: The space intelligent flying robot is modeled as a six-degree-of-freedom rigid body, and its translational kinematic model is established as follows: ； in, Indicates the position of the center of gravity of the space intelligent flying robot, is the z-axis vector of the robot system, and are collective thrust and total mass respectively, is the gravity vector, It represents the exogenous aerodynamic drag during high-speed flight; The rotational kinematics model of the space intelligent flying robot is established as: ； The dynamic equation of the space intelligent flying robot is established as: ； Among them, q represents the quaternion, represents the quaternion multiplication operator, represents the angular acceleration of the robot system, is the angular velocity of the robot system, represents the inertia matrix of the entire body, is the resulting torque produced by the rotor, is the interference torque.

4. The method for trajectory planning and tracking control of a flying robot under microgravity according to claim 3, wherein: The unknown obstacle avoidance constraint in step S130 is specifically for situations where an obstacle that cannot be identified at a distance suddenly appears in the motion path and the obstacle is not updated in the map, and the trajectory planning algorithm cannot immediately update the trajectory to guide the robot to avoid it; The posture angle and angular velocity constraints specifically limit the posture angle and angular velocity during trajectory tracking so that the robot can complete smooth movement: ； ； in and are the angle and angular acceleration of the robot when it turns, and are the minimum and maximum angles when the robot turns, and are the minimum and maximum angular accelerations of the robot when turning; The acceleration constraint specifically limits the acceleration output: ； ； ； in, 、 and are the accelerations along the x, y, and z directions, 、 and are the minimum accelerations along the x, y, and z directions, respectively, 、 and are the maximum accelerations along the x, y, and z directions, respectively.

5. The method for trajectory planning and tracking control of a flying robot under microgravity according to claim 3, characterized in that: The optimization objective function in step S140 is specifically: ； Among them, Q, R, S and are the appropriate dimensional weights for penalizing state tracking error, control action, control change rate, and violation of soft constraints, respectively. is the control input change rate, k is the cumulative time, To predict the time domain length, For reference of all states, is all the states from time t to time t+k, is all the control inputs from time t to time t+k, The rate of change of all control inputs from time t to time t+k is, Violation of soft constraints.

6. The method for trajectory planning and tracking control of a flying robot under microgravity according to claim 5, characterized in that: Step S210 includes the following process: The control quantity in the control system is defined as the robot's three-axis flywheel, and the robot's posture dynamics model is established as: ； in, is the robot's moment of inertia, is the robot's angular velocity, is the angular momentum of the flywheel relative to the robot, is the flywheel output torque, is the interference torque acting on the flywheel; Since the robot's posture angles change at a relatively low speed during the execution of the task, the robot's posture dynamics model is simplified as follows: ； ； ； in 、 and are the moments of inertia of the robot in the x-axis, y-axis and z-axis directions, 、 and is the output torque of the flywheel in the x-axis, y-axis and z-axis directions, 、 and is the disturbance torque on the flywheel in the x-axis, y-axis and z-axis directions, is the x-axis angular acceleration, is the y-axis angular acceleration, is the z-axis angular acceleration.

7. The method for trajectory planning and tracking control of a flying robot under microgravity according to claim 6, wherein: Step S220 includes the following process: For the robot posture dynamics model, considering that the interference torque on the flywheel is a constant torque, the proportional-integral-differential control law is designed as follows: ； in, is the torque output by the controller, is the current posture of the robot, is the robot target posture, is the attitude control error, are proportional, differential, and integral coefficients respectively; The closed-loop system model is: ； in, For the external disturbance torque, the control coefficient is designed through pole configuration to realize the control of the robot posture.

8. The method for trajectory planning and tracking control of a flying robot under microgravity according to claim 7, wherein: The flywheel unloading model in step S230 is specifically: ； in, is the aerodynamic torque generated by the fan, Indicates deviation from expectation, is the proportionality coefficient, is the current angular momentum of the robot, is the target angular momentum of the robot.

9. A trajectory planning and tracking control system for a flying robot in microgravity, characterized in that: The system has a program module corresponding to the steps of the method described in any one of claims 1 to 8 above, and executes the steps in the above-mentioned method for trajectory planning and tracking control of a flying robot under microgravity when running.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps of the trajectory planning and tracking control method for a flying robot under microgravity according to any one of claims 1 to 8 when called by a processor.

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