33 results about "Motion optimization" patented technology

Provided in the present invention are an exoskeleton shared control method, system and device based on AI co-driving, and a storage medium. The exoskeleton shared control method based on AI co-driving comprises: acquiring real-time pose data of an exoskeleton wearer; on the basis of the pose data, performing switching between a local trajectory correction mode and a hybrid long-short-term motion optimization mode in real time, wherein the local trajectory correction mode is completed on the basis of an AI co-driving policy network model; and performing multi-task tracking on the direction of a three-dimensional motion of an exoskeleton and a walking trajectory thereof, so as to achieve motion tracking control over the exoskeleton. By using the method provided in the present application, when encountering a sudden obstacle, an initial path can be corrected to an optimal path in combination with pose data and target point image feature data, thereby helping an exoskeleton to realize omnidirectional walking in a three-dimensional complex environment.

The invention relates to the technical field of robot control. A humanoid robot gait generation system based on hierarchical control is characterized by comprising a multi-layer control system operating at different refresh frequencies, and the multi-layer control system comprises a high-layer gait and trajectory planning layer operating at a low frequency and used for generating a macroscopic motion instruction; the middle-layer whole-body motion optimization layer runs at medium frequency, serves as a bridge of high-layer planning and bottom-layer control and is used for converting expected plantar and trunk tracks into reference motion of all joints of the whole body; the bottom joint servo control layer runs at the highest frequency and is responsible for accurately tracking a joint-level instruction output by the whole-body motion optimization layer; the state sensing and feedback closed-loop layer is used for feeding back state information, obtained in real time, of the body state of the robot to all the layers, providing a basis for track re-planning of a high layer and providing a real-time state initial value for optimization of a middle layer; and a feedback signal is provided for servo control of a bottom layer.

The invention discloses a collaborative configuration optimization method for an intelligent self-reconfiguration robot. The invention relates to the field of modular robot configuration-motion collaborative optimization in extraterrestrial planet surface inspection and detection tasks, constructs a multi-constraint dynamic graph optimization model for unified representation of configuration and motion control, and aims to improve the configuration reconstruction efficiency of a modular robot and obtain an optimal configuration-motion reconstruction scheme. The method comprises the following steps: carrying out restrictive configuration characterization on a robot by utilizing a rooted tree structure; constructing a sensing-driving integrated neural network to perform robot motion control representation; constructing a multi-constraint dynamic graph optimization model to uniformly represent configuration and motion control; constructing a fitness function for reconstruction optimization according to the evaluation function corresponding to the task; and performing configuration-motion optimization solution by using a non-dominated sorting genetic algorithm with an elitist strategy.

The invention belongs to the technical field related to robot motion control, and discloses a reaction type robot motion deadlock avoiding method and device, and the method comprises the steps: (1) building a collision avoidance constraint used for guaranteeing that a robot does not collide with an environment object based on a control barrier function; a deformable control Lyapunov function is utilized to construct a convergence constraint for ensuring the robot to move to a target; (2) establishing a robot collision avoidance motion optimization model by taking minimum deviation between the robot and model control input as an objective function based on collision avoidance constraint, convergence constraint conditions and a robot dynamic model; and (3) detecting the motion deadlock risk of the robot in real time based on the robot collision avoidance motion optimization model, and regulating and controlling the shape parameters of the deformable control Lyapunov function based on the obtained deadlock type so as to avoid the motion deadlock of the robot. According to the method, deadlock is avoided by regulating and controlling the deformable control Lyapunov function, the method can depend on a prior environment model or a fixed rule, and generalization is high.

A method for controlling the movement of a tool (10) relative to a workpiece (20) within an apparatus (1) is provided. The apparatus (1) comprises a tool manipulator (11) and / or a workpiece manipulator (21) to enable the relative movement according to a plurality k of movement axes (A1-A8), thereby defining a number k of available degrees of freedom for the relative movement. The method comprises the following steps: a) determining a target path (T) for the relative movement, which contains a number I of specified coordinates which is smaller than the number k of available degrees of freedom, thereby giving rise to a number m = k-l of excess degrees of freedom, b) defining an objective function (F) which contains a plurality of optimization criteria for the movement, c) extremizing the objective function (F) based on a computer-assisted simulation (SIM) of a plurality of overall movement paths which implement the target path (T), wherein the objective function (F) is extremized by varying at least one movement coordinate corresponding to at least one excess degree of freedom, thereby obtaining an optimum configuration (OPT) for said at least one movement coordinate, d) controlling the relative movement according to an overall movement path which implements the target path (T), wherein the at least one movement coordinate is set to its optimum configuration (OPT) for at least part of the movement path. Furthermore, a computer program product and an apparatus for carrying out this method are provided.

The invention discloses a mechanical arm control method and system based on real-time attitude estimation, and the method comprises the steps: obtaining a real-time point cloud image of a mechanical arm, and carrying out the attitude estimation based on the real-time point cloud image to obtain an initial attitude of the mechanical arm; the target posture of the mechanical arm is determined, the target posture and the initial posture are analyzed, and the posture error difference is determined; the attitude error difference is analyzed, the error state of the mechanical arm in each direction is determined, and a motion optimization coefficient matrix of the mechanical arm is determined based on the error state; the operation parameters of the mechanical arm are optimized on the basis of the motion optimization coefficient matrix, and motion of the mechanical arm is controlled on the basis of the optimized operation parameters; and after the mechanical arm moves, the steps are repeated, and closed-loop circulation control over the mechanical arm is completed. Visual monitoring is converted into real-time dynamic monitoring from static monitoring, continuous closed-loop deviation correction can be achieved in the movement process of the mechanical arm, accordingly, dynamic deviation correction and accurate operation are achieved, and the grabbing and assembling success rate and efficiency of the mechanical arm are greatly improved.

The invention relates to a multi-unmanned aerial vehicle four-dimensional space-time cooperative distributed path planning method, and belongs to the technical field of path planning. Comprising the following steps: collecting flight area information of an unmanned aerial vehicle cluster, starting point and terminal point position information of each unmanned aerial vehicle, threats in the area and an operation model of the unmanned aerial vehicle for modeling; using a DCOP model to decompose the cluster flight target function into target functions for path planning of each unmanned aerial vehicle individual; the individual objective function comprises external threats, dynamics and four-dimensional space-time cooperative constraints determined according to the modeling information; a distributed computing architecture is adopted, a moon motion optimizer is utilized, and the flight path of each unmanned aerial vehicle is solved through parallel iteration in combination with an elitist strategy and a self-adaptive population scale adjustment mechanism; in the moon motion optimizer, the positions of the moon, the earth and the sun respectively correspond to an unmanned aerial vehicle path, an optimal planning path and a suboptimal planning path. According to the invention, three-dimensional space collision avoidance and space-time collaborative optimization are realized, so that the expandability and robustness of the system are improved.

The invention relates to the technical field of load motion platform control, in particular to a vibration control method and device of a load motion platform, equipment and a storage medium. The method comprises the following steps: collecting state parameters of a load motion platform to obtain a multi-dimensional state parameter set, and executing parameter fusion processing to obtain a fusion state vector; updating the model parameter set according to the fusion state vector, and obtaining predicted deformation based on the fusion state vector and the elastic deformation parameter; determining a load change trend in combination with historical load data and the fusion state vector, and determining an optimization curve parameter in combination with a load characteristic parameter; generating a first acceleration and deceleration curve in combination with the basic curve parameter and the optimized curve parameter; determining a speed correction amount according to the predicted deformation amount, superposing the speed correction amount to the first acceleration and deceleration curve, and rejecting a target resonant frequency component through a vibration transmission parameter to generate a second acceleration and deceleration curve; and generating a motion optimization instruction based on the second acceleration and deceleration curve to execute motion control. The platform control stability can be improved.

Embodiments of the present disclosure relate to a neural network architecture for generating high-quality, multi-modal trajectories as seed trajectories for optimization-based motion planners. The neural network architecture utilizes an observation encoder configured to encode environmental observations, and a noise prediction network configured to perform denoising based on the observations. The neural network architecture is configured to generate multiple seed trajectories in parallel by simultaneously running several instances of the noise prediction network. In contrast to conventional techniques, this architecture produces multiple high-quality seed trajectories at once, significantly enhancing the efficiency and speed of motion planning tasks.

The invention discloses a martial arts motion optimization training method combined with a virtual reality technology, and relates to the technical field of motion image analysis and processing. The martial arts motion optimization training method combined with the virtual reality technology is characterized in that complex martial arts motions are disassembled into mechanical fractal units which can be recursively combined through fractal mechanical decomposition training; a trainer performs targeted intensive training on each fractal unit with the help of the VR tactile glove, deeply understands the action mechanics principle, greatly improves the control ability of action details, generates a personalized optimal action path light track for the trainer, guides the trainer to complete actions with the minimum joint load, not only reduces action errors, but also improves the training efficiency. In addition, the martial arts action entropy evaluation system quantifies the action deviation in real time, through dynamic adjustment of virtual fluid resistance, a trainer is prompted to correct wrong actions in time, and a set of complete and efficient training system is formed.

The application provides a logistics warehouse picking training and motion optimization method and system based on virtual reality, and relates to the technical field of intelligent warehousing. The method constructs a virtual warehouse environment linked with a database in real time and generates a picking task guide; collects depth images, inertial measurement data and physiological signals, and fuses to generate whole body posture data of a trainee; identifies non-standard actions based on the posture data and calculates work efficiency; combines the carrying weight, physiological state and motion data to perform ergonomics scoring, fatigue accumulation analysis and injury risk prediction; dynamically adjusts the task difficulty and controls the training process according to the training state; and finally generates a training evaluation report through motion data comparison. The application can improve the quality of logistics picking training and reduce the risk of work injury.

The present invention provides a verification device and method for a ship-bridge active collision avoidance system in a bridge area water area. The device includes: a first data transmission module for receiving ship navigation data in the bridge area water area; a digital twin simulation module for obtaining virtual ship motion parameters based on the ship navigation data in the bridge area water area and a preset ship motion situation; a second data transmission module for sending the virtual ship motion parameters to the ship-bridge active collision avoidance system and receiving entity ship motion optimization data; and a function evaluation module for determining that the ship-bridge active collision avoidance system is effective when it is determined based on the virtual ship motion parameters and the entity ship motion optimization data that the entity ship motion optimization data is not distorted and the virtual ship will not collide with the bridge pier. The present invention can test and verify the effectiveness of the function of the ship-bridge active collision avoidance system in the laboratory stage without installing it in the bridge area water area.

A deformation-viewpoint dynamic coupling robot viewpoint intelligent planning method and system, the method comprising: constructing a deformation-view mapping model and training; constructing a space-time graph convolution network and training; constructing a multi-objective hierarchical decoupling optimization network, inputting the multi-modal information in the actual into the trained deformation-view mapping model, generating a coverage prediction value, generating a compensated view according to the deformation compensation layer; the motion optimization layer uses a deep reinforcement learning strategy network and combines an adaptive importance sampling strategy to screen the compensated view and generate a screened candidate viewpoint set; generating a space-time safety corridor according to the trained space-time graph convolution network, scoring the collision risk of the screened candidate viewpoint set according to the safety constraint layer, constructing a multi-objective fitness function and solving, obtaining the optimal viewpoint set. The present application is suitable for high-precision visual detection scene, which significantly improves the real-time performance and robustness of robot viewpoint planning.

The embodiment of the invention provides a universal motion processor for controlling motion of a physical entity. The core structure of the processor comprises at least one programmable motion calculation core used for processing calculation tasks related to dynamics and motion optimization of the physical entity; the at least one model interface is used for receiving model data describing the mechanical structure of the physical entity; and the at least one instruction interface is used for receiving motion task instructions. Wherein the motion computing core is configured to process a motion task instruction received through the instruction interface based on model data received through the model interface to generate a control signal for driving the physical entity. Based on the scheme, the invention provides a special hardware core to ensure the calculation performance and real-time performance of motion control, and meanwhile, through a standardized interface design, the system can adapt to different physical entities, thereby fundamentally solving the contradiction between'special 'and'universal'.

This invention relates to the field of program-controlled robot motion technology, specifically to a motion optimization method, system, and device for amphibious robots. The invention first acquires motion sample information of the amphibious robot in each motion scenario. Then, based on the distribution characteristics and control effect parameters of control parameter combinations at all historical motion moments, it filters out all reference control parameter combinations, thereby determining the distribution reference range of road condition data corresponding to each reference control parameter combination. Finally, based on the distribution reference range of the amphibious robot's current road condition data, it determines the control parameters of the amphibious robot and performs motion control. This invention, based on the effective reference provided by the amphibious robot's historical motion data and control strategies, can adaptively match control strategies based on the current motion situation, avoiding rule base conflicts in different scenarios and improving the motion control effect of the amphibious robot.

The application discloses a kind of double-arm motion planning methods based on speed constraint space, belong to robot technical field.Method includes: first give the starting point and target point of double-arm carrying object;Then the speed constraint space of object is calculated to carry out uniform sampling, obtain the sampling speed set of object;Then distance deviation, pose deviation, joint torque and task operability are used as index to establish evaluation function, the sampling speed is scored by evaluation function, selects the highest score sampling speed as the speed of object next time;Finally, the optimal sampling speed of object each time is constantly selected until the complete trajectory from starting point to target point is obtained.The application realizes the motion optimization of double-arm coordination under the kinematic and dynamic constraint conditions, improves the task operability of double-arm, reduces the joint torque of robot arm, guarantees the stability and safety of double-arm carrying process.

This disclosure provides a motion optimization obstacle avoidance control method and system for vehicle-like robots, relating to the field of robot path planning and obstacle avoidance technology. The method includes: defining the state space of the vehicle-like robot; planning the global path of the vehicle-like robot; fitting the sparse state sequence using Hermite curves to obtain a time-space continuous trajectory; establishing a collision model between the vehicle-like robot and obstacles; transforming the geometric separation conditions between the robot and obstacles into a set of continuously differentiable nonlinear algebraic constraints based on duality theory; using the time-space continuous trajectory as a reference trajectory; and constructing a trajectory optimization objective function. This function can solve for the motion control quantities of the vehicle-like robot while satisfying its dynamic constraints, input constraints, and acceleration smoothing constraints, thus achieving obstacle avoidance planning and control of polygonal obstacles. This disclosure enables real-time, full-size robot planning and obstacle avoidance control.

The invention discloses a digital control method for multi-machine cooperative hoisting of flat arm type tower cranes, which synchronously controls rotation and amplitude variation of two tower cranes and trolley movement through a computer control system, optimizes a cooperative path and balances a load. The method specifically comprises the steps that a kinematics model is established, a kinematics equation is established according to the position relation between a tower crane and a component, and a related equation of the rotation angular speed of a large arm and the movement speed of a trolley is established; trajectory optimization control: fitting a trajectory by adopting a cubic polynomial, and iteratively solving an optimal control equation in combination with the hoisting performance parameters of the tower crane; real-time monitoring and closed-loop correction: calculating the eccentric amplitude of the lifting hook and the load rate of the tower crane by acquiring theoretical coordinates of the lifting hook, and correcting when the eccentric amplitude and the load rate exceed the range; and after the component reaches a designated position, in-place posture adjustment is achieved. According to the invention, full-process digital control of multi-machine collaborative operation can be realized, the hoisting synchronization precision, the load balance and the construction safety are obviously improved, the manual intervention is reduced, and the method is suitable for hoisting heavy components of super high-rise buildings.

To provide a technique for optimizing a robot collision avoidance motion by using a distance field constraint function.SOLUTION: CAD or sensor data indicating obstacles in a robot workspace are converted to voxels, and a three-dimensional binary matrix of voxel occupancy is created. A corresponding distance map matrix is then computed, where each cell in the distance map matrix contains a distance to a nearest occupied cell. The distance map matrix is used as a constraint function in a motion planning optimization problem, where the optimization problem is convexified and then iteratively solved to yield a robot motion profile which avoids the obstacles and minimizes an objective function such as travel distance. A distance field optimization technique is quickly computed and has a computation time which is independent of the number of obstacles. The disclosed optimization technique is easy to set up, as it requires no creation of geometry primitives to approximate robot and obstacle shapes.SELECTED DRAWING: Figure 8

The invention discloses a fixed base mechanical arm point-to-point trajectory optimization method based on a near-end strategy optimization algorithm, solves the problem that a high-dimensional continuous action space exists when deep reinforcement learning is directly used for a fixed base mechanical arm tail end position to reach a task, and belongs to the field of robot operation control and mechanical arm autonomous motion optimization. The method comprises the steps that a mechanical arm kinematics model is established, and constraint conditions are defined; modeling a tail end point-to-point arrival task as a sequential decision process, constructing a state vector as the input of a strategy network, and taking a tail end displacement increment direction vector as the action output of the strategy network; mapping the tail end displacement increment direction vector into a joint increment through differential inverse kinematics, and performing constraint processing according to a constraint condition to generate an executable joint control instruction; and training the strategy network by adopting a near-end strategy optimization algorithm, obtaining a single-step reward through a multi-target reward function, and evaluating the performance output by the action of the strategy network according to the single-step reward until convergence.

The invention discloses a multi-object vector sketch animation generation method based on grouping and motion trail prior. According to the method, a two-stage process including'motion initialization 'and'motion optimization' is adopted. In a first stage, through an interactive canvas, a user can semantically group input static vector sketches, define a key frame motion trail for each group, and then generate a rough animation sequence through interpolation. In a second stage, the coarse animation sequence is input into a novel, group-based displacement network. The network independently predicts a displacement field for each semantic group to optimize motion and uses priori knowledge of a pre-trained text-to-video model for guidance and optimization. According to the method, manual priori of grouping and motion trails is introduced, so that motion among multiple objects is effectively decoupled, the animation quality and time sequence consistency are remarkably improved, and the method is particularly suitable for complex scenes containing multi-object interaction.

Robotic motion planning techniques that use an accurate initial reference path. When using motion optimization to compute a new path, a candidate reference path is selected from memory that was previously computed and has similar start and goal points and collision avoidance environmental constraints as the new path. Taking into account the differences between the start and goal points of the new path and the start and goal points of the previously computed path, the candidate reference path is adjusted at all state points along its length to create an initial reference path. The initial reference path that has been adjusted to fit the start and goal points is then used as the start state for the motion optimization computation. By using an initial reference path that is similar to the final converged new path, the optimization computation converges faster than if a naive initial reference path is used.

The invention discloses a mechanical arm motion optimization method for undemanded inverse gradient solving, and relates to the field of mechanical arm motion optimization, comprising the following steps: constructing a mechanical arm motion optimization problem needing to be solved; a mathematical model corresponding to the mechanical arm motion optimization problem needing to be solved is established, and the quadratic programming problem of the mathematical model is converted into a dynamic linear matrix equation; constructing a neurodynamics improved model based on the linear matrix equation; and obtaining parameters for motion optimization of the mechanical arm by solving the neurodynamics improved model so as to complete motion optimization of the mechanical arm. The problems that an existing gradient neurodynamics algorithm cannot effectively solve the mechanical arm motion optimization problem in real time, and an existing zero neurodynamics algorithm is complex in calculation and possibly has no analytical solution when solving the mechanical arm motion optimization problem are solved.

The invention discloses a soft micro-robot multi-mode magnetic driving and motion optimization method oriented to a natural orifice environment, and relates to the technical field of micro-robot control. The method comprises the steps that multi-source information of a natural cavity environment where the soft micro-robot is located is obtained, wherein the multi-source information at least comprises parameters reflecting the narrowing degree, the bending angle or the wrinkle distribution of the cavity; extracting a topographic feature vector representing the environment; based on a preset environment feature weight, a form-motion weight matrix and the topographic feature vector, an optimal robot form primitive and a corresponding motion mode are determined through a dynamic matching decision-making mechanism, and the motion mode at least comprises one of curling rolling, bending inrush or stretching crawling; according to the determined motion mode, a preset driving magnetic field corresponding to the motion mode is selected and applied, the driving magnetic field at least comprises one of a rotating magnetic field, an alternating pulse magnetic field or an oscillating magnetic field, and the driving magnetic field can be generated through a gradient-torque decoupling modulation strategy. According to the method, the optimal motion mode can be adaptively selected according to the complex and changeable cavity environment, and the motion efficiency, stability and operation performance of the soft micro-robot in the natural cavity environment are remarkably improved.

The invention discloses an ankle pump motion data analysis method and system with a memory function, and the method comprises the steps: collecting the motion data of a user in real time through a wearable device in an ankle pump motion process, and obtaining multi-mode motion data; according to the multi-modal motion data, a motion mode recognition model based on deep learning is adopted, features of ankle pump motion are extracted, and feature representation of a motion mode is obtained; dynamically updating a personalized exercise memory bank of the user by adopting a memory bank construction algorithm based on incremental learning according to the feature representation of the exercise mode to obtain a personalized exercise memory bank; and according to historical data in the personalized exercise memory bank, in combination with real-time exercise data of the user, an exercise optimization suggestion is generated, and through a multi-objective optimization technology, exercise parameters are dynamically adjusted to obtain an optimized exercise feedback suggestion. By means of the embodiment of the invention, the effect of ankle pump exercise can be improved, and health management and exercise performance improvement of a user are promoted.

The present invention relates to the technical field of workpiece drilling, and more specifically, to a workpiece drilling action optimization method and system based on matrix operations, which obtains hole position data and drill bit data, generates a hole and drill matching table, wherein each row in the hole and drill matching table reflects all drill bits that can drill any hole position when processing the hole position; and generates a data table of the head action based on the current head position and the hole and drill matching table. The present invention simplifies a 0-1 matrix by using a set simplification rule to obtain the final solution, which is the optimal solution for the action set. In addition, the entire matrix simplification calculation process only involves three operations: bitwise AND, bitwise OR, and bitwise XOR of binary data. The 0-1 matrix can be simplified at a relatively fast speed, thereby obtaining the minimum number of action combinations, and then finding the optimal drilling action, thereby improving overall processing efficiency. The present invention is suitable for processing large-scale drilling action set simplification optimization problems.

The invention provides a fiber winding multi-target coordinated motion optimization method for a composite shell robot, relates to the field of fiber winding optimization, and solves the technical problem that multi-target performance in a whole winding path cannot be integrally optimized due to the fact that a greedy or single-target optimization strategy is mostly adopted when redundant variables are processed in the prior art. The method comprises the steps that a target winding track and position information of a plurality of doffing points in the target winding track are generated under the boundary limiting condition according to geometric structure parameters and a non-geodesic equation of a shell core model, and a plurality of sub-models are constructed in three-dimensional software based on a fiber winding robot and the shell core model, and constructing a kinematic model according to the position relationship and the spatial constraint relationship among the plurality of sub-models, and performing spatial rotation and translation transformation on the target winding trajectory according to the position information of the plurality of doffing points to obtain a task trajectory point of an end effector of the winding robot. The device is used for the fiber winding process of the large-size composite material shell robot.

The invention is applicable to the field of medical equipment, and provides a teleoperation method, electronic equipment and a computer readable storage medium, the method comprises the following steps: determining a motion pose of an operation master hand, determining an expected pose of a target slave arm according to the motion pose of the operation master hand, the target slave arm being a slave arm mapped by the operation master hand, performing servo execution constraint optimization on the motion of the target slave arm based on the expected pose to obtain an optimized motion instruction; the servo execution constraint comprises at least one of position constraint, speed constraint and acceleration constraint; the motion optimization instruction is used for indicating the motion of the target slave arm, and the motion optimization instruction is sent to the target slave arm. According to the invention, the stability of master-slave mapping during teleoperation can be improved.

Robot collision avoidance motion optimization technique using distance field constraint function. CAD or sensor data depicting obstacles in a robot work cell is converted to voxels and a three-dimensional binary matrix of voxel occupancy is created. A corresponding distance map matrix is then computed, where each cell in the distance map matrix contains the distance to the nearest occupied cell. The distance map matrix is used as a constraint function in a motion planning optimization problem, where the optimization problem is convexified and then iteratively solved to produce a robot motion profile that avoids obstacles and minimizes an objective function such as distance traveled. The distance field optimization technique is fast to compute and has a computation time that is independent of the number of obstacles. The disclosed optimization technique is easy to set up because it does not require the creation of geometric primitives to approximate robot and obstacle shapes.

This invention discloses a pose-coupled path planning method for unmanned aerial vehicles (UAVs) based on dual quaternions. The method uses dual quaternions to uniformly represent the spatial position and three-dimensional attitude of the UAV, achieving pose-coupled modeling. The flight path is discretized into a series of path nodes, and the pose parameter of each node is parameterized as a motion vector. A dual quaternion optimization model is constructed. Through predefined motion operators and unit dual quaternion operators, a mapping is established between the dual quaternion space and the motion vector space, thereby transforming the dual quaternion optimization model into a motion optimization model in the motion vector space. The motion optimization model is iteratively solved in the motion vector space, updating the path node sequence to obtain the optimal path that satisfies all constraints. The optimized path nodes are then smoothed to generate the flight trajectory. This invention significantly improves path planning efficiency and path quality.