215 results about "Optimal trajectory" patented technology

This document describes trajectory planning in a three-dimensional (3D) search space with a space-time artificial potential field. An example system includes a processor that obtains an initial pose, a goal pose, and an obstacle map for an environment. The processor uses a parking trajectory algorithm to determine a trajectory by searching in a space-time artificial potential field. The trajectory includes a series of 3D waypoints, including two-dimensional (2D) positional coordinates and time coordinates, to navigate from the initial pose towards the goal pose. Operation of the host vehicle is then controlled to maneuver along the trajectory using an assisted-driving or autonomous-driving system. In this way, path searching is performed in the entire 3D search space rather than iterating between two 2D search planes. This allows motion planning for autonomous parking, especially with static and dynamic objects present, to determine an optimal trajectory in a single iteration.

According to several aspects, a method for path planning for a vehicle includes determining a predicted trajectory of a remote vehicle. The predicted trajectory of the remote vehicle includes a plurality of predicted trajectory nodes. The method further includes determining a plurality of possible trajectories for the vehicle. The plurality of possible trajectories includes a plurality of possible trajectory nodes. The method further includes determining one or more evaluation metrics of each of the plurality of possible trajectory nodes based at least in part on a weather condition in an environment surrounding the vehicle. The method further includes selecting an optimal trajectory for the vehicle from the plurality of possible trajectories based at least in part on the one or more evaluation metrics of each of the plurality of possible trajectory nodes. The method further includes performing a first action based at least in part on the optimal trajectory.

The invention relates to the field of four-rotor control, and discloses an optimal trajectory planning method for a four-rotor suspension payload system, which comprises the following steps of: establishing a four-rotor suspension payload system kinetic model; for unknown lumped disturbance in the established model, estimating the lumped disturbance through a predefined time nonlinear disturbance observer to obtain a compensated nominal model; based on the real-time positions of the four rotors, a dynamic window particle swarm optimization algorithm is adopted in the detection range of a sensor carried by the four rotors to minimize a cost function about path points, and an initial optimal track is found; and taking the compensated nominal model as a new prediction model, taking the initial optimal trajectory as a reference trajectory, and using nonlinear model prediction control to minimize a cost function so as to obtain a prediction trajectory meeting system constraints. According to the method disclosed by the invention, the load transportation reliability and stability can be improved, and the control precision is effectively ensured.

The invention discloses a directional drilling track accurate control technology based on machine learning, and relates to the technical field of drilling engineering. The directional drilling track accurate control technology comprises the following steps that underground parameters of a drilling tool during underground operation are obtained; establishing a dynamic model based on the drilling tool structure and the motion state; based on the dynamic model, introducing a long-short-term memory neural network, and constructing a hybrid prediction model; drilling parameters are input into the hybrid prediction model, and trend information of multiple tracks is output; based on current geological conditions, drilling tool configuration and operation safety constraints, performing simulation evaluation on the trend information of the plurality of candidate tracks, and screening out an optimal track meeting track precision and underground safety requirements from the candidate tracks; based on the optimal track, control strategy input is constructed, and a state space containing a tool face angle, target azimuth deviation and a drilling tool state is set; and through a deep reinforcement learning method, an advanced adjustment instruction for the guiding tool is generated, and drilling operation is executed according to the optimal track and the advanced adjustment instruction.

The invention discloses an unmanned aerial vehicle flight path planning method based on an improved star-graffiti algorithm, and belongs to the field of unmanned aerial vehicle flight path planning. The invention relates to a method for carrying out flight path planning on a single task executed by an unmanned aerial vehicle. Firstly, a starting point, a target point position and a threat area range are determined; establishing a route planning cost model based on path cost, angle cost, height cost, speed cost and threat cost; optimizing the established cost model, on the basis of a basic individual algorithm, updating the position of the population by adopting an updating formula with nonlinear parameters hybridized with the individual algorithm, and embedding an elite selection method to improve the optimizing capability; and when an iteration upper limit is reached, obtaining an optimal individual position, namely an optimal track, and realizing track planning. According to the invention, optimization of the algorithm is realized, the calculation result is more flexible and simpler, and premature convergence is avoided.

Techniques are discussed herein for determining optimal driving trajectories for autonomous vehicles in complex multi-agent driving environments. A baseline trajectory may be perturbed and parameterized into a vector of vehicle states associated with different segments (or portions) of the trajectory. Such a vector may be modified to ensure the resultant perturbed trajectory is kino-dynamically feasible. The vectorized perturbed trajectory may be input, including a representation of the current driving environment and additional agents, into a prediction model trained to output a predicted future driving scene. The predicted future driving scene, including predicted future states for the vehicle and predicted trajectories for the additional agents in the environment, may be evaluated to determine costs associated with each perturbed trajectory. Based on the determined costs, the optimization algorithm may determine subsequent perturbations and / or the optimal trajectory for controlling the vehicle in the driving environment.

The invention discloses a multi-constraint multi-target coupled multi-vehicle cooperative safe lane changing trajectory planning method, which belongs to the field of intelligent traffic vehicle trajectory optimization, and comprises the following steps: S1, establishing a kinematics model of an automatic driving vehicle and discretizing the kinematics model; s2, on the basis of the discretized kinematics model, constraint conditions which need to be met during trajectory planning of the autonomous vehicle are established; s3, building a multi-target cost function of multiple vehicles by considering external factors, so as to convert a trajectory planning problem of the multiple vehicles into an optimization problem for solving a plurality of equality constraints and inequality constraints; and S4, under the condition that constraint conditions are met, the multi-target cost function constructed in the step S3 is minimized, and the optimal track of multi-vehicle cooperative lane changing is obtained. According to the multi-constraint multi-target coupling multi-vehicle cooperative safe lane changing trajectory planning method, the optimal trajectory is provided for the vehicles of the current road section, and multi-vehicle trajectory planning in a complex dynamic environment is achieved.

The invention discloses a tower crane motion planning method and device in a complex environment, and the method comprises the steps: building a nonlinear model for comprehensively describing the motion characteristics and transient behaviors of a crane-load system on the basis of kinetic analysis, and carrying out differential flat analysis; a BiRRT * algorithm based on direction bias is provided, a node expansion process is optimized by introducing a target bias mechanism of fusion region probability sampling and based on an improved potential field function direction guiding mechanism, and the efficiency and quality of path planning are improved. An improved random tree extension mechanism is combined with a depth deterministic policy gradient (DDPG) of reinforcement learning, and adaptive adjustment of sampling direction and step length parameters is realized. Path points obtained through path planning serve as profile value points of trajectory planning, system full-state constraint conditions such as load obstacle avoidance and shimmy reduction are fully considered, multi-target trajectory planning is carried out based on an NURBS curve, and a multi-target comprehensive optimal trajectory on the aspects of total hoisting time, operation energy consumption and load shimmy suppression is obtained.

This document describes trajectory planning in a three-dimensional (3D) search space with a space-time artificial potential field. An example system includes a processor that obtains an initial pose, a goal pose, and an obstacle map for an environment. The processor uses a parking trajectory algorithm to determine a trajectory by searching in a space-time artificial potential field. The trajectory includes a series of 3D waypoints, including two-dimensional (2D) positional coordinates and time coordinates, to navigate from the initial pose towards the goal pose. Operation of the host vehicle is then controlled to maneuver along the trajectory using an assisted-driving or autonomous-driving system. In this way, path searching is performed in the entire 3D search space rather than iterating between two 2D search planes. This allows motion planning for autonomous parking, especially with static and dynamic objects present, to determine an optimal trajectory in a single iteration.

A method for machine tool motion control which determines a time-optimal trajectory for a multi-segment tool path motion. Start and end waypoints for each segment of the overall motion are defined, along with other conditions such as cutting feed speed. An initial motion profile for each segment is computed based on the waypoint geometry and other constraints, and motion states at the waypoints which join the segments are optimized to provide the shortest total trajectory time. The optimized waypoint states include velocities and accelerations with non-zero values. An extra waypoint may be added to the trajectory, such as for collision avoidance, and the waypoint states are again optimized for minimum total time of the complete trajectory. Heuristic and gradient descent techniques are applied for computation of the optimum waypoint state values.

The invention belongs to the field of track planning and intelligent control of a reusable vehicle, and relates to an intelligent track planning method for a cruise section of the reusable vehicle. The method comprises the following steps: constructing a three-degree-of-freedom centroid motion dynamics model of a cruise section vehicle, generating an offline optimal trajectory sample library covering wide working conditions based on a Gaussian pseudo-spectral method, designing a mapping relation between deep neural network learning task parameters and trajectory features, and outputting a high-quality trajectory planning initial value online. The distribution of collocation points is dynamically adjusted in combination with a self-adaptive collocation point method, a nonlinear programming problem is iteratively solved with a high-quality initial value as a starting point, and efficient and accurate optimization of the trajectory is achieved. Simulation results show that the method significantly reduces the sensitivity of online optimization to initial guess, still has fast response ability and high robustness under complex constraint and uncertainty working conditions, effectively improves the autonomous trajectory planning ability of the cruise section of the reusable vehicle, and ensures flight reliability and control precision.

The invention discloses a hydraulic mechanical transmission device control method based on optimal trajectory optimization, and relates to the technical field of equipment intelligent auxiliary operation and control, and the method comprises the following steps: S1, building a physical model of a hydraulic mechanical transmission device; s2, taking the shortest motion time, the most smooth trajectory and the minimum energy consumption as optimization objectives, and obtaining an optimal trajectory based on a trajectory planning algorithm combining an improved sparrow algorithm and a quintic non-uniform B-spline curve; s3, oil return control is optimized; and S4, oil inlet control is optimized, specifically, an oil inlet flow controller is optimized based on a time-varying secant obstacle Lyapunov function and an RBF neural network. According to the trajectory planning algorithm, the trajectory of the hydraulic transmission device can be optimized, oil return and oil inlet are optimized based on the optimal trajectory, and working trajectory planning and high-precision control over the hydraulic equipment executing mechanism are achieved.

A method for programming a multi-segment motion plan for a machine tool which uses program points defined directly on a workpiece surface, and computes a time-optimal trajectory which transitions from air cut to cutting without stopping, while arriving at a cutting start waypoint traveling at a specified cutting feed speed. The programming method also combines what are traditionally separate air cut and cutting commands into a single command, and computes the time-optimal trajectory for all segments. The underlying time-optimal trajectory computation calculates an initial motion profile for each segment based on the waypoint geometry and other constraints, and motion states at the waypoints which join the segments are optimized to provide the shortest total trajectory time. The optimized waypoint states include velocities and accelerations with non-zero values.

According to several aspects, a method for path planning for a vehicle includes determining a predicted trajectory of a remote vehicle. The predicted trajectory of the remote vehicle includes a plurality of predicted trajectory nodes. The method further includes determining a plurality of possible trajectories for the vehicle. The plurality of possible trajectories includes a plurality of possible trajectory nodes. The method further includes determining one or more evaluation metrics of each of the plurality of possible trajectory nodes based at least in part on a weather condition in an environment surrounding the vehicle. The method further includes selecting an optimal trajectory for the vehicle from the plurality of possible trajectories based at least in part on the one or more evaluation metrics of each of the plurality of possible trajectory nodes. The method further includes performing a first action based at least in part on the optimal trajectory.

The invention provides a multi-section multi-constraint pseudo-spectrum online optimal guidance method. The method comprises the steps that 1, a combined power aircraft model is established; step 2, establishing a multi-section multi-constraint optimal control problem model with undetermined end time of each section; 3, solving a first-order necessary condition and a cross-section condition; 4, linearizing the kinetic model and first-order necessary conditions; 5, carrying out pseudo-spectrum discretization to obtain an optimal instruction updating strategy; and step 6, applying the optimal instruction updating strategy to form an online guidance law. Through the method, rapid and accurate guidance can be carried out, the characteristics of combined power in different speed domains and airspaces are utilized to the maximum extent, and the transport load of the aircraft is improved. Compared with the optimal trajectory calculated by a GPOPS-II tool under different examples, the method provided by the invention is verified to be capable of adapting to single-stage orbit injection in different scenes, the convergence speed is high, the precision is high, and the obtained trajectory has optimality.

The invention relates to a mechanical arm control method and device, computer equipment and a readable storage medium. The method comprises the steps of generating a plurality of candidate trajectories for a target task through a target diffusion strategy model; the target diffusion strategy model is obtained by training an original diffusion strategy model based on collected example data; evaluating and screening the plurality of candidate trajectories according to a single-step value discriminator to obtain an optimal trajectory in the plurality of candidate trajectories; the single-step value discriminator is a multi-modal feature regression model constructed based on a target diffusion strategy model; and the target mechanical arm is controlled to execute the target task according to the optimal track. By the adoption of the control method and device, the control accuracy and safety of the mechanical arm can be improved.

The invention belongs to the technical field of intelligent transportation, and particularly relates to a large transport track planning method considering internal and external comprehensive safety. Comprising the following steps: acquiring road information, and constructing a three-dimensional safety corridor; obtaining a dynamic model of the large transportation system with cargo connection characteristics; setting road trafficability safety boundary conditions, namely external constraint conditions; planning a driving track of the large transportation system, and optimizing a driving track set by adopting a multi-objective optimization algorithm; and an evaluation index is selected to design a trafficability efficiency comprehensive evaluation system of the large transport system, the comprehensive trafficability efficiency of each node on different transport routes is evaluated, and an optimal track is selected. According to the invention, the problems of passing safety and passing efficiency of trajectory planning of a large-piece transportation system in a complex environment are solved.

The invention provides a heavy-load train trajectory optimization method, device and system based on a multi-mass-point model. The method comprises the steps that a dynamic model and initial constraint conditions of a heavy-load train in the running process are constructed; according to the dynamic model and the initial constraint condition, an initial target function is constructed by taking train operation energy consumption and coupler force minimization as targets; based on the single-phase optimal control model, determining a state variable and a control variable in the train operation process; according to the state variable and the control variable, converting the initial objective function to obtain an objective function based on a single-phase optimal control model; and according to the target function and the constraint condition based on the single-phase optimal control model, determining the optimal track in the running process of the heavy haul train by using a pseudo-spectral method. The optimization problem is constructed based on the multi-mass-point model, and the train running track is optimized, so that the safety and the energy efficiency of train running are improved, and the maintenance cost is reduced.

The present invention discloses an end-to-end UAV autonomous navigation method based on differential theory, including: obtaining several depth maps and starting point information and end point information of trajectories, using a trajectory generation network based on motion primitives to generate several groups of flight corridors, constructing a constrained spatiotemporal optimization problem based on the lowest energy trajectory class, solving the spatiotemporal optimization problem to obtain the optimal trajectory corresponding to each group of flight corridors; calculating the trajectory loss of each optimal trajectory, and using the optimal trajectory with the lowest trajectory loss as the trajectory required to be executed by the UAV, thereby realizing autonomous navigation of the UAV. This method makes the spatiotemporal optimization problem differentiable through differential theory, so that a neural network can be used to learn corridors in an end-to-end manner, extracting a safe guidance area from a depth image and reconstructing it into a geometric space constraint for trajectory optimization, and then combining the kinematic constraints specified by the user to efficiently and robustly converge (about 1 millisecond) to a high-quality trajectory.

The invention discloses a moon leap trajectory planning method, system and device and a medium. The method comprises the steps of obtaining target landing point data; based on the acquired data, establishing a moon surface leap device kinetic model by defining a specific coordinate system and a state variable; based on the kinetic model, discretizing the kinetic equation through a numerical integration algorithm, constructing and solving a leap trajectory convex optimization problem with optimal burnup, and obtaining an obstacle avoidance-free optimal trajectory; based on the obstacle avoidance-free optimal trajectory, performing obstacle avoidance iteration through an iteration obstacle increase strategy to obtain a static obstacle avoidance trajectory; and on the basis of the static obstacle avoidance trajectory, the flight trajectory is adjusted again by calculating the trajectory error band width and the maximum extension distance, and a dynamic safety enhanced trajectory is obtained. According to the method, safe, robust and efficient moon surface leap trajectory planning in a complex and uncertain environment is realized.

The invention discloses an over-driven flight system teleoperation planning method based on a MINCO trajectory, and relates to the field of flight system trajectory planning. The problems that efficiency is reduced due to non-uniqueness, caused by redundancy characteristics, of inverse kinematics of a system during teleoperation of an overdrive vector rotor flight system in the prior art, and the process of seeking optimal solutions of multiple planned feasible solutions is complex in planning, long in consumed time and incapable of guaranteeing tracking precision are solved. According to the method, the waypoint of the trajectory and the time parameterization trajectory are directly utilized, the energy consumption of the system, the stability of the trajectory and the obstacle avoidance characteristic serve as optimization targets, the quasi-Newton method is used for accelerating convergence to the optimal solution, and the optimal trajectory is obtained to guarantee the tracking precision of the follow-up tracking process. The method is mainly used for trajectory planning of the teleoperation system.

The invention relates to the technical field of trajectory planning, and provides a rapid evaluation and search method and system for an optimal trajectory and terminal equipment, and the method comprises the steps: sampling a plurality of state points in a pre-planning space, and carrying out the pre-collision detection of the state points to remove points conflicting with an obstacle, and obtaining effective sampling points; performing cost evaluation on the effective sampling points, wherein the cost evaluation comprises heuristic cost based on historical planning trajectory information and static cost based on motion features; and iteratively searching from the initial state point based on the result of the cost evaluation: calculating the trajectory cost of the neighborhood state point and updating the search path along the gradient direction until the optimal feasible trajectory is output. According to the method, invalid sampling points are screened out through pre-collision detection, a gradient iteration search mechanism is adopted, and the track search efficiency is improved.

The invention discloses an intersection dynamic path planning method and system based on vehicle-road cooperation, and the method comprises the steps: obtaining vehicle trajectory data, carrying out the preprocessing of the vehicle trajectory data, carrying out the trajectory prediction based on the preprocessed trajectory data, carrying out the global path planning based on the predicted trajectory, and carrying out the conflict analysis and S-T diagram construction based on the determined global path planning. And then carrying out local trajectory planning, trajectory evaluation and optimal trajectory selection. According to the scheme, trajectory smoothing and lane matching are integrated to improve the accuracy of trajectory prediction, trajectory level analysis is carried out on potential conflicts between vehicles to enhance the refinement and pertinence of conflict detection, and in the global path planning stage, the path planning efficiency is improved. By improving a heuristic function of a Hybrid A * algorithm, a generated path is smoother and has fewer nodes while meeting vehicle kinematics constraints; in the local trajectory planning stage, a Lattice trajectory sampling method is fused, it is ensured that a fine-grained feasible trajectory can still be generated in a complex dynamic environment, and high calculation efficiency is maintained.

The invention discloses a double-AGV (Automatic Guided Vehicle) linkage omnidirectional dynamic path planning method based on a dynamic window method, and the method designs a real-time dynamic path planning scheme based on an omnidirectional differential composite motion model by utilizing the omnidirectional movement characteristic of Mecanum wheels. The method comprises the following steps: firstly, introducing a Y-direction linear velocity in a velocity sampling stage, and constructing an omnidirectional motion model to improve the omnidirectional motion capability; and meanwhile, by combining angular velocity sampling, dynamic adjustment of the attitude of the AGV is realized, an omnidirectional differential composite motion model is constructed, and the omnidirectional motion flexibility is enhanced. And secondly, fusing an artificial potential field method, designing an omnidirectional motion evaluation sub-function, carrying out multi-dimensional comprehensive evaluation on candidate trajectories, screening out an optimal trajectory facing a target point, and effectively improving the efficiency and safety of path planning. And finally, in combination with the contour parameters of the double-AGV linkage system, ensuring that the pilot AGV and the following AGV can be prevented from colliding with obstacles in the environment in the navigation process. According to the method, the transportation efficiency and the system stability are remarkably improved.

The invention discloses a self-adaptive laser head trajectory planning method for a complex workpiece, which relates to the technical field of intelligent manufacturing, and comprises the following steps: establishing a multi-constraint symbol distance field according to a workpiece point cloud model in combination with nozzle appearance geometric parameters, A / C axis motion range constraint and clamp forbidden area information, and calculating a reachable probability value of each voxel; forming a dynamic voxel reachable field reflecting the space feasibility of the workpiece; based on a dynamic voxel reachable field, generating a plurality of candidate trajectories in the reachable area, performing swept volume obstacle avoidance simulation, calculating a clearance distance evaluation value of each candidate trajectory, and selecting an optimal trajectory with the lowest cost in combination with the clearance distance and trajectory curvature features to generate an initial path. According to the method, the spatial quantitative evaluation of the machining feasibility of the complex workpiece is realized, and an accurate reachability map is provided for track planning.

Techniques are discussed herein for determining optimal driving trajectories for autonomous vehicles in complex multi-agent driving environments. A baseline trajectory may be perturbed and parameterized into a vector of vehicle states associated with different segments (or portions) of the trajectory. Such a vector may be modified to ensure the resultant perturbed trajectory is kino-dynamically feasible. The vectorized perturbed trajectory may be input, including a representation of the current driving environment and additional agents, into a prediction model trained to output a predicted future driving scene. The predicted future driving scene, including predicted future states for the vehicle and predicted trajectories for the additional agents in the environment, may be evaluated to determine costs associated with each perturbed trajectory. Based on the determined costs, the optimization algorithm may determine subsequent perturbations and / or the optimal trajectory for controlling the vehicle in the driving environment.

To provide a method for programming multi-segment motion planing for a machine tool which uses program points defined directly on a workpiece surface for computing a time-optimal trajectory for moving at a specified cutting feed speed to reach a cutting start waypoint and to transition from air cutting to cutting without stopping.SOLUTION: A programming method also combines an air-cutting command and a cutting command, which are separate from each other in the prior art, into a single command to compute the time-optimal trajectory for all segments. A basic time-optimal trajectory computation computes an initial motion profile for each segment based on the waypoint geometry and other constraints, and optimizes motion states at the waypoints which join the segments so as to provide the shortest total trajectory time. The optimized waypoint states include velocities and accelerations with non-zero values.SELECTED DRAWING: Figure 9

The invention particularly relates to a special vehicle trajectory planning method considering perceptual uncertainty, and the method comprises the steps: obtaining the position information and uncertainty of an obstacle in an environment based on environment three-dimensional point cloud data, calculating the position uncertainty based on a preset motion model, obtaining a position uncertainty reference trajectory of the obstacle, and obtaining the position uncertainty reference trajectory of the obstacle; determining a collision probability based on the collision probability, and constructing a risk assessment model based on the collision probability according to a preset progressive decrease type dynamic confidence distribution strategy and a preset collision risk assessment condition; and sampling the at least one determined target state point by using a preset coordinate system to obtain a candidate trajectory set, constructing a chance constraint based on the collision probability, screening the candidate trajectory set based on a deterministic boundary constraint obtained by the chance constraint to obtain a plurality of trajectories, and selecting an optimal trajectory from the plurality of trajectories based on the risk assessment model. Therefore, the problems of sensing uncertainty and the like of the special vehicle caused by the environment are solved.

The invention discloses a multi-unmanned-ship multi-trajectory planning method and system based on a synchronization time consensus mechanism, and solves the problems of low precision and poor trajectory adaptability of multi-unmanned-ship cooperation synchronization in a complex water area environment by constructing a cooperation framework of the synchronization time consensus mechanism and dynamic trajectory optimization. The method is characterized in that the time information interaction frequency between the unmanned ships is dynamically adjusted by adopting an event trigger type communication mechanism, and the global optimal synchronization time is determined through a distributed consensus algorithm; and on the basis of the quintic polynomial parameterized trajectory, curvature minimization, path minimization and obstacle avoidance safety are taken as multiple trajectory planning targets, and an optimal trajectory adaptive to synchronization time is generated in combination with numerical differentiation and iterative optimization algorithms. According to the invention, the method can achieve the precise time synchronization and high-precision trajectory tracking of a plurality of unmanned ships in a scene with the limited network bandwidth and the dynamic change of environment interference, and remarkably improves the efficiency and reliability of an unmanned ship cluster in tasks such as cooperative hunting and regional patrol.

The invention discloses a robot smooth path planning method and system based on an improved TEB algorithm and a storage medium, and the method comprises the following steps: S1, creating a robot motion environment, generating a cost map, and planning a global initial path; s2, performing adaptive dynamic sampling on path points on the global path according to the bending degree of the global path; s3, screening global path points; s4, initializing a track to be optimized; s5, updating the track to be optimized and the cost map; s6, constructing a total objective function, and creating a hypergraph; s7, solving the hypergraph to obtain an optimal track; s8, confirming whether the optimal track is feasible or not, and if not, returning to the step S4; s9, controlling the robot to move; and after the robot moves, re-screening the global path points and returning to the step S4 until the robot reaches the target point. According to the invention, frequent pose regulation and control and frequent speed fluctuation of the robot can be reduced.