2023 results about "Trajectory planning" patented technology

Disclosed in the present invention are a multi-agent vehicle-road-cloud integrated collaborative decision-making and control architecture system and method based on federated reinforcement learning. A multi-agent federated reinforcement learning decision-making and control framework having embedded vehicle dynamics characteristics is used, so as to solve the problem of in-depth integration of an intelligent traffic system and intelligent vehicles, and realize autonomous driving with vehicle-traffic in-depth decision-making and control collaboration; a semantic matrix is generated at a road side to serve as an input for vehicle-side reinforcement learning, so as to construct vehicle-side global and local trajectory planning guided by the road side; an integrated reward function for vehicle-side reinforcement learning is designed on the basis of a driving safety field constructed by the road side, so as to realize comprehensive consideration of vehicle-side safety and comfort; on the basis of road-side federated learning, vehicle-side neural network parameters are uploaded by means of V2I communication, so as to solve the problem of vehicle-road information asymmetry caused by privacy awareness; and for different environmental sample distributions, a local optimal policy for a current environment is selected by means of neural network screening, so as to synthesize a shared model benefiting from different environments, thus realizing a balance between sample efficiency and model robustness.

The invention relates to the technical field of equipment control, and provides an autonomous tracking anti-interference control method and system.The confidence coefficient of a sensor is determined by combining the historical precision of the sensor and the current signal quality through a sensing recognition module, and the fusion weight is dynamically adjusted based on the confidence coefficient, so that the target tracking state can accurately reflect the actual motion characteristics of a target; the trajectory planning module takes an interference type, an interference degree and a target tracking state as input parameters cooperatively, and combines an extended state space and a reward function containing an anti-interference reward, so that an explored tracking trajectory can actively adapt to an interference scene; meanwhile, the predicted collision probability is compared with a probability threshold value, so that the global updating triggering opportunity is ensured to be accurate, and the collision risk caused by an unreasonable track is effectively avoided; and the control optimization module generates a targeted feed-forward compensation amount according to the interference type and the interference degree, and generates a control instruction after superposing the basic control amount, thereby realizing cooperation of anti-interference compensation and trajectory tracking control.

The invention relates to the technical field of automatic driving, and discloses an automatic driving lane changing trajectory planning method based on deep learning, and the method comprises the steps: carrying out the data collection and preprocessing of a multi-modal sensor; performing spatial feature extraction and time sequence modeling on the preprocessed multi-modal data by adopting a CNN-LSTM hybrid architecture, performing feature fusion through an attention mechanism, and outputting a first feature extraction vector; taking the detected vehicles as graph nodes to construct a traffic graph, learning an interaction relationship between the vehicles through a graph attention network and a message passing mechanism, and calculating a scene urgency score and a safety score; generating a lane changing decision based on the deep Q network and the strategy gradient; and generating a trajectory based on the generative adversarial network. The technical problems that an existing lane changing track planning method cannot adapt to the dynamic traffic environment, lacks the ability of understanding complex multi-vehicle interaction and is difficult to balance safety and urgent conflict requirements are solved, and intelligent, safe and efficient automatic driving lane changing track planning is achieved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an intelligent flight path planning and energy management system and method for a long-endurance fixed-wing unmanned aerial vehicle. Comprising an environment sensing unit; the flight path planning unit is used for planning a flight path meeting task requirements, safety requirements and energy constraints based on the flight environment information of the unmanned aerial vehicle acquired by the environment sensing unit and pre-stored performance parameters of the unmanned aerial vehicle; and the energy management unit realizes energy dynamic management and optimization according to the real-time energy state of the unmanned aerial vehicle, the flight task and the planning result of the flight path planning unit. The flight path planning unit can call working condition energy consumption data such as navigational speed and height output by the energy management unit in real time, and dynamically adjust the weight of the path to avoid high-energy-consumption flight segments; the energy management unit synchronously and intelligently adjusts a main / standby battery charging and discharging strategy to optimize energy distribution according to a task time sequence (such as waypoint priority and track curvature) of a track.

The invention discloses an end-to-end automatic driving method based on dynamic multi-modal fusion in a complex scene, and belongs to the technical field of automatic driving. In order to solve the problems of sensor perception deficiency, cross-modal feature mismatching, unstable trajectory planning and the like easily occurring in night, low-illumination and complex dynamic environments in the existing end-to-end automatic driving method, texture details of a camera mode and geometric structure features of a laser radar mode are respectively enhanced through a double-flow feature refining mechanism; the characteristic difference between different modes is relieved; an information-driven dynamic fusion strategy is designed, the fusion weight is adaptively adjusted according to scene factors such as environment illumination and obstacle density, and the scene sensitivity and discrimination ability of the model are improved; asymmetric convolution and a low-rank-sparse decoupling technology are introduced, multi-order reconstruction of key channels is carried out on the multi-modal features, and the path change modeling capability is enhanced; and in combination with time sequence dependence of waypoints, outputting a future trajectory through an autoregression decoder to realize high-precision trajectory prediction and stable decision control.

The invention relates to the field of navigation, and more particularly discloses a gradient optimization driven unmanned aerial vehicle real-time obstacle avoidance multi-stage trajectory planning method, which comprises the following steps of: in a trajectory initialization stage, firstly, searching a collision-free geometric path considering steering limitation of an unmanned aerial vehicle on an occupied grid map by utilizing an improved algorithm to generate an initial B-spline control point; then, in a trajectory optimization stage, constructing a multi-target trajectory optimization problem, introducing an obstacle avoidance constraint based on an Euclidean distance field map, and optimizing the initial control point set in combination with unmanned aerial vehicle parameters and optimization weights to obtain a trajectory meeting an obstacle avoidance requirement; and finally, for the optimized trajectory, performing dynamic feasibility evaluation based on unmanned aerial vehicle kinematics limitation in a trajectory correction stage, and if the trajectory does not meet the constraint, performing correction based on the minimum curvature constraint on the control point to ensure that the finally generated trajectory is not only obstacle-avoiding but also feasible in dynamics, and finally, determining that the trajectory does not meet the constraint. Therefore, the real-time obstacle avoidance capability of the unmanned aerial vehicle in a complex environment is effectively improved.

The invention relates to an autonomous energy-saving soaring flight path planning method for a small-sized low-cost aircraft, belongs to the technical field of aircraft trajectory planning, solves the problem of low-cost wind field energy acquisition of the small-sized low-cost aircraft in the prior art, and comprises the following steps: S1, configuring a sensor for the aircraft, and measuring through the sensor to obtain observation parameters; s2, establishing a state vector of the aircraft; s3, establishing an aerodynamic force model, introducing a dynamic equation and a state transition equation, and performing accurate modeling on aerodynamic force; s4, performing multi-source data fusion by adopting extended Kalman filtering, establishing an extended Kalman filter of a nonlinear system, and executing real-time wind vector high-precision sensing; and S5, performing global wind field modeling, estimating a wind field environment, and performing energy-obtaining flight path planning to obtain an optimal energy-obtaining soaring flight path planning scheme.

The invention discloses an inspection robot navigation method, system and device based on Beidou and binocular vision fusion and a storage medium, and relates to the field of robot navigation and obstacle avoidance, and the method comprises the following steps: carrying out multi-modal data fusion processing based on an initial coordinate of an inspection robot and an image captured by a binocular vision system to obtain an environment sensing result; carrying out global planning and local optimization based on an environment perception result, and carrying out cooperation to generate a trajectory planning scheme; based on the environment sensing result and the trajectory planning scheme, intelligent navigation cooperative regulation and control are carried out, and an inspection robot motion control strategy is obtained; by improving the environmental perception accuracy, optimizing the path planning and performing intelligent navigation regulation and control, the navigation problem of the inspection robot in complex environments such as a transformer substation can be effectively solved, the inspection safety and efficiency are improved, and the method has remarkable practical application value.

The invention belongs to the technical field of unmanned aerial vehicle autonomous navigation, provides an unmanned aerial vehicle trajectory planning method and an unmanned aerial vehicle design applying the method, and aims to realize real-time and efficient environmental perception and unmanned aerial vehicle autonomous obstacle avoidance trajectory generation. A group of primitive sets is predefined in a three-dimensional state space to explore the whole search space so as to realize complete coverage of a feasible region, multi-mode perception input of'depth image, current state and target direction 'is adopted, and the depth image is acquired by a depth camera; the current state is obtained by the airborne vision positioning module; multi-modal sensing input is processed by a deep learning network, future expected position, speed and acceleration information is calculated according to output of the deep learning network and serves as input of a bottom layer controller of the unmanned aerial vehicle for trajectory tracking, and finally obstacle avoidance flight in a complex environment is achieved. The method is mainly applied to unmanned aerial vehicle design and manufacturing occasions.

The invention provides an industrial multi-robot intelligent collaborative planning method based on deep learning. The industrial multi-robot intelligent collaborative planning method comprises six parts including environment modeling, feature extraction, task allocation, trajectory planning, control instruction generation and rule distillation. The method comprises the following steps: acquiring multi-robot environment information by constructing a probability grid map and a topological structure, and extracting state and task features to form a comprehensive feature matrix; training an optimal task allocation strategy by adopting deep reinforcement learning, and combining CVAE and CEM joint modeling to optimize trajectory generation; an adaptive impedance controller based on MADDPG is further designed, and dynamic adjustment of interaction parameters is achieved; and finally, the control strategy is converted into a decision rule set through knowledge distillation, the control interpretability is improved, and the deployment complexity is reduced. According to the invention, the task cooperation efficiency and the control stability of the multi-robot system in a complex industrial environment can be effectively improved.

The invention discloses a spraying robot trajectory planning method based on depth camera scanning. The method comprises the steps that wall surface boundary polygon data, an initial spraying stroke set and a spraying dosage model parameter set are obtained; calculating a predicted coating thickness field, and performing difference calculation on the predicted coating thickness field and a preset target thickness to generate a thickness error field; performing connected domain clustering on the thickness error field to generate topological thickness error regions, and determining region type labels for the topological thickness error regions one by one; calling a matched editing operator from a discrete stroke editing operator library, performing geometric constraint verification, and generating a candidate editing scheme; and evaluating the candidate editing schemes based on a preset comprehensive scoring function, updating the initial spraying stroke set by using the scheme with the optimal score, obtaining an optimized spraying stroke set, and converting the optimized spraying stroke set into a robot control instruction. According to the method, the problem that global coverage and local thickness uniformity cannot be considered in traditional geometric planning is solved, and high-quality full-automatic spraying is achieved.

The invention discloses an enamel product and an intelligent spraying system thereof, and belongs to the technical field of enamel product spraying, and the enamel product comprises a high-precision visual guidance module, an environment intelligent control module and an auxiliary function module, the intelligent environment control module is used for monitoring and dynamically regulating and controlling parameters such as temperature and humidity and dust concentration of a spraying environment in real time, ensuring stable coating quality and reducing energy consumption, and the auxiliary function module is used for realizing accurate spraying track planning and quality control through process connection, safety guarantee and data collaboration. And high efficiency and reliability of the spraying process are guaranteed. On the basis that spraying of the enamel product is achieved, the quality of the whole process is improved, and comprehensive benefits can be optimized.

The invention belongs to the technical field of parallel robot controllers, and discloses a high-performance parallel robot controller based on an arm + fpga architecture, through deep heterogeneous fusion of an ARM and an FPGA, the control period is shortened to be within 10 microseconds, the trajectory tracking error is controlled to be 0.1 mm or below, and the performance bottleneck of a traditional architecture in a high-speed scene is solved; the multi-core ARM undertakes complex tasks such as global trajectory planning and dynamics solution, and realizes parallel processing by means of an NEON instruction set; the FPGA fully releases the hardware parallel characteristic of the FPGA, real-time tasks such as multi-axis motion control and sensor data fusion are synchronously completed through a distributed logic unit, and a complex decision-real-time execution assembly line cooperation mode is formed. Inertial parameters and load changes of the mechanical arm are estimated in real time through an LSTM neural network, and feedforward compensation is carried out on interference such as mechanical vibration and load abrupt change in combination with an extended state observer achieved through FPGA hardware; the innovatively designed double closed-loop control architecture supports seamless switching between a force control mode and a position control mode.

The invention relates to a distributed trajectory planning method and system for an unmanned aerial vehicle cluster in an obstacle environment. The method comprises the steps of initializing a system, constructing a local Euclidean symbol distance site map, and realizing real-time interaction of state information of the unmanned aerial vehicle. An initial collision-free path is generated using jump point search. And solving an optimal control point through an L-BFGS algorithm through multi-constraint trajectory optimization in combination with a load swing dynamics model, four-rotor dynamics limitation, cluster collision avoidance and environment obstacle avoidance requirements. And a dynamic time redistribution strategy is adopted, and the track time interval is adjusted according to speed and acceleration overrun conditions. The method further comprises an adaptive re-planning mechanism, and local target points are updated in real time and adjacent aircraft collaborative optimization is triggered based on local map boundary detection and quadrotor track safety detection. According to the method, efficient, safe and stable trajectory planning of the hanging load quad-rotor unmanned aerial vehicle cluster in a complex environment is realized, the requirement of autonomously and efficiently completing tasks is met, and the task execution efficiency and safety are improved.

The invention discloses a petrochemical explosion-proof humanoid robot trajectory planning method, device and equipment, and the method comprises the steps: collecting petrochemical environment gas concentration data, robot joint motion parameters and target position information, carrying out the dangerous region calibration of the gas concentration data, generating a forbidden map, and determining safety nodes and joint motion ranges; constructing an initial path skeleton based on target position information and safety node distribution, identifying a local dangerous section and a major dangerous section, and generating a motion constraint condition and a poison avoidance path; generating a motion primitive library based on the motion constraint condition and the joint motion range, performing stability filtering to generate stable motion primitives, and forming an advancing scheme set; acquiring a joint load peak value to construct a dynamic envelope, and judging execution fitness to form an execution probability field; and constructing a three-dimensional decision space based on the poison avoidance path, the advancing scheme set and the execution probability field, searching an optimal convergence point to generate a final planning trajectory, and providing a safe and efficient trajectory planning scheme for the petrochemical explosion-proof humanoid robot.

The invention discloses an automatic spraying device for inkjet printing equipment, belongs to the technical field of spraying, solves the problem that existing equipment can only process planes and cannot process curved surfaces and irregular surfaces, and comprises a workbench with a rotary clamping piece, a multi-dimensional adjustable spraying assembly and a processing unit. During working, the clamping piece fixes a workpiece and is driven by the motor to rotate, the laser scanner scans the workpiece to generate a three-dimensional model, and dynamic digital twin bodies are constructed in combination with equipment parameters; and the processing unit drives the nozzle to dynamically adjust the position and attitude under the cooperation of the motor through surface parameterization, trajectory planning and model prediction control, so that the optimal inkjet distance is ensured, and a multi-sensor fusion and error monitoring mechanism can correct the deviation in real time and automatically update the model and the trajectory when exceeding the limit. According to the invention, accurate spray painting of special-shaped workpieces with curved surfaces, concave surfaces and the like is realized, the spray painting precision and stability are improved, the application scene is widened, and the manual intervention requirement is reduced.

The invention discloses a vehicle trajectory planning method and device and a vehicle, and relates to the technical field of intelligent driving. The method comprises the following steps: obtaining vehicle state information of a target vehicle, a reference trajectory and environment perception data containing available traffic width, and performing trajectory optimization processing according to the vehicle state information, the reference trajectory and the environment perception data to obtain a trajectory optimization result of the reference trajectory; in the trajectory optimization processing process, taking a first passing cost required for minimizing a trajectory optimization result as a target; a passing width cost item in the first passing cost is a power function taking the residual passing width as an independent variable, and the passing width cost item is increased along with the reduction of the residual passing width; the remaining passing width is the difference between the available passing width and the vehicle width of the target vehicle. Therefore, in a narrow traffic scene, the traffic width cost item is increased in a super-linear manner, the constraint strength on the transverse position of the track is enhanced, the generation of a high-risk track excessively close to an obstacle is effectively avoided, the planning interruption frequency is reduced, and the trafficability of the vehicle in the narrow scene is remarkably improved.

The present invention proposes an autonomous driving testing method based on multi-alliance cluster confrontation, aiming to improve the efficiency and accuracy of autonomous driving simulation test, and comprising S1—initialization of testing environment of autonomous driving; S2—decision-making for dividing background vehicle clusters; S3—decision-making for confrontation behaviors of the background vehicles; S4—trajectory planning for the background vehicles; and S5—looping through the steps S2, S3, and S4 until cluster confrontation testing tasks are completed. The method of the present invention uses reinforcement learning and alliance games to dynamically generate testing scenarios that are highly confrontational to vehicles being tested, which can find dangerous boundary scenarios for autonomous driving more quickly and improve the efficiency of simulation testing.

The invention belongs to the technical field of trajectory planning, and discloses an urban road trajectory planning and control method and system, and the method comprises the steps: generating an obstacle set and a Frenet projection of a road physical boundary through employing a spatial clustering algorithm; utilizing an extreme value method and a self-adaptive smoothing algorithm to dynamically construct a feasible trajectory region trajectory, and synchronously performing interval constraint expansion on a future prediction trajectory of the dynamic obstacle; constructing a path optimization objective function with interval constraints, and solving an optimal path variable in real time by using a numerical optimization method; an expected speed and acceleration sequence of each sampling point is generated based on a space-time domain dynamic planning method, and a longitudinal motion curve is optimized in real time by using an objective function; and inputting the space path and the longitudinal speed sequence into an MPC system, and calculating an optimal front wheel steering angle and acceleration control instruction to realize vehicle trajectory tracking. The method can sense errors and environment changes in a self-adaptive mode, the response speed and robustness of dynamic traffic flow are improved, and multi-target collaborative optimization is achieved.

The invention relates to an unmanned aerial vehicle group-oriented sensing communication integrated network multi-resource joint scheduling method, and belongs to the technical field of wireless communication, and the method comprises the steps: building a system model of multiple UAV-ISAC tasks, and defining a joint optimization problem; extracting spatio-temporal features from the dynamic heterogeneous graph in which the unmanned aerial vehicle, the user and the sensing target are abstracted as nodes and the relationship is abstracted as edges; taking the features as input, and adopting a layered multi-agent reinforcement learning architecture to solve the joint optimization problem on line; in the architecture, resource allocation and trajectory planning actions are generated through cooperation of a central Actor and all unmanned aerial vehicle Actors, and system performance is evaluated by a central Critic; constructing a multi-target weighted reward function, stabilizing a training process by combining experience playback and a Mini-batch sampling mechanism, and updating network parameters in parallel; and obtaining an optimal resource allocation and unmanned aerial vehicle trajectory strategy through training. The sensing performance is improved, the communication quality is guaranteed, and the defects in the aspect of dynamic resource scheduling in the prior art are overcome.

The invention provides a real-time monitoring system and method for sorting of a digital twin-driven Delta mechanical arm. According to the system, firstly, a physical experiment platform is built, and a virtual model is built through SolidWorks, 3DMAX and Unity 3D. A communication framework is designed, and wireless communication of the PLC, the sensor, the mechanical arm, the conveying belt and the digital twin system is achieved. Positive and inverse kinematics analysis and Cartesian space trajectory planning are carried out on a Delta robot, and a related algorithm is packaged into an MATLAB function and compiled into a DLL library for C # calling. The system collects joint angle data in real time based on a rotary encoder, transmits the joint angle data to a digital twin system through an OPC UA protocol, and drives a virtual mechanical arm model after resolving, so that high-precision and low-delay virtual-real synchronous pose reconstruction is realized. In addition, the system also develops a collision detection function based on a bounding box algorithm, and performs visual detection by using camera calibration and a YOLO model.

The invention discloses a double-arm collaborative planning method, system and device based on reinforcement learning and a medium, and belongs to the technical field of mechanical arm control. According to the current state in the state space, a control action is generated, and three-dimensional displacement increment instructions of the left arm end effector and the right arm end effector are obtained; after the three-dimensional displacement increment instruction is responded to and double-arm cooperative control is executed, a mixed reward function is calculated; experience enhancement processing is carried out on the execution track, target resetting is carried out on the failure track, pseudo target experience is generated, and the original experience and the pseudo target experience are stored in a playback buffer; and updating parameters of the strategy network and the Q value network according to the empirical samples in the playback buffer, and completing optimization of the double-arm collaborative trajectory planning strategy. According to the method, the problems of sparse reward and local optimum in two-arm collaborative planning are effectively solved by fusing maximum entropy reinforcement learning and an experience playback mechanism, and the training efficiency and the strategy generalization ability are improved.

The invention discloses an automatic driving track generation method and device, equipment and a medium. The method comprises the steps that target state information of a vehicle is obtained, and the target state information comprises vehicle body state information, obstacle information, road structure information and traffic state information; the target state information is input into a trajectory planning model for trajectory generation, a plurality of candidate driving trajectories and trajectory evaluation results corresponding to the candidate driving trajectories are determined, and the trajectory planning model is constructed based on a deep neural network; and determining a target driving trajectory based on the plurality of candidate driving trajectories and the trajectory evaluation result corresponding to each candidate driving trajectory. According to the method, the automatic driving track of the vehicle can be automatically and accurately generated, emergencies in dynamic traffic are covered, the generalization ability during cross-scene migration is improved, the track generation flexibility is improved, and therefore the track precision and safety in complex scenes are guaranteed.

The invention discloses a diffusion model trajectory planning method based on historical guidance and collision suppression. The method comprises the following steps: collecting own vehicle information, environment information and routing lane information in normal vehicle driving data; constructing negative sample scene data containing vehicle collision; during model training, encoding static obstacle states, dynamic obstacle historical tracks and surrounding lane structure information in a current environment; training a diffusion model decoder based on the future trajectory of the own vehicle, inputting the environmental condition information, the routing lane information and the own vehicle planning result of the model at the previous moment as condition information into the decoder, and enhancing the time continuity of the planning result; and training a classifier based on the collision data, and applying negative guidance to a collision category by using a classifier guidance mechanism during reasoning and sampling of the diffusion model so as to suppress a trajectory generation result with a high collision risk. According to the invention, efficient, safe and stable planning track generation can be realized in a real road scene.

The invention provides a dynamic task allocation method for multi-robot collaborative operation, and relates to the technical field of multi-agent reinforcement learning, and the method comprises the steps: a central server carries out the time-space fusion of a real-time order data flow and a dynamic environment perception parameter, and generates a priority-weighted global task coordinate set; the edge computing node receives the global task coordinate set, performs spatial registration with a local real-time grid map, outputs a robot exclusive task subset, and extracts topological relation feature vectors of dynamic entities in a task area; and calculating an instantaneous change rate of relative displacement between the dynamic entities based on the topological relation feature vector, and generating a path correction coefficient matrix of trajectory planning. According to the invention, efficient task allocation and accurate execution in a high dynamic environment are realized.

The invention relates to the technical field of robot control, and discloses a mechanical arm trajectory planning control method and system based on a BAFARNN model. The method comprises the steps that a mechanical arm kinematics model is established, and a trajectory tracking problem is converted into a time-varying equation; designing a bounded adaptive function to activate a recurrent neural network model, defining an error function and constructing a dynamic equation; designing a piecewise adaptive coefficient function, and dynamically adjusting the gain according to an error norm and time; setting a Lissajous curve as an expected trajectory, and initializing a simulation environment; the joint speed is solved in real time through an ODE numerical method, and the mechanical arm is driven to move; actual motion data is collected and compared with an instruction, and closed-loop feedback control is triggered when the actual motion data exceed a threshold value. According to the method, rapid convergence is achieved through the piecewise adaptive coefficient function, the bounded activation function and the negative feedback mechanism are adopted to suppress noise, and high-precision and real-time trajectory tracking of the mechanical arm in the dynamic environment is achieved.

The invention relates to the technical field of unmanned aerial vehicle trajectory planning, in particular to a bidirectional tree random search method based on a complex environment node cost function, which comprises the steps of surveying obstacle data in a flight area and modeling, then introducing a target deviation strategy and a bidirectional search tree mechanism, and combining with an improved minimum cost method to obtain a random search result. Alternately expanding the two trees to generate an initial path, and integrating the path length, the obstacle distance and the flight height energy consumption by a cost function; and finally, pruning the initial path, removing redundant nodes, smoothing the trajectory by adopting a cubic spline interpolation method, ensuring curvature continuity, and generating an optimal path meeting the flight performance of the unmanned aerial vehicle. According to the invention, through an improved bidirectional minimum cost fast expansion random tree algorithm, dynamic association of obstacle constraint, target deviation and path cost in unmanned aerial vehicle path planning in a complex environment is represented, and a more efficient search convergence speed and better path quality are obtained. And rapid convergence and global optimization of the track of the unmanned aerial vehicle in a high-dynamic environment are realized.

The invention discloses a five-axis machining tool path planning method for a complex curved surface, and relates to the technical field of numerical control machining. Subregions are divided based on the Gaussian curvature of a curved surface model, and a region equation is fitted; projecting the sub-regions and screening to obtain a boundary point set, fitting a curved surface equation by adopting cubic NURBS, and performing normal vector and curvature constraint optimization to splice an optimal curved surface equation; setting a section plane, calculating a section curve, calculating an arc length based on a Simpson formula, and planning a track point coordinate sequence according to the curvature; constructing a spiral equation of adjacent section planes, calculating a spiral step length based on a Simpson formula according to a position and tangent continuous constraint solving coefficient, and generating a cross-plane track point sequence; in the section plane, based on lossless and cutting direction constraints, an optimal angle sequence is obtained through optimization of a genetic algorithm, and a cross-plane angle sequence is obtained through cosine interpolation; and a machining pose sequence is generated through integration, and high-precision machining of the complex curved surface is achieved.

The invention provides an intelligent heavy truck end-to-end driving method based on focus attention and probabilistic game, and belongs to the technical field of intelligent driving. The method comprises the following steps: firstly, acquiring multi-source data such as a laser radar, a look-around camera, an IMU (Inertial Measurement Unit) and a load signal, and generating an anti-jitter space-time BEV feature through cross-dimensional position coding based on motion compensation, near-field non-uniform sampling, focus attention and time sequence fusion; then, vectorization decoding is carried out on the features to obtain obstacle vehicle movement and map element vector information. And finally, on the basis of the expert track prior space, probability game interaction is carried out through multi-round iterative prediction and planning, a self-vehicle track conforming to heavy truck variable load dynamics constraints is generated, and the self-vehicle track is output after safety verification. According to the method, the problems of heavy truck high-position sensor vibration distortion, hinge structure blind area and trajectory planning under the large-inertia variable-load working condition are effectively solved, and the driving safety and robustness are remarkably improved.

The invention discloses a control method for collaborative trajectory planning of a container inspection robot. The control method comprises the following steps: S1, constructing a global task and a map; s2, obtaining real-time state sensing and positioning of the inspection robot (6); s3, establishing a unified three-dimensional cooperative control decision algorithm, and cooperatively controlling the height of an electric rod on the robot and the pitch angle (pitch), the yaw angle (yaw) and the roll angle (roll) of a holder based on the real-time pose of the robot, the distance between the ideal track and the target container and the standard size of the pre-modeled container; s4, the robot runs according to the planned track to execute the task and collects data; s5, performing global state management and safety strategy of the electric rod of the robot; the method has the beneficial effects that a detection blind area is eliminated, and three-dimensional cooperative control is realized; a camera can lock a target in a three-dimensional space at an optimal pose, image quality consistency is ensured, and jitter is inhibited through active holder movement; and full-process automation and safety protection are realized.