303 results about "Kinematics equations" patented technology

The invention provides an equipment fault early warning method based on a neural network, a medium and equipment, and the method comprises the steps: collecting equipment state information at regular time, recognizing abnormal equipment, obtaining operation parameters and structure parameters of the abnormal equipment and a layout schematic diagram of a system where the abnormal equipment is located, constructing a dynamic coupling topology link, and extracting associated parameters; verifying and generating a first error weight in combination with a physical kinematics equation, generating a second error weight by matching historical fault data, and comprehensively generating fault state information; a detection mode is switched based on risk level evaluation, a system dynamic coupling topology link, associated parameters, abnormal equipment information and fault state information are input into a neural network model, fault link information is output, and finally first early warning information and second early warning information are generated according to a dynamic detection mode to realize two-stage early warning. According to the method, the fault conduction relation between equipment is modeled through the graph neural network, and fault path prediction and systematic risk early warning are realized.

The invention discloses a four-rotor unmanned aerial vehicle model prediction control method based on a deep Q-network (DQN). The method comprises the following steps: firstly, establishing an inertial coordinate system and a body coordinate system, and establishing a kinematic equation and a kinetic equation of the four-rotor unmanned aerial vehicle based on a Newton second law and an Euler-Lagrange equation; according to the method, an unmanned aerial vehicle robust controller based on model predictive control (MPC) is designed, optimal control input is generated through dynamic optimization, and the trajectory tracking performance of the unmanned aerial vehicle is improved. For various disturbances existing in the whole tracking process, a DQN reinforcement learning algorithm and an MPC method are combined to design a flight control system, control rate errors caused by the disturbances are compensated, the stability of attitude control is enhanced, and the tracking precision and the anti-interference capability are improved. And finally, verifying the robust performance of the flight control system through a simulation experiment.

The present disclosure discloses a method for designing a terminal guidance law based on deep reinforcement learning, and relates to the field of missile and rocket guidance. The method includes: establishing a relative kinematics equation between a missile and a target in a longitudinal plane of a target interception terminal guidance section of the missile; to adapt to a research paradigm of reinforcement learning, abstracting a research problem and modeling as a Markov decision process; building an algorithm network, and setting algorithm parameters, where a selected deep reinforcement learning algorithm is a deep Q-network (DQN); in a terminal guidance process of each round, obtaining a sufficient number of training samples through Q-learning, training a neural network and updating a target network at fixed frequencies respectively, and continuously repeating the above process before set learning rounds are reached.

The invention discloses a high-speed train multi-information fusion positioning method based on a Beidou satellite, and relates to the technical field of satellite navigation and positioning. The method comprises the following steps: designing a high-speed train multi-information fusion positioning scheme; based on a train multi-information fusion positioning scheme, establishing a nonlinear train state model based on a train kinematics equation; the position, speed, acceleration, course angle and pitch angle of the train are used as state vectors, and a train measurement model is constructed; based on a tracking target set by a train state model and a train measurement model, the train state is estimated by fusing IMM-UKF / PF models of unscented Kalman filter (UKF) and particle filter (PF) and introducing ACW and a short-term error feedback mechanism in state fusion. According to the method, the positioning precision and robustness can be improved, and the dynamic response capability in a complex scene can be enhanced.

The invention relates to an automatic guided vehicle (AGV) adaptive path tracking method based on MPC. The method comprises the following steps: S1, establishing a kinematic discretization error model based on the kinematic characteristics of the two-wheel differential AGV, processing a continuous kinematic equation by adopting an Euler discretization method, expressing a dynamic change relationship between a transverse deviation distance and an angle deviation in a state-space equation form, and generating a kinematic discrete state-space model; the method has the advantages that the continuous equation is processed by establishing the kinematics discretization error model and adopting the Euler discretization method, the deviation dynamic relation is expressed through the state space, the calculation process is simplified, the precision is kept, sensor data are fused, the wheel type odometer, IMU and laser data are integrated through the extended Kalman filtering algorithm, and the precision is improved. The real-time position and angle deviation are calculated, the positioning accuracy is improved, a model prediction controller objective function is designed, a weight matrix and boundary constraint are introduced according to constraint conditions, and the effect of obstacle avoidance constraint is combined.

The invention discloses a compensation operation simulation method for a telescopic active wave compensation trestle, and the method comprises the steps: taking sea condition parameters as input, combining a wave frequency response factor of a ship, and simulating the six-degree-of-freedom wave frequency motion of the ship; establishing a series coordinate system connected with a compensation mechanism on the telescopic active wave compensation trestle, and constructing a forward motion equation and a reverse solution equation of compensation motion; according to the compensation target position of the tail end of the telescopic active wave compensation trestle, the six-degree-of-freedom motion amplitude of the ship is combined, and the motion stroke of a compensation mechanism is reversely solved; differentiating the motion stroke of the compensation mechanism, calculating the speed and acceleration of the compensation mechanism, and determining the statistical value of the motion time sequence of the compensation mechanism; and finally, judging whether the telescopic active wave compensation trestle can compensate the ship motion caused by the sea condition or not according to the statistical value. Whether the compensation mechanism design of the trestle can compensate the ship motion under the sea condition or not can be judged according to the simulation result.

The invention relates to the technical field of two-arm robot pipeline butt joint cooperative motion planning and control in a complex environment, in particular to a two-arm cooperative robot pipeline butt joint method based on relative Jacobi. According to the method, a double-arm kinematics equation relative to a Jacobian matrix is constructed, constraint conditions are constructed in combination with pipeline butt joint requirements, pose errors and integral information are introduced to design an anti-noise motion planning scheme, an end effector obstacle avoidance strategy is designed, the anti-noise scheme is converted into a quadratic optimization problem, and the quadratic optimization problem is solved through a numerical algorithm. According to the method, the problems that an existing scheme is insufficient in precision, inaccurate in constraint condition and poor in anti-noise and obstacle avoidance capacity are solved, the uniformity and accuracy of butt joint of the two-arm pipelines are improved, the influence of environmental noise is effectively restrained, butt joint safety is guaranteed, butt joint efficiency and reliability are improved, and the high-precision pipeline butt joint requirement under the complex environment is met.

The invention discloses a multistage voxel adaptive laser radar pedestrian tracking method, which comprises the following steps: S1, acquiring laser radar point cloud data, and adding ground filtering and elevation filtering; s2, performing hierarchical voxel grid processing on the basis of the point cloud data after ground filtering and elevation filtering, inputting the point cloud data after hierarchical voxel grid processing into a DV-Det model to detect a point cloud pedestrian target of a current frame, and adding a Soft-NMS for post-processing optimization; s3, designing an unscented Kalman filter based on a pedestrian kinematics equation, predicting the motion state of a pedestrian target, and adding a joint probability data interconnection algorithm and an adaptive life cycle management strategy; and S4, the mass center of the pedestrian target is calculated and detected, the relative position and the movement speed of the pedestrian target are solved, and a display panel of pedestrian state information is added to a visual interface. In conclusion, the defects in the prior art are overcome, high-precision and low-delay pedestrian detection and tracking can be achieved in multiple indoor and outdoor scenes, and the method has high social use value.

The invention relates to the technical field of magnetic suspension conveying lines, in particular to a variable curvature trajectory prediction control method and system for a magnetic suspension conveying line and a storage medium. The method comprises the steps that a variable-curvature trajectory prediction model of the magnetic suspension conveying line is established, and the variable-curvature trajectory prediction model describes the dynamic behavior of a conveying carrier on a variable-curvature path through a kinematics equation and geometric constraints and is used for predicting trajectory deviation in real time. According to the invention, by constructing a variable curvature trajectory prediction model and a multi-target rolling optimization control system, the problems of trajectory tracking and suspension stabilization of a magnetic suspension carrier under a complex path are systematically solved. The method has the advantages that a time domain motion equation is mapped to a space domain path coordinate system based on a curve coordinate system of arc length parameterization and a prediction model constructed by a French-Serret formula, the influence of curvature change on lateral displacement dynamics is accurately described, and dynamics mismatch caused by traditional fixed curvature approximation is eliminated from the model level.

The invention relates to the technical field of underwater robot control, in particular to an autonomous line patrol motion control method of an underwater cable detection robot, which comprises the following steps of: establishing a six-degree-of-freedom kinematics equation and a kinetic model of the robot, and collecting position information of the robot and a current heading angle of the robot; the target heading angle of the robot is calculated through an optimal deflection angle fusion algorithm, the current heading angle, collected by a sensor, of the robot is compared with the target heading angle, an error epsi is obtained, and the error epsi is added into a single-ring PID algorithm for adjustment; and controlling the thrust and direction of a propeller in the underwater cable detection robot according to a control signal tau (t) output by the single-loop PID algorithm and robot position information, judging whether a target heading angle of the underwater cable detection robot is consistent with a current heading angle, if so, completing autonomous line patrol, otherwise, continuing to correct, and if not, completing the detection of the underwater cable detection robot. The attitude control precision and the path navigation stability of the robot in the underwater environment are improved.

The invention discloses a mechanical arm repetitive motion inverse solution solving method and system considering iteration initial value selection. The problems that when a mechanical arm inverse solution is solved through a numerical iteration method based on a Jacobian matrix, dependence on iteration initial value selection is high, and motion cannot be repeated are solved. Comprising the steps that an accurate reachable working space of the mechanical arm is obtained, a certain number of reference inverse solutions optimized through an operability index are additionally stored in the accurate reachable working space, and an enhanced reachable working space is obtained; according to the expected pose information of the mechanical arm and the enhanced reachable working space, an iteration initial value for solving the inverse solution of the pose is selected and obtained; acquiring a tail end pose error kinematical equation of the mechanical arm at a joint position layer, and deducing to obtain an iterative updating equation of a joint position; and a repeatable motion index function is designed, a joint position iteration updating equation with the repeatable motion function is obtained, and according to the iteration updating equation and the selected iteration initial value, a mechanical arm repeated motion inverse solution solving scheme selected by considering the iteration initial value is obtained.

The invention provides an unmanned aerial vehicle six-degree-of-freedom hardware acceleration IP core implementation method and electronic equipment, and belongs to the technical field of calculation acceleration, and the method comprises the steps: packaging a six-degree-of-freedom kinematics equation of an unmanned aerial vehicle and parameters thereof to form an IP core; an IP core is loaded on the FPGA; the six-degree-of-freedom calculation of the unmanned aerial vehicle is carried out on the FPGA through the loaded IP core, and the calculation period of the six-degree-of-freedom of the unmanned aerial vehicle is controlled based on the clock period of the FPGA. According to the invention, the six-degree-of-freedom calculation period of the unmanned aerial vehicle is controlled through the clock period of the FPGA, so that the updating process from the input data of the IP core to the output data of the IP core is a fixed period and does not fluctuate along with the change of the input data or the calculation state; therefore, the technical problem of serious periodic jitter during six-degree-of-freedom calculation of the existing unmanned aerial vehicle is solved, and the credibility and the control stability of the simulation model are improved.

The invention discloses a satellite attitude system anti-interference finite time control method under complex dynamic disturbance, and the method comprises the steps: building a rigid satellite attitude maneuver control model according to satellite attitude dynamics and kinematics equations; designing a self-adaptive fine disturbance observer to estimate the total disturbance of a satellite attitude system by combining the influence of the external disturbance variable on the rigid satellite attitude maneuver process, and outputting an estimated value of the external disturbance variable; a three-stage segmented sliding mode control method is designed, dynamic continuity of a three-stage sliding mode is ensured, and segmented sliding mode control output is obtained; and compensating the segmented sliding mode control output by using an estimated value of an external disturbance variable output by a self-adaptive fine disturbance observer to realize satellite attitude stable control. According to the method, the problem that the stability of the satellite is reduced in the attitude maneuvering performance process when external disturbance exists is solved, better observation performance and shorter attitude stabilization time are achieved, and the control precision of the satellite is effectively improved.

The invention discloses a feeding motion calculation method for a double-output-shaft five-axis numerical control tool grinding machine, and the method specifically comprises the steps: building a unified coordinate system according to the mechanism characteristics and solving demands of the grinding machine, and the unified coordinate system comprises a machine tool coordinate system, a machining coordinate system, a workpiece coordinate system and a grinding wheel coordinate system; grinding machine structure parameters are set and comprise the offset of the original point of a machining coordinate system relative to the C axis, grinding machine structure offset examples of the offset of the original point of the machining coordinate system relative to the B axis, the grinding wheel installation distance and the grinding wheel installation direction; a universal grinding machine feeding kinematics equation is established by considering the inward and outward mounting conditions of main shafts at the left end and the right end of the grinding wheel; and solving based on grinding wheel installation transformation and grinding machine stroke constraint. According to the method for solving the partitioned selective solution based on the actual travel limit of the machine tool, the solution is more universal and practical, the actual travel limit of the machine tool is met, and the method has good adaptability.

The invention provides a space target trajectory parallel mapping method based on a physical information network, and belongs to the field of time sequence data processing in deep learning. Aiming at the problems that a large amount of repetitive work exists and time and labor are consumed in the process of space target motion physical system analysis modeling and accumulative error correction, the method comprises the steps of fitting a kinematics equation of a space target in a parallel digital space through a parameterized neural network, and introducing known physical equation information; and the constraint network searches an optimal hypothesis process in a hypothesis space, and a parallel space modeling mapping process is converted into solution of a parameter inversion problem in a target domain, so that rapid construction of a parallel digital space is realized. Meanwhile, along with the increase of input sample data, the ability of the cross-cycle space-time attention module designed by the method for capturing the motion characteristics of the space target is stronger and stronger, and dynamic correction of errors is realized in a continuous iteration process.

The invention discloses a self-adaptive recognition method and system based on a four-bar linkage single station machine motion trail, and belongs to the technical field of motion trail recognition. The method comprises the following steps: acquiring a multi-source synchronous data stream, detecting a peak interference frequency, adjusting an adaptive notch filter in real time, obtaining a pure geomagnetic field signal, calculating the equivalent length of a connecting rod by using angle and force data, and establishing a kinematics equation to solve track coordinates; establishing a magnetic field space gradient tensor model through a magnetometer array, fitting a nonlinear mapping relation between an angle and a gradient tensor, and generating a metal magnetic interference compensation model; judging a motion mode and dynamically adjusting filter parameters; constructing a fast and slow dual-time scale filter to fuse multi-source information, and introducing a fading factor to prevent divergence; a future angle is predicted by using a long-short-term memory neural network to serve as a virtual observed quantity to enhance the fault-tolerant capability; and optimizing the reconstruction trajectory through multiple physical constraints. According to the method, the track identification precision, the anti-interference capability and the system robustness are remarkably improved.

The invention discloses a laser radar inertial odometer method based on block-by-block updating, which comprises the following steps: constructing a kinematics equation and an observation equation of a laser radar inertial odometer system based on a block-by-block updating scheme, and performing state propagation on point cloud block laser radar data and inertial measurement data to obtain a prior prediction state; compensating the pose of each point cloud point in the point cloud block, and aligning the point cloud point to the pose of the point cloud block at the last moment; registering the aligned point cloud blocks with a local map, executing nearest neighbor plane search, and then calculating a point-surface residual error; combining the prior state prediction with the posterior state residual error, and performing state iteration updating and iteration convergence to obtain a final result; and then local map updating is carried out. According to the method, the continuous laser radar point cloud data is divided into a plurality of small blocks for batch processing according to the extremely short time interval, so that the time overhead of single update can be remarkably shortened.

The invention provides an automatic calibration method and device for a mechanical arm, electronic equipment and a storage medium, and the method comprises the steps that the mechanical arm is controlled to conduct RCM motion in the Y-Z axis direction, and the pitch angle range is determined; performing multiple RCM rotation sampling in the pitch angle range, and determining a motion deviation error of each rotation position point based on the Y-axis unit pixel actual distance, the first needle point pixel coordinate, the first space point coordinate, and the second needle point pixel coordinate and the second space point coordinate of the rotation position point under each RCM rotation sampling; determining a calibration matrix based on the motor position of each rotation position point and a forward kinematics equation of the mechanical arm; and least square method processing is conducted on the multiple motion deviation errors and the calibration matrix, and the calibration amount of the to-be-calibrated parameters of the mechanical arm in the YZ axis calibration direction is determined. The device is low in cost, low in calibration time consumption, high in calibration accuracy and automatic, and factory calibration of the mechanical arms can be carried out on a large scale.

The invention provides a modeling simulation method for a digital twin model of a fixed-wing unmanned aerial vehicle, and relates to the technical field of digital twin. The method comprises the following steps: establishing a fixed-wing unmanned aerial vehicle three-dimensional model Establishing dynamics and kinematics equations of the fixed-wing unmanned aerial vehicle; deducing a linear model of the fixed-wing unmanned aerial vehicle and an actuator fault model; establishing a digital twinborn model of the fixed-wing unmanned aerial vehicle; establishing a real-time communication relation between the fixed-wing unmanned aerial vehicle digital twin model and the entity fixed-wing unmanned aerial vehicle model; and operating the digital twin model of the fixed-wing unmanned aerial vehicle for simulation, and outputting flight state data in a visual interface. The method has the characteristics of high synchronism and coordination of the equipment entity and the equipment model, and the purposes of comprehensively controlling the actual operation state of the equipment and establishing the digital twin model of the fixed-wing unmanned aerial vehicle can be achieved.

The invention relates to a quadrotor unmanned aerial vehicle control method based on priori knowledge and a neural network model, and the method comprises the steps: introducing flight interference and uncertainty based on a first principle, and constructing a quadrotor kinematics equation; parameterizing the flight uncertainty by using a pre-trained neural network model, and constructing a four-rotor kinematics model; constructing a disturbance observer based on a pre-trained neural network model, predicting the interference of the flight, and obtaining a disturbance compensation amount; based on the four-rotor kinematic model and a disturbance observer, generating an optimal control quantity of the four-rotor unmanned aerial vehicle by solving a numerical optimization problem in each control period of the four-rotor unmanned aerial vehicle; and superposing the disturbance compensation quantity and the optimal control quantity to obtain control input of the four-rotor unmanned aerial vehicle. Compared with the prior art, the method solves the problem that the trajectory tracking precision of the quad-rotor unmanned aerial vehicle is insufficient due to model uncertainty and external disturbance.

An apparatus for deploying the wings of a movable-wing aircraft includes a spherical four-bar mechanism. The spherical four-bar mechanism has a first bar to which a first wing is attached, a second bar to which a second wing is attached, a third, movable bar rotatably or movably linking the first bar to the second bar at a first respective point on each bar, and a fourth bar grounding the first and second bar at a second respective point on each bar. The spherical four-bar mechanism may be mathematically optimized for space, size, and movement by a system of kinematic equations to allow the first and second wing to open at substantially the same time without colliding with one another. The wings of the aircraft may further include a mid-wing hinge with a movable stiffening spar to allow the wings to fold to a more compact size during storage.

The invention discloses a traffic safety risk early warning method and system based on intersection traffic flow information, and relates to the technical field of traffic safety monitoring, and the method comprises the steps: dividing an intersection region into space-time grids, generating a pixel-level optical flow field through a minimization energy function, converting the pixel-level optical flow field into a physical optical flow field, and carrying out the early warning of the traffic safety risk based on the physical optical flow field. Mapping the physical optical flow field to a space-time grid, and fusing the average optical flow vector and the velocity vector through a least square method to generate a fused velocity vector; predicting the future trajectory of each vehicle based on a kinematic equation, generating a grid risk by integrating the traffic flow density, the normalized grid confusion degree and the optimized strategy probability vector, and calculating the overall risk of the intersection through weighted average; and carrying out risk early warning on the overall risk of the intersection. According to the method, optical flow field analysis is combined with velocity vector fusion, the representation precision of the motion state of the vehicle is improved, kinematics prediction is combined with trajectory intersection analysis, and the capability of finding potential collision risks in advance is improved.

The invention discloses a mobile manipulator man-machine collaborative operation motion control method based on quadratic programming, which comprises the following steps of: 1, constructing a kinematic equation of a mobile manipulator, and designing a quadratic programming solver to describe a mobile manipulator coordinated control problem in a man-machine collaborative operation scene; 2, a constraint function of a quadratic programming solver is designed, equality constraints are utilized to meet end speed tracking in a cooperative task, and inequality constraints are utilized to realize joint limit position avoidance; 3, designing a translation operability index, a collaborative operation formation optimization index, a relaxation cost dynamic control function and a joint speed cost dynamic control function which are suitable for a man-machine collaborative task, and integrating the indexes and the functions into a target function of a quadratic programming solver, and a mobile mechanical arm man-machine collaborative operation motion control frame based on quadratic programming is formed.

The invention discloses a double-magnetic-conductance control method and system of an AGV, the AGV, equipment and a medium, the centers of two magnetic sensors are used as control target points, forward kinematics equations of each wheel and the control target points are constructed, the current speed of a vehicle body and the current speed deflection angles of the two control target points are obtained through inverse kinematics solution, and the control target points of the AGV are obtained. And calculating a predicted deviation correction angle of each control target point by combining a track deviation measurement result of the magnetic sensor, and finally performing forward kinematics solution to obtain a target speed and a target speed deflection angle of each wheel. According to the method, the positions of the two magnetic sensors serve as control targets, a front control point and a rear control point can be well considered by adopting a double-control-target-point angle deviation correction mode, rapid deviation correction of the AGV can be achieved, the deviation correction response speed is greatly increased, the method can be suitable for complex and variable paths, various variable paths can be well followed, and the method is suitable for large-scale popularization and application. And the path following precision is improved, and the control error is greatly reduced.

The invention relates to a flight path tracking control method for an unmanned autonomous submersible vehicle, and belongs to the technical field of unmanned submersible vehicle control. The method comprises the following steps: firstly, establishing a nonlinear dynamic model of the submersible vehicle, analyzing kinematics and dynamic characteristics of the nonlinear dynamic model, considering various underwater forces and moments, and defining an inertial coordinate system and a submersible vehicle body coordinate system to obtain a kinematics equation and a dynamic equation so as to provide a basis for control design; secondly, a non-singular integral terminal sliding mode control method is adopted, a sliding mode surface is designed, the singular problem of traditional sliding mode control is avoided, a sliding mode control law is deduced, system finite time convergence to a balance point is achieved, and an expected state is rapidly tracked. And finally, a brand new and simple adaptive control strategy is provided, an adaptive law is designed according to system errors and sliding mode surface information, a gain matrix is adjusted in real time, and system uncertainty and external interference are compensated. The method has the advantages of being high in robustness, rapid in convergence, simple and easy to implement.

A medical devices en-vivo positional determination system using a plurality of optical time of flight (TDF) transceivers along with gyroscopic angular and mechanical distance sensors for the determination of absolute linear and angular positional information of the tool bit tip for the purposes of more accurate hand-held drilling or cutting, on the workpiece using forward kinematic equations calculated on a microcomputer to provide a real-time display of the 3-linear position and 2-angular orientation of the tool bit (such as a drill, scalpel, or wire driver).

The invention relates to the technical field of communication, in particular to an aviation airborne miniature 5G ad hoc network multimode communication terminal, which comprises a flight state prediction module, a state vector and time step length linear transformation relation is constructed by adopting a kinematics equation based on real-time GPS position, velocity vector, acceleration and attitude angle data of the terminal and neighbor nodes, and the state vector and time step length linear transformation relation is obtained; and by using the residual error between the newly received kinematics data and the predicted value at the previous moment, weighting the correction state vector and the predicted covariance, circulating a recursive correction process, outputting three-dimensional space coordinates of multiple nodes under a plurality of time steps in the future, and obtaining a future position set of the nodes. According to the method, the basis of routing decision is changed by directly processing real-time GPS position, velocity vector and attitude angle data and adopting three-dimensional space coordinates of continuous nodes of a kinematics equation in a future time window. It no longer relies on instantaneous snapshots of the current network state, but is based on future predictions.

The invention discloses an unmanned aerial vehicle path deception jamming method based on an intelligent optimization algorithm, belongs to the technical field of unmanned aerial vehicle path deception jamming, is used for unmanned aerial vehicle path deception jamming, and comprises the steps of establishing an unmanned aerial vehicle kinetic equation and a kinematics equation, and obtaining an expected flight trajectory of an unmanned aerial vehicle; a particle filtering method based on firefly algorithm optimization is introduced, and the state of the unmanned aerial vehicle is tracked and estimated; an improved particle swarm optimization algorithm is introduced to calculate the public delay amount in the forwarding interference, and the positioning information of the unmanned aerial vehicle is interfered and guided. Compared with the prior art, the real flight process and maneuvering change of the unmanned aerial vehicle are simulated, and the attitude change and dynamic response of the unmanned aerial vehicle are reflected; the problems of particle degradation and sample depletion are relieved, and the tracking stability and precision under noisy observation are improved; the deception positioning precision and the system robustness are improved, and the overall deception jamming effect is enhanced; and a technical support is provided for improving the deception jamming capability of a tail end defense system.

The invention discloses a backhoe ship bucket tooth tip spatial position positioning device and an inland waterway excavation method. The positioning device is composed of a positioning module, an angle sensor, a data processing module, a waterproof pressure sensor and a display terminal, the multiple tilt angle sensors are distributed at key positions of the backhoe ship, and the waterproof pressure sensor is located at the root of bucket teeth. The device can accurately position the space position of the tooth tip of the bucket tooth and feed back related construction information in real time. The inland waterway excavation method is based on the positioning device, and comprises the links of preparation before construction, excavation operation and data recording and analysis. Constructing a model and debugging equipment before construction; during excavation, an operator can control the backhoe boat to work accurately according to data of the visual human-computer interaction interface; during construction, data is automatically recorded to assist decision making and process optimization. The real-time position of the tooth tip of the bucket tooth is determined according to a multi-connecting-rod structure kinematics equation of the mechanical arm of the backhoe ship, the excavation precision of the inland waterway can be improved, back break is reduced, the construction efficiency is improved, the cost and the safety risk are reduced, and the method is suitable for the underwater excavation engineering of the inland waterway.

The invention discloses a lightweight seven-degree-of-freedom mechanical arm connecting rod obstacle avoidance method and system in a structured environment, and the method comprises the steps: constructing a differential kinematics equation based on a Jacobian matrix of a mechanical arm, and obtaining a null space projection matrix of the mechanical arm; acquiring position vectors of the position vector of the center of the obstacle in the preset area relative to perpendicular feet of a first simplified connecting rod and a second simplified connecting rod of the mechanical arm; based on the position vectors of the two perpendicular feet, the direction vector and the speed value of the Cartesian repulsion speed of the mechanical arm are obtained; and based on the Jacobian matrixes of all the end points of the first simplified connecting rod and the second simplified connecting rod, the Cartesian repulsion speed of the mechanical arm is mapped to the joint space, the expected target joint speed is obtained, and the joint speed needed by obstacle avoidance of the mechanical arm is obtained in combination with the null-space projection matrix. The null-space projection matrix is obtained by constructing the differential kinematics equation, the Cartesian rejection velocity is calculated, the obstacle avoidance joint velocity is obtained by combining the null-space projection matrix, and the calculated amount during obstacle avoidance is greatly reduced.