175 results about "Random tree" patented technology

A suitable waypoint is selected using a goal score, a section from a start point to a goal point through the waypoint is divided into a plurality of sections based on the waypoint with a solution of inverse kinematics, and trees are simultaneously expanded in the sections using a Best First Search & Rapidly Random Tree (BF-RRT) algorithm so as to generate a path. By this configuration, a probability of local minima occurring is decreased compared with the case where the waypoint is randomly selected. In addition, since the trees are simultaneously expanded in the sections each having the waypoint with a solution of inverse kinematics, the solution may be rapidly obtained. A time consumed to search for an optimal motion path may be shortened and path plan performance may be improved.

The invention discloses a mobile robot path planning method based on an improved RRT* algorithm. The method introduces a target biasing strategy into a standard RRT* algorithm so as to reduce the randomness of sampling points; provides an avoidance step length extension method in order that a random tree can reasonably stay away from an obstacle area and avoids falling into a local minimum; and smoothes a path obtained by the improved RRT* algorithm by using a reverse sequence connection method smoothing strategy, so as to reduce the direction-changing operations of the robot and achieve the stable movement of the robot. Compared with an original standard RRT* algorithm, the improved RRT* algorithm has a better planned path and takes less time.

The invention discloses an instantaneous impact point based unmanned aerial vehicle non-collaborative real-time obstacle avoidance method. The instantaneous impact point based unmanned aerial vehicle non-collaborative real-time obstacle avoidance method comprises the steps of 1 performing obstacle detection, namely obtaining a relative motion state of an obstacle, 2 performing obstacle motion estimation, namely obtaining machine state information based on satellite navigation information and calculating a motion state of an inertial space obstacle, 3 performing impact conflict detection, namely judging whether obstacle impact occurs or not, 4 performing obstacle avoidance decision making, namely making an obstacle avoidance decision based on a detection result in the step 3, 5 performing obstacle avoidance route re-planning based on a quick expanding random tree algorithm considering an instantaneous impact point, namely introducing route evaluation heuristic information based on the instantaneous impact point and achieving obstacle avoidance route re-planning. The instantaneous impact point based unmanned aerial vehicle non-collaborative real-time obstacle avoidance method has the advantages of being simple in principle, easy to achieve, capable of improving unmanned aerial vehicle safety and the like.

The invention relates to a smooth path planning method for mobile robots based on a dynamic complex environment, and belongs to the field of robot path planning. The method comprises the steps of starting; searching for an optimal path; conducting smooth treatment on the optimal path; ending. The step of searching for the optimal path uses a two-way RRT algorithm with the idea of gravity of an artificial potential field to perform path searching, that is, the artificial potential field gravity guides a random tree to grow toward a target direction, and the RRT algorithm is prevented from randomly sampling the global; a two-way A* algorithm after a heuristic function is improved is adopted to add angle factors based on distance factors, the units of distances and angles are normalized, thepath planning time is greatly reduced, and the convergence speed of paths is improved; smoothing treatment is conducted on the optimal path, a Freudian smoothing algorithm is adopted to remove redundant nodes, a B-spline method is used three times for smoothly connecting remaining nodes, and the smooth optimal path is finally obtained.

The invention discloses a motion planning and controlling method for a tractor-trailer mobile robot in a complex environment. The method includes the steps: a) defining initial and terminal configurations; b) detecting obstacles obs<k>(x<k>, y<k>, cer<k>) by a sonar sensor; c) rejecting the obstacles with confidence coefficient cer<k> smaller than delta; d) defining an obstacle space OBS; e) generating a region Rgn<1>=(obs<k>); f) judging whether the obstacles belong to the space Rgn<j>, j=1, 2, ...n; g) judging whether the obstacle space is built or not; h) building bidirectional search random trees Tree_H and Tree_G; i) randomly generating a configuration X<rand>; j) acquiring new configurations X<new_H> and X<new_G>; k) judging whether the trees Tree_H and Tree_G are connected or not; l) fitting straight paths; m) controlling motion; n) judging whether new obstacles exist or not. The motion path planning and controlling method for the tractor-trailer mobile robot in the complex environment is scientific, reasonable and complete, and lays a theoretical foundation for researching running of the unmanned tractor-trailer mobile robot in the environment with the obstacles.

The invention discloses a reality augmenting method and a reality augmenting system based on image characteristic point extraction and random tree classification. The method comprises the following steps: initializing a system environment and configuring system parameters; selecting or extracting one marker front view and training a marker to obtain training data; calculating and correcting camera inner parameters of a marker image; correcting each frame of a real environment shot by a camera, and based on the training data, identifying the marker and calculating a relative position matrix of the marker in a camera coordinate system; and searching a corresponding virtual model by the identified marker, determining the position of the model by utilizing the position matrix of the extracted marker, and drawing the virtual model. The method and the system greatly reduce the limitation on the marker, can be used for marking maps and complex two-dimensional images, and can synthesize a three-dimensional model on any two-dimensional image to achieve three-dimensional and vivid effects.

The invention relates to a path planning method for improving RRT algorithm, and belongs to the technical field of robot path planning. The method introduces a target gravity strategy to overcome theshortcomings of the RRT algorithm. In the process of expansion, a random tree is randomly expanded when encountering obstacles; when no obstacles are encountered, the target gravity strategy is introduced to correct the expansion direction of the random tree; and a two-way expansion method is introduced to extend from a starting point and a target point respectively. The improved method improves planning efficiency and smoothes a planned path. Finally, the planned path is smoothed to be smoother.

The invention discloses a safe data-outsourcing machine learning and data analysis method. A random tree is transformed in a homoenergetic mode into properties of a binary tree; a trained decision tree prediction model is transformed into the binary tree, the binary tree is induced into a special polynomial of which a shape is a sum of an infinite number of multiplier items, and each piece of data in the model is encrypted by an RSA and is uploaded to a cloud platform; then data which needs to be decided is also encrypted by the RSA and is uploaded to the cloud platform; the encrypted data of the model and the encrypted data which needs to be decided are correspondingly computed by utilizing the multiplication homomorphic encryption property of the RSA to obtain a ciphertext result of each multiplier item; and the results are returned to a data user for decryption so as to obtain a decision result. By transforming the binary tree into the polynomial, a decision tree which originally can be implemented by various computations is transformed into the decision tree which can be implemented by one computation, so that the machine learning process of the decision tree can be rapidly completed by using a multiplication homomorphic algorithm, a complex degree of carrying out such decision tree machine learning algorithm on a ciphertext is greatly reduced, and computing efficiency is promoted.

The invention relates to a mobile-robot route planning method based on an improved genetic algorithm. A raster model is adopted to preprocess a working space of a mobile robot, in a rasterized map, an improved rapid traversing random tree is adopted to generate connections of several clusters between a start point and a target point, portions for the mobile robot to freely walk on in the working space are converted into directed acyclic graphs, and a backtracking method is adopted to generate an initial population which is abundant in diversity and has no infeasible path on the basis of the directed acyclic graphs. Three genetic operators, namely a selection operator, a crossover operator and a mutation operator, are adopted to evolve the population, wherein the selection operator uses a tournament selection strategy, the crossover operator adopts a single-point crossover strategy, and the mutation operator adopts a mutation strategy which displaces an aberrance point with an optimal point in eight-neighbor points of the aberrance point. A quadratic b-spline curve is adopted to smooth an optimal route, and finally, a smooth optimal route is generated. According to the method, the route planning capability of the mobile robot under a complex dynamic environment is effectively improved.

The invention relates to an improved method for planning paths of driverless vehicles by the aid of rapidly random-exploring trees. The improved method is characterized by particularly comprising steps of 1), searching and acquiring an initial path from a starting point to an end point by the aid of RRT (rapidly random-exploring tree) algorithms; 2), pruning the initial path by the aid of one-waypruning processes to obtain preliminary pruned paths; 3), further reducing the lengths of the preliminary pruned paths by the aid of bidirectional pruning processes; 4), removing sharp included angleson the paths by the aid of peak clipping processes; 5), carrying out smoothing processing on the paths without clipped peaks to obtain follow paths of the driverless vehicles. Compared with the priorart, the improved method has the advantages that the paths are short, non-holonomic constraints are taken into consideration, and the like.

The embodiment of the invention provides an obstacle avoidance path planning method of obstacle avoidance task unrelated artificial potential field guide. The obstacle avoidance path planning method comprises the steps of using the energy consumption, end speed and frictional wear of a manipulator as optimization objectives according to the use cost of the space manipulator, and providing a minimum singular value index to which a speed limit is introduced on the basis; providing a gradient potential field offline construction method considering the operating cost of the manipulator according to the minimum singular value of a jacoby matrix to which the speed limit is introduced and the operability, condition number and joint limits of the manipulator; and expanding by using constructed random trees in a gradient potential field guided RRT algorithm, and planning to obtain an obstacle-free operating path of the space manipulator. According to the obstacle avoidance path planning method provided by the embodiment of the invention, when an obstacle avoidance path planning task of the redundant space manipulator is executed, the efficiency of an obstacle avoidance planning algorithm can be improved, and the use cost of the manipulator can be reduced.

The invention provides a quadruped robot motion planning method for facing complex terrain. According to moving object of a quadruped robot, and local environment information and terrain information which are detected by a sensor, movement direction at the current moment of the quadruped robot is confirmed and generate local moving object of the quadruped robot in Cartesian space. According to the local moving object of the quadruped robot in the Cartesian space and the terrain information detected by the sensor, a motion path sequence is generated in quadruped robot configuration space through a rapid random tree algorithm, and the quadruped robot moves according to the motion path sequence until reaches the local moving object. Above steps are repeated until the quadruped robot reaches a set target. Due to the fact that a body motion planning in the Cartesian space and each joint configuration planning in the configuration space are combined, and a motion path sequence of each joint of the quadruped robot is generated according to present environment of the quadruped robot, safe and stable voluntary movement of the quadruped robot in the complex terrain is ensured.

The invention discloses a UAV path optimization method based on an improved bidirectional rapidly-exploring random-tree algorithm. The method comprises the following steps: 1, establishing a UAV kinematic constraint model and determining a starting point and a target point in a two-dimensional space; 2, based on a backward sector region sampling method, exploring the nodes of the bidirectional random tree; 3, based on a dynamic adaptive step size method, solving the growth limitations near an obstacle; and 4, by connecting the nodes of a bidirectional search tree, forming a complete flight path. Based on a classical B-RRT* algorithm, the method, based on the backward sector region sampling method, restricts random tree sampling points to the backward sector region when the nodes are explored, solves the growth limitations near the obstacle based on the dynamic adaptive step size method, and finally connects respective nodes to generate a flight path that conforms to the real flight ofthe UAV, thereby providing a feasible method for autonomous navigation of the UAV at low altitude.

The invention discloses an intelligent vehicle path planning method based on bidirectional extension random trees. The method comprises the following steps of generating a curve, and constructing twoextension trees which are opposite in starting point when an obstacle exists on the curve; generating a random point on the map, and searching for a node of the extension tree nearest to the random point; judging whether an obstacle exists on a connecting line of the random point and the node; if yes, generating a random point again, and if not, taking a new node on the connecting line of the random point and the node; judging whether an obstacle exists on the connecting line between the nearest node and the new node; if yes, generating the random point again, otherwise, updating the node according to the obstacle safety distance constraint; afterwards, selecting a father node and a child node of the node according to the intelligent vehicle maximum steering constraint and the node; judging whether the distance between the two extension trees is smaller than a set threshold value or not, if being greater than the set threshold value, generating a random point again, otherwise, reversely selecting the path of the node to the starting point and the path of the node to the target point according to the nodes of the two extension trees to form an intelligent vehicle planned path.

The invention discloses a dynamic path planning method for AUV (Autonomous Underwater Vehicle) submarine topography matching, and belongs to the technical field of AUV navigation. The dynamic path planning method comprises environment modeling, offline path planning and online path re-planning. An environment model is constructed by topography information and topography source confidence, whereina topography characteristic is represented with a topography standard deviation. Offline path planning is based on a rapid search random tree algorithm, improves a new node and father node selection algorithm, and processes the topography information by roulette and a mahalanobis distance so as to avoid a low topography information region. When an AUV navigates along an offline path and detects changes of the topography information, online re-planning implements judgment on the topography changes by methods of judging the topography changes, searching a local navigation optimal node, re-optimizing a global path and the like, and ensures navigation accuracy. The dynamic path planning method is applicable to long-voyage high-accuracy AUV path planning, and particularly when the topography information is changed, by modifying the path in real time, navigation accuracy and a path distance are ensured.

The invention relates to a multi-robot map exploration method based on a rapidly-exploring random tree. The method comprises the following steps of step 1, detecting boundary points by using a boundary detector based on the RRT (Rapidly-Exploring Random Tree) including a global boundary detector and a local boundary detector; step 2, filtering the obtained boundary points, that is, removing the detected boundary points and clustering some dense boundary points, and then storing the filtered boundary points in a boundary point list; step 3, assigning specific boundary points to a robot with thehighest profit by calculating the costs and benefits by using a multi-robot task allocation strategy based on the market economy according to the positions of each boundary point and the robot, so that a local map of the robot is established; and step 4, sharing and integrating map information obtained by each robot through network communication to get a final map. The multi-robot map explorationmethod based on the rapidly-exploring random tree provided by the invention can overcome the defects of the prior art and guide multiple robots to independently explore the unknown environment and establish an environmental map.

The invention relates to a dynamic step length-based target gravity bidirectional RRT path planning method, and belongs to the field of intelligent robot path planning. A dynamic step length concept is introduced based on a bidirectional RRT algorithm of target gravity idea, and the new node Xnew of an RRT random tree is extended by utilizing the dynamic change of the step length, which can not only utilize the randomness of the bidirectional RRT algorithm, but also utilize the characteristic that the new node Xnew is extended toward the target point Xgoal direction by utilizing the target gravity idea, and simultaneously solves the problem of target unreachability in the multi-obstacle environment, and can effectively improve the path search success rate and enhance the obstacle avoidanceability.

The invention discloses a method and a system for planning a sea path of an unmanned ship based on an RRT (rapidly-exploring random tree) algorithm. The method comprises the following steps of obtaining data of a sea map, so as to obtain complete information of the sea map; according to the definition of a polygon of the sea map, displaying the conditions of the sea surface, and rasterizing the original vector data, so as to obtain a complete interface of the sea map; enabling a user to set passing points on the interface of the sea map, and planning the path within the preset time threshold value by an improved RRT algorithm, so as to obtain the final asymptotically optimal effective path; converting the final effective path into the practical longitudinal and altitude information, outputting the longitudinal and altitude information of turning points on the path, and providing for a power control system of the unmanned ship to use. The method has the advantages that the RRT algorithmis further improved, the path passing the multiple preset passing points is planned, and a path compression algorithm is applied so as to effectively reduce the path distance.

The invention relates to a hybrid path planning method for a mobile robot. A global map is constructed, and on the basis of the global map, hybrid path planning is conducted through a combination of arapid exploring random tree algorithm and a time elastic band algorithm. The hybrid path planning method for the mobile robot combines global path planning and local path planning to achieve mutual compensation under a constraint condition and improve quality and efficiency of a path planning solution; and compared with a single planning method, the hybrid path planning method for the mobile robot has the characteristics that a global path can be quickly planned, dynamic planning and obstacle avoidance functions are realized, and real-time capability and safety of a mobile robot in motion areensured.

The invention provides a machine learning method based on weighted depth forests, wherein the depth forests include multi-granularity scanning and cascading forests; multi-granularity scanning can generate corresponding class vectors by acquiring a plurality of feature subsets, and the generated class vectors are spliced into the original sample feature space as the input features of the followingcascaded forests; cascaded forests are used to realize token learning and include random forests and completely random tree forests. Forest cascades are formed by hierarchical structure. By calculating the corresponding weight of prediction accuracy of each sub-tree in each forest, and then summing up the prediction probability vectors of each sub-tree by weight, the prediction results of the whole forest can be found out. It not only improves the prediction accuracy of depth forest, but also reduces the cascade series.

The invention discloses a six-wheel / leg robot compound movement path programming method, comprising steps of establishing a DEM graph of a robot working environment, performing geomorphic feature extraction, adopting the mapping between the geomorphic feature and the evaluation index, traversing each grid in the DEM image to obtain a trafficability map of the robot and a leg type / wheel type motion price graph, using a starting point of the robot as a root node of a random tree, adopting a pure leg type motion, using a standard random tree algorithm to perform expansion, gradually increasing the leaf nodes until the leaf nodes in the random tree contain a terminal point, obtaining a pure leg type motion target path between the starting point and the terminal point, evaluating the price of the path which is used as an initial price to perform iterative solving until the used time exceeds the maximum time limit set by the user. The six-wheel / leg robot compound movement path programming method provides a solution method for mixing the motion programming, and the motion programming result can dramatically improve the trafficability and the average operation speed.

The invention provides a three-dimensional automatic wiring method and a three-dimensional automatic wiring device based on a rapidly-exploring random tree. The three-dimensional automatic wiring method comprises the following steps: according to an available space, a starting point and a finishing point of a cable to be arranged to obtain an initial path of the cable to be arranged; detailing the initial path to obtain a middle path; through a cable physical model, modeling the middle path to determine a final path of the cable to be arranged. Through the three-dimensional automatic wiring method and the three-dimensional automatic wiring device, cable arrangement can be achieved easily, rapidly and accurately.

The invention relates to a path planning method based on a concentric circle sampling guide RRT algorithm. The method comprises the following steps of: 1, taking the starting point of path planning asthe root node of a random tree; 2, generating a random point by concentric circle sampling; 3, judging whether the random point is in a map range or not, if so, turning to the step 4, otherwise, returning to the step 2 to regenerate a random point; 4, finding out a node closest to the random point as an adjacent point; 5, generating a new node by extending step length from the adjacent point to the random point; 6, judging whether an obstacle exists between the new node and the adjacent point or not, if so, returning to the step 2 to perform resampling, and otherwise, adding the new node intothe random tree; 7, judging whether the new node is in a target point area or not, if so, adding a target point into the random tree, backtracking from the target point to the root node to obtain thepath points of a planned path, and otherwise, returning to the step 2; and 8, fitting the path points to obtain a final path. The method is beneficial to improving path finding efficiency and quicklyfinding the planned path.

The invention belongs to the technical field of drone operation and discloses a drone real-time path planning method based on an improved RRT (Rapidly-exploring Random Tree). The drone real-time pathplanning method comprises the following steps: constructing a local rolling window; setting local sub-goal points; carrying out random sampling and planning by adopting a local RRT algorithm; and carrying out an algorithm termination principle. According to the drone real-time path planning method, an original algorithm, which is only applicable to global planning, is improved and fused; the window is constructed according to local known environment information; and the sub-goal points are determined through a certain method and a global planning algorithm is used for exploring in the environment. In a process of rolling and frontward moving the window, the environment information in the window is continuously updated and planning mapping and feedback are realized; finally, a goal point isrealized. With regard to the RRT algorithm, the improvement method based on the rolling window does not need to carry out random exploring on a whole space when being compared with global planning; however, planning is limited in numerous windows which are continuously updated; a random exploring range is reduced and the calculation amount is reduced; and online planning can be realized.

The inventor provides a vehicle motion planning method and an unmanned vehicle, and relates to the field of unmanned vehicles, and especially relates to an unmanned vehicle motion planning. The method comprises: according to n travelling paths, updating probability distribution of an experienced region, in a vehicle motion process, according to the updated probability distribution of the experienced region, combined with probability of vehicle state conversion, calculating probability to transfer to an adjacent grid; according to the probability to transfer to the adjacent grid, expanding a random tree, and doing collision detection, if the vehicle passes the collision detection, using the corresponding grid to expand the random tree, and updating a current state; until all grids have been tried and the vehicle cannot pass the collision detection, or the vehicle has reached an objective grid. In the method, n actual paths are converted to probability to represent, and are applied in the path planning method, and according to the state information of the vehicle, the probability to transfer to the corresponding grid is calculated, so a motion planning module makes full use of the experiential knowledge to improve search efficiency.

The invention discloses a robot path planning method in a complex narrow environment. An RRT-Connect algorithm is combined with a reinforcement learning algorithm, and certain randomness of a random tree is reserved. According to a degree of obstacle density, an appropriate step size is selected so that a robot can realize rapid and random exploration in an obstacle-dense environment, can quicklypass through in a sparse obstacle environment; orientation of random tree distribution is increased, and a convergence speed is improved; and planning performance can be increased in the process of interaction with the environment so that a planned path is close to an optimal path and a local minimum can be avoided. An improved algorithm is compared with an original standard algorithm, the plannedpath is better and consumed time is less.

The invention discloses a dynamic ship route generating method based on sea chart data. The dynamic ship route generating method based on the sea chart data comprises the following steps of: extracting and processing a ship obstacle zone; generating a buffer zone; generating a ship route using a two-way extended random tree; performing path optimization; and performing dynamic update on the ship route. The dynamic ship route generating method based on the sea chart data can automatically calculate a better safety ship route according to the starting point and end point input by the user or obtained from the sea chart, can provide warnings and prompts when the navigation deviates from the planned route, and dynamically update the ship route according to the change of the position and environment of the ship, so as to guarantee the safety of personnel and ships, and improve the efficiency and economy of the ship route. In addition, the effect of the dynamic ship route generating method based on the sea chart data is verified in the development of the XX marine police system.

The invention discloses a method for detecting and tracking multi targets at real time in a monitoring videotape based on characteristic point classification. The method comprises a step of off-time pre-processing: according to the distribution of the characteristic points on a target object, dividing the target object into a plurality of sections, taking the character of each section to train a classifier. The method also comprises a step of taking a character around the character points on line: confirming the object part corresponding to the character point by using the trained classifier and calculating a center point of the corresponding target, detecting the target according to the distribution of the center point and finally tracking the target object based on a tracked character point. The method needs no step of estimating the static background, so the method has better robustness on illumination change and the camera vibration. The method utilizes the rapid stable random tree as the classifier and the gradient of the around character points as the classification data, so the method has excellent detecting and tracking effect and meets the real-time demand.

The invention relates to a natural-fracture-containing tight-reservoir volume-fracturing tree-shape random fracture net description method. Rock samples which do not contain natural fractures are selected to carry out a hydraulic fracturing simulation experiment; the fractal dimension each section of fracture is calculated, and the average rotation angle of each fracture is counted and calculated; major fractures and secondary fractures are determined, and the secondary fractures are graded; statistics is carried out to the number and the total length of each grade secondary fractures of a first section of fracture closest to the root of the fracture; a generator main stem pitch number is given, generator parameters of a fractal system are determined, and a generator production formula of the multistage random tree-shape fracture net is obtained; the length, the width and the height of each major fracture are calculated, and the proportional relation between the width and the height of each grade secondary fractures and the width and the height of the major fractures is set; a three-dimensional tree-shape fracture net figure is generated; the section number and the bifurcate grade of natural fractures which can be opened are determined; and a three-dimensional tree-shape fracture net figure of the stratum containing natural fractures is generated. According to the invention, the shape of the natural-fracture-containing tight-reservoir volume-fracturing tree-shape random fracture net can be described.

A suitable waypoint is selected using a goal score, a section from a start point to a goal point through the waypoint is divided into a plurality of sections based on the waypoint with a solution of inverse kinematics, and trees are simultaneously expanded in the sections using a Best First Search & Rapidly Random Tree (BF-RRT) algorithm so as to generate a path. By this configuration, a probability of local minima occurring is decreased compared with the case where the waypoint is randomly selected. In addition, since the trees are simultaneously expanded in the sections each having the waypoint with a solution of inverse kinematics, the solution may be rapidly obtained. A time consumed to search for an optimal motion path may be shortened and path plan performance may be improved.