1149 results about "Path search" patented technology

The invention provides a traffic information display method which presents traffic information so as to quantatively indicate the reliability and superiority of traffic information. The invention also provides a traffic information providing system which clearly communicates an “unknown” section to the receiving party. The invention represents the state volume of traffic information by the state volume of each sampling point set by segmenting a target road and represents traffic information by using the state volume of traffic information and gray scale information which displays the reliability of the state volume in multiple levels. The user thus understands how reliable the traffic information is and properly evaluates the traffic information. The traffic information may be used to perform a high-accuracy path search and provide traffic information at a reasonable cost. The traffic information providing system of the invention includes traffic information providing apparatus for providing, as traffic information, the state volume of traffic information of each distance quantization unit set by segmenting a target road and mask bit information indicating that the state volume is valid or invalid, and traffic information utilization apparatus for reproducing a valid state volume by using the mask bit information. In the traffic information providing system, the receiving party clearly recognizes, based on the mask bit information, an “unknown” section enclosed by an ellipse:

The invention discloses a mobile robot path planning and obstacle avoidance method and system. The mobile robot path planning method comprises the following steps: establishing a two-dimensional grid map by utilizing known obstacle environment information; in the two-dimensional grid map, establishing a global coordinate system at the place of a mobile robot, and setting a starting point and a terminal point of the mobile robot; determining the shortest path between the starting point and the terminal point through a jump point search algorithm, wherein the shortest path comprises a plurality of local target points connected in sequence; and in the process of controlling the mobile robot to move to each of the local target points, utilizing a local obstacle avoidance algorithm to avoid a dynamic obstacle. The mobile robot path planning and obstacle avoidance method adopts the jump point search algorithm to obtain the shortest path quickly, so that path search efficiency can be improved, and storage space is saved; and through the local obstacle avoidance algorithm, accuracy and real-time performance of mobile robot path planning and obstacle avoidance can be ensured, and autonomous navigation of the mobile robot is realized.

In a storage management system and method which virtualizes a plurality of storage devices distributed over a network to be managed, a storage state manager, in a storage management server, manages a storage usage state in each storage device, a network state manager manages a traffic state of each network device, and a path searching/setup portion determines a storage data path to be set up in the network device based on the storage usage state and the traffic state. In a host, a virtualization mapper associates a storage space replied from the storage management server with a virtual storage, and a path information setup portion transmits/receives storage data with the storage data path.

The invention provides a mobile robot path planning algorithm based on single-chain sequential backtracking Q-learning. According to the mobile robot path planning algorithm based on the single-chain sequential backtracking Q-learning, a two-dimensional environment is expressed by using a grid method, each environment area block corresponds to a discrete location, the state of a mobile robot at some moment is expressed by an environment location where the robot is located, the search of each step of the mobile robot is based on a Q-learning iterative formula of a non-deterministic Markov decision process, progressively sequential backtracking is carried out from the Q value of the tail end of a single chain, namely the current state, to the Q value of the head end of the single chain until a target state is reached, the mobile robot cyclically and repeatedly finds out paths to the target state from an original state, the search of each step is carried out according to the steps, and Q values of states are continuously iterated and optimized until the Q values are converged. The mobile robot path planning algorithm based on the single-chain sequential backtracking Q-learning has the advantages that the number of steps required for optimal path searching is far less than that of a classic Q-learning algorithm and a Q(lambda) algorithm, the learning time is shorter, and the learning efficiency is higher; and particularly for large environments, the mobile robot path planning algorithm based on the single-chain sequential backtracking Q-learning has more obvious advantages.

The problem of searching for a low cost path from a source location to a target location through a traversable region partitioned into a plurality of tiles is solved using source and target cost functions. Each tile in the traversable region is defined by boundary segments. The source cost function provides a cost for traversing from the source location to the boundary segment in question. The target cost function provides a cost for traversing from the boundary segment in question to the target location. The target cost function is estimated, and the source cost function is calculated. A path cost function is determined by adding the source and target cost functions. If the target location is a tile, then the target cost may be estimated using a convex hull of the target tile and the boundary segment in question. To facilitate the cost function calculations, multiple forms of cost function propagation between segments are disclosed.

The invention belongs to the field of unmanned path planning, and discloses an unmanned local path planning method based on equal-step sampling A* algorithm in order to meet the kinematic constraintsand actual traffic restrictions of automobiles. The method concretely comprises the following steps: 1, defining a search step size and a search security domain; 2, determining the starting point andthe target point of path search in a local grid map; 3, creating an Open list and a Closed list; 4, solving the cost functions of grid points in the Open list; 5, selecting the grid point with the lowest cost function value from the Open list; 6, respectively investing all security neighbor nodes of current nodes; 7, performing no sub-node processing on the current nodes in the search process; and8, repeating step 5 to step 7 until conditions are met, and returning to a feasible path or to search fail. The method is mainly applied to unmanned roads.

A motion path search device which searches for a motion path of a movable part of a robot capable of being taught a motion by direct teaching in which the robot is directly moved by an operator includes: a first space identification unit which identifies a space swept through by the movable part of the robot in the direct teaching; a second space identification unit which identifies a space swept through by at least a portion of a body of the operator in the direct teaching; a space combining unit which calculates, as an accessible space, a union of the space identified by the first space identification unit and the space identified by the second space identification unit; and a path search unit which searches for a motion path of the movable part within the accessible space calculated by the space combining unit.

A validation authority for certificates searches for and verifies paths and certificate revocation lists periodically, and classifies the paths into valid paths and invalid paths in accordance with the results of the validations, so as to register the paths in databases beforehand. Besides, in a case where a request for authenticating the validity of a certificate has been received from an end entity, the validation authority judges the validity of the public key certificate by checking in which of the valid-path database and the invalid-path database a path corresponding to the request is registered. On the other hand, in a case where the path corresponding to the validity authentication request is not registered in either of the databases, the validity of the public key certificate is authenticated by performing path search and validation anew.

The invention provides a coding/decoding method of polar codes. According to the method, blocking is carried out on an SCL coding data packet at a coding end; a CRC check bit is added in each block of data; for a path search structure on a code tree at a decoding end, candidate paths of which measure values not satisfying a condition are deleted; a location at which current decoding is located is judged; if the current coding is located at a CRC check location, CRC check is carried out again; the paths not passing the check are deleted from the surplus candidate routes; when the decoding is expanded to a leaf node, one path which passes the CRC check and of which measure value is the maximum is selected as a correct decoding path. According to the method, through combination of the CRC check and a traditional measure value path deleting thought, repeated search for a wrong path is avoided, and the decoding complexity of the polar codes is greatly reduced.

The invention discloses an ArcGIS-based map building and intelligent vehicle autonomous navigation method and system. The method comprises the following steps: 1, acquiring longitude and latitude information of a region to be navigated through equipment; 2, building and drawing a two-dimensional vector map and a road section text file of a fork road by using an ArcGIS tool; 3, obtaining the shortest path by using a path planning tool, and extracting a track from the path; 4, carrying out coordinate system conversion on the track, wherein during autonomous navigation, a 5-meter interpolation algorithm is used; obtained path points are very smooth; the number of the path points is increased, path precision of the road section is improved, and driving steadiness is guaranteed; furthermore, the time for path searching is not obviously prolonged. The method can supply more precise and more intensive tracking points to intelligent vehicle autonomous navigation under a condition of use of a map with the same complexity, and the tracking points are converted into coordinates under an intelligent vehicle coordinate system, so that the curve fitting difficulty is reduced.

The invention relates to a method for predicting electromagnetic wave propagation based on a ray tracking method, which comprises the steps of ray path searching, reflected and diffracted ray calculation, field intensity receiving, and path loss calculation. The conception of the method comprises the following steps: firstly, determining the position of an emission source, and finding out all propagation paths from the emission source to the ray of each test point according to building characteristics and distribution on a 3D map; secondly, determining reflection and diffraction losses and the like according to a Fresnel equation, a geometrical diffraction theory/uniform diffraction theory and the like so as to correspondingly obtain the field intensity from each path to each test point; and finally, performing coherence stack on the field intensities of all the arrived paths at the same test point to obtain the total received field intensity at each test point. The method has the advantage of better adaptability compared with the conventional statistical model. The method can be applied to predicting the electric wave propagation in a wireless communication system and adapts to the requirements on wireless network programming and designing.

Provided is a single mobile robot optimal itineration control method based on a target tracking control strategy. The method comprises seven steps of map construction, task description, topology construction, optimal path searching, task decomposition, target tracking control, and pose information feedback. The method comprises: firstly, based on a linear temporal logic theory, combining a constructed weighting switching system and a linear temporal logic task formula, establishing task feasible network topology; then, using an optimal path searching method based on a Dijkstra algorithm to search an optimal path, and storing discrete coordinate points in environment corresponding to the optimal path in cache, and sending the discrete coordinate points to a target tracking controller in order; then, using a target tracking control method to give a controlled quantity to control a robot to move to a target point according to distance between the robot and the target point and deviation of angles; and finally, in an operation process of the robot, using a global camera to acquire present position coordinate and orientation information of the robot in real time, and feeding back to the target tracking controller, so as to realize closed-loop control.

The invention discloses a deep learning-based vehicle path optimization method and system. The method includes the following steps that: real-time road data and historical road data are acquired, the acquired data are preprocessed, so that a tagged data set can be formed; a deep belief network model is constructed, and the deep belief network model is trained; the trained deep belief network model is utilized to predict all the paths from a vehicle to a destination, and the congestion coefficients of each path are output; and the paths are evaluated comprehensively based on two indexes, namely the congestion coefficients and distances, and an optimal path is outputted, wherein the optimal path is a path corresponding to the minimum linear accumulation result of the two indexes, namely a corresponding congestion coefficient and a distance. According to the method and system, through the powerful feature extraction function of the deep belief network model, required information can be obtained from multi-dimensional road traffic data, interference can be reduced, a congestion situation is predicted accurately and reasonably, path search efficiency can be improved, human-made mistakes can be reduced, and valuable time can be saved for disaster relief work.

The invention discloses a physical modeling-based robot obstacle avoidance path planning method, which comprises the following steps of: setting a gravitational field grid and a distance information grid of a robot working region to establish a robot double-grid information graph; based on the double-grid information graph, searching all feasible paths by using a directional ergodic method; calculating a comprehensive evaluation value of a gravity value and a distance value; and taking a path scheme corresponding to the maximum value as a robot optimal obstacle avoidance path planning scheme.By using the method, the defect that geometric attributes of moving objects and obstacles in robot path planning are not considered is overcome; during path searching after double grids are established by the method, robot obstacle avoidance path planning is performed according to the value of the double grids; and thus, the problems of the shortest path and moving safety are both taken into consideration, the efficiency of path planning is increased, and damage accidents which probably occur in path optimization are reduced.

A communication device includes at least one of path search means for detecting respective timings of path components included a signal received via a multipath propagation path using pilot symbols of a known phase included in said received signal and channel estimation means for estimating channel variation using the pilot symbols. The path search means includes a first path search part for detecting respective timings of path components using pilot symbols and a second path search part for detecting respective timings of path components using information symbols derived from a signal demodulated according to the timings detected in the first path search part and pilot symbols. The channel estimation means includes a pilot symbol acquiring part for acquiring pilot symbols included in the received signal and a channel estimation part for implementing channel estimation using the acquired pilot symbols.

The invention discloses a dynamic road segment division based vehicle route guidance method, which is characterized in that a dynamic rod network connected graph is built through dynamic road segment division for the purpose of searching optimal path search and realizing dynamic navigation. The method specifically comprises the following steps: first, acquiring vehicle real-time information through a vehicle networking technology by the traffic center, and utilizing an algorithm of Density-based Spatial Clustering Of Applications With Noise (DBSCAN) to regularly and dynamically divide the regional rods, so as to generate the dynamic rod network connected graph; secondly, sending the position and destination of a vehicle itself to a traffic information center for asking for the optimal path; and finally, generating the optimal path on the dynamic rod network connected graph through utilizing a shortest path algorithm by the traffic information center according to the position and destination of the vehicle, and sending the information to the vehicle and realizing path guidance. The method has the advantages that the generated dynamic rod network connected graph which is accurate and real-time can provide the optimal path guidance for a traveler, thereby alleviating city traffic jam and improving running efficiency.

The invention relates to a mobile robot path planning method and system based on a genetic ant colony algorithm. The mobile robot path planning method includes the steps that 1, modeling is conducted on the environment by establishing a coordinate system; 2, part of optimal solutions obtained through the genetic algorithm are converted to pheromones initial values of the ant colony algorithm; 3, optimum path search is conducted again through the ant colony algorithm, after optimum path search is ended, interlace operation is conducted on paths meeting the requirements of conditions, and the optimum path is finally obtained. The mobile robot path planning method and system overcome inevitable defects existing in a single ant colony algorithm, in other words, the ant colony algorithm is greater in blindness at the initial stage of search, the ant colony algorithm and the genetic algorithm are complementary in advantages, the search range of path search is shortened, and search efficiency of the optimum path is improved.

The invention relates to a method and device for generating a path of an unmanned vehicle on a construction section, computer equipment and a storage medium. The method comprises the following steps:when detecting a construction section, acquiring the detected obstacle information, wherein the obstacle information includes the type and feature information of the obstacle, and the feature information includes position information; determining current accessible area of the construction section according to the type and position information of the obstacle, and determining each way point included in the current accessible area; determining the terminal way point of a target travel path according to current accessible area; regarding current position of the vehicle as a start way point, performing path search in the way points included in the current accessible area according to the start way point and the terminal way point, and determining the passing way points of the target travel path in the construction section; generating a target travel path according to the start way point, the passing way points and the terminal way point. The scheme provided by the invention can be adaptedto the actual conditions of the road in the construction section.

The invention discloses a system and a method for planning paths of intelligent vehicles based on control sampling. The system comprises an environment map module, a vehicle status calculation module and a path searching module, wherein the environment map module is used for storing environment information and vehicle status information detected by a vehicle-mounted sensor and constructing a real-time environment map for planning the paths; the vehicle status calculation module is used for providing functions of expanding vehicle status, evaluating the vehicle status and checking collision of the status based on an environment mode and a vehicle status expansion module; the path searching module is used for executing an initial path searching algorithm to obtain initial paths, and then interpolating the initial path by using secondary B sample bars with tangent constraint to obtain continuous final paths. The system and the method for planning the path have high real-time performance and convergence performance; the planned paths meet vehicle kinematics constraints; the system and the method are high in executability.

The invention discloses a path determination method, a path determination device, and a navigation instrument. The path determination method comprises the following steps: obtaining a starting point and an ending point of a to-be-determined path; obtaining data of obstacles in corresponding regions of the starting point and the ending point, wherein each obstacle is expressed by a polygon; the data of the obstacles are corresponding data of the polygons; determining general views of the corresponding regions according to the starting point, the ending point and the obstacle data; calculating the adjacent cost matrixes of the general views; and determining the shortest path from the starting point to the ending point according to the adjacent cost matrixes. Through the path determination method and the path determination device, the path is determined on the basis of the general views; the path determination process without a road network region is transformed into the path determination process with the road network region; and the path search accuracy is high in accuracy.