375 results about "Waypoint" patented technology

The present disclosure belongs to a field of UAVs exploration technology, discloses a high-efficient autonomous exploration method, system, and terminal for UAVs, comprising: S1, heuristic waypoint generation: setting an exploration scope and waypoint spacing, and generating waypoints through waypoint generation algorithms; S2, global path planning: after generating heuristic waypoints, an A * algorithm is used to generate the global planning path; S3, real-time positioning and mapping: using point clouds for real-time positioning and mapping; S4, local B-spline trajectory generation: using B-spline parameterization method to generate local trajectories; S5, real-time obstacle avoidance and dynamic feasibility constraints: optimizing the trajectories to achieve fast convergence, generating smooth, collision-free, and dynamically feasible trajectories; S6, local real-time replanning: using a time sliding window for local replanning; S7, flight control: Using UAV control algorithms for controlling of UAVs robustly.

The invention provides an energy optimal path planning method and device for an unmanned surface vessel in a complex marine environment, and belongs to the technical field of path planning. The method provided by the invention comprises the following steps: constructing a comprehensive energy consumption model; constructing a hybrid enhanced particle swarm optimization algorithm; constructing a safety space set and an adjacent graph through a grid method, generating an initial optimal path by adopting an A * algorithm, and carrying out bounded random disturbance and safety correction on target particle waypoints; smoothing the path generated by iteration by adopting a B-spline technology, resampling the smoothed optimal solution, and then reinjecting the optimal solution into the population; adjusting an inertia weight and a learning factor based on the number of iterations, and introducing double guide factors to carry out secondary adjustment on a cognitive item of particle speed updating; and embedding the comprehensive energy consumption model as a fitness function into a hybrid enhanced particle swarm optimization algorithm, performing real-time energy consumption evaluation, individual and global optimal path updating and path smooth optimization on each candidate path in an algorithm iteration process, and outputting an optimal navigation path of the unmanned surface vessel.

The invention discloses an unmanned aerial vehicle cooperative inspection route optimization method and device and electronic equipment, and relates to the field of unmanned aerial vehicle inspection, and the method comprises the steps: carrying out the deep cleaning, anomaly detection, multi-source feature fusion and dimension reduction, space-time calculation and result mapping of multi-source heterogeneous data of a to-be-inspected region; carrying out fusion, electromagnetic interference evaluation and optimization on the generated dynamic environment probability map and the flight risk evaluation model, carrying out key waypoint segmentation extraction and trajectory adjustment on the improved route scheme, generating a dynamic route adjustment scheme, and carrying out cooperative distribution of multiple unmanned aerial vehicle tasks on the dynamic route adjustment scheme, so as to obtain a dynamic route adjustment scheme; and carrying out route optimization on the distributed multi-unmanned aerial vehicle cooperative inspection scheme, and carrying out multi-dimensional flyability analysis and adaptive optimization on the multi-unmanned aerial vehicle cooperative inspection scheme to obtain an unmanned aerial vehicle inspection route. According to the invention, the technical problem of low efficiency and quality of unmanned aerial vehicle inspection due to the fact that the unmanned aerial vehicle inspection route only focuses on optimization of a single factor in the prior art is solved.

The invention relates to the technical field of electric power, in particular to an unmanned aerial vehicle route automatic planning method and system for a complex space of a transformer substation. Analyzing at least one inspection task instruction input by a user to obtain a task type corresponding to each inspection task instruction and a data acquisition requirement corresponding to the task type; according to a preset three-dimensional semantic map of the transformer substation and the data acquisition requirement of each inspection task instruction, a flight route of at least one unmanned aerial vehicle is automatically generated by adopting a path planning algorithm, and the flight route comprises a waypoint sequence, a flight height, a shooting angle and a holder action instruction; the three-dimensional semantic map comprises no-fly zone information in the transformer substation, a plurality of devices, the position of each device, the device type, the electrification attribute and the safety rule label. And controlling the corresponding unmanned aerial vehicle to inspect target equipment in the transformer substation according to the flight route of each unmanned aerial vehicle. And large-scale and standardized inspection operation is realized.

The invention provides an unmanned aerial vehicle route planning operation safety monitoring system and method based on virtual-real fusion, belongs to the technical field of unmanned aerial vehicle safety monitoring, and aims to solve the problems that the test training difficulty of an unmanned aerial vehicle is increased and the experiment cost of the unmanned aerial vehicle is high due to interference and terrain transformation in the task execution process of the unmanned aerial vehicle. The safety monitoring system comprises a ground distributed semi-physical virtual-real fusion simulation platform and an air experiment module. The ground distributed semi-physical virtual-real fusion simulation platform is used for constructing a virtual test scene of task execution of the unmanned aerial vehicle, optimizing a waypoint planning decision according to feedback information in a task execution process, and performing safety judgment and real-time obstacle avoidance on the current waypoint planning decision so as to optimize the waypoint planning decision; and the space-time experiment module is used for monitoring the flight state of the unmanned aerial vehicle and executing the optimized waypoint planning decision.

The invention discloses an intelligent navigation method and device of an unmanned aerial vehicle, equipment and a storage medium. The method comprises the following steps: acquiring point cloud data and motion data, and calibrating to obtain a relative position and time synchronization parameter; performing map construction on the point cloud data and the motion data to generate a three-dimensional point cloud map; analyzing the boundary coordinates of the flight area, the flight height and the route step length, and generating route planning parameters; the boundary of the target inspection area is defined by combining the three-dimensional point cloud map, and an optimized route and corresponding waypoint sequence data are generated through route planning; acquiring real-time pose data of the unmanned aerial vehicle, comparing the current position with the waypoint sequence, and triggering image acquisition when a preset waypoint reaches a tolerance range; inspection data is obtained; and integrating the real-time pose data, the three-dimensional point cloud map, the flight state and the inspection data to form an intelligent navigation route. According to the invention, high-precision positioning and linkage of waypoints and data acquisition are realized, and the navigation precision, the inspection efficiency and the operation safety are improved.

The invention provides a self-adaptive accurate rust removal method and system based on unmanned aerial vehicle visual feedback, and relates to the technical field of large metal structure surface maintenance and anticorrosion treatment.The rust removal method comprises the steps that a global path is planned, and a global flight waypoint sequence is generated; a local image of the current to-be-derusted area is collected; the corrosion characteristics of the current to-be-derusted area are analyzed, and initial derusting parameters and initial derusting time are set; after each derusting operation is finished, a frame of intermediate result image is collected; based on the difference between the intermediate result image and the historical image, performing quantitative evaluation on the effect of the previous rust removal operation, and performing feature analysis on the residual rust region; self-adaptively adjusting and generating rust removal parameters of the next stage; and the steps of derusting operation, image collection, evaluation and parameter adjustment are repeatedly executed, and after the termination condition is met, the unmanned aerial vehicle is switched to the next to-be-derusted area.

The invention discloses an unmanned aerial vehicle waypoint generation method and device, and relates to the technical field of unmanned aerial vehicles. Based on the bridge boundary point set, determining an inspection area topology boundary, and constructing a minimum closed inspection area; generating parallel scanning lines in the minimum closed enclosing rectangle of the minimum closed inspection area, obtaining intersection points of each scanning line and the boundary of the minimum closed inspection area and each side line of the obstacle polygon, and obtaining a safety interval and an obstacle interval on each scanning line by judging the positions of the intersection points; safety interval waypoints are generated in the safety interval on the scanning line, obstacle avoidance waypoints are generated at the boundary of the obstacle interval, and a preliminary waypoint sequence is formed; and generating a coverage path by adopting an alternate scanning mode, performing interpolation and de-duplication processing on the initial waypoint sequence, generating a final waypoint sequence, outputting the final waypoint sequence as a waypoint file, and docking with an unmanned aerial vehicle flight control system through an interface to realize autonomous inspection flight. The problems that in the prior art, waypoint setting efficiency is low, and potential safety hazards exist are solved.

Systems and methods for implementing mobility foundation models in accordance with some embodiments of the invention are illustrated. One embodiment includes a method for operating a mobile device. The method receives initial state tokens, wherein each corresponds to data reflecting a previous state of a mobile device. The method determines a sub-task for the mobile device by applying an LLM to the initial state tokens. The method encodes sensor data into patch tokens. Each of the patch tokens reflects a recent state of the mobile device. The method updates the initial state tokens into updated state tokens, based on the patch tokens and the sub-task. The method produces navigation waypoints from the updated state tokens, wherein each of the navigation waypoints represents a distinct destination for the mobile device. The method controlling the mobile device according to the navigation waypoints.

A method is provided. An implementation solution includes: obtaining first information and a plurality of waypoint sequences of a target vehicle; performing, for each waypoint sequence of the plurality of waypoint sequences, trajectory prediction based on the first information under the guidance of the waypoint sequence to obtain a predicted trajectory corresponding to the waypoint sequence and a score for the predicted trajectory; and determining, based on respective scores for a plurality of predicted trajectories corresponding to the plurality of waypoint sequences, a target predicted trajectory among the plurality of predicted trajectories.

The invention discloses a real-time visual language navigation method based on frontier exploration and neural relationship inference. The method comprises the following steps: constructing an initial occupation grid map and a confidence value map; based on the occupied grid map, identifying a boundary between the explored area and the unexplored area, and generating a candidate leading edge waypoint set; a vision-language model is used for detecting instance objects existing in the current visual field, and points closest to the robot are extracted to serve as a candidate instance route point set; calculating visual language comprehensive scores of all waypoints in the candidate frontier waypoint set and the candidate instance waypoint set, and selecting the waypoint with the highest score as a next navigation target; and based on the selected target waypoint, calling a local path planner to generate a collision-free path, driving the robot to move, and updating the occupied grid map and the confidence value map in a rolling manner. Task specific training is not needed, vision and language semantic information can be efficiently fused, and intelligent exploration and target navigation in an unknown environment are achieved.

The invention discloses a navigation-free autonomous flight bridge bottom inspection control method and unmanned aerial vehicle-mounted control equipment, and the method comprises the steps: responding to the obtained three-dimensional point cloud data of a to-be-inspected bridge, planning and selecting a bridge bottom inspection path through the three-dimensional point cloud data, and enabling the starting point of the bridge bottom inspection path to start from the outside of the bridge; and generating a bridge bottom inspection route based on the bridge bottom inspection path and executing inspection, wherein the inspection comprises bridge bottom navigation-free flight control, autonomous obstacle avoidance, roof-imitating flight and inspection waypoint image acquisition. After the unmanned aerial vehicle flies out of the bridge, when the electric quantity of the unmanned aerial vehicle is too low or the unmanned aerial vehicle is manually triggered to return, the return flight route of the unmanned aerial vehicle is automatically called to execute a return flight task during return flight. The method disclosed by the invention can be efficiently operated on light embedded equipment, so that the unmanned aerial vehicle can realize real-time perception and real-time flight control on the bridge in the flight process, a more efficient and safer bridge bottom inspection flight control solution is provided, and the development of the unmanned aerial vehicle in the bridge inspection field is promoted.

The invention discloses a semantic and physical perception-based unmanned aerial vehicle navigation extension method during testing. The method comprises the following steps: S1, constructing multi-modal navigation input information of an unmanned aerial vehicle; s2, based on multi-modal navigation input information, judging whether a unique candidate navigation task target conforming to the description of the navigation task instruction exists in the current view image or not, and correspondingly constructing an initial candidate waypoint set; s3, generating corrected candidate waypoints and a corrected candidate waypoint set by using the visual language model; s4, performing multi-dimensional quantitative scoring on the corrected candidate waypoints in the corrected candidate waypoint set; and S5, selecting the corrected candidate waypoint with the highest multi-dimensional quantitative score as an optimal waypoint, and generating a track containing a continuous pose sequence based on the optimal waypoint to control the unmanned aerial vehicle. According to the method, the navigation planning and self-correction capability can be enhanced, and the navigation decision accuracy and reliability are effectively improved.

The invention belongs to the technical field of trajectory planning, and particularly relates to a trajectory planning method and system of motion camouflage re-parameterization and homotopy perception topology. The method comprises the following steps: S1, acquiring a starting point and a terminal point of a robot and obstacle information of an environment, and carrying out trajectory planning problem modeling; s2, based on homotopy perception topology, planning a plurality of initial paths with different topologies in a free space of the environment; s3, aiming at each initial path, adopting a motion camouflage re-parameterization technology, taking the initial path as a virtual prey path, and mapping multi-dimensional waypoint position parameters on the path into a one-dimensional scalar control parameter sequence based on a selected reference point so as to construct a low-dimensional optimization problem; s4, in the low-dimensional optimization problem, optimizing the one-dimensional scalar control parameter sequence and the trajectory segment time, and generating a smooth trajectory satisfying the dynamic constraint and the obstacle avoidance constraint; and S5, selecting the trajectory with the lowest cost from the plurality of optimized trajectories as a final trajectory to be output.

A method for determining a reference station setup point for setting up a reference station includes moving a construction machine to a waypoint. At this waypoint, position data describing the position of a construction machine reference point on the construction machine are determined on the basis of the satellite signals of a global navigation satellite system and the correction signals of a reference station already set up on site in the vicinity of the earthmoving machine. The position data describing the position of the reference station setup point are determined based on the position data describing the position of the construction machine reference point on the construction machine at this waypoint and on the basis of a specified spatial relationship between the construction machine reference point and the point specified as the reference station setup point on the ground surface.

An airspace flow prediction method and device based on an ensemble learning algorithm are provided. The method includes the steps: collecting historical airspace flow data and related spatial structure data, and preprocessing; constructing a GNN model, and calculating an influence degree of each node and an influence degree between the nodes in an airspace network by using the GNN model, the node being any airport or any waypoint; performing, by the GNN model, feature conversion and attention fusion on the influence degree of the node, the influence degree between the nodes and time series data to acquire a fused feature vector; inputting the fused feature vector into an LSTM model to acquire a predicted airspace flow of the node; and applying the predicted airspace flow of the node to manage navigation of traffic in the airspace network.

The invention relates to the technical field of ship route planning, in particular to a ship navigation real-time optimal route planning method based on an improved genetic algorithm, and the method comprises the steps: obtaining an input data set containing ship static parameters, multi-period environment prediction data and navigation constraints; an improved path search algorithm based on a comprehensive cost function is adopted to process input data, an initial navigation path is generated, and the cost function synchronously considers leg length, navigation risk and violation penalty. And carrying out speed scheme optimization on the initial path by adopting an improved genetic algorithm containing specific chromosome coding and a multi-target fitness function to generate an optimal speed distribution scheme. And fusing the initial path and the speed scheme, and outputting a route planning result containing a route point sequence and the recommended navigational speed of each route segment. According to the invention, integrated collaborative optimization of the path and the speed is realized.

The invention relates to a real-time low-energy-consumption unmanned aerial vehicle multifunctional automatic inspection method and system, and belongs to the field of automatic inspection. The method comprises the following steps: performing semantic field conversion based on a fuzzy geographic instruction, and outputting a geographic semantic field; performing vision-field coupling navigation based on a geographic semantic field, and outputting a real-time flight path and environment preliminary perception data; topological risk identification is carried out based on the real-time flight path and the environment preliminary perception data, and a 3D environment model with risk annotation is obtained; fractal energy consumption optimization is carried out based on the 3D environment model with the risk label, and an optimized waypoint sequence is output; and carrying out multi-modal data acquisition and real-time risk verification based on the optimized waypoint sequence. According to the invention, intelligent inspection of understanding natural language instructions, fusing multi-sensor information, dynamically evaluating risks and optimizing energy consumption is realized.

The invention discloses a waypoint prediction method fusing instruction landmark features in visual language navigation, and belongs to the crossing field of artificial intelligence and computer vision. The method is realized through the following steps: firstly, extracting landmarks and co-occurrence landmarks thereof from a natural language instruction by utilizing a large language model, and generating a candidate landmark sequence; then, the occurrence probability of the candidate landmarks in actual observation is calculated based on a visual large model CLIP, and noise interference is dynamically corrected and suppressed through a learnable co-occurrence scoring module; secondly, fusing the corrected landmark features with the depth visual features, inputting a double-layer Transform model to model a spatial relationship, generating a waypoint probability heat map, and outputting adjacent waypoints through non-maximum suppression (NMS); and finally, topological mapping is carried out on the predicted waypoints, an optimal path is obtained through cross-modal path planning, and an intelligent agent is guided to execute low-level actions. The method does not need to depend on a predefined environment map or manually annotate data, navigation errors in a continuous environment are remarkably reduced, the generalization ability of unseen scenes is improved, and the method is suitable for open world navigation scenes such as robot navigation and automatic driving.

The invention relates to the technical field of aircraft navigation, and discloses an aircraft return path construction method based on visual intelligence. According to the method, a flight environment multi-view image sequence is collected through an airborne vision sensor, and vision observation data with a unified timestamp and space coordinates are generated through space-time alignment processing; calculating a motion vector field of the aircraft relative to a ground reference object based on continuous inter-frame optical flow characteristics in the data, and establishing a three-dimensional environment kinematics model by combining attitude data of the inertial measurement unit; reversely deducing a current flight path by using the model, extracting a historical path key waypoint set, and constructing a return path initial topology network according to the spatial distribution density of the historical path key waypoint set; carrying out dynamic weight distribution on a network node connection relationship, and generating a wind disturbance resistance candidate return path set in combination with a real-time meteorological wind field intensity parameter; and evaluating the candidate set by using a multi-objective optimization algorithm, and outputting an optimal return path meeting a fuel consumption threshold value and a flight time constraint.

The invention discloses a multi-rotor unmanned aerial vehicle target guidance method and device combined with visual detection, and relates to the field of guidance. The method comprises the following steps: receiving target initial information sent by a superior system; analyzing the target initial information, generating a preliminary target waypoint, and controlling the multi-rotor unmanned aerial vehicle to autonomously fly to a preset target airspace where the preliminary target waypoint is located; in a preset target airspace, driving the multi-rotor unmanned aerial vehicle and the airborne holder to execute a composite search action, and collecting a real-time image stream by using an airborne visual camera; establishing a stable tracking state; in a stable tracking state, continuously tracking the aerial target through a target tracking algorithm, and generating three-dimensional relative position and motion state data of the aerial target relative to the multi-rotor unmanned aerial vehicle in real time; and determining an interception route and judging whether a preset interception condition is met or not, and if yes, sending an execution instruction to the airborne physical strike load. By implementing the technical scheme provided by the invention, the cost of the low-altitude target guidance method is reduced.

The application discloses an intelligent path planning method and system for a mechanical arm spray head of a deicing vehicle, and can realize automatic and accurate deicing on a complex three-dimensional surface of an airplane. The method comprises the following steps: target surface extraction and reconstruction are performed on a deicing area of the surface of the airplane to generate a continuous three-dimensional surface; the three-dimensional surface is mapped to a two-dimensional parameter plane by surface parameterization; a 2D waypoint sequence is generated on the two-dimensional parameter plane in combination with the effective spraying width of the nozzle; 3D space inverse mapping is performed based on the 2D waypoint sequence, the spray head posture of the deicing vehicle is aligned based on the result of the inverse mapping, and the three-dimensional target posture of the nozzle at each waypoint is obtained; the target posture of the nozzle is converted into specific angle instructions of each joint of the mechanical arm of the deicing vehicle; on the basis of the joint angle instructions, joint trajectory smoothing and S-type speed planning are performed, and finally the action path of the spray head of the mechanical arm of the deicing vehicle is generated.

The invention relates to the field of intelligent navigation of unmanned aerial vehicles, and provides a multi-mode intelligent navigation method and system for an unmanned aerial vehicle in order to solve the limitation of an existing visual language navigation method in the aspects of environment mapping and decision level splitting. The multi-mode unmanned aerial vehicle intelligent navigation method comprises the following steps: performing semantic analysis on a natural language navigation instruction to generate a multi-source semantic enhanced two-dimensional global semantic map; generating waypoint features for guiding a navigation direction; the features of the three-dimensional local semantic point cloud are extracted, cross-modal attention interaction is carried out on the features of the three-dimensional local semantic point cloud and the waypoint features to generate refined waypoint features, and then the refined waypoint features are decoded to obtain specific actions needing to be executed by the unmanned aerial vehicle at the next moment; therefore, autonomous path planning and navigation of the unmanned aerial vehicle in a complex unknown environment based on the natural language navigation instruction are realized.

The invention discloses an autonomous navigation system and method for an electric power inspection unmanned aerial vehicle, and belongs to the technical field of artificial intelligence and unmanned aerial vehicle control. The system comprises a multi-mode sensing module, a VLM navigation module and an electric power scene adaptation and flight control instruction conversion module, an instruction semantic adaptation unit disassembles a natural language inspection instruction into triple features, and an electric power equipment knowledge base is called to map an equipment type into target equipment features which can be identified by an SPF; the multi-modal feature fusion unit carries out feature fusion to obtain fusion features; the decision output unit combines the fusion features and the triple features and then inputs the combined features and the triple features into a VLM navigation decision model based on SPF frame 2D space grounding logic, and an initial 2D waypoint is output; and the flight control adaptation unit performs flight control instruction conversion based on the 3D displacement vector corresponding to the 2D waypoint. The natural language inspection instruction is mapped into target equipment characteristics which can be identified by the SPF, so that accurate conversion from the natural language inspection instruction to the flight control instruction can be realized.

The invention discloses a multi-unmanned aerial vehicle path following and obstacle avoidance method and device under a complex digital elevation terrain, and the method comprises the steps: determining the total number of unmanned aerial vehicles, a reference waypoint set and a flight kinematics model, and limiting constraint conditions; calculating a virtual waypoint and transverse and longitudinal angle deviation through an adaptive coefficient, and re-planning an optimal waypoint in a feasible region based on barycentric coordinates of an obstacle and a maximum transverse expansion radius when the obstacle encounters an emergent obstacle; then, a guidance rate coefficient conforming to constraints is selected according to the angle deviation, and a guidance rate instruction is generated to stabilize the deviation; the unmanned aerial vehicle constructs an adjacent communication node set according to the communication intensity and the communication radius; and finally, calculating a consistency index including the flight progress and the target point distance, updating the index through distributed communication, and adjusting the reference centroid rate, thereby realizing space-time collaboration of multiple unmanned aerial vehicles. The method does not need a central node, has a distributed coordination characteristic, can resist wind field disturbance and individual damage, and quickly responds to sudden obstacles.

The application discloses a kind of support to drag and drop sorting waypoint list management interaction system, belong to ground station waypoint management field.Belong to ground station waypoint management field.It includes waypoint list display module, drag and drop sorting interaction module, waypoint management operation module, associated function adaptation module;Waypoint list display module presents the key parameters such as waypoint height, flight speed with entry form;Drag and drop sorting interaction module supports user to drag and drop waypoint entry adjustment order, real-time feedback state and synchronously flight execution logic;Waypoint management operation module provides waypoint deletion, save, read and upload function;Associated function adaptation module realizes linkage with template editor, relative coordinate editor and flight control module.This system operation is intuitive, data synchronization is efficient, and function integration is strong, effectively solve the problem that traditional waypoint management operation is complicated, data linkage is insufficient, improve the convenience and accuracy of flight mission planning.

The present disclosure discloses a method for target assignment and route planning for multi-agent unmanned surface vehicles (USVs). Task information is transmitted to a task allocator, which identifies unassigned targets and available USVs. Based on a comprehensive cost function considering navigation distance and turning penalties between targets and USVs, a target is assigned to each USV A path planner then generates a smooth waypoint sequence for each assigned USV and target pair, serving as the navigation path. During navigation, a navigation controller constructs a velocity optimization model according to cooperative collision avoidance and maritime boundary constraints, and controls the USVs in real-time to navigate along the waypoint sequences. The present disclosure achieves efficient matching of multiple targets and multi-agent USVs, generates smooth navigation paths satisfying constraints, and enables intelligent navigation control while meeting cooperative collision avoidance requirements and maritime boundary constraints.

A route planning method, apparatus, electronic device, and computer-readable storage medium are disclosed. The method includes: determining route constraint information for a test area and a test route; dividing the test area into regions to obtain a first sub-region; determining a second sub-region and points of interest (POIs) traversed by the test route based on the route constraint information of the test route and the first sub-region; determining waypoints traversed by the test route based on the POIs of the second sub-region; determining the traversal order of the waypoints based on the location information of the second sub-region or the location information of the waypoints; and generating the test route based on the waypoints and the traversal order. This disclosure can improve the efficiency of urban generalization testing of intelligent driving systems.

A method for generating intermediate waypoints for a navigation system of a robot includes receiving a navigation route. The navigation route includes a series of high-level waypoints that begin at a starting location and end at a destination location and is based on high-level navigation data. The high-level navigation data is representative of locations of static obstacles in an area the robot is to navigate. The method also includes receiving image data of an environment about the robot from an image sensor and generating at least one intermediate waypoint based on the image data. The method also includes adding the at least one intermediate waypoint to the series of high-level waypoints of the navigation route and navigating the robot from the starting location along the series of high-level waypoints and the at least one intermediate waypoint toward the destination location.

A route planning device is provided with a display configured to display a chart on a screen, an operation interface configured to specify a position on the screen, a storage configured to store navigation publication related to a book referred to during past route planning in association with the position, a waypoint setting module configured to set a waypoint on the chart in response to a user's operation received by the operation interface, a navigation publication acquisition module configured to acquire navigation publication within a range of the set waypoint as a reference from the storage, and a display control module configured to display the acquired navigation publication on the screen of the display.