132 results about "Random tree" patented technology

The invention provides a robot intelligent path planning method based on K-value adaptive multi-algorithm cooperation, and belongs to the field of robot path planning. The robot path planning method mainly comprises the following steps: preprocessing an environment map, and constructing a three-layer data structure of a quadtree, a Voronoi graph and a connected graph; generating an initial path based on an improved fast extension random tree algorithm, and extracting key points; establishing a K value evaluation system including five dimensions of obstacle density, local complexity, channel characteristics, path curvature and topology complexity, and realizing accurate quantification of the environment; carrying out adaptive region division based on the K value, and dividing the environment into an open region, a narrow channel region and an obstacle dense region; aiming at the characteristics of different regions, an improved bidirectional A algorithm and an improved fast expansion random tree algorithm are respectively adopted to carry out collaborative planning; and finally, smoothing the path through a Bezier curve. According to the method, the quality and the planning efficiency of path planning in a complex environment can be improved, and higher robustness is achieved.

The invention is suitable for the technical field of unmanned aerial vehicles, and provides an unmanned aerial vehicle cruise detection method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the global path planning through employing an improved fast extension random tree algorithm; when a conflict risk is detected, a dynamic reward function is designed with returning to a global flight path after obstacle avoidance as a target, and a deep reinforcement learning model is adopted to realize control of actions of the unmanned aerial vehicle; and controlling the unmanned aerial vehicle to fly along the global aircraft path and the local obstacle avoidance path, and executing a target detection task based on the target detection model in the flight process until the task is completed and / or a target position is reached. According to the invention, global path planning is carried out by adopting the improved fast extended random tree algorithm, a local obstacle avoidance path is obtained in combination with the deep reinforcement learning model when facing an emergent obstacle, the autonomous cruising ability of the unmanned aerial vehicle is improved, real-time target detection is realized based on the target detection model in the flight process, and the task execution efficiency of the unmanned aerial vehicle is improved.

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

The invention relates to an adaptive step length Combine-RRT * path planning method, which belongs to the technical field of robot path planning, and comprises the following steps: modeling and binarizing according to an obstacle map, determining a starting point and a target point, and setting initial parameters; initializing a random tree according to the obstacle map; determining a normal distribution sampling process and parameters; a sampling strategy of a sampling process is selected by using a dynamic greedy factor for sampling to generate sampling points; determining a dynamic expansion step length during node expansion by using a variable step length growth strategy, and generating a new expansion node; performing collision detection, updating the tree atlas, and outputting an initial path; determining an elliptical sampling range; using a dynamic greedy factor to select elliptical region sampling or normal distribution sampling to generate sampling points; re-searching father nodes and re-connecting in a specified range of the new extended nodes, and searching a path with the minimum cost; and a B spline curve rear end optimization path is introduced.

The invention relates to the technical field of robot path planning, and discloses an improved robot path planning method based on an RRT algorithm, and the method comprises the steps: carrying out the global path searching through an improved fast exploration random tree (RRT) algorithm, and employing a special random sampling strategy and a KD-Tree to accelerate the nearest neighbor searching, and improving the searching efficiency; a greedy strategy is utilized to optimize a path, and redundant nodes are reduced; a smooth trajectory is generated by means of cubic spline interpolation, and control points are dynamically adjusted through collision detection. The method effectively solves the problems of path redundancy and poor smoothness of a traditional RRT algorithm, is suitable for the fields of robot navigation, logistics transportation and the like, and can remarkably improve the path planning efficiency and quality.

The invention relates to the technical field of path planning, in particular to a mechanical arm path planning method and system based on environment parameter self-adaptive adjustment, and the method comprises the steps: taking the tail end position of a mechanical arm as a starting node, taking the position of a target object as a target node to construct a random tree, and setting a search radius to dynamically obtain obstacle data; the step length is dynamically adjusted according to obstacle data, an adaptive sampling strategy is introduced, and an initial path is generated by selecting an optimal father node and reconnecting peripheral nodes; smoothing the initial path, adjusting an offset point, optimizing a target node search area and constraining a path direction included angle to obtain a final path; and the path node coordinates are converted into mechanical arm physical coordinate system coordinates, and a mechanical arm is driven to execute an obstacle avoidance grabbing task. According to the method, the path planning efficiency is remarkably improved, the planning time is shortened, the path quality is optimized, and the safety and stability in a complex environment are enhanced.

The invention discloses a tower crane motion planning method and device in a complex environment, and the method comprises the steps: building a nonlinear model for comprehensively describing the motion characteristics and transient behaviors of a crane-load system on the basis of kinetic analysis, and carrying out differential flat analysis; a BiRRT * algorithm based on direction bias is provided, a node expansion process is optimized by introducing a target bias mechanism of fusion region probability sampling and based on an improved potential field function direction guiding mechanism, and the efficiency and quality of path planning are improved. An improved random tree extension mechanism is combined with a depth deterministic policy gradient (DDPG) of reinforcement learning, and adaptive adjustment of sampling direction and step length parameters is realized. Path points obtained through path planning serve as profile value points of trajectory planning, system full-state constraint conditions such as load obstacle avoidance and shimmy reduction are fully considered, multi-target trajectory planning is carried out based on an NURBS curve, and a multi-target comprehensive optimal trajectory on the aspects of total hoisting time, operation energy consumption and load shimmy suppression is obtained.

The invention provides an incremental reinforcement learning path planning method based on dynamic reward remodeling, which is used for solving the problems of training efficiency and composite optimization in path planning. According to the method, a rapid exploration random tree and an artificial potential field method are combined with reinforcement learning, a dynamic reward function is designed, real-time feedback is provided for an intelligent agent, and strategy learning is accelerated. The system specifically comprises a reward remodeling module which provides process rewards through a fast exploration random tree and an artificial potential field method; the sub-target course learning module is used for gradually increasing task complexity and optimizing sample utilization; the progressive reward adjustment module dynamically adjusts the reward weight and solves the reward coupling problem; and the strategy fine tuning module dynamically adjusts the learning rate and supports strategy optimization. Experimental results show that in static and dynamic path planning tasks, compared with a traditional method, the method has the advantages that the training efficiency and the composite optimization performance are remarkably improved, the reward coupling problem is effectively solved, and the intelligent agent can efficiently learn and converge to a global optimal strategy.

The invention discloses an automatic wiring method and system of a secondary screen cabinet, a medium and a product, and belongs to the field of automatic wiring of the secondary screen cabinet, and the method comprises the steps: obtaining the design drawing data of the secondary screen cabinet, and extracting the installation position of each component and the insertion starting point and end point of a wiring harness; setting a plurality of bundling middle points for each wire harness based on the positions of the components, and forming a node sequence by combining starting and ending points; generating path fragments for each pair of adjacent nodes in the sequence by adopting a fast exploration random tree connection algorithm with sampling region constraint and an obstacle approaching penalty term; and sequentially splicing all the segments into a complete wiring path, and controlling a wiring device to automatically complete wiring harness arrangement and fixation according to the complete wiring path. By implementing the method, the problems of low efficiency and poor safety of automatic wiring path planning of the wiring harnesses in the secondary screen cabinet in the prior art can be solved.

The invention relates to the technical field of path planning, in particular to an automatic path planning method for a mechanical arm. According to the technical scheme, the method comprises the steps of environment perception and modeling, global rough planning, learning parameter initialization based on a deep reinforcement algorithm, local fine planning and learning optimization, and path execution and real-time feedback adjustment. The rapid exploration random tree algorithm is adopted for global rough planning, a basic framework is provided for subsequent local fine planning, the global optimal path can be effectively searched through the deep reinforcement learning algorithm, the path is further optimized through local fine planning, the problem that a traditional intelligent path planning algorithm is prone to falling into a local optimal solution is solved, and the path planning efficiency is improved. The multi-sensor fusion technology is used for sensing environment information in real time, the environment model and the state space are updated in time, the robustness and reliability of the system are improved, the mechanical arm can adapt to diversified working environments and task requirements, and more intelligent decision making and path planning are achieved.

The invention discloses a multi-robot affine formation path optimization method driven by a virtual navigator, and the method comprises the steps: firstly setting a point set composed of the virtual navigator and followers, enabling the virtual navigator to form a convex hull and meet an affine localization condition, so as to guarantee that the followers are always located in the convex hull; generating a collision-free reference trajectory of the mass center of the virtual navigator based on a fast random tree algorithm; constructing a nonlinear constraint optimization problem NCO which contains linear transformation constraint and meets smoothness, obstacle avoidance, speed and angular speed constraint; and establishing a communication graph of the virtual navigator and the follower, calculating balance stress, designing a smoother follower distributed differential speed control law, and realizing formation path optimization and obstacle avoidance control. Compared with an existing method, the formation path generation method has the advantages that the smooth and stable formation path fitting the reference trajectory can be generated while obstacle avoidance and physical feasibility are guaranteed, and the cooperative motion capability and the control performance of a multi-robot system in a complex environment are improved.

The invention discloses a path planning method for cooperative operation of multiple unmanned aerial vehicles, and belongs to the technical field of unmanned aerial vehicle control. The method comprises the following steps: acquiring real-time state information of each unmanned aerial vehicle; dynamic tasks are allocated based on a lightweight auction protocol, and distance, energy consumption, task urgency and load are comprehensively considered in a bidding function; generating a global reference path for each unmanned aerial vehicle, and applying for a space-time reserved window of the three-dimensional space logic grid unit in a specific time interval from the coordination agent; the coordination agent performs conflict detection and feeds back a reservation result; each unmanned aerial vehicle carries out local re-planning in combination with an improved artificial potential field-fast extended random tree algorithm so as to avoid dynamic obstacles; and when the task is changed or the unmanned aerial vehicle fails, a task release and redistribution mechanism is triggered. According to the method, safe, efficient and extensible cooperative path planning of multiple unmanned aerial vehicles in a high-dynamic environment is realized, and the method is suitable for scenes such as logistics distribution, electric power inspection, emergency search and rescue and the like.

The invention discloses a crane working path planning method and system and application thereof. The method comprises the following steps: 1) constructing a crane operation demonstration database; 2) pre-training a convolutional neural network by using a demonstration database, and extracting a mapping relation between environment features and tracks; 3) inputting the current starting point position, the target point position and the environment information obtained by the laser radar into a path generation network, and outputting an initial action sequence of the rotation degree of freedom, the dolly motion degree of freedom and the hook degree of freedom; 4) performing local re-planning on the initial action sequence by adopting a fast random tree algorithm based on physical constraint, wherein the optimization criterion is the weighted sum minimization of the path length, the load swing angle and the action sequence variation; and (5) the crane is controlled to move according to the optimized action sequence, the load swing angle is monitored in real time, and emergency braking is triggered when the swing angle exceeds a threshold value. By means of the method, efficient and safe operation of the crane in the complex environment can be achieved, and good engineering practicability and popularization value are achieved.

The invention provides a mobile robot path planning method and device, electronic equipment and a storage medium, and belongs to the technical field of robot motion control path planning, and the method comprises the steps: constructing a first hyper-ellipsoid based on the real-time progress of bidirectional rapid exploration random tree exploration, adaptively adjusting the sampling probability of the first hyper-ellipsoid according to the size of the first hyper-ellipsoid, and randomly sampling to obtain a first sampling point; expanding the bidirectional rapid exploration random tree; after expansion, if direct connection cannot be realized and the number of tree nodes of the reverse tree is greater than the number of tree nodes of the forward tree, the forward tree and the reverse tree are mutually exchanged and then the step 1 is executed until the forward tree and the reverse tree can be directly connected, and the forward tree and the reverse tree are combined to obtain an initial path solution; and calculating the time consumed by the end of the loop, and if the time is not less than a time threshold, outputting the initial path solution as an optimal solution. The technical problem that in the prior art, the path planning efficiency is low when a complex environment is processed is effectively solved.

The invention discloses a multi-sensor data fusion path planning method and system for a pipeline robot, and the method comprises the steps: collecting laser radar and inertial measurement data through an environment perception module, and executing time-space calibration in a semantic map construction module to generate a three-dimensional semantic map containing obstacle attributes; the global path planning module adopts an algorithm to dynamically adjust a heuristic function for a straight pipe, a bent pipe and a branch pipe to generate an initial path; and the local dynamic adjustment module establishes a rolling window based on the advancing speed, and replans the dynamic obstacle by using a fast expansion random tree algorithm. And the path execution conversion module performs smooth optimization on the path through quintic polynomial fitting and curvature change rate constraint and generates a bottom layer control instruction. Through the multi-source fusion and hierarchical planning strategy, the problems of pipeline environment positioning drifting and dynamic obstacle avoidance are effectively solved, and the stability and safety of robot advancing are remarkably improved.

The invention provides a pneumatic soft mechanical arm obstacle avoidance trajectory planning method and system, and relates to the technical field of intelligent robot path planning and obstacle avoidance control, and the method comprises the steps that task space information of a target mechanical arm is acquired, and the task space information comprises starting point coordinates and target point coordinates of the tail end; the method comprises the steps of taking a starting point as a current node of a random tree, constructing an effective sampling space between the current node and a target point, generating a new sampling point in the effective sampling space by adopting a triple-guidance adaptive sampling strategy integrating target guidance, obstacle guidance and gradient guidance, constructing a new current node of the random tree by utilizing the new sampling point, and performing target guidance and obstacle guidance on the new current node. Iterative expansion of the random tree is realized until a target point is reached, and a preliminary collision-free path is obtained; the preliminary collision-free path is optimized, and a final collision-free path is obtained and used for controlling the obstacle avoidance track of the target mechanical arm from the starting point to the target point; according to the method, the path planning capability and the execution stability in a complex environment are improved.

The invention discloses a mechanical arm path planning method based on repulsive force range constraint, which belongs to the technical field of intelligent manufacturing, and comprises the following steps: introducing constraint sampling of probability target bias to improve planning efficiency and directivity; artificial potential field expansion based on repulsive force range constraint is adopted to guide the random tree to avoid obstacles and accelerate to approach a target point; and local optimum processing of the virtual target point is carried out to guide the random tree to effectively avoid local optimum. And redundancy detection processing: carrying out secondary optimization processing on the path by adopting a pruning method so as to eliminate redundant nodes. The method has good path planning efficiency, is suitable for actual operation and operation of the mechanical arm, and has remarkable advantages in the aspects of planning time, path length and stability.

The invention relates to a mechanical arm path planning method of a self-adaptive sampling domain under dense obstacles. The method comprises the following steps: firstly, initializing map information and a random tree, and setting related parameters; secondly, an initial value of a sampling ellipsoid domain is defined, the long axis direction of the ellipsoid points to the target position in real time, the range of the ellipsoid sampling domain is expanded every time a given iteration number is increased, and when the number of obstacles in the sampling domain exceeds a given number or repulsive force borne by a current point exceeds a threshold value, the range of the ellipsoid sampling domain is narrowed, and flexible adjustment of the sampling domain is achieved. New nodes are expanded through an artificial potential field method, and collision detection and path optimization are carried out; and finally, repeating the steps until the maximum number of iterations is met, and outputting a drawn path. According to the method, the sampling domain range can be adaptively adjusted according to the change of the surrounding environment of the current node, the number of redundant nodes is reduced, the search efficiency is improved, the path length is reduced, and the search time of the mechanical arm under dense obstacles is greatly shortened.

The invention relates to an unmanned vehicle path planning method based on a dynamic expansion guidance RRT algorithm, and belongs to the technical field of intelligent driving path planning and autonomous navigation. According to the method, in a global path planning algorithm, firstly, an unmanned vehicle kinematics model is established by referring to vehicle incomplete kinematics constraints; then, proposing a dynamic expansion guide RRT algorithm which is low in RRT effective sampling efficiency and the like, and proposing a feasible region division strategy; providing a variable probability target guidance and vector superposition strategy for the problems of low path finding efficiency and the like caused by blindness of random tree expansion and lack of target guidance; for the problems that dense and useless expansion branches exist in a random tree structure, a planned path is tortuous and the like, a random tree sparsification and corner constraint strategy is provided so as to improve the utilization rate of the expansion tree branches and reduce the tortuosity of the path; and double father node reselection is provided to further optimize the path length, and key point extraction and path smoothing are utilized to obtain a global optimal path.

The invention discloses an unmanned aerial vehicle path navigation planning method based on urban low-altitude airspace risk constraints, and belongs to the technical field of unmanned aerial vehicle path planning. Random sampling and node expansion are carried out by adopting a fast exploration random tree algorithm; performing path local optimization by adopting a beetle antennae algorithm, and generating an expansion node; adopting a collision detection function to carry out obstacle detection between the expansion nodes; constructing a tree, and searching and outputting an optimal path; according to the method, the random expansion characteristic of the rapid exploration random tree and the local optimization capability of the longhorn beetle search are combined, so that a better safe path is rapidly generated in a three-dimensional environment, generation of redundant nodes can be effectively reduced, the path planning efficiency and the convergence speed are improved, and the method is more suitable for rapid path planning of the unmanned aerial vehicle in the urban airspace.

The invention provides a mobile robot path planning method, device, equipment and medium, and relates to the technical field of path planning, and the method comprises the steps: guiding the expansion direction of a random tree of an RRT * algorithm through an artificial potential field method according to a starting point and a target point of a mobile robot, preferentially selecting sampling points of the random tree, and deleting redundant nodes, generating a global planning path; determining a current position and a local target point based on the global planning path; and according to the current position and the local target point, adopting the artificial potential field method and the simulated annealing algorithm strategy to generate a local planning path. The method can solve the problems that an RRT * algorithm is poor in guidance and difficult to approach to a target point quickly, rejects invalid nodes far away from a target, improves the sampling pertinence, and can guide a robot to be separated from a local extreme value.

The application discloses a drilling and anchoring robot drilling arm trajectory planning method and system and electronic equipment, relates to the technical field of trajectory planning, and comprises the following steps: acquiring a three-dimensional working area model of a drilling and anchoring robot, a working starting point and a drilling point; determining an initial drilling arm path of the drilling and anchoring robot by using a rapidly-exploring random tree algorithm; the initial drilling arm path is a path of a drilling arm of the drilling and anchoring robot from the working starting point to the drilling point in the three-dimensional working area model; removing redundant points in the initial drilling arm path of the drilling and anchoring robot by using a connection method to obtain a first optimized path; and fitting the first optimized path by using a second-order Bezier curve to obtain a final drilling arm path of the drilling and anchoring robot. The drilling and anchoring robot drilling arm trajectory is optimized to assist in realizing fast and accurate hole finding and drilling.

The application discloses a UAV robust path planning method and system for uncertain dynamic environment, relates to the field of path planning, and realizes real-time tracking of dynamic obstacles by using EKF, and constructs a probability repulsive field based on Mahalanobis distance, effectively solves the sensing vulnerability and static model limitation of a traditional APF algorithm, and provides a robust dynamic obstacle avoidance guide for random tree expansion; secondly, a fuzzy logic adaptive step (FLC-AS) module is designed; the module intelligently adjusts the expansion step according to the risk degree of a local environment and an exploration stage, and realizes dynamic balance of exploration safety and search efficiency; comparison and analysis of the method and various mainstream algorithms show that the PFLS-RRT* algorithm has excellent adaptability in a complex environment, and achieves the best comprehensive performance in key indexes such as path quality, planning efficiency and robustness.

The present invention provides a robot arm obstacle avoidance path planning method based on the RRT*FN algorithm, comprising the following steps: establishing a robot arm kinematic model; collision detection; initializing a workspace; using an improved RRT*FN algorithm applicable to multiple scenarios to plan a global path for the robot arm, wherein the random sampling point s rand Generation: Based on the nearest random tree node s nearest Based on the properties of the algorithm, new nodes are generated using a binary greedy expansion method, a safe expansion strategy, and a local environment sampling boundary expansion strategy. These new nodes are then further expanded toward the target point, a process known as secondary expansion. Feasibility screening is used to determine whether to add or discard these new nodes, while an ellipsoid is used to limit the total number of nodes in the search tree. A method based on triangle inequalities is used to determine and optimize the current path. This method meets the requirements for a real-time optimal path while being adaptable to a variety of scenarios.

The invention discloses a welding manipulator path planning method based on a fast expansion random forest. The method comprises the steps that a welding manipulator kinetic model is designed; an RRF * algorithm tree selection problem is formalized into an MAB problem, a more desirable tree in tree extension is actively selected, and resources are effectively allocated to the most desirable tree; designing a Bayesian updating process, wherein Bayesian updating is gradually improved through an observation result; local sampling is carried out, the local connectivity of the space is utilized through Markov chain random sampling, and the random sampling is updated through a Bayesian suggestion distribution sequence; collision detection is carried out, if a new node falls in a safe feasible region, the new node is added into the random tree, node sampling continues to be carried out, and otherwise, the node is removed and sampling is carried out again; and performing path extraction. According to the welding manipulator path planning optimization algorithm provided by the invention, the planned path can be smoother, and the algorithm efficiency is improved.

The application discloses a path planning method based on deep reinforcement learning-fast exploration random tree, comprising the following steps: S1, obtaining a starting point and an ending point; S2, calculating candidate path points and selecting an action in an action state according to a Q value; S3, calculating a reward value and a new action state after the action is executed; S4, storing the action state, the action, the reward value and the new action state to an experience pool, in response to the number of stored experience values in the experience pool being greater than a batch size, randomly selecting experience values of the batch size, and updating the Q value and a time difference error through a policy network; S5, updating policy network parameters through a mean square error loss, and calculating a target network update step according to the time difference error; S6, judging whether a searched path reaches the ending point or satisfies a set maximum path point search number, if yes, outputting a current path, and if not, returning to S2. The application improves the search efficiency of the algorithm without increasing the search time of the algorithm.

The invention provides a vehicle path planning method based on improved RRT. The method comprises the following steps: step 1, constructing an optimal action strategy network; 2, basic information of vehicle path planning is set, and a fast search random tree is initialized; 3, expanding path nodes by using the optimal action strategy network to obtain an optimal path node set; and step 4, smoothing the optimal path node set to obtain an optimal path, and completing vehicle path planning based on the improved RRT. According to the method, the deep Q network reinforcement learning method and the RRT path planning are combined for path planning, efficient, executable and smooth vehicle path planning can be realized, and the method is suitable for automatic driving and other vehicle path decision scenes.

The global path planning method based on potential energy guidance fast search random tree algorithm belongs to the technical field of intelligent vehicle path planning, and solves the technical problem that the traditional fast search random tree algorithm can cause the planned trajectory to travel close to the obstacles. First, the position of the obstacles and the start and end positions are determined through the two-dimensional space map planned by the vehicle. For any point in the map, the potential energy value thereof is calculated, and the potential energy values of all the obstacles are superimposed to obtain the potential energy value of the arbitrary point. The potential energy values of all the arbitrary points in the two-dimensional space map are obtained in the same way. Then, the potential energy values of each point of the whole map are normalized to obtain a potential energy map matrix. The potential energy map matrix is introduced into the fast search random tree algorithm, a potential energy threshold is set, and the randomly generated points are screened. After screening, the potential energy values are introduced into the cost function of the fast search random tree algorithm, and rewiring optimization is performed, so as to perform vehicle path planning.

The present invention discloses a robot path planning sampling method that incorporates a candidate expansion queue, comprising the following steps: obtaining a candidate expansion queue based on the positional relationship between a starting point and a target point; initializing a random tree; forming a guide path to the target point; searching the random tree for the node Qnearest closest to the expansion point; generating a new node; determining whether a collision occurs between nodes Qnear and Qnew and an obstacle; and determining whether the target point Qgoal or any other node in the guide path has been reached. Because the present invention incorporates a new candidate expansion queue, probabilistic expansion is no longer limited to the target point, resolving the problem with the original Goal-based RRT method of easily falling into a local optimum when approaching the target point. Because the present invention incorporates a new target point guide path, it can generate an initial path more quickly and ensure path quality.

The present invention discloses an improved ship path planning method based on the Rapidly-exploring Random Tree (RRT). First, the concept of gravitational potential field centered at the destination is introduced. By superimposing the direction of randomly expanding nodes and the direction of the gravitational potential field, a new node expansion direction is determined to constrain the expansion direction of the random tree in the RRT algorithm, ensuring that the expansion direction of the tree always approaches the destination, thereby reducing the invalid branches during the growth of the tree. And during the expansion process of the path tree, the situation of obstacles around the ship is considered, so that the target point can be reached faster. In addition, this method uses the inscribed circle of a triangle method to smooth the path inflection points, making the finally generated path more suitable for practical applications. Finally, simulation data under the same conditions are used to verify the effectiveness and certain advancement of this method, which is very suitable for real-time path planning in dynamic scenarios, especially for ship path planning in the navigation field.