122 results about "Dynamic window approach" patented technology

The invention relates to a path planning method for a mobile charging robot in an unstructured environment, which combines global path planning and local dynamic obstacle avoidance, performs global path planning through an improved A * algorithm, and performs real-time local obstacle avoidance by using a dynamic window method. The global path and the local path are fused through dynamic weight adjustment, and it is ensured that the robot keeps the continuity of the global path while avoiding obstacles. In the path planning process, factors such as terrain complexity and robot advancing speed are considered, and the energy consumption of the path is minimized by adopting an energy consumption optimization algorithm, so that the working time of the robot is prolonged, and the execution efficiency is improved. The path planning method is suitable for storage, industry, field inspection and other complex dynamic environments, has high adaptability, real-time performance and robustness, and provides an effective solution for efficient application of the mobile charging robot in an unstructured environment.

The invention discloses an intelligent navigation system of a sweeping robot, which relates to the technical field of household intelligent equipment and comprises a multi-source sensor array, a dynamic environment modeling module, a path planning engine, a multi-mode positioning module, an autonomous decision controller, a cloud collaborative optimization unit and a fault degradation processing module. Omnibearing environment perception is provided through a multi-source sensor array, data are fused in real time through a dynamic environment modeling module, a grid map is updated in an incremental mode, obstacle semantic information is marked, an improved A * algorithm and a dynamic window method are fused through a path planning engine, and an optimized path giving consideration to global efficiency and local dynamic obstacle avoidance is generated. The multi-modal positioning module fuses SLAM resolving and IMU data through tight coupling to achieve centimeter-level high-precision and high-robustness positioning, and the autonomous decision controller intelligently and dynamically adjusts the cleaning priority, the advancing speed and the exception handling strategy according to the path, environment state and pose information, so that the cleaning behavior better meets the requirement.

The invention discloses a building robot multi-machine cooperative construction planning method and system, and belongs to the technical field of building construction automation, and the method comprises the steps: collecting three-dimensional space data of a construction site, combining with a timestamp, constructing a four-dimensional space-time semantic map, recognizing a process stage, dynamically allocating tasks, generating an initial path through an improved A * algorithm, and constructing a three-dimensional space-time semantic map. And the path is adjusted in real time by adopting a dynamic window method. According to the three-dimensional collaborative modeling design method of the building robot, the problem of dynamic environment adaptability is solved through combination of four-dimensional map construction, Nash equilibrium allocation and an improved A * algorithm.

The invention discloses an AUV (Autonomous Underwater Vehicle) path planning and control integrated method for a complex marine environment, and provides an intelligent path control scheme integrating reinforcement learning and local obstacle avoidance mainly aiming at underwater navigation problems such as submarine topography fluctuation, frequent ocean current disturbance and uncertain dynamic obstacle distribution. According to the method, a realistic three-dimensional ocean grid environment is constructed, an improved Q-learning algorithm is utilized to complete global path planning, a group of optimal path point sequences are generated, and it is ensured that the AUV has the macroscopic navigation capability. Local obstacle avoidance is performed in combination with a three-dimensional dynamic window method (3D DWA), and sudden obstacles are dynamically avoided through speed sampling and trajectory prediction. A DDPG control strategy based on deep reinforcement learning is further introduced, end-to-end training of path planning and control execution is realized, and the system can directly output a control instruction according to a real-time sensing state. A cosine attenuation learning rate, a self-adaptive exploration mechanism, an energy consumption penalty function and a dynamic obstacle prediction and control action filtering technology are fused in the method, so that the intelligence of path planning, the stability of control output and the robustness of obstacle avoidance response are effectively improved. According to the invention, the autonomous navigation capability, the environmental adaptability and the safety task completion efficiency of the AUV in a complex marine environment can be obviously enhanced, and reliable intelligent control technical support is provided for underwater operation.

The invention relates to the technical field of sensor fusion, in particular to an AGV (Automatic Guided Vehicle) dynamic obstacle avoidance control system based on multi-sensor fusion, which comprises a visual path planning module used for determining an obstacle boundary position according to an obstacle image detected by a visual sensor through an obstacle recognition model based on an improved YOLOv10 neural network, generating a visual obstacle avoidance map according to the obstacle boundary position; the electromagnetic tracing module is used for generating an electromagnetic obstacle position according to the electromagnetic wire track detected by the electromagnetic sensor; the infrared obstacle avoidance module is used for generating an infrared obstacle position according to the obstacle distance detected by the infrared sensor; and the dynamic obstacle avoidance control module is used for substituting the visual obstacle avoidance map, the electromagnetic obstacle position and the infrared obstacle position into an improved local path planning model based on a dynamic window method to generate a linear velocity and an angular velocity for AGV dynamic obstacle avoidance control. According to the invention, intelligent dynamic obstacle avoidance control of the AGV is realized.

The invention discloses a closed denial environment adaptive path planning algorithm, and belongs to the technical field of mobile robot navigation. According to the algorithm, aiming at the characteristics of dense obstacles and many dynamic interferences in a closed denial environment, an artificial potential field method is innovatively combined with a sampling-based RRT algorithm and a dynamic window method, so that organic fusion of global and local path planning is realized. According to the algorithm, an artificial potential field gravitational field is introduced into an RRT sampling process, so that new node generation always deviates to a target direction, and adaptive change is realized by dynamically adjusting a sampling sector angle; in local path planning, a global artificial potential field gravitational field is utilized to optimize a DWA evaluation algorithm, and a better dynamic obstacle avoidance effect is achieved. The algorithm is suitable for path planning requirements of unmanned aerial vehicles, specialized robots and the like in a closed denial environment.

The invention discloses an unmanned aerial vehicle local path planning method based on an improved dynamic window method, and the method comprises the steps: S1, initializing the state, a target point and an environment map of an unmanned aerial vehicle, and creating a grid map of the environment; s2, a dynamic window optimization algorithm is improved, and the improvement points are as follows: (1) according to the obstacle density, a dynamic window parameter adaptive adjustment strategy is introduced, the speed and acceleration range of the unmanned aerial vehicle are dynamically adjusted, and meanwhile, the distance from the unmanned aerial vehicle to an end point is taken as an influence factor, so that the unmanned aerial vehicle automatically decelerates when approaching the end point; (2) setting a dynamic adjustment sampling resolution, and optimizing the number of candidate paths according to environmental complexity; (3) introducing a sparrow search algorithm for optimizing the index weight of a trajectory evaluation function; and S3, inputting the created path grid map into the improved dynamic window optimization algorithm, and outputting an optimal path. The method can be more effectively applied to local path planning of the unmanned aerial vehicle, and the safety and effectiveness of autonomous navigation of the unmanned aerial vehicle in a complex environment are improved.

The invention proposes a robot path planning algorithm based on a particle swarm optimization algorithm and a dynamic window method, and relates to the field of control theories and electronic information, and the method comprises the steps: introducing an inertia weight updating strategy based on a state factor, setting adaptive parameters and crossover and mutation operators to improve the global search capability, increase the population diversity, and improve the robot path planning precision. On-line self-adaptive updating of inertia weight and learning factors is realized on line in combination with Q-learning, gene combination modes are enriched through crossover operators, convergence and exploratory performance of the algorithm are improved, an obstacle avoidance strategy of a traditional DWA algorithm is improved, weight parameters of a dynamic window evaluation function are dynamically adjusted according to real-time information of a target and an obstacle, and an obstacle avoidance algorithm is established. According to the method, the global planning is adopted, the path points generated through global planning are adopted as temporary targets, fusion of MOQLCOPSO and the improved DWA algorithm is achieved, local optimum is effectively avoided, the planning efficiency and path safety are improved, and the method is suitable for mobile robot navigation under the complex three-dimensional terrain.

The invention relates to a distributed unmanned ship cluster collaborative awareness and control integration method and system in a high sea condition environment. The method comprises the steps of equipment installation and distributed communication network construction; task target and environment information is collected and subjected to preprocessing, space-time calibration and data fusion, fused data is transmitted and shared through a distributed communication network, and a distributed Kalman filtering algorithm is used for joint data processing; on the basis of an unmanned ship dynamics model containing interference factors, a model prediction control algorithm is adopted to carry out rolling optimization control input, hierarchical path planning is carried out through an improved A * algorithm and a dynamic window method, cooperation among unmanned ships is kept through a cooperative control protocol, and control parameters are automatically adjusted according to sea conditions; and constructing a collaborative optimization objective function, adjusting collaborative perception and collaborative control strategies according to the minimum value of the objective function, and performing closed-loop feedback optimization on the collaborative perception process. Compared with the prior art, the sensing accuracy, the control stability and the collaborative operation efficiency of the unmanned ship cluster in a high sea condition environment can be effectively improved, and the ability of the unmanned ship cluster to cope with a severe sea environment is remarkably enhanced.

The invention discloses an unmanned surface vehicle formation path planning method, and relates to the technical field of formation path planning, and the method comprises the steps: carrying out the rasterization processing of a map, adjusting the initial unmanned surface vehicle formation, determining the starting point and terminal point information of a navigator, and determining the position of a dynamic obstacle and the information of a static obstacle. An attenuation coefficient is introduced into a heuristic function of the A-Star algorithm, and path planning is carried out by adopting the improved A-Star algorithm; path smoothing processing is carried out through a Bezier curve of a self-adaptive selection control point; introducing an improved dynamic window method to carry out dynamic local obstacle avoidance; and according to the initial unmanned ship formation pattern and the positions of followers in the formation, combining the global path of the navigator to generate a planned path of the followers, and keeping the followers at the same speed and deflection angle as the navigator. The method ensures the optimality of the path planning of the navigator, also ensures that the follower can adaptively adjust in real time, and effectively improves the efficiency, stability and practicability of the formation path planning.

The invention discloses a trajectory planning and tracking method suitable for a small underwater robot, and belongs to the technical field of automatic control, and the method comprises the steps: generating a three-dimensional underwater environment global path of the small underwater robot through employing an improved adaptive ant colony algorithm; a dynamic obstacle is considered, a dynamic window method is fused to carry out local optimization on the generated global path, and an optimized path is obtained; the ocean current field environment is considered, a quantum particle swarm algorithm is used for optimizing the generated global path, and an optimized path is obtained; and improving the MPC model by using an improved particle swarm optimization algorithm, and tracking the trajectory generated by the small underwater robot through the improved MPC model. By adopting the method, the problems of low trajectory planning efficiency, poor path smoothness, high energy consumption, insufficient trajectory tracking precision and the like in a dynamic environment in the prior art are solved, and the task execution capability and adaptability of the underwater robot in a complex environment are remarkably improved.

The invention provides an autonomous navigation and intelligent obstacle avoidance method of a biomimetic robot, and relates to the technical field of biomimetic robots, which realizes real-time estimation of six-degree-of-freedom poses by collecting environmental data; visual features are extracted by using a convolutional network, an enhanced memory unit is constructed in combination with pose information, a triple index path memory library is maintained through a dynamic memory updating mechanism, and feature similarity retrieval and navigation instruction generation in a backtracking mode are supported; defining a state vector based on a resultant force of a robot pose, a target direction and an artificial potential field, training a strategy network through deep reinforcement learning, and outputting a motion instruction according to Q value maximization; fusing an artificial potential field and a dynamic window method, and optimizing an objective function to obtain optimal linear velocity and angular velocity; and solving a joint angle through inverse kinematics, and combining attitude error feedback to realize body attitude maintenance and foot end trajectory tracking.

The invention provides a ground path planning method and device, and the method comprises the steps: determining a two-dimensional map based on the ground environment of a target planning region, the two-dimensional map comprising a starting node, a target node and an obstacle; sequentially searching from the starting node by adopting an improved A star algorithm to obtain each intermediate node between the starting node and the target node so as to obtain a planned path, each intermediate node is determined by estimating the cost of each non-obstacle node on the basis of each non-obstacle node adjacent to the previous node of the intermediate node and by adopting a heuristic function based on a dynamic weight; determining whether every three continuous nodes in the planned path in the direction from the starting node to the target node are collinear or not or whether a connecting line between two nodes except the node located in the middle in the three nodes does not penetrate through an obstacle or not to globally optimize the planned path, and obtaining a globally optimized planned path; and carrying out local path optimization on the globally optimized planning path by adopting a dynamic window method.

The invention provides a path planning method and device, and the method comprises the steps: determining a map based on the environment of a target planning region, the map comprising a starting node, a target node and an obstacle; an improved A star algorithm is adopted to search from a starting node and a target node in two directions in sequence to obtain all intermediate nodes between the starting node and the target node, so that a global planning path is obtained, and all the intermediate nodes pass through all non-obstacle nodes in a planar neighborhood or a spatial neighborhood based on the previous nodes of the intermediate nodes; the path cost of each non-obstacle node is estimated by adopting a path cost function based on a safety penalty factor and a turning penalty factor; determining whether each included angle formed by each node in the global planning path and a previous node and a next node is within a set angle range or not to globally optimize the global planning path to obtain a globally optimized global planning path; and carrying out local path optimization on the globally optimized global planning path by adopting a dynamic window method.

The invention provides a vehicle path planning method fusing improved RRT and Trajectory Mamb-DWA, and the method is suitable for a dynamic traffic environment. In the global planning stage, the search efficiency of the RRT algorithm is optimized through KD-Tree, the random sampling range is limited in combination with a safe sampling area, the directivity of the path is enhanced by utilizing adaptive target gravitation bias, and a global optimal path is generated by adopting Bezier curve smoothing. In the local obstacle avoidance stage, a Trajectory Mama trajectory prediction model is embedded in a dynamic window method, the movement trends of surrounding vehicles and pedestrians are predicted, the speed of the vehicle is restrained through time sequence collision detection, and an obstacle avoidance trajectory with both real-time performance and foresight is generated. And aiming at the path connection problem after obstacle avoidance, designing a dynamic re-planning mechanism: according to the current environment state, comparing expected time for returning to an original path or generating a new path with a collision risk, and adaptively selecting an optimal scheme. The method provides key technical support for efficient navigation of the autonomous vehicle, and is of great significance to development of a driving assistance system.

The invention discloses an industrial transfer robot path planning method based on artificial intelligence, relates to the technical field of industrial transfer robot path planning and multi-robot cooperative control, and provides a systematic breakthrough for narrow roadway passage of an industrial transfer robot through deep cooperation of the game theory and federal learning. A robot meeting scene is modeled as a limited action game, and a revenue function is designed in combination with priority weight and security constraint, so that the robot can autonomously generate a forward, yielding or keeping strategy, and bidirectional deadlock is avoided from the source; through a synchronous output mechanism of action probability distribution and a time window, the strategy performability is remarkably improved; the track layer directly generates a synchronization path on the cost graph according to the time window, and motion interruption caused by repeated sampling in a dynamic window method is avoided; even if track deviation or communication abnormity occurs, the rollback strategy can guarantee that the system recovers to be stable immediately, and continuous operation of the production line is maintained.

The invention discloses an anti-impact drilling robot navigation system and method based on multi-mode 3D target detection. The anti-impact drilling robot navigation system comprises a multi-sensor sensing module, a 3D target detection module, a path planning module and a control module. The multi-sensor sensing module is used for acquiring surrounding environment data and the real-time position and posture of the anti-impact drilling robot; the 3D target detection module adopts an improved MVXNet model to identify environmental data; the path planning module is of a double-layer architecture, wherein one layer adopts an improved dung beetle optimization algorithm to carry out global path planning; on the other layer, an improved dynamic window method is adopted for dynamic obstacle avoidance and local planning adjustment of a global path, and finally a corresponding control instruction is generated through a control module to enable the anti-impact drilling robot to advance according to a plan. Therefore, high-robustness, high-adaptability and high-precision navigation driving of the anti-impact drilling robot in a complex environment is realized, and the driving efficiency of the anti-impact drilling robot is effectively improved on the premise of ensuring the advancing safety of the anti-impact drilling robot.

The invention discloses a control method for ultra-low-altitude flight of a large unmanned aerial vehicle, and the method comprises the following steps: building a kinetic model of the unmanned aerial vehicle, the kinetic model comprising dynamic equations of position, speed, attitude control and control input; performing error tracking control on the attitude and the speed of the unmanned aerial vehicle by adopting a Backstepping nonlinear control method based on the dynamic model; estimating external disturbance and dynamic parameters of the unmanned aerial vehicle in real time by combining an L1 adaptive control method, and adaptively adjusting control input to compensate wind speed change; predicting a future flight state by using a model prediction control algorithm, optimizing a flight path and compensating wind speed disturbance; a safe trajectory is planned in real time through a dynamic window method, and obstacles are avoided in combination with dynamic constraints. According to the invention, through organic combination of Backstepping control, L1 adaptive control, MPC and DWA algorithms, the complex problems of wind speed disturbance, obstacle avoidance, high-precision operation and the like in ultra-low-altitude flight can be solved.

The invention discloses an intelligent luggage case obstacle avoidance method based on an ultra wide band, and relates to the technical field of intelligent mobile equipment control, and the method comprises the steps: collecting and preprocessing multi-source environment perception data, and generating an environment perception fusion data set; correcting projection in the ranging direction and constructing a local environment grid map based on grid division and grid occupation probability; comparing the bottom direction TOF height value with a ground reference height by adopting a threshold value judgment method, identifying a height difference risk area, and generating a path guide point through Cartesian coordinate conversion; based on the path guide points and the local environment grid map, generating a trajectory candidate set by adopting a dynamic window method, and removing impassable trajectories through a collision detection algorithm in combination with obstacle grid information; through a multi-target scoring function and a double-DIP controller, predicting a collision risk score of the feasible trajectory and generating an obstacle avoidance control instruction; the obstacle avoidance control instruction is executed, environment sudden change data are collected in real time, and the obstacle avoidance control instruction is adjusted in real time through a path replanning algorithm.

The invention belongs to the technical field of industrial robot control and detection, and particularly relates to an inspection robot detection system based on image recognition. After the system is started, an image acquisition and preprocessing module synchronously acquires visible light and thermal imaging images of an industrial scene through a multispectral sub-module; the image quality is optimized through a multi-color space adaptive threshold segmentation algorithm; the industrial visual identification module extracts defect features based on a YOLOv-SCSA network, matches a device template and locates defect positions; the motion control module realizes accurate positioning through a multi-sensor dynamic fusion algorithm, plans an obstacle avoidance path in combination with a dynamic window method, and drives the robot to autonomously inspect; the abnormality diagnosis module judges defect types by using a hybrid classification algorithm, evaluates risks according to parameters and triggers early warning; the data storage and transmission module processes data locally and uploads the data to the cloud, the man-machine interaction module displays the data in real time and supports remote intervention, and a closed-loop inspection process is formed.

The invention provides an intelligent inspection robot system, and relates to the technical field of robot inspection. Environment data are collected through a multi-mode sensor; preprocessing the environmental data, including image denoising and enhancement, point cloud filtering and attitude data smoothing, to generate multi-modal fusion input data; inputting the preprocessed data into a Vision Transform-based multi-modal sensing network, extracting global features through a self-attention mechanism, and performing joint embedding with laser radar point cloud and IMU data to generate an environment semantic feature map; based on the environment semantic feature map, utilizing a neural radiation field technology to reconstruct a dynamic 3D scene model in real time, and generating a self-adaptive inspection path through a meta-reinforcement learning algorithm; and the smoothness and safety of the inspection path are optimized by combining a dynamic window method, and a robot motion module is controlled to execute obstacle avoidance and navigation. The technical effect of improving the accuracy and rationality of automatic inspection in the industrial inspection process is achieved.

The invention discloses a plant protection unmanned aerial vehicle operation method and system based on Beidou high-precision positioning and hyperspectral nitrogen nutrition estimation, and the method comprises the steps: determining a plant protection unmanned aerial vehicle operation region, and calculating the flight height of an unmanned aerial vehicle through hyperspectral camera parameters and navigation positioning data; acquiring a hyperspectral image of crops in the operation area of the plant protection unmanned aerial vehicle in real time through a hyperspectral camera, and determining a nitrogen nutrition deficiency area by using a spectral feature selection algorithm based on spectral data in the hyperspectral image; converting the pixel coordinates of the nitrogen nutrition deficiency region into real geographic coordinates; based on the real geographic coordinates of the nitrogen nutrition deficient area, the performance parameters and task requirements of the unmanned aerial vehicle are combined, a dynamic window method is adopted for sowing path planning, and finally accurate sowing irrigation is achieved. According to the method, a dynamic threshold value and maximum entropy threshold value method is introduced, Canny edge detection and multi-source correction are combined, region boundary jitter and area calculation errors are reduced, and a space reference is provided for precise sowing.

The invention discloses a double-AGV (Automatic Guided Vehicle) linkage omnidirectional dynamic path planning method based on a dynamic window method, and the method designs a real-time dynamic path planning scheme based on an omnidirectional differential composite motion model by utilizing the omnidirectional movement characteristic of Mecanum wheels. The method comprises the following steps: firstly, introducing a Y-direction linear velocity in a velocity sampling stage, and constructing an omnidirectional motion model to improve the omnidirectional motion capability; and meanwhile, by combining angular velocity sampling, dynamic adjustment of the attitude of the AGV is realized, an omnidirectional differential composite motion model is constructed, and the omnidirectional motion flexibility is enhanced. And secondly, fusing an artificial potential field method, designing an omnidirectional motion evaluation sub-function, carrying out multi-dimensional comprehensive evaluation on candidate trajectories, screening out an optimal trajectory facing a target point, and effectively improving the efficiency and safety of path planning. And finally, in combination with the contour parameters of the double-AGV linkage system, ensuring that the pilot AGV and the following AGV can be prevented from colliding with obstacles in the environment in the navigation process. According to the method, the transportation efficiency and the system stability are remarkably improved.

The invention discloses an improved unmanned aerial vehicle dynamic window obstacle avoidance method based on an artificial potential field method. The method comprises the following steps: selecting local path points from a global path at certain intervals as sub-target path points for guiding local path planning; carrying out sampling combination on the speed space of the unmanned aerial vehicle in the current state, and generating a plurality of feasible candidate trajectories in a time window; introducing an improved artificial potential field function into an evaluation function of a dynamic window method, and comprehensively evaluating each candidate trajectory; and selecting the trajectory control instruction with the optimal comprehensive evaluation score as the motion input of the next control period, and realizing real-time obstacle avoidance and path correction of the unmanned aerial vehicle in a complex dynamic environment. Obstacle information and the positions of the sub-target path points are updated in real time in the flight process, if the distance between the current position and the positions of the sub-target path points is smaller than a set sub-target path point arrival value, it is regarded that the unmanned aerial vehicle arrives at the sub-target path points, and updating is carried out according to the obtained sub-target path points; and updating the next sub-target path point in the sub-target path point set to realize dynamic path adjustment. The device comprises a processor and a memory.

The invention relates to a hybrid reinforcement learning navigation method and system based on hierarchical course learning and an attention mechanism, and belongs to the technical field of intelligent robots and autonomous navigation. The method comprises the steps of obtaining robot state and environment information through a sensor; extracting context features of the laser radar sequence by using a self-attention network and generating a sub-target candidate set; verifying the track by adopting a dynamic window method; constructing a multi-target reward function and a hierarchical course learning framework; and finally updating the strategy through a mixed loss function. The system correspondingly comprises a sensor module, a feature extraction module, a sub-target generation module, a track verification module, a reward function construction module, a course learning control module and a strategy updating module. The problems that a traditional planner lacks long-term planning and reinforcement learning is low in training efficiency and poor in safety are solved, and the navigation intelligence, safety and learning efficiency of the robot in an unknown complex environment are remarkably improved.

The invention provides an inspection robot obstacle avoidance method based on fusion of an artificial potential field method and a dynamic window method, which belongs to the technical field of robot path planning and comprises the following steps: firstly, respectively constructing a gravitational potential field function and a repulsive potential field function according to the distance from a robot to a target point and the distance from the robot to an obstacle; a traditional repulsive force potential field function is modified into a sampling interval, negative gradients of the gravitation potential field function and the repulsive force potential field function are solved to obtain a resultant force set borne by the robot, an optimal path is selected from multiple paths according to an evaluation function, and finally the cyclic sampling prediction process is repeated until the robot reaches the end point. According to the method, the local minimum value points can be effectively avoided, the path with the least redundant nodes is selected, and the obstacle avoidance efficiency is improved on the premise of ensuring safety.

The invention discloses a traffic cone robot path planning method and system based on dynamic coefficient adjustment, and the method comprises the steps: building a grid map through obtaining the environment information of a traffic cone robot; realizing global path planning and local path planning from the starting point to the target point of the traffic cone robot by using an A star algorithm and a dynamic window method respectively; in the global path planning search process, a dynamic weight coefficient changing along with the distance from the current node to the starting point is introduced to serve as a coefficient for estimating the cost to calculate the comprehensive cost of each node, the problem that an existing A star algorithm is low in search efficiency is solved, and the speed of obtaining the optimal path is increased; meanwhile, the coefficient of the distance evaluation function in the evaluation function is dynamically adjusted, so that the dynamic window method can adjust the speed of the traffic cone robot according to the distance between the position of the traffic cone robot and the obstacle, and the adaptability to the moving obstacle is remarkably improved.

The invention discloses a two-way A star algorithm and dynamic window method fused path planning method and system. The method comprises the following steps: calculating a global path from a starting point to an end point by using a two-way A star algorithm; traversing the global path to remove redundant nodes in the global path; finding an initial current node in the global path from which the redundant nodes are removed; judging whether the current node is an end point, if the current node is the end point, fusing local paths among the nodes to obtain a final planned path from the starting point to the end point, otherwise, determining a next current node according to the current node and the global path; and calculating a local path from the current node to the next current node by using a dynamic window method, and returning the next current node as a new current node to continue iteration. The invention aims to solve the problems of calculation efficiency and path smoothness of the existing path planning method, improve the navigation capability in a dynamic environment, and improve the safety, movement efficiency and real-time obstacle avoidance capability of the robot.

The invention provides an inland river navigation collision avoidance method and system fusing improved A * and a dynamic window method, and the method comprises the following steps: 1, constructing a two-dimensional grid map of an inland river navigation environment, setting a starting point and an ending point of ship navigation, and determining the distribution information of obstacles in the map; 2, performing global path planning based on an improved A algorithm; step 3, optimizing the global path; 4, performing local planning and fusion based on a dynamic window method DWA; and step 5, repeating the step 4 until the local target point is an end point, and outputting a final navigation path to realize autonomous collision avoidance of the inland ship. According to the technical scheme, navigation safety, efficiency and environmental adaptability are considered.

The invention relates to a self-adaptive safe path planning method for a gas inspection robot, and belongs to the technical field of robot path planning. According to the method, firstly, a global path is generated by improving an A star algorithm, smoothness is optimized, then a multi-sensor data sensing environment is fused, local real-time obstacle avoidance planning is carried out by adopting a dynamic window method, and a deep reinforcement learning model is introduced to realize dynamic decision making. According to the method, the problems that in the prior art, in a dynamic complex environment, path planning is rigid, obstacle avoidance is not timely, and high-risk areas cannot be adaptively avoided are effectively solved, the efficiency and safety of path planning can be remarkably improved, and the autonomous operation capacity and the intelligent decision-making level of the robot in the underground complex environment are enhanced.