4results about How to "Improve path planning efficiency" patented technology

This invention belongs to the field of dust collection robot technology, specifically relating to a factory intelligent dust collection spray robot system based on multi-sensor collaboration. The system includes a vehicle body, a sweeper mounted on the vehicle body, an electronic control system within the vehicle body, a vision recognition module mounted on the vehicle body, a dust collection module mounted on the vehicle body, and a dry fog module mounted on the vehicle body. The electronic control system controls the operation of the vision recognition module, dust collection module, and dry fog module. The vision recognition module is used to construct a map and optimize the dynamic path in real time. The dust collection module absorbs dust through graded filtration. The dry fog module dynamically adjusts the atomized particle size and spray flow rate of the dry fog nozzles according to the dust concentration to prevent dust diffusion. This invention features improved dust collection efficiency, reduced development and maintenance costs, and dust suppression effects.

The invention relates to a mine robot cooperative path planning method based on multi-agent reinforcement learning, and belongs to the technical field of mine robot cooperative control and intelligent decision, and the method comprises the following steps: constructing refined environment characterization based on multi-source sensor information through a terrain cost modeling module, and calculating a terrain cost value; path planning is carried out by adopting a centralized training-decentralized execution mechanism through a multi-agent reinforcement learning framework; motion conflicts between the robots are monitored and solved in real time through a dynamic conflict resolution module; and cross-robot knowledge transmission and strategy migration are realized through the knowledge sharing unit. According to the method, the path planning efficiency, the collaborative operation safety and the learning adaptability of multiple robots in complex dynamic environments such as mine catastrophe are effectively improved, and the method is suitable for collaborative detection, rescue and transportation tasks of multiple types of mine robots.

The present invention provides a multi-topography integrated path planning method incorporating road factors. Based on the image information of the target area, at least one terrain feature included in the target area is determined, and a grid map corresponding to the target area is constructed. In response to a path search instruction input for indicating searching for a driving path between a starting point and an ending point in the target area, at least one path is determined in the grid map. The sum of the values of the cost function corresponding to each grid passed by each path is obtained. The cost function is associated with the slope value, type of the terrain feature included in the corresponding grid, the speed of a preset vehicle passing through the terrain feature included in the corresponding grid, and the Euclidean distance between the starting point and the ending point. The path corresponding to the minimum value of the sum is determined as the target path. Determining the path corresponding to the target path as the driving path in the target area can improve the passing efficiency of the user.

This invention discloses a method, system, storage medium, and product for unmanned vehicle (UAV) path planning in field environments under UAV collaboration, belonging to the field of UAV path planning, and solving the problem of low data accuracy in the global path planning of UAVs in existing technologies. This invention acquires a top-down visible light image of the passable area and performs image pose calculation and sparse point cloud data generation; then generates a two-dimensional orthophoto and digital surface model of the passable area; a semantic segmentation model performs semantic segmentation on the two-dimensional orthophoto and extracts ground feature classification data; after gridding the digital surface model and ground feature classification data, slope values ​​and ground feature classification values ​​are calculated; based on the slope value and ground feature classification value of each grid cell, the passage cost of each grid cell is calculated; the starting and ending coordinates of the UAV are input, and UAV path planning is performed based on the path planning algorithm and the obtained passage cost map. This invention is used for UAV-collaborated path planning in field environments.