3results about How to "Fast planning" patented technology

The invention discloses an optimal path planning method of a pulse coupling neural network based on double constraints. The method comprises the following steps: mapping a path planning environment to a DC-PCNN network; a DC-PCNN neural network model is constructed, and all neurons are initialized; activating a target neuron, and recording the current neuron as a father node; calculating an exponential decay function of the torque deviation, and multiplying the calculated value as a penalty factor by an update item of the internal activity item; calculating a gravitational function value of the flow field constraint, and using the calculated value to update a current neuron dynamic threshold value; comparing the internal activity item with a dynamic threshold; when gt; if yes, activating the neuron, and recording the current neuron as a father node; repeating the steps S4-S6 until the initial neuron is activated; and backtracking all activated nodes, and planning an optimal path. According to the method, the search efficiency is remarkably improved while the path optimality is ensured.

This invention discloses a collaborative operation system and method for a drone and a robotic dog in a mining scenario. The system includes a drone, a robotic dog, and a wireless communication module. The drone and the robotic dog transmit data through the wireless communication module. The drone includes a laser terrain scanner, and the robotic dog is equipped with a high-definition camera and an infrared sensor. The laser terrain scanner is used to perform a global terrain scan of the mining blasting area to obtain laser point cloud data. The high-definition camera is used to identify obstacle elevation values, and the external sensors are used to collect road slope data. This invention utilizes a bidirectional BFS search algorithm combined with adjacent grid selection weights and priorities to generate multiple planned paths. The drone sends the planned paths to the robotic dog in advance, and the robotic dog only needs to select the optimal path based on the actual road conditions. The lightweight MobileNet model improves the robotic dog's computation speed and time, and the selection of the optimal path based on actual road conditions requires low hardware computing power.

The application provides a kind of mechanical arm motion planning method and system based on deep neural network.The method includes processing obstacle space point cloud data collected by depth camera to obtain processed point cloud data;Convert the processed point cloud data into octomap chart and import into moveit mechanical arm workspace;In moveit mechanical arm workspace, the complete collision-free path from the initial configuration state to the target configuration state is output by the mechanical arm motion planning algorithm based on deep neural network;Optimize the complete collision-free path to generate the optimal complete collision-free path;Control the mechanical arm to move according to the generated optimal complete collision-free path.The application proposes that the deep neural network predicts the configuration state of the mechanical arm at a certain time, reduces the calculation amount in the process of mechanical arm obstacle avoidance planning through collision detection and bidirectional iterative exploration, greatly speeds up the path planning speed, shortens the path planning time, and at the same time ensures that the planned path is close to the optimal path.