Dibi (chongqing) Intelligent Technology Research Institute Co., Ltd.

Chongqing University

Star Institute of Intelligent Systems

Star (chongqing) Intelligent Equipment Technology Research Institute Co., Ltd.

Lihui Tan

Yongduan Song

Shilei Tan

Junfeng Lai

Ziqiang Jiang

Li Huang

Jian Liu

Congyi Zhang

Yankai Zhang

Shuai Wang

Lei Fang

Saiyu Wang

The invention discloses an adaptive path planning method based on neutral networks trained by the evolutional algorithms, the neutral network training method comprises input and output of the data acquired by the mobile sensors installed on the mobile robots as the neutral networks, and training and optimization of the recurrent neutral networks based on the evolutional algorithms; the path planning method refers to the application of the trained neutral networks to the path planning of the mobile robot, the invention effectively improves local quick search capability and global search capability of the algorithms by applying the evolutional algorithms to the optimization of the recurrent neutral networks, so that the robot can plan a rational path in a dense and uncertain environment.

Adaptive path planning method based on neutral networks trained by the evolutional algorithms

The present disclosure provides a method for object detection and recognition based on a neural network. The method includes: adding a detection layer following three detection layers of an existing YOLOv5 network model, to construct a new YOLOv5 network model; then, training the new YOLOv5 network model by considering an overlapping area between a predicted box and a ground truth box, a center-to-center distance between the two boxes, and an aspect ratio of the two boxes; and finally, inputting a to-be-detected image into the trained new YOLOv5 network model, outputting a predicted box of an object and probability values corresponding to a class to which the object belongs, and setting a class corresponding to a maximum probability value as a predicted class of the object in the to-be-detected image. This method can quickly and effectively detect multiple classes of objects. Especially, a detection effect for small objects is more ideal.

Method for object detection and recognition based on neural network

The present disclosure discloses a method for route optimization based on dynamic window and redundant node filtering, comprising using an existing raster map data set to determine the coordinate information of a starting position and a destination position of movement, and to mark a destination node and an obstacle node in the raster map; using A* algorithm to plan a global route; globally optimizing the global route planned by A* algorithm, and filtering redundant nodes out; combining a dynamic window algorithm to perform the local optimization section by section on the optimized global route so as to obtain a final global route. According to the present disclosure, the combination of algorithms reduces a single movement duration of a mobile robot and improves the smoothness of the movement route curve. At the same time, the problems of the robot occurring on the route during the static driving are alleviated.

Method for route optimization based on dynamic window and redundant node filtering

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Chongqing, China

Lihui Tan