智能軌跡控制割草機器人設計——基于FPGA神經(jīng)網(wǎng)絡
發(fā)布時間：2018-10-23 12:57
【摘要】：為了提高割草機器人自主導航和定位的精確性和智能性,設計了一種新型的基于FPGA神經(jīng)網(wǎng)絡算法的割草機器人。該設計采用FPGA可重構技術,以3層誤差反向傳播神經(jīng)網(wǎng)絡作為典型的模型來展開;利用成熟的BP算法公式,設計了割草機器人智能控制的模型;利用FPGA技術,設計了割草機器人的硬件系統(tǒng);最后采用文本輸入的設計方法,利用田間試驗的方式,對機器人的軌跡規(guī)劃能力和控制精度進行了驗證。試驗結果表明:利用FPGA和神經(jīng)網(wǎng)絡模型可以有效地穿越5個障礙物,并可得到滿意的軌跡規(guī)劃結果。將普通的PID控制器和神經(jīng)網(wǎng)絡PID控制器得到的控制結果誤差進行了對比,結果表明:神經(jīng)網(wǎng)絡PID控制器得到的割草機器人控制誤差明顯比傳統(tǒng)的PID控制器誤差小。該方法為神經(jīng)網(wǎng)絡的硬件實現(xiàn)提供了可靠的理論基礎。
[Abstract]:In order to improve the accuracy and intelligence of autonomous navigation and localization of grass mowing robot, a new kind of grass mowing robot based on FPGA neural network algorithm is designed. The design adopts FPGA reconfigurable technology, takes 3-layer error back propagation neural network as the typical model, uses mature BP algorithm formula, designs the intelligent control model of mowing robot, and makes use of FPGA technology. The hardware system of the mowing robot is designed, and the trajectory planning ability and control precision of the robot are verified by using the design method of text input and the way of field experiment. The experimental results show that the FPGA and neural network models can effectively cross five obstacles and obtain satisfactory trajectory planning results. The error of the conventional PID controller and the neural network PID controller is compared. The results show that the error of the neural network PID controller is obviously smaller than that of the traditional PID controller. This method provides a reliable theoretical basis for the hardware implementation of neural network.
【作者單位】： 河南工業(yè)職業(yè)技術學院;
【基金】：河南省自然科學基金項目(2015ZCB115) 南陽市科技攻關項目(2012GG029)
【分類號】：S817.111;TP242
本文編號：2289312
[Abstract]:In order to improve the accuracy and intelligence of autonomous navigation and localization of grass mowing robot, a new kind of grass mowing robot based on FPGA neural network algorithm is designed. The design adopts FPGA reconfigurable technology, takes 3-layer error back propagation neural network as the typical model, uses mature BP algorithm formula, designs the intelligent control model of mowing robot, and makes use of FPGA technology. The hardware system of the mowing robot is designed, and the trajectory planning ability and control precision of the robot are verified by using the design method of text input and the way of field experiment. The experimental results show that the FPGA and neural network models can effectively cross five obstacles and obtain satisfactory trajectory planning results. The error of the conventional PID controller and the neural network PID controller is compared. The results show that the error of the neural network PID controller is obviously smaller than that of the traditional PID controller. This method provides a reliable theoretical basis for the hardware implementation of neural network.
【作者單位】： 河南工業(yè)職業(yè)技術學院;
【基金】：河南省自然科學基金項目(2015ZCB115) 南陽市科技攻關項目(2012GG029)
【分類號】：S817.111;TP242
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