本文介紹了基于神經(jīng)網(wǎng)絡(luò)和微粒群優(yōu)化算法的移動機(jī)器人動態(tài)避障路徑規(guī)劃算法。通過神經(jīng)網(wǎng)絡(luò)改進(jìn)的微粒群算法，充分利用了神經(jīng)網(wǎng)絡(luò)的融合性和并行性來提高微粒群算法中適應(yīng)度函數(shù)的準(zhǔn)確性。通過神經(jīng)網(wǎng)絡(luò)描述機(jī)器人工作空間的動態(tài)環(huán)境約束并找到最優(yōu)的適應(yīng)度函數(shù)，在微粒群算法中使用該函數(shù)，求得微粒群算法最優(yōu)無碰路徑。
關(guān)鍵詞：神經(jīng)網(wǎng)絡(luò)；微粒群優(yōu)化算法；動態(tài)避障；路徑規(guī)劃
Abstract: This paper presents a mobile robot obstacle avoidance dynamic path planning
algorithm based on neural network and Particle Swarm Optimization (PSO). Using neural network to improve the PSO algorithm, and make integration and parallel of neural network to enhance the accuracy of PSO algorithm’s fitness function. Optimization function for the extreme, points to determine the equations of motion, and finally to the point iterative path tends to set the optimal path planning, use this function in PSO algorithm, finally get PSO obtained optimal collision path.
Key words: neural network; particle swarm optimization; obstacle avoidance dynamic; path
planning