基于灰狼优化算法的机器人羽流追踪方法

针对在室内扩散环境无法获得可靠的羽流流向/流速信息的情况下,解决寻源机器人源定位效率低、成功率低的问题,提出了一种基于灰狼优化算法的机器人羽流追踪方法.该方法以气体浓度值作为个体适应度,在不搭载羽流流速/流向传感器的情况下,通过寻源机器人模拟灰狼种群的社会机制与狩猎行为进行位置更新,使寻源机器人能高效地追踪羽流并定位源位置.分别将灰狼优化算法、粒子群算法、遗传算法、Z字形搜索策略进行四组机器人羽流追踪仿真实验,基于灰狼优化算法的寻源机器人的定位成功率分别为92％、94％、94％、94％.实验结果表明,基于灰狼优化算法的寻源机器人的定位成功率分别为95％、90％、90％,验证了基于灰狼优化算法的机器人羽流追踪方法的可行性和有效性.

Research on Robot Plume Tracking Method Based on Grey Wolf Optimization Algorithm

In the case that reliable plume flow direction/velocity information cannot be obtained in an indoor diffusion environment, to solve the problem of low source positioning efficiency, low success rate of the source seeking robot, a plume tracking method for source seeking robot based on gray wolf optimization algorithm is proposed. In this method, the gas concentration value is used as an individual fitness. Without the plume flow rate/flow direction sensor, the social mechanism and hunting behavior of the gray wolf population are simulated by the source-seeking robot to update the position, so that the source-seeking robot can efficiently track plume and locate source location. The gray wolf optimization algorithm, particle swarm optimization algorithm, genetic algorithm, and zigzag search strategy were used in four sets of robot plume tracking simulation experiments, and the positioning success rates of the source- seeking robot based on the gray wolf optimization algorithm were 92%, 94%, 94%, and 94%. The on-site test results show that the positioning success rates of the source- seeking robot based on the gray wolf optimization algorithm were 95%, 90%, 90%, which verifies the feasibility and effectiveness of the robot plume tracking method based on the grey wolf optimization algorithm.