Extremum Seeking for Mobile Robots
Extremum Seeking for Mobile Robots
复制标题
移动机器人的极值搜索
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Nima Ghods
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作者：
Nima Ghods
本文介绍了极值搜索法应用于车载(S)的理论和实验结果，目的是定位一个未知的、非线性的信号场的源。对于位置信息不可用的环境，极值搜索方法被应用于自动驾驶车辆，作为导航的一种手段，以找到车辆可以在本地测量的某些信号源。信号在震源处处于最大强度，并且随着距离震源的距离而减小。虽然我们在实验中只假设信号场有一个最大值，但为了证明理论上的稳定性，我们使用了信号场的二次型局部近似。我们探索了处理非常慢或漂移的传感器的想法，并给出了一维优化极值搜索方案和具有点质量车辆动力学的二维源定位的几种不同变化的稳定性结果。给出了非完整飞行器正向调速转向寻源算法的详细收敛分析和仿真结果。我们在连续体中开发了一种确定性算法来部署一组能够测量相对邻居位置的自主车辆(代理)，在一维空间中，在可测量信号源附近的代理密度较高，而在一维内远离信号源的代理密度较低。我们还考虑了2D中的随机蜂群算法，该算法迫使代理网络扩展、维持队形，并在没有位置信息的情况下寻找源，从而为每个代理提供信号场的局部测量和与邻居的相对距离。给出了极值搜索应用于移动车辆对光源进行定位、跟踪和水平集跟踪的实验结果。我们使用极值对多辆车进行了实验，不仅寻求对光源的定位，而且还寻求避开物体和彼此。最后，我们讨论了建立烟羽特征实验台的细节和烟羽探源实验的结果。
The work in this thesis describes theoretical and experimental results of extremum seeking applied to vehicle(s) with the objective of localizing the source of an unknown, nonlinear, signal field. For environments where position information is unavailable, the extremum seeking method is applied to autonomous vehicles as a means of navigating to find the source of some signal which the vehicles can measure locally. The signal is at maximum intensity at the source and decreases with distance away from the source. Although we only assume that the signal field has a maximum in experiments, to prove theoretical stability we use quadratic form a local approximation of the signal field. We explore the idea of dealing with a very slow or drifting sensor and provide stability results for several distinct variations of an extremum seeking scheme for 1D optimization and 2D source localization with point-mass vehicle dynamics. Detailed convergence analysis and simulations for steering-based source seeking with forward velocity regulation applied to nonholonomic vehicles are provided. We develop a deterministic algorithm in a continuum to deploy a group of autonomous vehicles (agents) capable of measuring relative position to neighbors, in a line formation, which has a higher density of agents near the source of a measurable signal and a lower density away from the source in 1D. We also consider stochastic swarming algorithms in 2D that force the net of agents to spread, maintain a formation, and seek a source without position information, whereby each agent is given a local measurement of signal field and the relative distance from neighbors. Experimental results of extremum seeking applied to mobile vehicles to perform localization, tracking, and level-set tracing of a light source are shown. We perform experiments with multiple vehicles using extremum seeking not only to localize the light source but also to avoid objects and each other. Finally, we discuss details of setting up a testbed to produce a characterized smoke plume and the results of plume source seeking experiments