用信号强度与机器学习实现机器人室内自主导航
Indoor Localization for Autonomous Robot Navigation
- 结合RSSI与机器学习预测位置，驱动机器人路径规划
- 在测试中机器人成功绕过拐角约50%的次数
- 适合对室内定位与机器人导航感兴趣的开发者
室内定位系统（IPS）随着室外导航的普及而受到关注。研究正积极探讨如何利用接收信号强度指示（RSSI）和机器学习（ML）实现智能手机的室内导航。当前IPS的应用场景仍需深入探索，本文旨在将IPS用于自主机器人室内导航。我们采集了数据集并训练模型，在机器人上进行测试，同时开发了A*路径规划算法，使机器人能根据预测方向自主导航。经过不同网络结构的对比测试，机器人在绕过拐角时的成功率约为50%。结果表明，利用IPS实现机器人自主导航是未来值得深入研究的方向。
原文摘要 · Abstract (English)
Indoor positioning systems (IPSs) have gained attention as outdoor navigation becomes prevalent in everyday life. Research is being actively conducted on how indoor smartphone navigation can be accomplished and improved using received signal strength indication (RSSI) and machine learning (ML). IPSs have more use cases that need further exploration, and we aim to explore using IPSs for the indoor navigation of an autonomous robot. We collected a dataset and trained models to test on a robot. We also developed an A* path-planning algorithm so that our robot could navigate itself using predicted directions. After testing different network structures, our robot was able to successfully navigate corners around 50 percent of the time. The findings of this paper indicate that using IPSs for autonomous robots is a promising area of future research.
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