Smart Autonomous Systems Lab, School of Mechanical & Automotive Engineering, Kunsan National University, Gunsan, Jeonbuk 54150, Korea.
Sensors (Basel). 2018 Oct 22;18(10):3575. doi: 10.3390/s18103575.
In recent years, machine learning (and as a result artificial intelligence) has experienced considerable progress. As a result, robots in different shapes and with different purposes have found their ways into our everyday life. These robots, which have been developed with the goal of human companionship, are here to help us in our everyday and routine life. These robots are different to the previous family of robots that were used in factories and static environments. These new robots are social robots that need to be able to adapt to our environment by themselves and to learn from their own experiences. In this paper, we contribute to the creation of robots with a high degree of autonomy, which is a must for social robots. We try to create an algorithm capable of autonomous exploration in and adaptation to unknown environments and implement it in a simulated robot. We go further than a simulation and implement our algorithm in a real robot, in which our sensor fusion method is able to overcome real-world noise and perform robust exploration.
近年来，机器学习（以及因此产生的人工智能）取得了相当大的进展。结果，不同形状和用途的机器人已经进入了我们的日常生活。这些机器人的开发目标是为了人类的陪伴，旨在帮助我们应对日常和例行的生活。这些机器人与之前在工厂和静态环境中使用的机器人家族不同。这些新型机器人是社交机器人，它们需要能够自行适应环境，并从自己的经验中学习。在本文中，我们致力于创建具有高度自主性的机器人，这是社交机器人的必备条件。我们尝试创建一种能够在未知环境中自主探索和适应的算法，并将其实现在模拟机器人中。我们不仅仅停留在模拟阶段，而是将我们的算法实现在一个真实的机器人中，在这个机器人中，我们的传感器融合方法能够克服现实世界中的噪声并进行稳健的探索。
