微型仿生机器人是一种仿照生物外形和运动形态制作设计的机器人,凭借其体型小、机动性高、环境适应性强等优点,在复杂环境探索、敌情侦察等特殊场景中具有较高的应用前景,近年来备受研究人员的关注.但是微型机器人运动稳定性弱、单目相...微型仿生机器人是一种仿照生物外形和运动形态制作设计的机器人,凭借其体型小、机动性高、环境适应性强等优点,在复杂环境探索、敌情侦察等特殊场景中具有较高的应用前景,近年来备受研究人员的关注.但是微型机器人运动稳定性弱、单目相机环境感知精度低等问题的存在一直制约着其实际应用.本文仿照昆虫“独角仙”设计了一种新的微型仿生爬虫机器人,开发了基于生物运动调控机制的六足机器人控制系统,改进了基于啮齿类动物空间导航原理的同时定位与建图系统.使用自研的微型仿生爬虫机器人在人造沙盘和真实室内走廊两个场景中进行了实验验证.在人造沙盘场景,由于微型仿生爬虫机器人提供的环境图像质量模糊,ORB-SLAM3算法无法完成回环检测,不能正确识别曾经见过的场景.类脑同步定位与地图构建(simultaneous localization and mapping,SLAM)系统回环准确率高达100%,比原RatSLAM算法提高了4.36%.在真实室内走廊场景,ORB-SLAM3算法和RatSLAM算法建图效果都比较差,而类脑SLAM不仅有着较好的建图结果,而且在准确率100%的同时召回率也高达97.87%.与ORB-SLAM3和RatSLAM算法相比,类脑SLAM系统取得了较好的建图结果.因此,自研的微型仿生爬虫机器人具有灵活的运动能力、鲁棒的导航定位能力,促使微型仿生机器人离实际应用更近了一步.展开更多
Reinforcement learning is one of the fastest growing areas in machine learning,and has obtained great achievements in biomedicine,Internet of Things(IoT),logistics,robotic control,etc.However,there are still many chal...Reinforcement learning is one of the fastest growing areas in machine learning,and has obtained great achievements in biomedicine,Internet of Things(IoT),logistics,robotic control,etc.However,there are still many challenges for engineering applications,such as how to speed up the learning process,how to balance the trade-of between exploration and exploitation.Quantum technology,which can solve complex problems faster than classical methods,especially in supercomputers,provides us a new paradigm to overcome these challenges in reinforcement learning.In this paper,a quantum-enhanced reinforcement learning is pictured for optimal control.In this algorithm,the states and actions of reinforcement learning are quantized by quantum technology.And then,a probability amplifcation method,which can efectively avoid the trade-of between exploration and exploitation via quantized technology,is presented.Finally,the optimal control policy is learnt during the process of reinforcement learning.The performance of this quantized algorithm is demonstrated in both MountainCar reinforcement learning environment and CartPole reinforcement learning environment—one kind of classical control reinforcement learning environment in the OpenAI Gym.The preliminary study results validate that,compared with Q-learning,this quantized reinforcement learning method has better control performance without considering the trade-of between exploration and exploitation.The learning performance of this new algorithm is stable with diferent learning rates from 0.01 to 0.10,which means it is promising to be employed in unknown dynamics systems.展开更多
文摘微型仿生机器人是一种仿照生物外形和运动形态制作设计的机器人,凭借其体型小、机动性高、环境适应性强等优点,在复杂环境探索、敌情侦察等特殊场景中具有较高的应用前景,近年来备受研究人员的关注.但是微型机器人运动稳定性弱、单目相机环境感知精度低等问题的存在一直制约着其实际应用.本文仿照昆虫“独角仙”设计了一种新的微型仿生爬虫机器人,开发了基于生物运动调控机制的六足机器人控制系统,改进了基于啮齿类动物空间导航原理的同时定位与建图系统.使用自研的微型仿生爬虫机器人在人造沙盘和真实室内走廊两个场景中进行了实验验证.在人造沙盘场景,由于微型仿生爬虫机器人提供的环境图像质量模糊,ORB-SLAM3算法无法完成回环检测,不能正确识别曾经见过的场景.类脑同步定位与地图构建(simultaneous localization and mapping,SLAM)系统回环准确率高达100%,比原RatSLAM算法提高了4.36%.在真实室内走廊场景,ORB-SLAM3算法和RatSLAM算法建图效果都比较差,而类脑SLAM不仅有着较好的建图结果,而且在准确率100%的同时召回率也高达97.87%.与ORB-SLAM3和RatSLAM算法相比,类脑SLAM系统取得了较好的建图结果.因此,自研的微型仿生爬虫机器人具有灵活的运动能力、鲁棒的导航定位能力,促使微型仿生机器人离实际应用更近了一步.
文摘Reinforcement learning is one of the fastest growing areas in machine learning,and has obtained great achievements in biomedicine,Internet of Things(IoT),logistics,robotic control,etc.However,there are still many challenges for engineering applications,such as how to speed up the learning process,how to balance the trade-of between exploration and exploitation.Quantum technology,which can solve complex problems faster than classical methods,especially in supercomputers,provides us a new paradigm to overcome these challenges in reinforcement learning.In this paper,a quantum-enhanced reinforcement learning is pictured for optimal control.In this algorithm,the states and actions of reinforcement learning are quantized by quantum technology.And then,a probability amplifcation method,which can efectively avoid the trade-of between exploration and exploitation via quantized technology,is presented.Finally,the optimal control policy is learnt during the process of reinforcement learning.The performance of this quantized algorithm is demonstrated in both MountainCar reinforcement learning environment and CartPole reinforcement learning environment—one kind of classical control reinforcement learning environment in the OpenAI Gym.The preliminary study results validate that,compared with Q-learning,this quantized reinforcement learning method has better control performance without considering the trade-of between exploration and exploitation.The learning performance of this new algorithm is stable with diferent learning rates from 0.01 to 0.10,which means it is promising to be employed in unknown dynamics systems.
