机器人(英语:Robot)包括一切模拟人类行为或思想与模拟其他生物的机械(如机器狗,机器猫等)。狭义上对机器人的定义还有很多分类法及争议,有些电脑程序甚至也被称为机器人。在当代工业中,机器人指能自动运行任务的人造机器设备,用以取代或协助人类工作,一般会是机电设备,由计算机程序或是电子电路控制。

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Elements of Robotics 是一本为高中生以上水平的读者写的机器人教科书。旨在弥合在学校里玩机器人与深入研究机器人学之间的市场空白和学习需求,让读者从工业和科研应用的角度了解机器人的主要研究课题。本书概述了不同类型的机器人以及用于构建机器人的组件,并侧重于介绍机器人算法。书中的算法描述只用到了高中生或者大学新生的数学知识,如微积分,代数和概率论等,深入浅出地解释了定位、绘图、图像处理、机器学习和群组机器人等高级主题算法。本书以开放获取的形式出版,自出版至今两年里已通过SpringerLink被读者下载61万多次。

https://www.springer.com/gp/book/9783319625324#:~:text=Elements%20of%20Robotics%20presents%20an,processing%2C%20machine%20learning%20and%20swarm

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For real-world deployments, it is critical to allow robots to navigate in complex environments autonomously. Traditional methods usually maintain an internal map of the environment, and then design several simple rules, in conjunction with a localization and planning approach, to navigate through the internal map. These approaches often involve a variety of assumptions and prior knowledge. In contrast, recent reinforcement learning (RL) methods can provide a model-free, self-learning mechanism as the robot interacts with an initially unknown environment, but are expensive to deploy in real-world scenarios due to inefficient exploration. In this paper, we focus on efficient navigation with the RL technique and combine the advantages of these two kinds of methods into a rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of time. First, we use the rule of wall-following to generate a closed-loop trajectory. Second, we employ a reduction rule to shrink the trajectory, which in turn effectively reduces the redundant exploration space. Besides, we give the detailed theoretical guarantee that the optimal navigation path is still in the reduced space. Third, in the reduced space, we utilize the Pledge rule to guide the exploration strategy for accelerating the RL process at the early stage. Experiments conducted on real robot navigation problems in hex-grid environments demonstrate that RuRL can achieve improved navigation performance.

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For real-world deployments, it is critical to allow robots to navigate in complex environments autonomously. Traditional methods usually maintain an internal map of the environment, and then design several simple rules, in conjunction with a localization and planning approach, to navigate through the internal map. These approaches often involve a variety of assumptions and prior knowledge. In contrast, recent reinforcement learning (RL) methods can provide a model-free, self-learning mechanism as the robot interacts with an initially unknown environment, but are expensive to deploy in real-world scenarios due to inefficient exploration. In this paper, we focus on efficient navigation with the RL technique and combine the advantages of these two kinds of methods into a rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of time. First, we use the rule of wall-following to generate a closed-loop trajectory. Second, we employ a reduction rule to shrink the trajectory, which in turn effectively reduces the redundant exploration space. Besides, we give the detailed theoretical guarantee that the optimal navigation path is still in the reduced space. Third, in the reduced space, we utilize the Pledge rule to guide the exploration strategy for accelerating the RL process at the early stage. Experiments conducted on real robot navigation problems in hex-grid environments demonstrate that RuRL can achieve improved navigation performance.

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