论文标题
通过移动机器人进行的深层Q学习，以进行能源约束的覆盖范围
Deep Recurrent Q-learning for Energy-constrained Coverage with a Mobile Robot
论文作者
论文摘要
在本文中，我们研究了在存在多个充电站的情况下，具有能量受限机器人的环境的覆盖率。由于机器人的车载电源有限，因此它可能没有足够的能量来涵盖环境中的所有点。取而代之的是，它需要停在一个或多个充电站，以间歇性充电。机器人不能违反能量限制，即访问具有负能量的位置。为了解决这个问题，我们提出了一个深入的Q学习框架，该框架产生了一项政策，以最大程度地提高覆盖范围并最大程度地减少预算违规行为。我们提出的框架还利用了复发性神经网络（RNN）的记忆，以更好地适合这个多目标优化问题。我们已经在具有充电站和各种障碍物配置的16 x 16个网格环境中测试了提出的框架。结果表明，我们提出的方法找到了可行的解决方案，并胜过可比的现有技术。
In this paper, we study the problem of coverage of an environment with an energy-constrained robot in the presence of multiple charging stations. As the robot's on-board power supply is limited, it might not have enough energy to cover all the points in the environment with a single charge. Instead, it will need to stop at one or more charging stations to recharge its battery intermittently. The robot cannot violate the energy constraint, i.e., visit a location with negative available energy. To solve this problem, we propose a deep Q-learning framework that produces a policy to maximize the coverage and minimize the budget violations. Our proposed framework also leverages the memory of a recurrent neural network (RNN) to better suit this multi-objective optimization problem. We have tested the presented framework within a 16 x 16 grid environment having charging stations and various obstacle configurations. Results show that our proposed method finds feasible solutions and outperforms a comparable existing technique.
