ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore.
Optoelectronics Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam.
Sensors (Basel). 2021 Feb 4;21(4):1067. doi: 10.3390/s21041067.
One of the essential attributes of a cleaning robot is to achieve complete area coverage. Current commercial indoor cleaning robots have fixed morphology and are restricted to clean only specific areas in a house. The results of maximum area coverage are sub-optimal in this case. Tiling robots are innovative solutions for such a coverage problem. These new kinds of robots can be deployed in the cases of cleaning, painting, maintenance, and inspection, which require complete area coverage. Tiling robots' objective is to cover the entire area by reconfiguring to different shapes as per the area requirements. In this context, it is vital to have a framework that enables the robot to maximize the area coverage while minimizing energy consumption. That means it is necessary for the robot to cover the maximum area with the least number of shape reconfigurations possible. The current paper proposes a complete area coverage planning module for the modified hTrihex, a honeycomb-shaped tiling robot, based on the deep reinforcement learning technique. This framework simultaneously generates the tiling shapes and the trajectory with minimum overall cost. In this regard, a convolutional neural network (CNN) with long short term memory (LSTM) layer was trained using the actor-critic experience replay (ACER) reinforcement learning algorithm. The simulation results obtained from the current implementation were compared against the results that were generated through traditional tiling theory models that included zigzag, spiral, and greedy search schemes. The model presented in the current paper was also compared against other methods where this problem was considered as a traveling salesman problem (TSP) solved through genetic algorithm (GA) and ant colony optimization (ACO) approaches. Our proposed scheme generates a path with a minimized cost at a lesser time.
清洁机器人的一个基本属性是实现完全区域覆盖。当前的商用室内清洁机器人具有固定的形态，只能清洁房屋中的特定区域。在这种情况下，最大区域覆盖的结果是次优的。平铺机器人是解决这种覆盖问题的创新解决方案。这些新型机器人可以在需要完全区域覆盖的清洁、绘画、维护和检查等情况下部署。平铺机器人的目标是通过根据区域要求重新配置为不同的形状来覆盖整个区域。在这种情况下，拥有一个使机器人能够在最小化能耗的同时最大化区域覆盖的框架是至关重要的。这意味着机器人需要用尽可能少的形状重新配置来覆盖最大的区域。本文提出了一种基于深度强化学习技术的改进 hTrihex 蜂窝状平铺机器人的完全区域覆盖规划模块。该框架同时生成具有最小总成本的平铺形状和轨迹。在这方面，使用演员-评论家经验回放（ACER）强化学习算法对具有长短时记忆（LSTM）层的卷积神经网络（CNN）进行了训练。当前实现获得的模拟结果与包括之字形、螺旋形和贪婪搜索方案在内的传统平铺理论模型生成的结果进行了比较。本文提出的模型还与其他将此问题视为通过遗传算法（GA）和蚁群优化（ACO）方法解决的旅行商问题（TSP）的方法进行了比较。我们提出的方案以较少的时间生成了成本最小的路径。
