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). 2020 Jun 3;20(11):3170. doi: 10.3390/s20113170.
Completed area coverage planning (CACP) plays an essential role in various fields of robotics, such as area exploration, search, rescue, security, cleaning, and maintenance. Tiling robots with the ability to change their shape is a feasible solution to enhance the ability to cover predefined map areas with flexible sizes and to access the narrow space constraints. By dividing the map into sub-areas with the same size as the changeable robot shapes, the robot can plan the optimal movement to predetermined locations, transform its morphologies to cover the specific area, and ensure that the map is completely covered. The optimal navigation planning problem, including the least changing shape, shortest travel distance, and the lowest travel time while ensuring complete coverage of the map area, are solved in this paper. To this end, we propose the CACP framework for a tiling robot called hTrihex with three honeycomb shape modules. The robot can shift its shape into three different morphologies ensuring coverage of the map with a predetermined size. However, the ability to change shape also raises the complexity issues of the moving mechanisms. Therefore, the process of optimizing trajectories of the complete coverage is modeled according to the Traveling Salesman Problem (TSP) problem and solved by evolutionary approaches Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Hence, the costweight to clear a pair of waypoints in the TSP is defined as the required energy shift the robot between the two locations. This energy corresponds to the three operating processes of the hTrihex robot: transformation, translation, and orientation correction. The CACP framework is verified both in the simulation environment and in the real environment. From the experimental results, proposed CACP capable of generating the Pareto-optimal outcome that navigates the robot from the goal to destination in various workspaces, and the algorithm could be adopted to other tiling robot platforms with multiple configurations.
完成区域覆盖规划（CACP）在机器人学的各个领域中起着至关重要的作用，例如区域探索、搜索、救援、安全、清洁和维护。具有改变形状能力的平铺机器人是增强覆盖具有灵活大小的预定义地图区域的能力并进入狭窄空间限制的可行解决方案。通过将地图划分为与可变形机器人形状相同大小的子区域，机器人可以规划最佳运动到预定位置，改变其形态以覆盖特定区域，并确保地图完全覆盖。本文解决了最优导航规划问题，包括最小的变形形状、最短的行进距离和最低的行进时间，同时确保了地图区域的完全覆盖。为此，我们提出了一种名为 hTrihex 的平铺机器人的 CACP 框架，它具有三个蜂窝形状的模块。机器人可以将其形状转换为三种不同的形态，以确保覆盖预定大小的地图。然而，改变形状的能力也增加了运动机制的复杂性问题。因此，根据旅行商问题（TSP）对完整覆盖轨迹的优化过程进行建模，并通过遗传算法（GA）和蚁群优化（ACO）等进化方法进行求解。因此，TSP 中清除一对航点的成本权重定义为机器人在两个位置之间移动所需的能量。该能量对应于 hTrihex 机器人的三个操作过程：转换、平移和方向校正。CACP 框架在仿真环境和真实环境中都得到了验证。从实验结果可以看出，所提出的 CACP 能够生成 Pareto 最优结果，即在各种工作空间中引导机器人从目标到目的地，并且该算法可以应用于具有多种配置的其他平铺机器人平台。
