Complete area-coverage path planners are essential for robots performing tasks like cleaning, inspection, and surveying. However, they often involve complex calculations, mapping, and determining movement directions, leading to high computational or processing overheads and the risk of deadlocks. This paper proposes an approach for cleaning, i.e., by linear wiping of generic and discontinuous surfaces in hospital settings using inhouse assembled mobile dual-arm (MDA) robotic system. The proposed framework introduces key features: (a) a less resource-intensive approach for MDA positioning and cleaning surface mapping, (b) Modified Glasius Bioinspired Neural Network through use of heuristics (GBNN+H) to optimize surface linear wiping while obstacle avoidance, and traversal across discontinuous surfaces. The advantages of the proposed algorithm are highlighted in simulation with GBNN+H significantly reduces the number of steps and flight time required for complete coverage compared to existing algorithms. The proposed framework is also experimentally demonstrated in a hospital setting, paving the way for improved automation in cleaning and disinfection tasks. Overall, this work presents a generic and versatile, applicable to various surface orientations and complexities.

完整的区域覆盖路径规划器对于机器人执行清洁、检查和测量等任务至关重要。然而，它们通常涉及复杂的计算、映射和确定移动方向，导致较高的计算或处理开销以及死锁的风险。本文提出了一种清洁方法，即使用内部组装的移动双臂（MDA）机器人系统对医院环境中的通用和不连续表面进行线性擦拭。所提出的框架引入了关键特征：（a）用于 MDA 定位和清洁表面映射的资源密集度较低的方法，（b）通过使用启发式（GBNN + H）改进的 Glasius Bioinspired 神经网络来优化表面线性擦拭，同时避障，以及遍历不连续的表面。与现有算法相比，GBNN+H 仿真中凸显了该算法的优点，显着减少了完全覆盖所需的步骤数和飞行时间。所提出的框架还在医院环境中进行了实验演示，为提高清洁和消毒任务的自动化铺平了道路。总的来说，这项工作呈现出通用性和多功能性，适用于各种表面方向和复杂性。