Control strategies for cleaning robots in domestic applications: A comprehensive review

Abstract

Service robots are built and developed for various applications to support humans as companion, caretaker, or domestic support. As the number of elderly people grows, service robots will be in increasing demand. Particularly, one of the main tasks performed by elderly people, and others, is the complex task of cleaning. Therefore, cleaning tasks, such as sweeping floors, washing dishes, and wiping windows, have been developed for the domestic environment using service robots or robot manipulators with several control approaches. This article is primarily focused on control methodology used for cleaning tasks. Specifically, this work mainly discusses classical control and learning-based controlled methods. The classical control approaches, which consist of position control, force control, and impedance control , are commonly used for cleaning purposes in a highly controlled environment. However, classical control methods cannot be generalized for cluttered environment so that learning-based control methods could be an alternative solution. Learning-based control methods for cleaning tasks can encompass three approaches: learning from demonstration (LfD), supervised learning (SL), and reinforcement learning (RL). These control approaches have their own capabilities to generalize the cleaning tasks in the new environment. For example, LfD, which many research groups have used for cleaning tasks, can generate complex cleaning trajectories based on human demonstration. Also, SL can support the prediction of dirt areas and cleaning motion using large number of data set. Finally, RL can learn cleaning actions and interact with the new environment by the robot itself. In this context, this article aims to provide a general overview of robotic cleaning tasks based on different types of control methods using manipulator. It also suggest a description of the future directions of cleaning tasks based on the evaluation of the control approaches.

Path planning optimization of six-degree-of-freedom robotic manipulators using evolutionary algorithms

“…The D-H method sets the orthonormal coordinate systems in each joint of the robotic manipulator, in such way that axis z k (where k is a counter determining the joint) matches the axis of movement of the joint k . 27 Once all the coordinate systems are positioned, the parameters θ k , d k , a k and α k are determined and they are placed in the homogeneous transformation matrix for the joint 28 –30…”

Design and Control of a Wall Cleaning Robot with Adhesion-Awareness

“…Moreover, the corresponding fuzzy consequents are clipped by the respective firing strength of a rule. Consequently, the membership function µ R of the inferred fuzzy results is given by (8), where n is the number of rules, since the fuzzy union operator is used for the aggregation.…”

“…Therefore, robots have been developed to cater to demands in cleaning activities in various domains of building environments, including floor cleaning [4], garden cleaning [5], window cleaning [6], and staircase cleaning [7] with the aid of different cleaning methods, such as vacuuming and wiping [8]. In this regard, nowadays, special attention has to be paid in the development of wall cleaning robots to eliminate the health and safety concerns of human labor involved in the conventional cleaning process of tall vertical structures [9,10].…”

A modified YOLOv3 detection method for vision-based water surface garbage capture robot

“…The researches about cleaning robot are gradually becoming prevalent, [2][3][4][5][6][7][8][9] and performances in the grasping are fascinating. [10][11][12][13] In this context, a water surface garbage cleaner robot has been developed in our lab, which can be applied to clean up water surface garbage, reduce the labor volume of sanitation workers, and improve the water ecological environment.…”