Motion analysis of screw drive in-pipe cleaning robot

In-pipe cleaning robots often need to carry cleaning tools, and their tails are connected with cables such as water pipes and air pipes. Especially when cleaning vertical straight pipes and curved pipes, a greater traction is required. Therefore, a new type of screw drive in-pipe cleaning robot was designed in this paper. The robot solves the problems of small traction, complex structure, and unstable motion of the in-pipe cleaning robot. The kinematics modeling was carried out on the screw drive in-pipe cleaning robot’s screw module for generating traction, and the force analysis was performed on this basis. The function model of the torque, air pressure, and traction of the screw module was established, which was verified by the simulation and experiment. The results show that the screw in-pipe cleaning robot has a large traction, stable operation, and can be well adapted to the vertical straight pipes and curved pipes.

AI-Enabled Predictive Maintenance Framework for Autonomous Mobile Cleaning Robots

Vibration is an indicator of performance degradation or operational safety issues of mobile cleaning robots. Therefore, predicting the source of vibration at an early stage will help to avoid functional losses and hazardous operational environments. This work presents an artificial intelligence (AI)-enabled predictive maintenance framework for mobile cleaning robots to identify performance degradation and operational safety issues through vibration signals. A four-layer 1D CNN framework was developed and trained with a vibration signals dataset generated from the in-house developed autonomous steam mopping robot ‘Snail’ with different health conditions and hazardous operational environments. The vibration signals were collected using an IMU sensor and categorized into five classes: normal operational vibration, hazardous terrain induced vibration, collision-induced vibration, loose assembly induced vibration, and structure imbalanced vibration signals. The performance of the trained predictive maintenance framework was evaluated with various real-time field trials with statistical measurement metrics. The experiment results indicate that our proposed predictive maintenance framework has accurately predicted the performance degradation and operational safety issues by analyzing the vibration signal patterns raised from the cleaning robot on different test scenarios. Finally, a predictive maintenance map was generated by fusing the vibration signal class on the cartographer SLAM algorithm-generated 2D environment map.

A Deep Learning-Based Dirt Detection Computer Vision System for Floor-Cleaning Robots with Improved Data Collection

Floor-cleaning robots are becoming increasingly more sophisticated over time and with the addition of digital cameras supported by a robust vision system they become more autonomous, both in terms of their navigation skills but also in their capabilities of analyzing the surrounding environment. This document proposes a vision system based on the YOLOv5 framework for detecting dirty spots on the floor. The purpose of such a vision system is to save energy and resources, since the cleaning system of the robot will be activated only when a dirty spot is detected and the quantity of resources will vary according to the dirty area. In this context, false positives are highly undesirable. On the other hand, false negatives will lead to a poor cleaning performance of the robot. For this reason, a synthetic data generator found in the literature was improved and adapted for this work to tackle the lack of real data in this area. This synthetic data generator allows for large datasets with numerous samples of floors and dirty spots. A novel approach in selecting floor images for the training dataset is proposed. In this approach, the floor is segmented from other objects in the image such that dirty spots are only generated on the floor and do not overlap those objects. This helps the models to distinguish between dirty spots and objects in the image, which reduces the number of false positives. Furthermore, a relevant dataset of the Automation and Control Institute (ACIN) was found to be partially labelled. Consequently, this dataset was annotated from scratch, tripling the number of labelled images and correcting some poor annotations from the original labels. Finally, this document shows the process of generating synthetic data which is used for training YOLOv5 models. These models were tested on a real dataset (ACIN) and the best model attained a mean average precision (mAP) of 0.874 for detecting solid dirt. These results further prove that our proposal is able to use synthetic data for the training step and effectively detect dirt on real data. According to our knowledge, there are no previous works reporting the use of YOLOv5 models in this application.

sTetro-Deep Learning Powered Staircase Cleaning and Maintenance Reconfigurable Robot

Staircase cleaning is a crucial and time-consuming task for maintenance of multistory apartments and commercial buildings. There are many commercially available autonomous cleaning robots in the market for building maintenance, but few of them are designed for staircase cleaning. A key challenge for automating staircase cleaning robots involves the design of Environmental Perception Systems (EPS), which assist the robot in determining and navigating staircases. This system also recognizes obstacles and debris for safe navigation and efficient cleaning while climbing the staircase. This work proposes an operational framework leveraging the vision based EPS for the modular re-configurable maintenance robot, called sTetro. The proposed system uses an SSD MobileNet real-time object detection model to recognize staircases, obstacles and debris. Furthermore, the model filters out false detection of staircases by fusion of depth information through the use of a MobileNet and SVM. The system uses a contour detection algorithm to localize the first step of the staircase and depth clustering scheme for obstacle and debris localization. The framework has been deployed on the sTetro robot using the Jetson Nano hardware from NVIDIA and tested with multistory staircases. The experimental results show that the entire framework takes an average of 310 ms to run and achieves an accuracy of 94.32% for staircase recognition tasks and 93.81% accuracy for obstacle and debris detection tasks during real operation of the robot.

Design of a Reconfigurable Wall Disinfection Robot

During a viral outbreak, such as COVID-19, autonomously operated robots are in high demand. Robots effectively improve the environmental concerns of contaminated surfaces in public spaces, such as airports, public transport areas and hospitals, that are considered high-risk areas. Indoor spaces walls made up most of the indoor areas in these public spaces and can be easily contaminated. Wall cleaning and disinfection processes are therefore critical for managing and mitigating the spread of viruses. Consequently, wall cleaning robots are preferred to address the demands. A wall cleaning robot needs to maintain a close and consistent distance away from a given wall during cleaning and disinfection processes. In this paper, a reconfigurable wall cleaning robot with autonomous wall following ability is proposed. The robot platform, Wasp, possess inter-reconfigurability, which enables it to be physically reconfigured into a wall-cleaning robot. The wall following ability has been implemented using a Fuzzy Logic System (FLS). The design of the robot and the FLS are presented in the paper. The platform and the FLS are tested and validated in several test cases. The experimental outcomes validate the real-world applicability of the proposed wall following method for a wall cleaning robot.

Automatic Recharging Path Planning for Cleaning Robots

To solve the problem of automatic recharging path planning for cleaning robots in complex industrial environments, this paper proposes two environmental path planning types based on designated charging location and multiple charging locations. First, we use the improved Maklink graph to plan the complex environment; then, we use the Dijkstra algorithm to plan the global path to reduce the complex two-dimensional path planning to one dimension; finally, we use the improved fruit fly optimization algorithm (IFOA) to adjust the path nodes for shorting the path length. Simulation experiments show that the effectiveness of using this path planning method in a complex industrial environment enables the cleaning robot to select a designated location or the nearest charging location to recharge when the power is limited. The proposed improved algorithm has the characteristics of a small amount of calculation, high precision, and fast convergence speed.

Development of following robot for supplying power to solar panel cleaning robot

Purpose Autonomous mobile cleaning robots are widely used to clean solar panels because of their flexibility and high efficiency. However, gravity is a challenge for cleaning robots on inclined solar panels, and robots have problems such as high working power and short battery life. This paper aims to develop a following robot to improve the working time and efficiency of the cleaning robot. Design/methodology/approach The mechanical structure of the robot is designed so that it can carry a large-capacity battery and continuously power the cleaning robot. The robot determines its position and orientation relative to the edge of solar panel by using optoelectronic sensors. Based on the following distance, the robot changes its state between moving and waiting to ensure that supply cable will not drag. Findings Prototype following robot test results show that the following robot can stably follow the cleaning robot and supply continuous power to cleaning robot. The linear error of the following robot moving along the solar panel is less than 0.3 m, and the following distance between the robot and the cleaning robot is in 0.5–1.5 m. Practical implications The working time of cleaning robots and working efficiency is improved by using following robot, thereby reducing the labor intensity of workers and saving the labor costs of cleaning. Originality/value The design of the following robot is innovative. Following robot works with the existing cleaning robots to make up for shortcomings of the existing cleaning system. It provides a more feasible and practical solution for using robots to clean solar panels.

Modeling and control of a novel facade cleaning robot with four-ducted fan drive

With the development of modern cities, the demand for cleaning glass curtain walls in urban skyscrapers is increasing, and therefore, many researches on facade cleaning robots have been carried out in the past few decades. In this article, a novel type of facade cleaning robot based on four-ducted fan is proposed. To improve the load capacity of the robot, the lifting and horizontal movement are, respectively, supported by an independent lifting mechanism and a lateral movement mechanism. Unlike the previous passive and active suction, the powerful suction power of the robot is provided by the four-ducted fan. Meanwhile, the obstacle-climbing force is caused by the forward of the four-ducted fan that improves the crossing obstacle ability. In addition, an incremental sliding mode algorithm is applied for controlling the posture when the robot crosses obstacles, which can ensure the stable performance of the cleaning robot under disturbance. Some simulations and experiments are conducted and the results demonstrate the effectiveness and robustness of the designed facade cleaning robot.

Coverage Path Planning Using Reinforcement Learning-Based TSP for hTetran—A Polyabolo-Inspired Self-Reconfigurable Tiling Robot

One of the critical challenges in deploying the cleaning robots is the completion of covering the entire area. Current tiling robots for area coverage have fixed forms and are limited to cleaning only certain areas. The reconfigurable system is the creative answer to such an optimal coverage problem. The tiling robot’s goal enables the complete coverage of the entire area by reconfiguring to different shapes according to the area’s needs. In the particular sequencing of navigation, it is essential to have a structure that allows the robot to extend the coverage range while saving energy usage during navigation. This implies that the robot is able to cover larger areas entirely with the least required actions. This paper presents a complete path planning (CPP) for hTetran, a polyabolo tiled robot, based on a TSP-based reinforcement learning optimization. This structure simultaneously produces robot shapes and sequential trajectories whilst maximizing the reward of the trained reinforcement learning (RL) model within the predefined polyabolo-based tileset. To this end, a reinforcement learning-based travel sales problem (TSP) with proximal policy optimization (PPO) algorithm was trained using the complementary learning computation of the TSP sequencing. The reconstructive results of the proposed RL-TSP-based CPP for hTetran were compared in terms of energy and time spent with the conventional tiled hypothetical models that incorporate TSP solved through an evolutionary based ant colony optimization (ACO) approach. The CPP demonstrates an ability to generate an ideal Pareto optima trajectory that enhances the robot’s navigation inside the real environment with the least energy and time spent in the company of conventional techniques.