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.

Design and optimization of intelligent cleaning robot in chemical fiber textile workshop

From artificial turf to medical fibers, chemical fiber materials have become a new type of material, and chemical fiber textile materials have long gone beyond the scope of clothing. They play an important role in the fields of medical, clinical, chemical and other fields. The handling is not optimistic. Therefore, this article analyzes the working principle and process of the intelligent cleaning machine, and uses this as the theoretical basis to design the structure of the intelligent cleaning robot for the textile workshop, and then proceed from the functionality and aesthetics to its appearance design and optimization. A simple but distinctive appearance, while also greatly improving work efficiency.

Design of the control system of the laser cleaning robot facing the inner wall of the tiny pipe

In order to solve the problem of cleaning small and micro pipelines, the control system of the cleaning robot for small and micro pipelines is designed using single-chip technology. Draw the circuit design flow chart, use PROTEUS software to design the display and control circuit of the DC adjustable regulated power supply, stepping motor. Write and debug the program that controls the start and stop of the motor, accelerate and decelerate, and rotate forward and reverse in keil4; import the program into the single-chip microcomputer, and use the PROTEUS software for simulation and simulation. The experimental results show that the designed control system can realize the two actions of the pipeline robot going straight in the pipeline and the cleaning head rotating.

Ocean Surface Cleaning Autonomous Robot (OSCAR) using Object Classification Technique and Path Planning Algorithm

Increasing water pollution is one of the biggest concerns in today’s world. It leads to a variety of problems including an increase in the level of toxic concentration in the water. This paper aims to introduce a concept of an ocean/water body cleaning robot that attempts to classify the wastes using a camera with a custom machine learning model and segregate accordingly using separators while collecting them on the basket attached, that can be recycled on the base station. The robot can be deployed on any water surface thus making it more effective than a largescale ocean pollution cleaning technique. It can be used to clean up oil spills from shipwreck and pipeline leakage and can monitor the water quality of the particular location and send a distress signal to the base station if the readings are abnormal. The water quality data and the information about the type of pollutants from the machine learning model can be used to formulate local laws to reduce pollution and create awareness about the type of material that ends up at the ocean/water body.