Improvement of robot learning with combination of decision making and machine learning for water analysis

This study published in Robot Learning has been focused on water analysis using the combination of decision making and machine learning for a recently developed robotic system. The unique procedure the researchers have applied has significant impact on the improvement of robots’ performance that will be able to detect, analyze and distinguish drinking water on Earth and other planets.

Robot learning is an important ability required for water analysis without human intervention. For this purpose, the development of robots making them learn appropriate tasks and perform activities efficiently is based on the application of their leveraging skills and training. The benefits of autonomous robots able to perform water analysis are rapid response in crisis situations, sustainable resource management, planetary exploration and reduction in human intervention. Although robot learning has been investigated for the development of robots’ different tasks such as object manipulation, item cleaning and interactive or multi-task learning, it has not been investigated for water analysis using the combination of decision-making and machine learning (ML).

Drinking water detection and distinction are important robots’ tasks. Heavy metals and organic materials are toxic water pollutants that have caused health and environmental problems worldwide. It is required to develop robots able to detect these contaminants and distinguish drinking water on Earth and other planets without human intervention. 

For years, ML has been investigated without combination with decision making for water analysis. However, human decision making based on categorization is a preliminary step for learning. Therefore, the combination of both processes has been required for a more appropriate analysis of water samples for robotics. Therefore, the researchers in the current work have applied the combination of decision making and ML for improvement of robot learning for water analysis.

For the first time, the researchers have applied the combination of decision making using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and ML using Microsoft Visual Studio codes in Python. The Random Forest Classifier, a supervised ML algorithm, was used for water analysis.

The information on more than 3200 water samples available in the dataset section of the Kaggle website; “Water Quality and Potability Dataset” was used for water analysis. Dataset preprocessing was performed by completing the data table before analysis.

The TOPSIS analysis of water samples showed that the candidates having high values of profit criteria and low values of cost criteria had a better rank. The same result was obtained in the analysis of the physicochemical properties of and ingredients of water samples. The ML simulation showed that using the modified code could improve the learning accuracy to 69%, which improved to 73% after using Synthetic Minority Over-sampling Technique (SMOTE) for class balancing and tuning the hyperparameters.

The robotic system that has been designed and developed for the application of simulation software includes electronic devices such as DC thruster or drive motors, battery, solar panel and DC/DC converter. In this system, control has been carried out via a remote control and a command receiver. The remote control having four channels showed an adjustable speed, Moreover, the direction could be adjusted well and easily to make the model go straight. The information receiving board at the input end showed reverse connection protection, the output has a self-recovery fuse, to adapt and stable signal.

The designed robotic system would be able to deposit the water samples in the storage area where they would be collected by steering the ship via an arm. The equipment needed to build the ship’s platform containing motors, battery, solar panel, DC/DC converter, robotic arm, and sensors have been developed for monitoring the physical and chemical parameters of water. After collecting water samples using the motorized platform and robotic arm. sensors would measure the physicochemical parameters of the samples in real time. These sensor readings are processed by the onboard system, where the trained ML model combined with the decision-making process would classify the water samples as drinkable or undrinkable. The classification results guide the robot’s decision-making process for storing the sample or discarding it. Therefore, hardware control would be integrated with intelligent analysis.

To address the growing need for efficient water resource management and exploration, advanced robotic systems would be equipped with autonomous water sample analysis capabilities. These enhancements will be realized through the integration of cutting-edge technologies in robotics, sensor systems, and artificial intelligence. The challenges related to sensor accuracy, data noise, and system scalability provide a balanced perspective on the feasibility of the developed robotic water analysis system by highlighting both potential limitations and strategies for overcoming them. Therefore, addressing these issues would ensure that the system remains practical and effective for real-world applications.

While the obtained results are promising for the future of robotics, further investigations including the application of sensors would be required for the implementation of the results of the current work in the developed robotic system. This could help develop a unique robotic platform for the detection, analysis and distinction of drinking water on Earth and other planets.

Javanbakht T, Pajaziti A, Buza S. Combination of decision making and machine learning for improvement of robot learning for water analysis. Robot Learn. 2025(2):0006, https://doi.org/10.55092/rl20250006