Robots Learn To Detect Drinking Water With Machine Learning And Smart Decision-Making

By merging human-like decision-making with machine learning, researchers are building robots that can autonomously analyze water quality, paving the way for cleaner resources on Earth and exploration of other planets.

Research: Combination of decision making and machine learning for improvement of robot learning for water analysis. Image Credit: Lotus_studio / Shutterstock

Research: Combination of decision making and machine learning for improvement of robot learning for water analysis. Image Credit: Lotus_studio / Shutterstock

A new study, published in Robot Learning, has focused on water analysis using a combination of decision-making and machine learning for a recently developed robotic system. The unique procedure the researchers have applied has a significant impact on the improvement of robots' performance, enabling them to detect, analyze, and distinguish drinking water on Earth and other planets.

The Need for Robot Learning in Water Analysis

Robot learning is an important ability required for water analysis without human intervention. For this purpose, the development of robots that make 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 conduct water analysis are rapid response in crises, 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 tasks for robots. 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.

Combining Decision Making and Machine Learning

For years, ML has been investigated without being combined with decision-making for water analysis. However, human decision-making based on categorization is a preliminary step for learning. Therefore, combining both processes is necessary for a more suitable analysis of water samples for robotics. In this work, the researchers have applied decision-making and ML together to enhance 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 the Synthetic Minority Over-sampling Technique (SMOTE) for class balancing and tuning the hyperparameters.

Robotic System Design and Components

The robotic system designed and developed for the application of simulation software includes electronic devices such as a DC thruster or drive motors, a battery, a solar panel, and a DC/DC converter. In this system, control has been carried out via a remote control and a command receiver. The remote control, which had 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 maintain a 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, has 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.

Future Potential and Challenges

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 to implement the current work's results 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.

Source:
Journal reference:
  • Taraneh Javanbakht, Arbnor Pajaziti and Buza, S. (2025). Combination of decision making and machine learning for improvement of robot learning for water analysis. Robot Learning, [online] 2(2), pp.1–2. doi: 10.55092/rl20250006, https://www.elspub.com/papers/j/1901525004991582208.html 

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