Design experimental of RF controlled beach cleaner robotic vehicle

Praveen, R.; Prabhu, L.; Premjith, P; Mohan, Adarsh. K.; Ajayraj,

doi:10.1088/1757-899x/993/1/012030

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…The application of autonomous robotics in beach cleaning represents a developing area of research, still in its early stages but showing significant potential for advancement [10,11]. Traditional beach-cleaning robots, which are predominantly large-scale [12], often require human supervision and/or control, limiting their autonomy and operational efficiency [13][14][15]. However, recent advancements in DRL and simulation technologies have introduced new opportunities for improving these systems.…”

Section: State Of Artmentioning

confidence: 99%

RL-Based Sim2Real Enhancements for Autonomous Beach-Cleaning Agents

Quiroga,

Hermosilla,

Varas

et al. 2024

Applied Sciences

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This paper explores the application of Deep Reinforcement Learning (DRL) and Sim2Real strategies to enhance the autonomy of beach-cleaning robots. Experiments demonstrate that DRL agents, initially refined in simulations, effectively transfer their navigation skills to real-world scenarios, achieving precise and efficient operation in complex natural environments. This method provides a scalable and effective solution for beach conservation, establishing a significant precedent for the use of autonomous robots in environmental management. The key advancements include the ability of robots to adhere to predefined routes and dynamically avoid obstacles. Additionally, a newly developed platform validates the Sim2Real strategy, proving its capability to bridge the gap between simulated training and practical application, thus offering a robust methodology for addressing real-life environmental challenges.

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Section: State Of Artmentioning

confidence: 99%

RL-Based Sim2Real Enhancements for Autonomous Beach-Cleaning Agents

Quiroga,

Hermosilla,

Varas

et al. 2024

Applied Sciences

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

Adarsh

Anush

Shrivarshan

et al. 2021

J. Phys.: Conf. Ser.

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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.

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