Solar Powered Beach Cleaning Robot Literature Review

This literature review surveys recent innovations in solar-powered beach cleaning robots. These systems aim to autonomously collect waste from sandy coastal areas using renewable energy, enhancing environmental sustainability and operational autonomy. The review includes peer-reviewed IEEE papers and academic journals, summarized with emphasis on control systems, autonomy, and design optimization.

Google Scholar and IEEE Xplore were searched using the terms: "beach cleaning robot", "solar powered beach cleaning robot", "autonomous garbage collection robot", and "solar energy for robotics", "amphibious beach cleaner". Additional sources were identified through ResearchGate and arXiv. Key sources were selected based on relevance, publication quality, and access.

Chen, Y.-H., & Pan, S.-Y. (2025). Design and implementation of a cleaning robot. Mechanical Engineering Advances, 3(1), 2150. https://doi.org/10.59400/mea2150

Indoor/outdoor robot for autonomous waste detection, classification, and collection

Integrates mechanical, circuit, and software design

YOLOv5 + CSRT used for real-time waste detection and tracking

Hardware includes Raspberry Pi, Arduino Mega, ultrasonic sensors, and mecanum wheels

Conveyor and belt-driven cleaning mechanisms

Successfully tested for accuracy and waste sorting (95% accuracy on standard shapes)

Focus on adaptability and computational efficiency in embedded systems

Rivadeneira, F., Martínez, S., Terán, A., Arauco, B., Flores, J., & Furukawa, R. (2023). Maqya: Design of an autonomous low cost beach-cleaner robot for endanger beaches. 2023 IEEE XXX International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Lima, Peru, pp. 1-6. DOI : https://doi.org/10.1109/INTERCON59652.2023.10326032

A low-cost autonomous robot is proposed to tackle beach pollution.

It uses YOLO V8 computer vision to detect garbage on sand and a mechanical system for collection.

Simulated in ROS, the robot's model achieved strong performance: 90.95% precision, 78.37% recall, and an 84.19% F1-Score.

The project addresses the severe global issue of marine litter, highlighting its environmental and health impacts, especially on beaches in Peru.

Mallikarathne, T., Abeysinghe, H., Rathnayake, C., & Perera, M. (2023). Design a beach cleaning robot based on AI and Node-RED interface for debris sorting and monitor the parameters. In Proceedings of the 7th SLAI International Conference on Artificial Intelligence (SLAAI-ICAI). IEEE. https://doi.org/10.1109/SLAAI-ICAI59257.2023.10365015

Developed an AI-enabled, solar-powered beach cleaning robot.

Uses sensors (LIDAR, DHT22, GPS, Load Cell, Gas sensors) for monitoring environmental parameters.

Incorporates a Node-RED interface for real-time parameter visualization and user interaction.

Performs AI-based debris sorting (plastic, organic, paper) using Support Vector Machines (SVM).

Robot construction uses Raspberry Pi and aluminum chassis for durability and efficiency.

Simulation and data processing conducted using Proteus and multiple linear regression to estimate cleaning efficiency.

Achieved an average cleaning efficiency of 83%, positively influenced by solar irradiance, temperature, and debris weight.

Ahmet B. Tatar et al., "A Conceptual Design of Solar-Powered Water Surface Garbage Cleaning Robot", 4th International Conference on Artificial Intelligence, Robotics and Control (AIRC), pp. 78–81, 2023.

https://doi.org/10.1109/AIRC57904.2023.10303130

Focuses on marine plastic pollution and proposes a low-cost robotic solution

Concept design created using SolidWorks for a solar-powered, surface-mounted garbage robot

Utilizes paddlewheel propulsion and front mesh conveyor to collect floating debris

Includes RGB camera, GNSS + RTK, IMU, and ultrasonic sensors in future hardware plan

Portable, lightweight garbage hopper made from PLA, designed for easy maintenance

Solar panels and batteries support full operation without external power

Intended for rivers, marinas, shallow waters; emphasizes autonomous and remote cleaning capability

A. B. Tatar et al., "A Conceptual Design of Solar-Powered Water Surface Garbage Cleaning Robot", 4th Int. Conf. on Artificial Intelligence, Robotics and Control (AIRC), pp. 78-81, 2023. https://doi.org/10.1109/AIRC57904.2023.10303130

Solar-powered paddlewheel robot for shallow water cleaning

Uses GNSS-RTK, IMU, ultrasonic sensors, RGB camera for nav & environment awareness

Waste collected using grid conveyor and stored in a detachable PLA hopper

NVIDIA Jetson Nano for onboard image processing and control

Remote-controlled within 2 km, low-cost and portable for river/lake use

Yanghai Zhang et al., "A Spiral-Propulsion Amphibious Intelligent Robot for Land Garbage Cleaning and Sea Garbage Cleaning", Journal of Marine Science and Engineering, Vol. 11(8), Article 1482, pp. 1–13, 2023.

https://doi.org/10.3390/jmse11081482

Amphibious robot with spiral drum propulsion suitable for beaches, marshes, and water surfaces

Mechanical shovel arm with 2 degrees of freedom for effective garbage collection

Constructed with stainless steel spiral and controlled via STM32 board and ESP32-CAM

Supports both infrared remote and app-based control with real-time video feedback

Can collect and offload trash up to 10 kg, with 0.5 m³ capacity, over a range of 80 m

Tested with average speed of 0.29 m/s on land and 0.33 m/s on water

Provides a multifunctional, mobile solution for cleaning hard- to-access litter zones

Herath, H. M. R. G., Jayasooriya, M. W. S. B., Weerasinghe, G. C. B., & Dharmarathna, K. K. N. (2022). Design and implement of beach cleaning robot. Conference Paper. https://www.researchgate.net/publication/365181947

Wireless beach cleaning robot using IoT and RF control

Designed for Sri Lanka’s polluted coastlines to reduce manual labor

Features garbage-level monitoring via Blynk app

Uses LiPo battery, WeMos Mini Wi-Fi, ultrasonic sensors

Built from locally sourced components for easy maintenance

Continuous track undercarriage for sandy terrain

Shovel and conveyor belt system for waste transfer

Focus on cost-effective, remotely monitored autonomous cleanup

Kong Ke Long, Chandrasekharan Nataraj, & Yvette Shaan-Li Susiapan. (2022). Autonomous Garbage-Collecting Robot For Beaches With Deep Learning Approach and Improved Cleaning Technique. JATI Journal of Applied Technology and Innovation, 6(2). Developed an autonomous beach-cleaning robot using deep learning

Used CNN with TensorFlow and a Pi Camera for object detection

Detected garbage using bounding boxes and adjusted position with motors

Collected waste using rotary blades and a mesh-net platform; emptied via a movable gate

Powered by rechargeable Li-ion batteries and solar panels

Aimed to reduce time, cost, and human effort in beach cleaning

Varghese, D., & Mohan, A. (2022). Binman: An autonomous beach cleaning robot. In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) (pp. 1–5). IEEE. DOI- https://doi.org/10.1109/MysuruCon55714.2022.9972499

Autonomous beach cleaning robot named “Binman”

Uses GPS and ROS for navigation and path planning

Equipped with a surf rake-style debris collection system

Designed to reduce human health risks and manual labor

Simulation tested in Gazebo for real-world viability

No solar or renewable energy mentioned – likely battery powered

Elkolali, M., Al-Tawil, A., Much, L., Schrader, R., Masset, O., Sayols, M., Jenkins, A., Alonso, S., Carella, A., & Alcocer, A. (2021). A low-cost wave-solar powered Unmanned Surface Vehicle. arXiv preprint arXiv:2112.03685.

USV prototype powered by both wave and solar energy for data collection and cleaning.

Designed to monitor water quality: pH, salinity, DO, temperature.

Uses low-cost sensors, autonomous navigation, and solar charging for long-term deployment.

Targeted for use in lakes, coastal areas, and estuarine environments.

CN217203825U. (2021). Sandy Beach Rubbish Clearance Robot. Chinese Utility Patent. https://patents.google.com/patent/CN217203825U/en

Solar-powered robot designed for beach garbage collection.

Equipped with electric drive and solar charging to minimize noise and energy use.

Utilizes storage battery for continuous autonomous operation.

Designed for efficient and sustainable cleaning of sandy environments

Bhalerao, A. S., Khandagale, S. P., Patil, R. A., & Jagtap, A. B. (2020). Sobot – Solar-Based Beach Cleaning Robot. International Journal of Scientific Research in Science and Technology, 7(3), 652–656. DOI- : https://doi.org/10.32628/IJSRST

Solar-powered for eco-friendly and remote operation.

Autonomous navigation using ultrasonic, IR sensors, GPS, and ESP32-CAM.

Image processing identifies waste (e.g., plastic bottles) via cloud comparison.

Waste collection system with 10-liter capacity and auto-return for unloading.

Reduces human effort and enhances beach cleanliness using sustainable tech.

M. N. Mohammed et al., "Design and Development of River Cleaning Robot Using IoT Technology", 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), pp. 84-87, 2020. https://doi.org/10.1109/CSPA48992.2020.9068718

Solar-powered with IoT monitoring for real-time operation

Controlled via smartphone, uses GPS and DC motors for semi-autonomous motion

Waste collection via floating conveyor and mechanical slab

pH and ultrasonic sensors for environment sensing and obstacle avoidance

CAD and Proteus simulation for design verification

Analene M. Nagayo et al., "Autonomous Trash Collector Robot with Wireless Charging System in a Campus Environment", BIUST Research and Innovation Symposium (RDAIS), Botswana, pp. 147–151, 2019.

https://biust.ac.bw/wp-content/uploads/2020/11/BIUST_Research_and_Innovation_Symposium_2019.pdf

Arduino-controlled autonomous robot using GSM and GPS modules for monitoring and localization

Uses QTR reflectance sensors, ultrasonic and color sensors for line-following, obstacle avoidance, and destination detection

Solar-powered wireless charging system with electromagnetic induction at docking station

Trash pickup by arm and claw mechanism, bin unloading with electromagnet and gravity

Achieves 88% line-following reliability and 80% pick-and-place accuracy

Designed for clean campus environments, encourages minimal human involvement

Recommends future integration of image processing and additional sensors for improved accuracy

The Searial Cleaners. (n.d.). BeBot: The Beach Cleaning Robot. Retrieved from https://searial-cleaners.com/our-cleaners/bebot-the-beach-cleaner/

BeBot is a 100% electric, solar-assisted robot developed for sustainable beach cleaning.

Sifts sand up to 10 cm deep, collecting microplastics and waste over 3,000 m²/hour.

Quiet, remote-controlled operation suitable for public beach areas.

Reduces environmental impact while promoting eco-tourism and manual labor savings.

Schmoeller da Roza, F., Ghizoni da Silva, V., Pereira, P. J., & Bertol, D. W. (2016). Modular robot used as a beach cleaner. Ingeniare. Revista chilena de ingeniería, 24(4), 643–653. https://revistas.uta.cl/pdf/2193/art09.pdf

Developed a modular mobile robot adapted for autonomous beach cleaning.

Robot collects aluminum cans using a servo-actuated excavator-style claw.

Uses computer vision with HSV color space and optical flow for object detection and obstacle avoidance.

Equipped with a differential drive system and high-torque DC motors.

Controlled via PID speed controller, tuned using the AMIGO method.

Citation: Raza, U., Aslam, M. W., & Shah, J. H. (2024). EcoDetect: Real-Time Lightweight Waste Detection for Robotic Sorting. Sensors, 24(14), 4666. https://doi.org/10.3390/s24144666

Model used: EcoDetect-YOLO (YOLOv5s with BiFPN and CBAM attention)

Accuracy: Achieved high precision in detecting cluttered, occluded, and small waste objects indoors

Special features: Tailored for real-time edge deployment; uses attention mechanisms (CBAM) and BiFPN for efficient multi-scale feature fusion

Citation: Kabra, A., Dakkireddy, K., & Grover, D. (2021). DeepWaste: A Dataset and Classification Model for Smart Waste Disposal. arXiv preprint arXiv:2101.05960. https://arxiv.org/abs/2101.05960

Model used: ResNet-based classifier (DeepWaste)

Accuracy: 88.1% accuracy on multi-class classification (compost, recycle, landfill)

Special features: Designed for mobile/embedded devices; low latency; can be adapted for on-site waste detection without cloud dependency

Citation: Choudhary, P., & Singh, R. (2023). ConvoWaste: A Convolutional Neural Network-Based Smart Waste Management System. arXiv preprint arXiv:2302.02976. https://arxiv.org/abs/2302.02976

Model used: Deep Convolutional Neural Network (DCNN)

Accuracy: 98% on a curated image dataset of recyclable vs. non-recyclable waste

Special features: Real-time classification and physical sorting mechanism; includes GSM-based alerts; best for structured indoor use

Citation: Kim, Y., & Han, J. (2022). Vision-Based Litter Detection for Aerial Surveillance. Symmetry, 14(5), 960. https://doi.org/10.3390/sym14050960

Model used: Custom CNN object detector optimized for UAVs

Accuracy: Demonstrated high accuracy on real-world aerial images of outdoor litter

Special features: Designed for use on drones; robust to camera tilt, sunlight, and varying terrain — ideal for open environments like beaches or parks

Zhang, Y., et al. (2022). An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet. International Journal of Environmental Research and Public Health, 19(23), 15987. DOI: https://doi.org/10.3390/ijerph192315987

Developed a lightweight and efficient waste classification model named GECM-EfficientNet, based on EfficientNetB0.

Integrated Efficient Channel Attention (ECA) and Coordinate Attention (CA) modules to enhance feature extraction.

Utilized transfer learning for model initialization, improving classification accuracy on the TrashNet dataset.

Achieved high accuracy with reduced computational requirements, suitable for real-time applications.

Citation: Li, X., et al. (2023). Focus-RCNet: A Lightweight Recyclable Waste Classification Algorithm Based on Focus and Knowledge Distillation. Visual Computing for Industry, Biomedicine, and Art, 6(1), 1-12.

DOI: https://doi.org/10.1186/s42492-023-00146-3

Introduced Focus-RCNet, a lightweight model for recyclable waste classification using knowledge distillation techniques.

Trained and evaluated on the TrashNet dataset, achieving high accuracy with reduced model size.

Implemented data augmentation methods, including random flips and brightness adjustments, to enhance model robustness.

Demonstrated suitability for deployment on resource-constrained devices. SpringerOpen

Chen, Y., et al. (2022). A Multi-Label Waste Detection Model Based on Transfer Learning. Resources, Conservation and Recycling, 181, 106235.

DOI: https://doi.org/10.1016/j.resconrec.2022.106235

Developed YOLO-WASTE, a multi-label waste classification model leveraging transfer learning techniques.

Constructed a dataset containing images with multiple waste categories per image to reflect real-world scenarios.

Achieved a mean Average Precision (mAP) of 92.23% with an average detection time of 0.424 seconds per image.

Enhanced the model's ability to detect and classify multiple waste types simultaneously, improving efficiency. ScienceDirect