Title: Autonomous System For Identifying and Capturing Floating Waste
 

Date: Friday, December 19th, 2025 

Time: 08:00-10:00 EST, 14:00-16:00 CET

Location: Georgia Tech-Europe - Pink room. Metz, France.

Remote option (Zoom):

    https://gatech.zoom.us/j/94516893977?pwd=Co6E9KKqLOfzxexutbgkiQ3MIROuUO.1

    Meeting ID: 945 1689 3977

    Passcode: 539957

Luis F. W. Batista

Computer Science Ph.D. Student

School of Interactive Computing in the College of Computing 

Georgia Institute of Technology

Committee

Dr. Cédric Pradalier (Advisor), Georgia Institute of Technology

Dr. Seth Hutchinson, Georgia Institute of Technology

Dr. Sehoon Ha, Georgia Institute of Technology

Dr. Alberto Ortiz, University of the Balearic Islands

Dr. Assia Benbihi, Sevensense

Dr. Claire Dune, Université de Toulon

Dr. Jeremy Fix, Centrale Supelec

Dr. Roland Lenain, INRAE

Abstract:

Plastic pollution in aquatic environments poses a critical challenge, causing significant harm to ecosystems and human health. Traditional cleanup methods are often labor-intensive and limited in scalability and coverage, prompting interest in autonomous robotic solutions. This dissertation investigates the development of an autonomous system for detecting and collecting floating waste, combining advanced visual perception and control based on reinforcement learning (RL) into an integrated system tailored for aquatic environments. The application not only addresses an important ecological problem but also presents substantial technical challenges in perception, navigation, and field deployment.

    The first contribution of this work is the enhancement of visual perception through polarimetric imaging. Specular reflections from water surfaces complicate detection tasks using standard cameras. To overcome this, the PoTATO dataset was created, containing images of floating plastic bottles captured with a polarimetric camera. The dataset includes multiple polarimetric modalities, and experiments demonstrate that polarimetric inputs, especially when fused with color information, improve detection accuracy under diverse lighting conditions.

    The second contribution is the development of a reinforcement learning framework for autonomous surface vehicle (ASV) navigation, incorporating hydrodynamic modeling and domain randomization to reduce the sim-to-real (simulation-to-reality) gap. The RL-based controller is evaluated with respect to energy efficiency and task completion time, and is tested under disturbances such as shifting payloads and asymmetric drag. Comprehensive field experiments demonstrate the robustness of the trained policies and validate the training approach.

    Finally, the dissertation integrates perception and control into an end-to-end pipeline for autonomous waste collection. Detected objects are mapped to navigation targets using calibrated transformations, and the ASV is guided by the RL agent. The integrated system is evaluated in both simulation and real-world settings, highlighting the feasibility for environmental cleanup applications.

    In summary, this work demonstrates a comprehensive approach to autonomous waste collection by advancing perception, control, and integration methods. Field trials are central to the evaluation, ensuring that the system performs reliably under real-world conditions. These findings highlight the potential of autonomous systems to contribute to environmental monitoring and waste mitigation.