Multiobjective Environmental Cleanup with AutonomousSurface Vehicle Fleets Using Multitask Multiagent DeepReinforcement Learning
Plastic pollution in water bodies threatens and disrupts aquatic life, requiring effective cleanup solutions. This paper proposes a strategy for plastic cleanup using a fleet of autonomous surface vehicles in a multitask scenario, with a focus on both exploration and cleaning tasks. The mission is d...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:dnet:idus________::b785026768685600e62b873e5f6d3192 |
| Acceso en línea: | https://hdl.handle.net/11441/185550 https://doi.org/10.1002/aisy.202500434 |
| Access Level: | acceso abierto |
| Palabra clave: | Autonomous surface vehicles Environmental monitoring Multiobjective optimization Multitask multiagent deep Reinforcement learning Partially observable Markov games |
| Sumario: | Plastic pollution in water bodies threatens and disrupts aquatic life, requiring effective cleanup solutions. This paper proposes a strategy for plastic cleanup using a fleet of autonomous surface vehicles in a multitask scenario, with a focus on both exploration and cleaning tasks. The mission is decoupled into two phases: an exploration phase for locating trash and a cleaning phase for collection. A Multitask Deep Q-Network with two heads estimates Q-values for each task, and all ASVs share the same policy through an egocentric state formulation to enhance scalability. A multiobjective learning approach is applied, resulting in distinct policies that balance the duration of the exploration and cleaning phases, leading to the construction of a Pareto front, which provides a visual representation of trade-offs between task priorities. The framework adapts to various environmental conditions, demonstrated in both the larger Malaga Port and the smaller Alamillo Lake. The study also highlights the importance of a dedicated exploration phase for larger areas, while minimal exploration is sufficient for smaller spaces. Compared to the decomposition weighting sum strategy, the approach consistently produces superior Pareto-optimal policies, ensuring broader and more effective exploration of the objective space. |
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