Robotic Detection of a Human-Comprehensible Gestural Language for Underwater Multi-Human-Robot Collaboration

In this paper, we present a motion-based robotic communication framework that enables non-verbal communication among autonomous underwater vehicles (AUVs) and human divers. We design a gestural language for AUV-to-AUV communication which can be easily understood by divers observing the conversation unlike typical radio frequency, light, or audio based AUV communication. To allow AUVs to visually understand a gesture from another AUV, we propose a deep network (RRCommNet) which exploits a self-attention mechanism to learn to recognize each message by extracting maximally discriminative spatio-temporal features. We train this network on diverse simulated and real-world data. Our experimental evaluations, both in simulation and in closed-water robot trials, demonstrate that the proposed RRCommNet architecture is able to decipher gesture-based messages with an average accuracy of 88-94% on simulated data, 73-83% on real data (depending on the version of the model used). Further, by performing a message transcription study with human participants, we also show that the proposed language can be understood by humans, with an overall transcription accuracy of 88%. Finally, we discuss the inference runtime of RRCommNet on embedded GPU hardware, for real-time use on board AUVs in the field

Is the use of deep learning an appropriate means to locate debris in the ocean without harming aquatic wildlife?

With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems. Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life

Is the use of deep learning an appropriate means to locate debris in the ocean without harming aquatic wildlife?

With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems.Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1,644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life

An ecotoxicological approach to microplastics on terrestrial and aquatic organisms: A systematic review in assessment, monitoring and biological impact

Marine and land plastic debris biodegrades at micro- and nanoscales through progressive fragmentation. Oceanographic model studies confirm the presence of up to ~2.41 million tons of microplastics across the Atlantic, Pacific, and Indian subtropical gyres. Microplastics distribute from primary (e.g., exfoliating cleansers) and secondary (e.g., chemical deterioration) sources in the environment. This anthropogenic phenomenon poses a threat to the flora and fauna of terrestrial and aquatic ecosystems as ingestion and entanglement cases increase over time. This review focuses on the impact of microplastics across taxa at suggested environmentally relevant concentrations, and advances the groundwork for future ecotoxicological-based research on microplastics including the main points: (i) adhesion of chemical pollutants (e.g., PCBs); (ii) biological effects (e.g., bioaccumulation, biomagnification, biotransportation) in terrestrial and aquatic organisms; (iii) physico-chemical properties (e.g., polybrominated diphenyl ethers) and biodegradation pathways in the environment (e.g., chemical stress, heat stress); and (iv) an ecotoxicological prospect for optimized impact assessments

Artificial intelligence as a strategic tool to reduce marine pollution - using expert interviews to identify strengths, weaknesses, opportunities and threats of integrating ai in the in-situ management of marine litter

Artificial Intelligence (AI) and Sustainable Development are phenomena disrupting our society. This work analyzes how the breakthrough technology AI can be used as a strategic tool to minimize marine litter and contribute to achieving SDG 14. Expert interviews were conducted, and the results were categorized into the dimensions of a SWOT framework, indicating the factors that affect the potential of AI in the context. The overall results suggest that AI is not a panacea in the world of sustainable development, but it is a great strategic tool for marine litter management that is likely to improve in the future