We present the design, development, and evaluation of HREyes: biomimetic communication devices which use light to communicate information and, for the first time, gaze direction from AUVs to humans. First, we introduce two types of information displays using the HREye devices: active lucemes and ocular lucemes. Active lucemes communicate information explicitly through animations, while ocular lucemes communicate gaze direction implicitly by mimicking human eyes. We present a human study in which our system is compared to the use of an embedded digital display that explicitly communicates information to a diver by displaying text. Our results demonstrate accurate recognition of active lucemes for trained interactants, limited intuitive understanding of these lucemes for untrained interactants, and relatively accurate perception of gaze direction for all interactants. The results on active luceme recognition demonstrate more accurate recognition than previous light-based communication systems for AUVs (albeit with different phrase sets). Additionally, the ocular lucemes we introduce in this work represent the first method for communicating gaze direction from an AUV, a critical aspect of nonverbal communication used in collabo-rative work. With readily available hardware as well as open-source and easily re-configurable programming, HREyes can be easily integrated into any AUV with the physical space for the devices and used to communicate effectively with divers in any underwater environment with appropriate visibility.

Direct communication between humans and autonomous underwater vehicles (AUVs) is a relatively under-explored area in human-robot interaction research, although many tasks (e.g., surveillance, inspection, and search-and-rescue) require close diver-robot collaboration. Suboptimal AUV positioning relative to its human collaborators can lead to poor quality interaction and lead to excessive cognitive and physical load for divers. In this paper, we introduce a novel method for AUVs to autonomously navigate and achieve diver-relative positioning to begin interaction. Our method is based only on monocular vision, requires no global localization, and is computationally efficient. We present our algorithm and its implementation on board a physical AUV, performing extensive evaluations in the form of closed-water tests in a controlled pool. Our results show that the proposed monocular vision-based algorithm performs reliably and efficiently, operating entirely on-board the AUV.

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 and 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.

In this article, we propose, implement, and evaluate a motion-based communication system for field robots: robots that operate in dynamic, unstructured, outdoor environments. We perform two pilot studies to guide our development of the system, then evaluate it alongside an audio communication system, an LCD display, and a system of blinking LEDs. We compare the usage of these four systems with three different robots from up to five different viewpoints of interaction in a large study administered via Amazon Mechanical Turk. We contribute in two ways to the development of a more robust form of field human-robot interaction, wherein robots can select the most appropriate communication vector for a given situation and context. First, we contribute a motion-based communication system for field robots along with three baseline systems against which to test it. Second, we present results from our development of this motion system, showing that it is easier to learn than a baseline blinking LED system, viable for use underwater, aerial, and terrestrial field robots, and less negatively affected by adverse viewpoints than other communication methods.

Autonomous Underwater Vehicles (AUVs) can be effective collaborators to human scuba divers in many applications, such as environmental surveying, mapping, or infrastructure repair. However, for these applications to be realized in the real world, it is essential that robots are able to both lead and follow their human collaborators. Current algorithms for diver following are not robust to non-uniform changes in the motion of the diver, and no framework currently exists for robots to lead divers. One method to improve the robustness of diver following and enable the capability of diver leading is to predict the future motion of a diver. In this paper, we present a vision-based approach for AUVs to predict the future motion trajectory of divers, utilizing the Vanilla-LSTM and Social-LSTM temporal deep neural networks. We also present a dense optical flow-based method to stabilize the input annotations from the dataset and reduce the effects of camera ego-motion. We analyze the results of these models on scenarios ranging from swimming pools to the open ocean and present the model’s accuracy at varying prediction lengths. We find that our LSTM models can generate predictions with significant accuracy 1.5 seconds into the future and that stabilizing LSTM models significantly improves trajectory prediction performance.

Deep neural networks are the leading solution to the object detection problem. However, challenges arise when applying these networks to the kind of real-time, first-person video data that a robotic platform must process: specifically, detections may not be consistent from frame to frame, and objects may frequently appear at viewpoints that are particularly challenging for the model, resulting in inaccurate detections. In this paper, we present our approach for addressing these challenges for our particular vision problem: diver detection onboard autonomous underwater vehicles (AUVs). We begin by producing and releasing a dataset of approximately 105,000 annotated images of divers sourced from videos in order to address the challenge of learning a wide variety of object rotations and translations. This is one of the largest and most varied diver detection datasets ever created, and we compare models trained and tested on both our dataset and a previous dataset to demonstrate that our dataset improves the state-of-the-art in diver detection. Then, in order to choose an object detection model that produces detections that are consistent from frame to frame, we evaluate several state-of-the-art object detection models on the temporal stability of their detections in addition to the typical accuracy and efficiency metrics, mean average precision (mAP) and frames per second. Importantly, our results showed that models with the highest mAP do not also have the highest temporal stability.

This paper presents an approach to address data scarcity problems in underwater image datasets for visual detection of marine debris. The proposed approach relies on a two-stage variational autoencoder (VAE) and a binary classifier to evaluate the generated imagery for quality and realism. From the images generated by the two-stage VAE, the binary classifier selects "good quality" images and augments the given dataset with them. Lastly, a multi-class classifier is used to evaluate the impact of the augmentation process by measuring the accuracy of an object detector trained on combinations of real and generated trash images. Our results show that the classifier trained with the augmented data outperforms the one trained only with the real data. This approach will not only be valid for the underwater trash classification problem presented in this paper, but it will also be useful for any data-dependent task for which collecting more images is challenging or infeasible.

In this paper we present the LoCO AUV, a Low-Cost, Open Autonomous Underwater Vehicle. LoCO is a general-purpose, single-person-deployable, vision-guided AUV, rated to a depth of 100 meters. We discuss the open and expandable design of this underwater robot, as well as the design of a simulator in Gazebo. Additionally, we explore the platform's preliminary local motion control and state estimation abilities, which enable it to perform maneuvers autonomously. In order to demonstrate its usefulness for a variety of tasks, we implement a variety of our previously presented human-robot interaction capabilities on LoCO, including gestural control, diver following, and robot communication via motion. Finally, we discuss the practical concerns of deployment and our experiences in using this robot in pools, lakes, and the ocean. All design details, instructions on assembly, and code will be released under a permissive, open-source license.

In this paper, we propose a novel method for underwater robot-to-human communication using the motion of the robot as “body language”. To evaluate this system, we develop simulated examples of the system's body language gestures, called kinemes, and compare them to a baseline system using flashing colored lights through a user study. Our work shows evidence that motion can be used as a successful communication vector which is accurate, easy to learn, and quick enough to be used, all without requiring any additional hardware to be added to our platform. We thus contribute to “closing the loop” for human-robot interaction underwater by proposing and testing this system, suggesting a library of possible body language gestures for underwater robots, and offering insight on the design of nonverbal robot-to-human communication methods.

Trash deposits in aquatic environments have a destructive effect on marine ecosystems and pose a long-term economic and environmental threat. Autonomous underwater vehicles (AUVs) could very well contribute to the solution of this problem by finding and eventually removing trash. This paper evaluates a number of deep-learning algorithms performing the task of visually detecting trash in realistic underwater environments, with the eventual goal of exploration, mapping, and extraction of such debris by using AUVs. A large and publicly-available dataset of actual debris in open-water locations is annotated for training a number of convolutional neural network architectures for object detection. The trained networks are then evaluated on a set of images from other portions of that dataset, providing insight into approaches for developing the detection capabilities of an AUV for underwater trash removal. In addition, the evaluation is performed on three different platforms of varying processing power, which serves to assess these algorithms' fitness for real-time applications.