We present a visual based approach for reactive autonomous navigation of an underwater vehicle. In particular, we are interested in the exploration and continuous monitoring of coral reefs in order to diagnose disease or physical damage. An autonomous underwater vehicle needs to decide in real time the best route while avoiding collisions with fragile marine life and structure. We have opted to use only visual information as input. We have improved the Simple Linear Iterative Cluster algorithm which, together with a simple nearest neighbor classifier, robustly segment and classify objects from water in a fast and efficient way, even in poor visibility conditions. From the resulting classification and the current robot's direction and orientation, the next possible free-collision route can be estimated. This is achieved by grouping together neighboring water superpixels (considered as “regions of interest”). Finally, we use a model-free robust control scheme that allows the robot to autonomously navigate through the free-collision routes obtained in the first step. The experimental results, both in simulations and in practice, show the effectiveness of the proposed navigation system.

In this paper we propose a design of a class of robotic legs (known as “Ninja legs”) that enable amphibious operation, both walking and swimming, for use on a class of hexapod robots. Amphibious legs equip the robot with a capability to explore diverse locations in the world encompassing both those that are on the ground as well as underwater. In this paper we work with a hexapod robot of the Aqua vehicle family (based on a body plan first developed by Buehler et al. [1]), which is an amphibious robot that employs legs for amphibious locomotion. Many different leg designs have been previously developed for Aqua-class vehicles, including both robust all-terrain legs for walking, and efficient flippers for swimming. But the walking legs have extremely poor thrust for swimming and the flippers are completely unsuitable for terrestrial operations. In this work we propose a single leg design with the advantages of both the walking legs and the swimming flippers. We design a cage-like circular enclosure for the flippers in order to protect the flippers during terrestrial operations. The enclosing structure also plays the role of the walking legs for terrestrial locomotion. The circular shape of the enclosure, as well, has the advantages of an offset wheel. We evaluate the performance of our design for terrestrial mobility by comparing the power efficiency and the physical speed of the robot equipped with the newly designed legs against that with the walking legs which are semi-circular in shape. The swimming performance is examined by measuring the thrust generated by newly designed legs and comparing the same with the thrust generated by the swimming flippers. In the field, we also verified that these legs are suitable for swimming through moderate surf, walking through the breakers on a beach (and thus through slurry), and onto wet and dry sand.

In this paper we describe a heterogeneous multi-robot system for assisting scientists in environmental monitoring tasks, such as the inspection of marine ecosystems. This team of robots is comprised of a fixed-wing aerial vehicle, an autonomous airboat, and an agile legged underwater robot. These robots interact with off-site scientists and operate in a hierarchical structure to autonomously collect visual footage of interesting underwater regions, from multiple scales and mediums. We discuss organizational and scheduling complexities associated with multi-robot experiments in a field robotics setting. We also present results from our field trials, where we demonstrated the use of this heterogeneous robot team to achieve multi-domain monitoring of coral reefs, based on real-time interaction with a remotely-located marine biologist.

We present MARE, an autonomous airboat robot that is suitable for exploration-oriented tasks, such as inspection of coral reefs and shallow seabeds. The combination of this platform's particular mechanical properties and its powerful software framework enables it to function in a multitude of potential capacities, including autonomous surveillance, mapping, and search operations. In this paper we describe two different exploration strategies and their implementation using the MARE platform. First, we discuss the application of an efficient coverage algorithm, for the purpose of achieving systematic exploration of a known and bounded environment. Second, we present an exploration strategy driven by surprise, which steers the robot on a path that might lead to potentially surprising observations.