Multi-robot systems (MRS) are a group of robots that are designed aiming to perform some collective behavior. By this collective behavior, some goals that are impossible for a single robot to achieve become feasible and attainable. There are several foreseen benefits of MRS compared to single robot systems such as the increased ability to resolve task complexity, increasing performance, reliability and simplicity in design. These benefits have attracted many researchers from academia and industry to investigate how to design and develop robust versatile MRS by solving a number of challenging problems such as complex task allocation, group formation, cooperative object detection and tracking, communication relaying and self-organization to name just a few. One of the most challenging problems of MRS is how to optimally assign a set of robots to a set of tasks in such a way that optimizes the overall system performance subject to a set of constraints. This problem is known as Multi-robot Task Allocation (MRTA) problem. MRTA is a complex problem especially when it comes to heterogeneous unreliable robots equipped with different capabilities that are required to perform various tasks with different requirements and constraints in an optimal way. This chapter provides a comprehensive review on challenging aspects of MRTA problem, recent approaches to tackle this problem and the future directions.

https://www.springer.com/cda/content/document/cda_downloaddocument/9783319182988-c2.pdf?SGWID=0-0-45-1507882-p177356409

Multi-robot Task Allocation: A Review of the State-of-the-Art

In this letter, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a large-scale dataset of a paired and an unpaired collection of underwater images (of ‘poor’ and ‘good’ quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available at https://github.com/xahidbuffon/funie-gan .

Fast Underwater Image Enhancement for Improved Visual Perception

In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a large-scale dataset of a paired and unpaired collection of underwater images (of `poor' and `good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available at this https URL.

18.03.15 KB. Ok to add accepted version to spiral, subject to 12 months embargo, now expired.

https://iopscience.iop.org/article/10.1088/1748-3182/9/3/031001/pdf

Launching the AquaMAV: bioinspired design for aerial-aquatic robotic platforms

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.

/pdf/multi-domain-monitoring-of-marine-environments-using-a-4kl7iqwtrb.pdf

Multi-domain monitoring of marine environments using a heterogeneous robot team

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.

/pdf/ninja-legs-amphibious-one-degree-of-freedom-robotic-legs-cbfbjw4132.pdf

Ninja legs: Amphibious one degree of freedom robotic legs

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.

/pdf/mare-marine-autonomous-robotic-explorer-3yfi7rc5lr.pdf

MARE: Marine Autonomous Robotic Explorer

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, an autonomous reactive navigation approach based on visual information for an underwater vehicle is presented. Our method is divided in two stages. In the first stage, we use our improved version of the Simple Linear Iterative Clustering (SLIC) superpixel algorithm (4) to robustly segment low-resolution images based on their color features. We exploit the fact that high-resolution information is not needed for navigation (see (5), (6) for studies on the subject) and also that for underwater images, the CIELab color space can be adjusted to highlight only the color on objects that do not represent water itself. Given that this algorithm has a linear computational complexity, the objects and RoI (water) can be segmented in a fast and efficient way. After this, a simple nearest neighbor classifier is applied to separate and detect objects from water. In the second stage, from the resulted classification and the current direction and orientation of the robot, the next possible free-collision route (also called direction of escape) can be estimated. This is achieved by obtaining the geometric center of the area covered by a RoI. We have previously used this approach for indoor mobile robot navigation with promising results (7). Finally, we present a model-free robust control scheme that allows the robot to autonomously navigate by mapping the image error (in terms of the geometric center of RoI) to robot motion directly. The experimental results show the effectiveness of the proposed navigation system.

Vision-based reactive autonomous navigation with obstacle avoidance: Towards a non-invasive and cautious exploration of marine habitat

While spherical robots are an interesting design that has been studied before, but due to their relative manufacturing complexity there are few such devices commercially available. Given the increased popularity of 3D printers and laser cutters, rapid prototyping has become an integral part of engineering practice. Leveraging these advances, we have developed Spherico. Spherico is an inexpensive spherical robot that utilizes both laser cutter and 3D printing technology combined with off-the-shelve (OTS) components. Driven by a Raspberry Pi, the ROS (Robotic Operating System) is used as the underlying software architecture for controlling the robot. Using all the off-the-shelve OTS components, rapid prototyping parts and ROS, Spherico takes less than 72Hrs to build. The resulting robot is an inexpensive and robust platform for research into spherical robot control.

Bir Bikram Dey

Papers

Multi-domain monitoring of marine environments using a heterogeneous robot team

Ninja legs: Amphibious one degree of freedom robotic legs

MARE: Marine Autonomous Robotic Explorer

Vision-based reactive autonomous navigation with obstacle avoidance: Towards a non-invasive and cautious exploration of marine habitat

Spherico: rapid prototyping a spherical robot