Pedro F. Santana

An autonomous surface-aerial marsupial robotic team for riverine environmental monitoring: Benefiting from coordinated aerial, underwater, and surface level perception

This paper presents RIVERWATCH, an autonomous surface-aerial marsupial robotic team for riverine environmental monitoring. The robotic system is composed of an Autonomous Surface Vehicle (ASV) piggybacking a multirotor Unmanned Aerial Vehicle (UAV) with vertical takeoff and landing capabilities. The ASV provides the team with longrange transportation in all-weather conditions, whereas the UAV assures an augmented perception of the environment. The coordinated aerial, underwater, and surface level perception allows the team to assess navigation cost from the near field to the far field, which is key for safe navigation and environmental monitoring data gathering. The robotic system is validated on a set of field trials.

Design of Communication and Control for Swarms of Aquatic Surface Drones

The availability of relatively capable and inexpensive hardware components has made it feasible to consider n nlarge-scale systems of autonomous aquatic drones for maritime tasks. In this paper, we present the CORATAM n nand HANCAD projects, which focus on the fundamental challenges related to communication and control in n nswarms of aquatic drones. We argue for: (i) the adoption of a heterogeneous approach to communication in n nwhich a small subset of the drones have long-range communication capabilities while the majority carry only n nshort-range communication hardware, and (ii) the use of decentralized control to facilitate inherent robustness n nand scalability. A heterogeneous communication system and decentralized control allow for the average n ndrone to be kept relatively simple and therefore inexpensive. To assess the proposed methodology, we are currently n nbuilding 25 prototype drones from off-the-shelf components. We present the current hardware designs n nand discuss the results of simulation-based experiments involving swarms of up to 1,000 aquatic drones that n nsuccessfully patrolled a 20 km-long strip for 24 hours.

On the Design of a Robotic System Composed of an Unmanned Surface Vehicle and a Piggybacked VTOL

This paper presents the core ideas of the RIVERWATCH experiment and describes its hardware architecture. The RIVERWATCH experiment considers the use of autonomous surface vehicles piggybacking multi-rotor unmanned aerial vehicles for the automatic monitoring of riverine environments. While the surface vehicle benefits from the aerial vehicle to extend its field of view, the aerial vehicle benefits from the surface vehicle to ensure long-range mobility. This symbiotic relation between both robots is expected to enhance the robustness and long lasting of the ensemble. The hardware architecture includes a considerable set of state-of-the-art sensory modalities and it is abstracted from the perception and navigation algorithms by using the Robotics Operating System (ROS). A set of field trials shows the ability of the prototype to scan a closed water body. The datasets obtained from the field trials are freely available to the robotics community.

A Vision-Based Approach to Fire Detection

This paper presents a vision-based method for fire detection from fixed surveillance smart cameras. The method integrates several well-known techniques properly adapted to cope with the challenges related to the actual deployment of the vision system. Concretely, background subtraction is performed with a context-based learning mechanism so as to attain higher accuracy and robustness. The computational cost of a frequency analysis of potential fire regions is reduced by means of focusing its operation with an attentive mechanism. For fast discrimination between fire regions and fire-coloured moving objects, a new colour-based model of fires appearance and a new wavelet-based model of fires frequency signature are proposed. To reduce the false alarm rate due to the presence of fire-coloured moving objects, the category and behaviour of each moving object is taken into account in the decision-making. To estimate the expected objects size in the image plane and to generate geo-referenced alarms, the camera-world mapping is approximated with a GPS-based calibration process. Experimental results demonstrate the ability of the proposed method to detect fires with an average success rate of 93.1% at a processing rate of 10 Hz, which is often sufficient for real-life applications.

A cooperative multi-robot team for the surveillance of shipwreck survivors at sea

The sea as a very extensive area, renders difficult a pre-emptive and long-lasting search for shipwreck survivors. The operational cost for deploying manned teams with such proactive strategy is high and, thus, these teams are only reactively deployed when a disaster like a shipwreck has been communicated. To reduce the involved financial costs, unmanned robotic systems could be used instead as background surveillance teams patrolling the seas. In this sense, a robotic team for search and rescue (SAR) operations at sea is presented in this work. Composed of an Unmanned Surface Vehicle (USV) piggybacking a watertight Unmanned Aerial Vehicle (UAV) with vertical take-off and landing capabilities, the proposed cooperative system is capable of search, track and provide basic life support while reporting the position of human survivors to better prepared manned rescue teams. The USV provides long-range transportation of the UAV and basic survival kits for victims. The UAV assures an augmented perception of the environment due to its high vantage point.

On Exploiting Haptic Cues for Self-Supervised Learning of Depth-Based Robot Navigation Affordances

This article presents a method for online learning of robot navigation affordances from spatiotemporally correlated haptic and depth cues. The method allows the robot to incrementally learn which objects present in the environment are actually traversable. This is a critical requirement for any wheeled robot performing in natural environments, in which the inability to discern vegetation from non-traversable obstacles frequently hampers terrain progression. A wheeled robot prototype was developed in order to experimentally validate the proposed method. The robot prototype obtains haptic and depth sensory feedback from a pan-tilt telescopic antenna and from a structured light sensor, respectively. With the presented method, the robot learns a mapping between objects’ descriptors, given the range data provided by the sensor, and objects’ stiffness, as estimated from the interaction between the antenna and the object. Learning confidence estimation is considered in order to progressively reduce the number of required physical interactions with acquainted objects. To raise the number of meaningful interactions per object under time pressure, the several segments of the object under analysis are prioritised according to a set of morphological criteria. Field trials show the ability of the robot to progressively learn which elements of the environment are traversable.

Self-supervised learning of depth-based navigation affordances from haptic cues

This paper presents a ground vehicle capable of exploiting haptic cues to learn navigation affordances from depth cues. A simple pan-tilt telescopic antenna and a Kinect sensor, both fitted to the robots body frame, provide the required haptic and depth sensory feedback, respectively. With the antenna, the robot determines whether an object is traversable by the robot. Then, the interaction outcome is associated to the objects depth-based descriptor. Later on, the robot to predict if a newly observed object is traversable just by inspecting its depth-based appearance uses this acquired knowledge. A set of field trials show the ability of the to robot progressively learn which elements of the environment are traversable.

On Collaborative Aerial and Surface Robots for Environmental Monitoring of Water Bodies

Remote monitoring is an essential task to help maintaining Earth ecosystems. A notorious example is the monitoring of riverine environments. The solution purposed in this paper is to use an electric boat (ASV - Autonomous Surface Vehicle) operating in symbiosis with a quadrotor (UAV – Unmanned Air Vehicle). We present the architecture and solutions adopted and at the same time compare it with other examples of collaborative robotics systems, in what we expected could be used as a survey for other persons doing collaborative robotics systems. The architecture here purposed will exploit the symbiotic partnership between both robots by covering the perception, navigation, coordination, and integration aspects.

Gaze-directed telemetry in high latency wireless communications: The case of robot teleoperation

The proposed telemetry system consists of a graphical user interface with reconfigurable multiple widgets whose transmission is prioritised based on the users gaze. The proposed approach encompasses a novel framework for telemetry of remote machines, based on ROS (Robot Operating System). It includes a modular ensemble of GUI, gaze device interoperability, and a ROS sensory topic modulator, which alternates different strategies to optimise all displayed information transmission quality, in a way that best suits the user. The approach was validated on a teleoperation scenario having multiple users control a robot in a designed course.

An open-source watertight unmanned aerial vehicle for water quality monitoring

This paper presents an open-source watertight multirotor Unmanned Aerial Vehicle (UAV) capable of vertical takeoff and landing on both solid terrain and water for environmental monitoring. The UAVs propulsion system has been designed so as to also enable the active control of the UAVs drift along the water surface. This low power locomotion method, novel to such a vehicle, aims to extend the available operation time on water bodies surveys. The ability to take-off from water allows the UAV to overcome any obstruction that appears on its path, such as a dam. A set of field trials show the UAVs water-tightness, its take-off and landing capabilities in both land and water, and also the ability to actively control its on-surface drifting.