Gonçalo Cabrita

Divergence-based odor source declaration

This paper explores the use of the divergence operator for odor source declaration in swarm-based algorithms. A set of simulations of a swarm of robots running the decentralized asynchronous particle swarm optimization, bacterial foraging optimization and ant colony optimization algorithms was used to generate multiple wind and odor biased vector fields to investigate the effectiveness of the divergence operator in odor source declaration. A set of real world experiments were also performed using the same swarm algorithms on a controlled environment to ascertain if the divergence operator can also be used on real data. The sparse gas sensor data acquired by the robots was interpolated using the Nadaraya-Watson estimator by means of a wind and odor biased kernel before the application of the divergence. Results show that the divergence operator excels at odor source declaration.

Bayesian sensor fusion for land-mine detection using a dual-sensor hand-held device

This work presents a methodology and practical implementation of sensor fusion for land-mine detection using a novel multi-sensor hand-held device composed by a triple coil metal detector and a gas sensor. The proposed approach consists on merging data from both sensors in order to reduce the false alarm rate, particularly by using odor information. A Bayesian approach is proposed for the sensor fusion. Results show a false alarm rate of 1.4 to 1, a mine detection rate of 100% and a mine localization mean absolute error of 3 cm. Furthermore the resulting mine presence probability distribution maps represent an important visualization tool for mine clearance hand-held device users.

Cooperative multi-agent mapping of three-dimensional structures for pipeline inspection applications

This article proposes two methods based on cooperation between climbing and ground robots in order to address the mapping problem for autonomous inspection of three-dimensional (3D) structures. A pole climbing robot was developed to autonomously inspect a 3D human-made structure. The robot is able to climb over 3D human-made structures with bends and T-junctions. In the previous version of the system, the robot operator had to provide a set of data, resembling the map of the 3D structure, to the path planning algorithm of the climbing robot. However, the necessity of a priori knowledge of the structure’s geometry reduces the autonomy of the system. In this article we propose two solutions in which the structure in front of the robot is mapped autonomously. In the first proposed method, ground robots act as a mobile observer with a wide coverage for a climbing robot, to detect and estimate the size of the structure being climbed. We will present a case study in which multiple terrain robots provide the model of a structure which should be explored by a pole climbing robot. Each terrain robot is equipped with a low-cost wide angle Visual Graphics Array (VGA) camera, and some markers are fixed on the climbing robot. At each navigation step, the climbing robot and terrain robots cooperate to model a part of the structure which should be explored by the climbing robot. We also present a second approach in which a depth image from a Microsoft Kinect (a motion sensing input device by Microsoft) is fused with the information from the camera in order to eliminate the dependency of the system on color and light conditions.

An autonomous all terrain robotic system for field demining missions

Mines deployed in post-war countries pose severe threats to civilians and hamper the reconstruction effort in war hit societies. In the scope of the EU FP7 TIRAMISU Project, a toolbox for humanitarian demining missions is being developed by the consortium members. In this article we present the FSR Husky, an affordable, lightweight and autonomous all terrain robotic system, developed to assist human demining operation teams. Intended to be easily deployable on the field, our robotic solution has the ultimate goal of keeping humans away from the threat, safeguarding their lives. A detailed description of the modular robotic system architecture is presented, and several real world experiments are carried out to validate the robots functionalities and illustrate continuous work in progress on minefield coverage, mine detection, outdoor localization, navigation, and environment perception. Proposal of a new affordable, lightweight and autonomous all-terrain robot for demining operations.Complete description of the mechanical system and design of the modular robotic architecture.Validation of the robots intelligent capabilities in outdoor experiments.Work in progress and lessons learned for minefield coverage, and mine detection.

Virtual cancelation plume for multiple odor source localization

This article presents a novel algorithm for multiple odor source localization by a multi-robot system based on a virtual cancelation plume approach. The proposed method is based on rendering a previously declared odor source invisible to the robots so that the declared source and the odor plume it generates do not interfere with the effects of other existing plumes, allowing the localization of the remaining sources. Exploration and plume tracking by the robots is achieved using a decentralized asynchronous particle swarm optimization algorithm. The divergence operator is used to declare the odor sources. A set of simulations and real world experiments are performed on two different scenarios on a controlled environment using a swarm of 5 robots to validate the proposed methodology. Results show that the virtual plume cancelation algorithm can be successfully used to find multiple odor sources, even when two plumes overlap. It can also extend the operation of many odor source localization algorithms developed for single source localization.

The 2017 Humanitarian Robotics and Automation Technology Challenge [Humanitarian Technology]

Presents information on the RASS 2015 Humanitarian Robotics and Automation Technology Challenge.

A Framework for Remote Field Robotics Competitions

Field robotics competitions usually require a degree of logistics that is out of reach for most of the academic and non-academic community. Nevertheless these events are a source of innovation. This article proposes a framework for remote field robotics competitions, allowing virtually anyone from around the world to participate in a robotics challenge, regardless of resources. This framework, addressed in depth in this article, allows challengers to develop their entries under a simulated environment, and later test their ideas on a state of the art robotics platform, always without ever getting into direct contact with the actual robot. The framework was validated on the Humanitarian Robotics and Automation Technology Challenge (HRATC), an international humanitarian demining competition.

Squirtle: An ASV for Inland Water Environmental Monitoring

This paper presents the Squirtle, an Autonomous Surface Vehicle (ASV) for autonomous navigation and capable of manoeuvring in tight river environments. Its main features include precise localization with RTK GPS and inertial sensors, high manoeuvrability and autonomy, depth sensing and obstacle detection. Its purpose is the exploration and mapping of the bottom of rivers and of their shores. The article includes a brief description of the systems developed to fulfil this task. Software is based on the Robot Operating System (ROS). Results from preliminary field trials are presented and discussed.

Estimation of Gaussian Plume Model Parameters Using the Simulated Annealing Algorithm

This article presents a novel cost function for estimating the parameters of the Gaussian plume model using simulated annealing. The novel cost function takes into account the meandering and intermittency phenomena found on dispersing plumes. The proposed method was validated using real gas sensor data sampled by a swarm of 5 robots performing the Decentralized Asynchronous Particle Swarm Optimization for plume tracing under a controlled environment.