Engineering the information system that runs a heterogeneous mobile sensor network is a complex task. In this paper we present the solution that was developed in the context of the ExpoLIS project. The goal of this project is to deploy a network of mobile (low-cost) sensors in city buses. Besides the software that needs to transfer, process, and store sensor data, we also developed a mobile application to increase awareness on air pollution, and a tool that allows scientists to subscribe to sensor data. We present the engineering solutions that form the backbone of the information system, and the structure and design of developing supporting tools. We discuss our choices regarding how sensor data are processed in order to make these data available for the common citizen. We mention possible future directions for the software that we have developed.

The proposed telemetry system consists of a graphical user interface with reconfigurable multiple widgets whose transmission is prioritised based on the user's 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.

This paper presents an incremental learning mechanism for context-aware switching between localisation methods which are available to the robots control system (e.g., GPS-based, map-based). The goal is to avoid the cumbersome and error prone manual mapping between localisation methods and environmental contexts. At each moment, the system determines which localisation method is performing best by comparison with the motion estimates produced by an odometer, assumed as accurate in the short-time. Then, the best performing method is associated to the current environmental context, which is defined by a novel descriptor built from the local occupancy grid. The result of this instance-based learning process is used online to estimate which localisation method performs the best in the current environmental context. The switching process is facilitated by the use of the de facto standard Robot Operating System (ROS) framework. The system was instantiated in a differential-wheeled robot equipped with a short-range 2-D laser scanner, and successfully validated on a set of field trials.

Testing and debugging real hardware is a time consuming task, in particular for the case of aquatic robots, for which it is necessary to transport and deploy the robots on the water. Performing waterborne and airborne field experiments with expensive hardware embedded in not yet fully functional prototypes is a highly risky endeavour. In this sense, physics-based 3D simulators are key for a fast paced and affordable development of such robotic systems. This paper contributes with a modular, open-source, and soon to be freely online available, ROS-based multi-robot simulator specially focused for aerial and water surface vehicles. This simulator is being developed as part of the RIVERWATCH experiment in the ECHORD european FP7 project. This experiment aims at demonstrating a multi-robot system for remote monitoring of riverine environments.

This paper presents ARES-III, a multi-purpose service robot for robust operation in all-terrain outdoor environments. Currently in pre-production phase, ARES-III is aimed to fulfil the requirements of a robotic platform that is able to support the development of real world applications in surveillance, agriculture, environmental monitoring, and other related domains. These demanding scenarios motivate a design focused on the reliability of the mechanical platform, the scalability of the control system, and the flexibility of its self-diagnosis and error recovery mechanisms. These are key features of ARES-III often disregarded in current commercial and research platforms. First, a comprehensive set of field trials demonstrated the ability of the ARES-III chassis, made of durable materials and with no-slip quasi-omnidirectional kinematic characteristics, to perform robustly in rough terrain. Second, supported by a control system fully compliant with the wide spread Robot Operating System (ROS), the scalability of ARES-III is enforced. Finally, the integration of active self-diagnosis and error recovery mechanisms in ARES-III control system fosters long lasting operation.

This paper presents a video-based early fire detection system. Focus is given to the challenges related to the actual deployment of the vision system. Most importantly, background subtraction is performed in a windowed way for improved accuracy, an attentive mechanism is employed to focus a computationally expensive frequency analysis of potential fire regions, interaction with a people detection and tracking system is included so as to enable model-based false alarms rejection, a new colour-based model of fire's appearance as well as a new Wavelet-based model of fire's frequency signature are proposed, and the camera-world mapping is approximated according to a GPS-based learning calibration process. An average success rate of 92.7% at a processing rate of 10 Hz shows the applicability of the model to real-life applications.

This paper presents a saliency-based solution to boost trail detection. The proposed model builds on the empirical observation that trails are usually conspicuous structures in natural environments. This hypothesis is confirmed by the experimental results, where a strong positive correlation between trail location and visual saliency has been observed. These results are due in part to the proposed extensions to a well known visual saliency computational model. This paper goes further and shows that, with a proper analysis of the saliency information alone, the ambiguity regarding both trail's position and approximate skeleton is reduced to three hypotheses in 98% of the tested natural images. This analysis is performed by a set of agents inhabiting the saliency and feature specific intermediate maps. These agents' behaviours exploit implicit, top-down knowledge about the object being sought in an active way. With the proposed model, computationally demanding accurate trail detectors are able to focus their activity in a fraction of the input image, thus promoting robustness and real-time performance.

Stereo-based off-road obstacle detection is a complex and still open problem. The challenges are in great extent related to computational cost and noise level. Previous work has shown that visual saliency and voting mechanisms are extremely effective in tackling these issues. This paper proposes a set of extensions to these mechanisms, to further improve the detector's speed-accuracy trade-off as well as its robustness. The observed enhancements are due in part to the adaptive way saliency is accounted for by the detector during the image scanning procedure. Additionally, detector's positive results are in turn used to boost the saliency map itself, thus reinforcing the analysis of relevant regions of the image. To enable detection in highly roughed terrain, the detector's invariance to the robot's posture is also enhanced. Experimental results show that, with the extended detector, denominated of ESalOD, higher levels of robustness, accuracy and computational efficiency are attained.

This paper proposes a model for trail detection that builds upon the observation that trails are salient structures in the robot's visual field. Due to the complexity of natural environments, the straightforward application of bottom-up visual saliency models is not sufficiently robust to predict the location of trails. As for other detection tasks, robustness can be increased by modulating the saliency computation with top-down knowledge about which pixel-wise visual features (e.g., colour) are the most representative of the object being sought. This paper proposes the use of the object's overall layout instead, as it is a more stable and predictable feature in the case of natural trails. This novel component of top-down knowledge is specified in terms of perception-action rules, which control the behaviour of simple agents performing as a swarm to compute the saliency map of the input image. For the purpose of multi-frame evidence accumulation about the trail location, a motion compensated dynamic neural field is used. Experimental results on a large data-set reveal the ability of the model to produce a success rate of 91% at 20Hz. The model shows to be robust in situations where previous trail detectors would fail, such as when the trail does not emerge from the lower part of the image or when it is considerably interrupted.

Inspection of an environment is often limited to a single video feed, provenient from a camera assembled on a mobile robot. This is known as the keyhole effect. In addition to this, the operator must often perform several tasks at the same time and specially in cluttered environments it can be hard to navigate the robot and at the same time keep the viewpoint of a camera aligned with a specific point of interest. This paper tackles this problem by proposing two mechanisms for assisting in teleoperation by positioning a pan-tilt camera. The goal consists in improving the teleoperatorpsilas situation awareness. Field trials with a pan-tilt-zoom (PTZ) camera mounted on an all-terrain robot validate the proposed mechanisms.

Human judgement is an integral part of the teleoperation process, heavily influenced by the rate in which commands are given to and the telemetry data is received from the robot. In this paper a multi-agent system for robot teleoperation is proposed. Several entities (e.g. robot or human) can easily be added and removed. In addition, this approach fosters a shared notion of reality to every entity present in the network, through a mechanism similar to the distributed blackboard architecture. This method provided an exchange of information regarding the teleoperation scenario, i.e. perceptual clues. This promoted a study on how perceptual clues influenced the operators’ judgement and performance. The experimental results in a physical suggest that the system is able to guarantee a close interaction between users and robots.

The unstructured nature of cross-country environments poses a great deal of challenges to obstacle detection algorithms. This paper presents an algorithm able to perform obstacle detection with small computational cycles, and extremely robust against outliers. To reduce the computational cost, foveation, i.e. space-variant image resolution adaptation, is employed. To reduce sensitivity to outliers, two voting filters are presented. Experimental results on two different stereo vision heads show that the proposed algorithm is able to perform at 10 Hz. Even in the presence of a low signal-to-noise ratio, caused by the use of a partially damaged lens, the algorithm is able to distinguish obstacles from the background.

This paper describes the DSAAR architecture, which aims the fast development and prototyping of multi-robot systems and it is based on Linux inter-process communication (IPC) mechanisms and multi-agent Jade software platform. This architecture is of special interest in domains where several heterogeneous actors have to interact both with and without realtime constraints. One of these domains is humanitarian demining, where technology transfer is also a requirement.

This article covers research work about unmanned helicopters that is being developed by the Portuguese SME Introsys, S.A. in the context of a generic project targeting the development of a multi-robot system for landmine detection. As helicopters are complex multivariable nonlinear underactuated systems with strong coupling in some control loops, they are an interesting subject of research for the control and robotic research communities. Therefore, a wide range of techniques have been proposed in the literature to control such vehicles, ranging from classic control to soft-computing methods. The main goal for this article is to present a survey about existing low level control architectures for unmanned helicopters and to discuss which control design criteria (design requirements) are relevant when they are operating in the context of humanitarian demining

This paper describes the development of a multi-robot system for area reduction in humanitarian demining. In spite of the specific requirements imposed to the work being carried out by the humanitarian demining domain, the results being achieved can also be used in other domains such as surveillance and remote monitoring. A multi-agent based architecture is responsible for coordinating a progressive stochastic analysis of the terrain. The all-terrain navigation used by the mobile robots is attained by a novel embodied reactive obstacle avoidance method. A control mission software has been developed to plan, configure and supervise the operations. Highly compliant legged and wheeled platforms have been developed accomplishing low-cost all-terrain robots. Digital signal processing algorithms have been applied for landmine detection using the payload sensors.